Seismic Forecasting, Seismotectonics and Geodynamic Evolution of the Himalayan Belt

#### **Chapter 7**

## Unveiling the Evolution Journey from Pangea to Present Himalayan Orogeny with Relation to Seismic Hazard Assessment

*Tandrila Sarkar, Abhishek Kumar Yadav, Suresh Kannaujiya, Paresh N.S. Roy and Charan Chaganti*

### **Abstract**

The objective is to understand incessant seismic activities in Northwest and Central Himalayan regions. GPS data acquired (2017–2020, Nepal; 2015–2019, Uttarakhand) from 65 GNSS stations are used to generate velocity solutions with respect to International Terrestrial Reference Frame 2014 & Indian fixed reference frame to determine the site's precise position. These velocities are further used to calculate the strain rate and prevailing convergence rate by the respective Triangulation method and Okada's formulation. The estimated mean maximum and minimum principal strain rate are 12.19 nano strain/yr. and � 102.94 nano strain/yr. respectively. And the respective mean shear strain and dilatation are 115.13 nano strain/yr. �90.75 nano strain, which implies that Higher Himalaya observes high compression rate compared to Outer and Lesser Himalayan region. Estimations have also elucidated presence of extensional deformation in the Northwestern part of the Himalayan arc. Accordingly, in Central Himalaya, paleoliquefaction investigations have deciphered turbidites, confirming that the seismic ruptures did not reach the surface during the 2015 Gorkha earthquake. The best-fit locking depth of 14 km and convergence rate of 21 mm/yr. (Nepal) & 18 mm/yr. (Uttarakhand) are obtained. The strain budget analysis indicates that Northwest and Central Himalaya can beckon a megathrust earthquake in the future.

**Keywords:** triangulation method, Okada formulation, palaeoliquefaction, best-fit estimation, Himalaya

#### **1. Introduction**

The collision between the Indian and Eurasian Plate gave birth to the world's youngest orogeny or fold mountain known as Himalaya. The Himalayan orogeny is very young compared to Aravallis and the Appalachian Mountains. During the mid-Permian, a shallow sea known as Tethys was present in the latitudinal area now covered by the Himalaya. In this era, the Pangea had also started splitting up into small landmasses. In this course, the Northern Eurasian landmass or Angara and the Southern Indian landmass or Gondwana deposited a reasonable amount of sediments into the Tethys, reducing the gap between the Eurasian and Indian landmass. The mountain building process took 55 Ma, which started in the Upper Cretaceous period and continued until the Middle Miocene. In the last stage, the formation of Siwaliks also took place that continued from Early Miocene to Neogene age [1, 2].

The convergence activity in the Himalayan arc has made it very vulnerable or important to high seismic activity [3], leading to megathrust earthquakes in the brittle part of the crust that has accumulated elastic strain for a prolonged period. Also, the complex rheology of Himalayan wedges and crustal structure is another reason for the seismic hazard potential. Past studies have shown that Uttarakhand and Western Nepal had been the most potential zone for megathrust earthquakes for 200–500 years [4–7]. Therefore, in the past half-century, the Northwest Himalaya was ruptured by various small and large earthquakes. Similarly, in the Central Himalaya, the recent Gorkha earthquake of Mw >7 that took place in Kathmandu, Nepal on 25 April 2015 resonated with the 1833 megathrust earthquake, thereby indicating an ongoing convergence rate of 14–20 mm/yr. (also consistent with the estimated value) as shown in Section no. 6 & 7 in the Himalayan region [8].

A dense network of GNSS stations with > 5 years long period also accomplices in crustal deformation study and help in detecting the slip rate asperity on decollement to unravel the zones/areas with a high risk of seismicity. A detailed study is needed to understand the factors and precursors that trigger megathrust earthquakes. This research work estimated the surface deformation to assess the future seismic hazard potential in Northwest-Central Himalayan region using a close-knit network of 65 GNSS stations (both permanent and campaign).

#### **2. Tectonic setting**

Himalaya is located along the southern fringe of Tibetan plateau with a stretch of 2500 km length and 250–300 km width, is bounded by Nanga Parbhat (Indus gorge) in northwest and Namcha Barwa (Tsangpo gorge) in the Northeast. The morphologic and structural framework of Himalaya is classified as Kashmir-Punjab of length 550 km, Kumaun-Garhwal of length 320 km, Assam segments of length 400 km and Nepal of length 800 km [9]. At the east and west syntaxes, the Tsangpo River separates Himalaya from the Indo-Burmese range in the east, and the Indus River separates Hindukush in the west, respectively. The Himalaya with peaks high resides on compressed, thrusted, folded and 70 km thick (twice the thickness from the normal continental crust) continental crust, witnessed through geophysical surveys (seismic reflection and gravity) [10–12]. MFT lies adjacent to Indo-Gangetic Plain a foreland formed due to convergence between the Indian and Eurasian Plate. The Main Himalayan Thrust acts as a décollement or detachment that dips at a shallow depth. The South Tibetan Detachment, the Main Central Thrust, the Main Boundary Thrust, the Himalayan Frontal Thrust coalesce and meet [13, 14]. The Formation of the Himalaya took place in five distinct phases, which are described in **Table 1** relative to their geology and age modified after [5, 15, 16].

#### **3. GPS velocity measurements within close-knitted GNSS stations**

Himalaya has a history of seismicity or earthquakes of very high magnitude that lead to devastation and casualties. To assess the seismic hazard potential, the interseismic strain that is getting accumulated for quite a long period is evaluated with the aid of a close-knitted network of continuous and campaign GNSS stations [5, 6, 17, 18]. These stations are located exquisitely along the Northwest and Central Himalayan stretch spanning the three major thrusts (HFT, MBT and MCT, **Figure 1**).


*Unveiling the Evolution Journey from Pangea to Present Himalayan Orogeny with Relation to… DOI: http://dx.doi.org/10.5772/intechopen.102683*

#### **Table 1.**

*Evolution phase of the Himalaya with associated tectonic processes and geology.*

#### **Figure 1.**

*A schematic diagram illustrating the study area along the northwest-central Himalayan stretch and the locations of the 65 GNSS stations. The square on the left figure is enlarged on the right highlighting the northwest Himalaya or Uttarakhand region along with the GNSS stations that are located in this realm. Note the white vectors indicate the velocity with respective to ITRF2014 and the white circles on it defines the error ellipse. While the red vectors represent the velocities derived with respect to India fixed reference frame (Jade et al. [19]) and the red circles on it define the seismicity of varying magnitude. The yellow lines feature the three Main thrusts of Himalaya; Himalayan frontal thrust (HFT), Main boundary thrust (MBT) and Main central thrust (MCT).*

The Continuous GNSS stations or CORS (Continuously Operating Reference Stations) are well equipped with robust & firmly constructed receivers from Trimble and Leica, Choke-ring geodetic antenna, <10° elevation angle and the time taken for data recording is at 15 s sampling interval. Consequently, the campaign or episodic GNSS stations are equipped with a Zephyr antenna. The data is recorded for 3–4 days repeatedly at regular intervals so that the hydrological mass distribution and seasonal variation effect in this region diminish accordingly. The data is acquired from 65 stations for 2015–2019 (Uttarakhand region) and 2017–2020 (Nepal region). GPS data acquired is processed in GAMIT/GLOBK (vs. 10.71) software along with nearby IGS (International GNSS Services) stations to calculate the precise position of these 65 stations [20]. It takes two steps to complete the GPS data processing. In the first step, the daily relative position of an individual station/site is estimated with respect to clock errors and orbital parameters of the satellite, along with error predicted due to ocean-tidal effect, ionospheric electron content and atmospheric water vapor. The Finite Element Solution (FES) 2012 and International Earth Rotation system (IERS) 2010 models are employed to reduce the respective ocean tidal and Earth tidal effects. While the Global Pressure (GPT) model and Global Mapping Function (GMF) correct the tropospheric delay, which results due to wet and dry water mass [21]. The final or the second step of this process uses GLOBK modules to analyze the loosely constrained solution for obtaining the site velocity with respect to International Terrestrial Reference Frame 2008 (ITRF08) and ITRF14 Uttarakhand and Nepal region, respectively. The velocities obtained from ITRF08 is converted to ITRF14 using the HTDP software (geodesy.noaa.gov/TOOLS/Htdp/Htdp.shtml). Also, the velocities for each station is calculated with respect to fixed Indian reference Frame/Euler Pole as suggested by [19] (**Table 2**, **Figure 1**). The velocities



*Unveiling the Evolution Journey from Pangea to Present Himalayan Orogeny with Relation to… DOI: http://dx.doi.org/10.5772/intechopen.102683*


#### **Table 2.**

*Estimated velocities from both continuous and campaign stations with respect to ITRF 2014 and Indian fixed reference frame [19].*

estimated with respect to ITRF14 vary from 34.70 � 1.5 (BADR) to 52.42 � 0.07 (BRN2), thereby indicating crustal shortening rate in the Higher Himalaya is very high compared to the stations that are located in the Outer Himalayan region. Accordingly, the velocities obtained from India Plate fixed reference frame, vary from 23.3 (CHLM) to 0.1 (DEHR). Such variation signifies that the surface deformation in the Himalayan stretch occurs due to the thrusting of the Indian Plate below the Eurasian Plate.

#### **4. Strain rate estimation on a localized scale using the triangulation method**

The estimated site velocities depend on the deformation rate and the translation motion [22]. The translation motion does not depend on the position in a localized zone and has a similar direction and magnitude for a particular reference frame. Whereas the deformation rate indicates a change in the displacement (Δ*ui*) or position of the site secularly, which is explained in 2D by the following equation.

$$
\Delta u\_i = L\_{i\bar{j}} \Delta x\_j \text{ for } i, j = \infty, y \tag{1}
$$

where Δ*x <sup>j</sup>* is the change in the site position and *Lij* is the displacement gradient tensor. The *Lij* or displacement gradient tensor is decomposed into asymmetric and symmetric parts (Eq. 2), thereby representing the rotation rate and the infinitesimal strain rate.

$$L\_{\vec{ij}} = \varepsilon\_{\vec{ij}} + o\_{\vec{ij}} = \frac{1}{2} \left( \frac{\partial u\_i}{\partial \mathbf{x}\_j} + \frac{\partial u\_j}{\partial \mathbf{x}\_i} \right) + \frac{1}{2} \left( \frac{\partial u\_i}{\partial \mathbf{x}\_j} - \frac{\partial u\_j}{\partial \mathbf{x}\_i} \right) \tag{2}$$

Within a close clustered network of GNSS stations that are distributed along the Himalayan stretch, the estimation of strain rate (on a local scale) and the rate of rotation can be carried with the aid of the Triangulation method [23]. In this method, the horizontal field velocity ð*u*\_ *<sup>x</sup>*, *u*\_ *<sup>y</sup>*) determines the deformation rate at the centroid of the triangle at three nonlinear stations. Each of the horizontal velocity component is expressed in terms of deformation rate (*ε*\_*xx*, *ε*\_*xy*, *ε*\_ *yx*, *ε*\_ *yy*) and

*Unveiling the Evolution Journey from Pangea to Present Himalayan Orogeny with Relation to… DOI: http://dx.doi.org/10.5772/intechopen.102683*

translation motion (*tx*, *ty*) with the recognized initial site location (*xo*, *yo*Þ. So, the velocity field at three different stations gives six equations (i.e. 2 for each site) alongwith six unknown, and it is expressed by the following equation.

$$
\begin{pmatrix} \mathbf{1}\_{\dot{\boldsymbol{\mu}}\_{\mathcal{X}}} \\ \mathbf{1}\_{\dot{\boldsymbol{\mu}}\_{\mathcal{Y}}} \\ \mathbf{1}\_{\dot{\boldsymbol{\mu}}\_{\mathcal{X}}} \\ \mathbf{1}\_{\dot{\boldsymbol{\mu}}\_{\mathcal{Y}}} \\ \mathbf{1}\_{\dot{\boldsymbol{\mu}}\_{\mathcal{X}}} \\ \mathbf{1}\_{\dot{\boldsymbol{\mu}}\_{\mathcal{X}}} \\ \mathbf{1}\_{\dot{\boldsymbol{\mu}}\_{\mathcal{Y}}} \end{pmatrix} = \begin{pmatrix} \mathbf{1} & \mathbf{0} & \mathbf{1}\_{\dot{\boldsymbol{\mu}}\_{\mathcal{O}}} & \mathbf{1}\_{\dot{\boldsymbol{\mu}}\_{\mathcal{O}}} & \mathbf{0} & \mathbf{0} \\ \mathbf{0} & \mathbf{1} & \mathbf{0} & \mathbf{0} & \mathbf{1}\_{\dot{\boldsymbol{\mu}}\_{\mathcal{O}}} & \mathbf{1}\_{\dot{\boldsymbol{\mu}}\_{\mathcal{O}}} \\ \mathbf{1} & \mathbf{0} & \mathbf{2}\_{\dot{\boldsymbol{\mu}}\_{\mathcal{O}}} & \mathbf{2}\_{\dot{\boldsymbol{\mu}}\_{\mathcal{O}}} & \mathbf{0} & \mathbf{0} \\ \mathbf{1} & \mathbf{0} & \mathbf{3}\_{\dot{\boldsymbol{\mu}}\_{\mathcal{O}}} & \mathbf{3}\_{\dot{\boldsymbol{\mu}}\_{\mathcal{O}}} & \mathbf{0} & \mathbf{0} \\ \mathbf{0} & \mathbf{1} & \mathbf{0} & \mathbf{0} & \mathbf{3}\_{\dot{\boldsymbol{\mu}}\_{\mathcal{O}}} & \mathbf{3}\_{\dot{\boldsymbol{\mu}}\_{\mathcal{O}}} \end{pmatrix} \begin{pmatrix} \mathbf{t}\_{\mathbf{x}} \\ \mathbf{t}\_{\mathcal{Y}} \\ \dot{\boldsymbol{\mu}}\_{\mathcal{X}} \\ \dot{\boldsymbol{\mu}}\_{\mathcal{X}} \\ \dot{\$$

The above equation and its solution for three non-collinear GPS stations can be written in the form of

$$
\dot{u} = Gm \tag{4}
$$

$$
\dot{m} = G^{-1}\dot{u} \tag{5}
$$

where *u*\_ is a 1 � 6 column matrix of known instantaneous displacement/velocity vectors at three stations, *m* is a 1 � 6 column matrix of unknown translation vector and the deformation gradient tensor, and *G* is a square matrix of 6 � 6 order, and *G*�<sup>1</sup> is its inverse matrix which includes zeros, ones and the location vector coordinates of the three GNSS stations.

In a triangulation network, the translation gradient, rotational gradient, maximum shear strain rate and dilatation is estimated using the derived model parameters [24].

The translation gradient is calculated as:

$$\text{Speed} = \sqrt{\mathbf{t}\_{\mathbf{x}}^2 + \mathbf{t}\_{\mathbf{y}}^2} \tag{6}$$

The rotation rate is expressed as:

$$
\dot{\boldsymbol{\omega}} = \begin{pmatrix} \mathbf{0} & \frac{\left(\dot{\boldsymbol{\varepsilon}}\_{\mathbf{xy}} - \dot{\boldsymbol{\varepsilon}}\_{\mathbf{yx}}\right)}{2} \\\\ \frac{\left(\dot{\boldsymbol{\varepsilon}}\_{\mathbf{yx}} - \dot{\boldsymbol{\varepsilon}}\_{\mathbf{xy}}\right)}{2} & \mathbf{0} \end{pmatrix} \tag{7}
$$

2D Lagrangian strain rate tensor described as;

$$
\dot{\varepsilon}\_{\vec{\eta}} = \begin{pmatrix}
\dot{\varepsilon}\_{\text{xx}} & \frac{\dot{\varepsilon}\_{\text{xy}} + \dot{\varepsilon}\_{\text{yx}}}{2} \\
\frac{\dot{\varepsilon}\_{\text{yx}} + \dot{\varepsilon}\_{\text{xy}}}{2} & \dot{\varepsilon}\_{\text{yy}}
\end{pmatrix} \tag{8}
$$

The magnitude and orientation of maximum ð*e*1) and minimum (*e*2) principal strain rate tensor. Also, compression and extension in a regime are determined by the respective negative and positive values of principal strain rates. And the maximum infinitesimal shear strain (*γ*max) is expressed by the following equation.

$$\gamma\_{\text{max}} = e\_1 - e\_2 = 2\sqrt{\left(\frac{\varepsilon\_{\text{xx}} - \varepsilon\_{\text{\eta}\eta}}{2}\right)^2 + \left(\varepsilon\_{\text{xy}}\right)^2} \tag{9}$$

The first invariant of the 2D strain tensor is the areal strain or dilatation ((ellipse area-circle area)/circle area) and is explained by the following equation.

$$\text{Area strain rate} / \text{Dilatation} = \mathbf{e}\_1 + \mathbf{e}\_2 \tag{10}$$

#### **5. Inferences from localized strain rate estimation**

The strain rate and rotation rate are estimated at the centroid of 89 triangular zones of the 65 sites forming an angle of 30° or more (**Table A1**). The maximum strain rate varies from �47.90 to 195.22 nano strain/yr. with an azimuth variation from 23.21°N to 161.02° N. Similarly, the minimum strain rate varies from �333.606 to �8.9 nano strain/yr. with an azimuth variation from 178.55 °N to 0.07 °N. The value of maximum shear strain lies between 13.47 to 301.51 nano strain/yr., and the angle of rotation range from �9.9 E-0.7 to 9.12E-0.7 (°/yr). Along with this, the change in dilatation or areal strain differs between 136.07 to �381.405 nano strain. From these observations, it can be stated that the stations (mainly station number 71–86) located in the Higher Himalaya (South East) of the Nepal region experiences a high rate of compression (**Figure 2**, **Table A1**), clockwise motion with a mean shear value of 115.13 nano strain/yr. (**Figure 4**) and negative dilatation (highlighted by blue to yellow color, **Figure 3**). On proceeding towards the Northwest Himalaya, the stations located there observe a low compression rate (especially along the MBT boundary). At the same time, the cluster of stations (mainly station number 36–50) located in the Higher Himalaya of Uttarakhand region (Northwest)

#### **Figure 2.**

*The strain rate analysis using the triangulation approach. The red and blue vectors indicate maximum and minimum principal strain rate respectively. Note that the blue vectors are highly dominant towards the south eastern part of Nepal region indicating a high rate of compression. While in the northwest part of Uttarakhand region the red vectors are much dominant thus indicating an extension. The maximum and minimum values estimated are tabulated in Table A1.*

*Unveiling the Evolution Journey from Pangea to Present Himalayan Orogeny with Relation to… DOI: http://dx.doi.org/10.5772/intechopen.102683*

#### **Figure 3.**

*The colored patches show the dilatation at the Centre of the respective triangles as described in Table A1. The prominent red patch in northwest of the Uttarakhand region defines extension (with reference to Figures 2 and 4, here the rate of extension is high). And the prominent blue patch in the Southeastern part of Nepal region indicates high compression rate.*

reflects positive dilatation (highlighted by dark red color, **Figure 3**), anticlockwise movement (**Figure 4**) and positive strain rate values (**Figure 2**) or extension.

The second invariant of the strain rate field, where the second invariant is defined as;

$$"\dot{\epsilon}^2 \epsilon^2 \, \_{\rho\rho} + "\dot{\epsilon}^2 \, \_{\theta\theta} + \text{"}\dot{\epsilon}^2 \, \_{\rho\theta} \tag{11}$$

where ˙*εφφ,* ˙*εθθ* and ˙*εφθ* are the horizontal components of the strain rate tensor. When the second invariant is estimated, the whole tectonic regime experiences moderate to high areal dilatation (color varies from yellow to orange to red, **Figure 5**), leaving out certain portions of the Eastern or NNE region (blue to yellow color, **Figure 5**). The 2D second invariant to the strain rate tensor has been estimated independently from the principal components of strain (**Figure 5**) to establish a relation between the rate of deformation, seismicity and the interseismic strain accumulating for a prolonged period [25, 26]. The map of the second invariant also focuses on the spatial distribution of earthquakes (**Figure 5**).

In an orogen of convergence, observation of such paradox situation becomes quite inquisitive [27]. Past studies have shown that prominent extensional deformation exists in an orogen of convergence like Andean cordillera, Scandinavian Caledonides etc. [28, 29]. In the Himalayan realm, intra-continental tectonic features, prominent folds, and several other deformational geological structures indicate a compressional tectonic area. But in recent studies, it has been observed that normal faults and many other extensional geological structures are also distributed in different Himalayan sectors [30], but these are not consistent with ongoing

#### **Figure 4.**

*The maximum shear strain rate is estimated by the triangulation approach. Note the green and blue lines represent right lateral (RL) and left lateral (LL) respectively. The respective estimates and direction of the angular rotation is described in Table A1.*

#### **Figure 5.**

*A 2D second invariant map estimated by the triangulation approach showing the spatial distribution of seismicity. Note the hollow red circles are the seismic events ranging in magnitude from 4 to >5.*

*Unveiling the Evolution Journey from Pangea to Present Himalayan Orogeny with Relation to… DOI: http://dx.doi.org/10.5772/intechopen.102683*

seismicity and thrusting in the southward direction. Also, the presence of large scale lineament, shifting and tilting of river, subsidence of older rocks, upliftment of river terraces & active thrusts and normal faults overriding the young Holocene sediments demonstrates that extensional and compressional features co-exist with each other in Himalaya [31, 32].

#### **6. Estimation of ongoing convergence rate and its future implications**

The rate of convergence along the active plate boundaries determines the seismic potential and the rate of seismicity occurring today. Previous studies have stated that currently, MFT is the most active fault because here, the crustal shortening is taking place due to the plate movement and evidence of GPS vectors [33]. The site velocities (obtained with respect to ITRF) are distributed along the strike direction (fault parallel or strike slip fault) (**Figures 6** and **7**) and across the strike direction (fault normal, or oblique fault) (**Figures 8** and **9**), and the structural trend of MFT is taken as 303° and 288° in the Nepal & Uttarakhand region. It is assumed that the fault normal velocity as dip slip and fault parallel velocity as strike slip on the plate interface, 'i.e.' MHT (Main Himalayan Thrust). In fault normal velocity, the uncertainty is calculated by applying the following formula

$$
\sigma\_{horz} = \sqrt{\frac{\sigma\_E^2 \sigma\_N^2}{\sigma\_N^2 \cos^2 \theta + \sigma\_E^2 \sin^2 \theta}}\tag{12}
$$

#### **Figure 6.**

*The rate of ongoing convergence in the Nepal region is estimated along the arc parallel or fault parallel direction. Note the green arrows are the estimated convergence rate while the red arrows define modeled convergence rate. The hollow blue circles indicate seismic events (2017–2021) of magnitude varying from 4 to >5.*

#### **Figure 7.**

*The rate of ongoing convergence in the Uttarakhand region is estimated along the arc parallel or fault parallel direction. Note the green arrows are the estimated convergence rate while the red arrows define modeled convergence rate. The hollow blue circles indicate seismic events (1960–2021) of magnitude varying from 4 to >6.*

#### **Figure 8.**

*The rate of ongoing convergence in the Nepal region is estimated across the arc normal or fault normal direction. Note the green arrows are the estimated convergence rate while the red arrows define modeled convergence rate. The hollow blue circles indicate seismic events (2017–2021) of magnitude varying from 4 to >5.*

*Unveiling the Evolution Journey from Pangea to Present Himalayan Orogeny with Relation to… DOI: http://dx.doi.org/10.5772/intechopen.102683*

#### **Figure 9.**

*The rate of ongoing convergence in the Uttarakhand region is estimated across the arc normal or fault normal direction. Note the green arrows are the estimated convergence rate while the red arrows define modeled convergence rate. The hollow blue circles indicate seismic events (1960–2021) of magnitude varying from 4 to >6.*

where *σ<sup>E</sup>* is the standard deviation of the east velocity, *σ<sup>N</sup>* is the standard deviation of the north velocity, and *θ* is the bearing of the convergence vector, counterclockwise from east. At the site (GNSS stations) location, the subsurface deformation generally depends on several fault parameters: rake, dip, strike, fault length, width, etc. This is due to finite subsurface dislocation, and it can be explained by the back slip approach as expressed by [34] in the following expression

$$\mathbf{u} = \mathbf{G}\mathbf{s} \tag{13}$$

where u is the surface deformation vectors, s is the vector of parameters, and G is the Green's functions matrix computed using the semi-analytical formulation published by [35].

The Okada dip slip dislocation approach [35] estimates the surface deformation at each GNSS site due to a finite rectangular dip slip dislocation on MHT for an elastic halfspace. In this approach, it is presumed that the frontal portion of MHT is completely locked to some extent and aseismic creeping occurs with a uniform slip rate at downdip zone of MHT. After that, reduced chi-square uncertainty is calculated because a grid search analysis was required to estimate the best fit value of slip rate and locking depth. The expression used for calculating the chi-square uncertainty is as follows;

$$\chi^2\_v = \frac{1}{\left(n - P\right)^2} \sum\_{i=1}^n \left(\frac{r\_i}{f\sigma\_i}\right)^2 \tag{14}$$

where *n* is the number of observations, *P* is the number of free parameters, *ri* is the residual between observed and calculated velocity, *σ<sup>i</sup>* is the formal data uncertainty, and *f* is the data scaling factor. The scaling factors helps to avoid the inclusion of any additional uncertainty and prevent influencing various other types of data. Also, the formal GPS velocity uncertainty estimates are generally underestimated by a factor of 2 to 5 [36].

#### **7. Inferences from ongoing plate convergence and seismicity assesment**

The plate convergence rate (fault normal) or the elastic strain accumulated in the brittle frontal part of MHT is 21 mm/yr. and 18 mm/yr. for Nepal and the Uttarakhand region, respectively, with 14 km locked depth and dip angle 10° (Nepal) and 7° (Uttarakhand) (**Figures 10** and **11**). But the fault parallel motion is relatively small (3–4 mm/yr) and varies inconsistently (**Figures 6** and **7**), so the fault normal motion is only considered. The slip deficit in the Uttarakhand region is calculated as 3.9 m, indicating that a significant amount of strain had accumulated since the last magnitude earthquake (1803 earthquake) [37]. And the estimated slip deficit corresponds to seismic moment 4.9 1021 Nm and Mw 8.3 by taking fault length 412 km and assuming its width as 111 Km. Therefore, the strain budget analysis in the Uttarakhand region signifies that this area bears the potential to produce a megathrust earthquake, and the results are consistent with [6, 16, 38, 39]. While the Nepal region was struck by a recent high magnitude (Mw >7) Gorkha earthquake that immensely disturbed the tectonic activity of this region. It has been observed that the 2015 seismicity took place along the planar MHT and also gives good information about ground motion frequencies, the direction of rupture etc. It is also presumed that earthquakes with Mw > 8 can rupture the surface devastatingly. But the earthquakes with Mw between 7 and 8 rupture mainly within depth and rarely reach the surface. Also, Paleoliquefaction investigations have deciphered turbidites that formed due to 2015 seismic activity from the Rara Lake, which presents alternative evidence for ruptures that did not reach the surface [40–44]. Therefore, it can be concluded that interseismic strain is building up within the Nepal Himalayan region to beckon the next calamitous earthquake in the near future.

#### **Figure 10.**

*A 3D reference model showing the cross section of the Himalayan geometry in Nepal region. Note the locked width is 96 km, locked depth is 14 km, dip angle 10° and slip rate estimated is 21 mm/yr. the hollow blue circles represent seismic events from 2017 to 2020 ranging between 3 to >5. Also, the concentration of seismicity is high near the Main central thrust, thereby indicating hypocentre of these earthquakes falls in and around the higher Himalaya.*

*Unveiling the Evolution Journey from Pangea to Present Himalayan Orogeny with Relation to… DOI: http://dx.doi.org/10.5772/intechopen.102683*

#### **Figure 11.**

*A 3D reference model showing the cross section of the Himalayan geometry in Uttarakhand region. Note the locked width is 111 km, locked depth is 14 km, dip angle 7° and slip rate estimated is 18 mm/yr. the hollow blue circles represent seismic events from 2015 to 2021 ranging between 3 to >5. Also, the concentration of seismicity is high near the Main central thrust, thereby indicating hypocentre of these earthquakes falls in and around the higher Himalaya.*

3D reference model (for both Nepal and the Uttarakhand region) illustrating the predominant occurrence of earthquakes ranging from low to high magnitude is developed using the parameters that have been applied in Okada's formulation. This model discusses the evolution of slip rate spatiotemporally and the related seismic events that happened in and around the Main Central Thrust fault, thereby indicating that the locations of hypocentre fall in the Higher Himalayan region (**Figures 10** and **11**) [41, 45, 46]. But previous studies have shown that many historic megathrust earthquakes have ruptured partially and did not reach the surface [7, 47]. Therefore, through magnitude moment calculations and 3D reference model, it can be presumed that a megathrust event similar to the Gorkha earthquake or even more catastrophic than that will occur instantaneously.

#### **8. Conclusion**

The surface deformation rate is measured along the Northwest -Central Himalayan belt using 65 GNSS stations located close to the wide spatial distribution. GPS data acquired (2015–2019, Uttarakhand and 2017–2020, Nepal) from these GNSS stations are used to estimate the velocity solution and determine the precise position of each site with respect to ITRF2014 and Indian fixed reference frame as suggested by Jade et al. [19]. These estimated velocities are then used to calculate the strain rate and prevailing convergence rate of the Himalayan stretch using the Triangulation method and Okada formulation. From the triangulation approach, the mean observed values of maximum principal strain are 12.19 nano strain/yr., minimum principal strain is �102.94 nano strain/yr., maximum shear strain is 115.13 nano strain/yr. and dilatation of �90.75 nano strain. Although the velocity solution from ITRF2014 implies an increase in convergence rate towards the Higher Himalaya, but results from triangulation have also featured comparatively high extensional deformation in the Northwest (Uttarakhand region) than in the Nepal region. Accordingly, using the grid search analysis, the best fit convergence calculated is 21 mm/yr. and 18 mm/yr. in Nepal and Uttarakhand regions. The locking depth is 14 km in both the regions with dip angle 10° (Nepal) and 7° (Uttarakhand).

Considering the last megathrust earthquake in the Uttarakhand region (1803 earthquake), the slip deficit estimated is 3.9 m corresponding to 4.9 <sup>10</sup><sup>21</sup> Nm seismic moment and Mw > 8. Whereas in Nepal, after the 2015 Gorkha earthquake (Mw 7.3), the tectonic regime is disturbed greatly and has significantly impacted the infrastructure and the GNSS stations present here. Secondary evidences like turbidites from palaeoseismic investigation show that 2015 seismic activity has not ruptured the surface to a megathrust front. This implies building up interseismic stress waiting to release into a calamitous earthquake in the near future.

### **Acknowledgements**

The authors are grateful to the Director of the Indian Institute of Remote Sensing for his consistent encouragement in carrying out this study. We acknowledge those scholars who have assisted us in the field surveys and those who have helped directly and indirectly in this work. We are thankful to Dr. Robert W. King (MIT) for providing GAMIT/GLOBK 10.70 software for processing GPS data. The authors heartily acknowledges the Editor-in-chief and Reviewer for helping in exemplifying their work exquisitely.


#### *Unveiling the Evolution Journey from Pangea to Present Himalayan Orogeny with Relation to… DOI: http://dx.doi.org/10.5772/intechopen.102683*

**A. Appendix**


*Earth's Crust and Its Evolution - From Pangea to the Present Continents*


#### *Unveiling the Evolution Journey from Pangea to Present Himalayan Orogeny with Relation to… DOI: http://dx.doi.org/10.5772/intechopen.102683*


*Earth's Crust and Its Evolution - From Pangea to the Present Continents*


**Table A1.** *The estimated maximum & minimum principal strain rate, maximum shear strain rate, rotation rate, dilatation with their corresponding azimuth and coordinated by Triangulation approach.*

#### *Unveiling the Evolution Journey from Pangea to Present Himalayan Orogeny with Relation to… DOI: http://dx.doi.org/10.5772/intechopen.102683*

#### **Author details**

Tandrila Sarkar<sup>1</sup> , Abhishek Kumar Yadav<sup>2</sup> , Suresh Kannaujiya<sup>3</sup> \*, Paresh N.S. Roy2 and Charan Chaganti<sup>3</sup>

1 Indian Institute of Technology (Indian School of Mines), Dhanbad, Jharkhand, India

2 Indian Institute of Technology Kharagpur, West Bengal, India

3 Indian Institute of Remote Sensing, Indian Space Research Organization, Dehradun, Uttarakhand, India

\*Address all correspondence to: skannaujiya@iirs.gov.in

© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Unveiling the Evolution Journey from Pangea to Present Himalayan Orogeny with Relation to… DOI: http://dx.doi.org/10.5772/intechopen.102683*

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#### **Chapter 8**

## Seismic Forecasting Using a Brownian Passage Time Distribution

*Edmore Utete*

#### **Abstract**

Seismic forecasting using a Brownian Passage Time distribution is presented in this chapter. Seismic forecasting is concerned with the probabilistic assessment of general seismic hazard, including the frequency and magnitude of earthquakes in a given area over a given period of time. Seismic forecasting generally look for trends that lead to an earthquake. The estimation of the time that a strong earthquake will occur requires the determination of the distribution that the earthquake recurrence time follows. Brownian Passage Time distribution describes reliably the physical processes related with earthquakes' occurrence. The model assumes that the evolution of the stress loading between two earthquakes depends on the constant loading rate, and a random component, which follows the Brownian Relaxation Oscillator. Its hazard function is in good agreement with the temporal evolution of earthquake occurrence as the hazard rate is very low after an earthquake, and then increases as time passes, and takes a maximum value at the mean recurrence time and since then, it decreases asymptotically exhibiting a pure quasi-periodic temporal occurrence.

**Keywords:** seismology, seismic forecasting, probability distribution, stochastic model

#### **1. Introduction to seismic forecasting**

Seismic forecasting is concerned with the probabilistic estimation of the frequency and magnitude of seismic events in a given area over a given period of time using advanced statistical and scientific methods. This can be distinguished from seismic prediction, which is the specification of the time, location, and magnitude of a future seismic event with sufficient precision that a warning can be issued. The two can be further distinguished from seismic warning systems, which is the detection of a possible seismic event in real time to regions that might be affected. Seismic warning systems focus on a very short time outlook ranging from few minutes to few days, seismic prediction looks at specific future time with acceptable time ranging from few minutes to few hours while seismic forecasting makes use of probability estimations with time scales ranging from few days up to several decades.

In this chapter on seismic forecasting, literature body on seismic formation process will be reviewed to develop a theoretical framework that validates and upholds ideas that will be further used to develop a stochastic model that can be used to forecast future seismic events. The elastic rebound theory will be validated, and an

analysis of recurrence time models will be done so as to select the best model that can be used in seismic forecasting. Given a homogeneous, consistent, and complete past seismic data of a region, the unbiased maximum-likelihood estimates of model parameters can be estimated and used as input parameter to seismic forecasting.

#### **1.1 History of seismic forecasting**

Since the ancient times attempts to predict seismic events were made, with people associating such events with the spiritual world. In some societies, such events were considered a sign of bad luck or punishment for disobedience from the supernatural beings. The scientific revolution was a game changer in this mistrial subject with scientist being optimistic that a practical method of seismic prediction would soon be found. By the end of the nineteenth century continued failure leads to many people questioning whether it was even possible to predict a seismic event. Scientific evidence of few predictions of large seismic events has not occurred, and a few claims of success prediction remain controversial. As a result, emphasis has been shifted from seismic prediction to seismic forecasting. Due to the high level of destruction and loss of lives, after larger seismic events, a lot of scientific and national government resources have been pooled and allocated into seismic forecasting rather than prediction as it has proved to be useful in seismic risk mitigation in areas such as establishment of building codes, insurance rate structures, awareness, preparedness programs, and public policy related to seismic events. Statistical methods used for seismic forecasting look for trends or patterns that lead to a seismic event. The trends involve many complex variables, and the advanced statistical techniques are needed to understand them. These approaches tend to have relatively long time periods, making them useful for seismic forecasting.

#### **1.2 Elastic-rebound model**

Previously, it was thought that ruptures of the surface were the result of strong ground shaking rather than the converse suggested by Harry Fielding Reid [1], the first scientist to explain the seismic formation process after the great 1906 San Francisco earthquake. The theory postulated that steady tectonic force causes strain to accumulate slowly in a rock and eventually become large up to a threshold constant value called the elastic limit. The elastic limit is the maximum strain that a rock can withstand without breaking. When the limit is exceeded, an earthquake will occur. At that time, a sudden movement occurs along the fault line, releasing the accumulated energy, and the rocks snap back to their original undeformed shape. After an event another cycle starts. When the accumulated strain is great enough to overcome the strength of the rocks, an earthquake occurs again. The duration of an "earthquake cycle" is the ratio of event-strain release to tectonic strain rate. Rocks in a fault plane are subjected to tectonic force caused by tectonic plate movement. Fault plane subjected to plate tectonic moves at the rate of a few centimeters per year, over a time period of decades. The stored energy is released during the rupture partly as heat, partly in damaging the rock, and partly as seismic waves.

The elastic-rebound model plays a central acceptable role in our understanding of earthquake mechanics. This approach, which has some observational grounding, has been the basis for long-term forecasting models. A number of statistical models have been proposed for seismic forecasting. Discrete probability models are proposed to forecast a number of events in a given time interval and continuous probability models have been proposed to forecast the time until the next seismic event.

It is not a good idea to model seismic recurrence using a normal distribution as it gives positive probability to negative intervals. Better models are the Weibull

#### *Seismic Forecasting Using a Brownian Passage Time Distribution DOI: http://dx.doi.org/10.5772/intechopen.101454*

gamma and log-normal distributions, and can be used as alternatives. In this chapter, we are going to consider the Weibull distribution as it is practically convenience as proven by its wide application in statistical quality control. Nishenko and Buland [2] identified several theoretical distributions after normalizing 15 characteristic seismic sequences data by looking at the shape of a generic distribution for recurrence intervals.

Nishenko and Buland [2], agreed with Hagiwara's [3] model preferences of the exponential, Weibull, gamma, and lognormal distributions by analyzing the reliability of the distributions. They further pointed that the log-normal provides the best fit to the distribution of normalized intervals, and there was no difference in the estimated parameters of the log-normal normal distribution in seismic data from different regions with different time scales. They all postulated that lognormal distribution was a good model for seismic risk assessment.

The elastic rebound model provides a framework seismic recurrence modeling. Extending advanced probabilistic modeling to this theory provides a basic model for seismic forecasting. The idea is the inclusion of the seismic random perturbations in the elastic rebound model.

The same approach was used by Kagan and Knopoff [4] in modeling time-dependent model in statistical seismic analysis. Empirical analysis does not use a Poisson process in many cases. Matthews et al. [5] disagreed with Kagan and Jackson [6] on the idea that seismic recurrence time intervals are shorter than the mean interval. The use of exponential distribution in modeling seismic recurrence is debatable, and currently, there is no much literature and it is still not clear whether seismic recurrence has a strong central tendency or not.

#### **1.3 Recurrence time models**

The recurrence time models that can be considered are the exponential, Weibull, gamma, and log-normal distributions. Use of these distributions has been motivated primarily on the grounds of familiarity, simplicity, and convenience.

#### *1.3.1 Exponential distribution*

The exponential distribution is the probability distribution of the time between seismic events in a Poisson point process. A number of events in a given time interval follow a poison distribution as it assume that events occur continuously and independently at a constant average rate. This means the time between events follows an exponential distribution. One-parameter exponential distribution has a property of memoryless implying the distribution of a waiting time until a certain event does not depend on how much time has elapsed already. This property disqualifies the exponential distribution as a possible model for seismic recurrence time model as it opposes the elastic rebound model.

#### *1.3.2 Gamma distribution*

Gamma distribution is a generalized exponential distribution. This distribution contains the densities of the sum of *n* independent exponentials. The shape parameter θ >1 has zero hazard rate at a time zero and increases to a finite asymptotic level that is always smaller than the mean recurrence rate, which is not the case with seismic events. This makes the Gamma distribution not a potential model for seismic recurrence time models.

#### *1.3.3 Weibull distribution*

Weibull distribution contains the densities of the minimum of *n* independent exponential distribution with rate *t* that have independent occurrence times, and then, the distribution has a Weibull (t, n). It is a distribution for which seismic occurrence rate is proportional to a power of time. The shape parameter, *k*, is that power plus one. A value of *k*< 1 indicates that the seismic occurrence rate decreases over time that is there is significant "infant mortality," or seismic event occurrence is high early and decreasing over time as the seismic events are "weeded" out of the population. This may be because the seismic building force stabilizes over time. A value of *k* ¼ 1 indicates that the seismic occurrence rate is constant over time. This is consistent with the elastic rebound model but the Weibull distribution reduces to an exponential distribution. A value of *k*>1 indicates that the seismic occurrence rate increases with time. This happens if there is an increase in seismic building process activities. The hazard-rate functions either start at zero and increase to ∞ or *vice versa*, depending on the parameter *k*, this disqualifies the weibull distribution as a potential seismic recurrence time model as the elastic rebound model do not support infinite or zero hazard rate.

#### *1.3.4 Log-normal distribution*

The log-normal distribution is obtained by taking the exponential of a normal distribution. The asymptotic hazard rate is always zero and hazard-rate functions that increase from zero at time *t* ¼ 0 and then eventually decrease to zero. The probability density function puts least weight in the left tail. The mean residual life increases without bound as t ! ∞. Its asymptotic properties disqualify the lognormal family as a reliable seismic recurrence model as this means the longer the time since the last seismic event, the longer it will be expected until the next seismic event. As suggested by Davis et al. [7] that the log-normal model provided only a slightly better fit than the gamma or Weibull models.

#### *1.3.5 The Brownian relaxation oscillator*

The Brownian Relaxation Oscillator can be used for seismic forecasting of time of next event. If seismic building process are fixed and tectonic forces load at a constant rate, seismic events will occur after a fixed time interval. The point process will form identical events. The only significant variable in such a deterministic model will be the "strain state," and after a long time of events, it forms a cycle of loading and instant relaxation oscillating over time forming a deterministic relaxation oscillator model. The strain state will go to zero soon after a seismic event and increase upward at a constant rate up to a fixed elastic limit value. Immediately after exceeding that value an event occurs that relaxes the strain level to zero. The cycle will continue over time making the time of next seismic event predictable.

This is in agreement with the elastic rebound model proposed by Reid [1] with strain level meaning the same as cumulative elastic strain. The strain level could also mean cumulative moment deficit or total stress level. The strain level can be described as the absolute rapture potential. **Figure 1** shows a diagrammatic representation of a deterministic relaxation oscillator.

Let *S(t)* be the strain in the fault plane at time *t* measured from a value *S*<sup>0</sup> after an event. The strain in the fault is the sum of initial strain after an event *S*<sup>0</sup> and an increase in strain φ due to tectonic loading. The elastic rebound model assumes the strain in the rock load constantly at a rate λ. An event will occur when stain level reaches a constant elastic limits value *SE*.

*Seismic Forecasting Using a Brownian Passage Time Distribution DOI: http://dx.doi.org/10.5772/intechopen.101454*

**Figure 1.** *Deterministic relaxation oscillator.*

$$
\varphi = \lambda t \tag{1}
$$

$$\mathbf{S}(t) = \mathbf{s}\_0 + \lambda t \tag{2}$$

Since φ will be set to 0 after an event and starts to increase constantly at a rate λ until the next event. If events occur at constant intervals tE and if *t* is the clock time, then

$$\mathbf{S}(t) = \mathbf{s}\_0 + \lambda(t - t\_E) \tag{3}$$

$$\mathbf{S}(t) = \mathbf{s}\_0 + \lambda t - \lambda t\_E \tag{4}$$

The deterministic process can be expressed as

$$\mathbf{S}(t) = \mathbf{s}\_0 + q\mathbf{t} - q\mathbf{t}\_E \tag{5}$$

The graphical representation of such a process is shown in **Figure 1** aforesaid.

The seismic formation process has other complex variables, which can be represented in the model with a random error term or white noise εt a random perturbation process. The stochastic relaxation oscillator can be expressed as

$$S(t) = \mathfrak{s}\_0 + \mathfrak{q}t - \mathfrak{q}t\_E + \mathfrak{e}\_t \tag{6}$$

The above stochastic relaxation oscillator equations show that the stain level is made up of a sum of three components.

*s*<sup>o</sup> is the initial constant stain pre-existing in a fault plane. The value of *s*<sup>o</sup> is a constant but differs from fault plane to faults depending on the type of rock.

*φt* � *φtE* is the difference between the strain level at any given time and the strain level after the previous seismic event. The value is as a result of stain accumulation due to seismic building process and depends on time since the last seismic event. Seismic forecasting involves being able to find the best probability model that best fits this component. The component is assumed to follow a Brownian

**Figure 2.** *Brownian relaxation oscillator with random term.*

Passage-Time Distributions as it depends on time past since the last event. In this model *s*<sup>0</sup> ≤*φtE*, this explains why some events may result in aftershock or event after of aftershock because in some events the relaxation may not release all the stains in the fault giving a possibility of an event after another event.

The component *ε<sup>t</sup>* is a random term that takes into the model the effect of other random variables that affect seismic stain building as a result of seismic perturbation. The term is also called the white noise and follows a Brownian motion or Gaussian or normal distribution with mean zero and constant variance. This term results in the formation of a graph with a path up or above the smooth theoretical graph of *St.*

The graphical representation of such a process is shown in red color in **Figure 2**. Such a path is like the drunken man's path. The value of constant variance determines how far the process moves from the line representing the deterministic model.

The seismic strain builds between the time interval *t*<sup>0</sup> and *tE* and builds at a rate. This means the seismic recurrence intervals will have average length μ ¼ <sup>δ</sup> λ. Indeed, the deterministic oscillator with identical recurrence intervals has a variance, σ ¼ 0*:*

#### **1.4 Brownian passage-time distributions**

Let *T* denote the first passage time to level *s*>0 by Brownian motion with drift rate λ >0 and diffusion rate σ2. The probability distribution of T has a well-known closed form with probability density function given by

$$f(t;s; \lambda; \sigma) = \frac{s}{\sqrt{2\pi\sigma^2 t^3}} \ \exp\left(-\frac{(s-\lambda t)^2}{2\sigma^2 t}\right), t \ge 0\tag{7}$$

The distribution is also called the inverse Gaussian or the Brownian passage time.

The cumulative Gaussian probability function is given by

$$F(t) = P(T \le t) \tag{8}$$

$$\begin{split} \text{Let } a &= \frac{\sigma}{\mu} \\ &= \Phi \left\{ \frac{1}{a} \left( \sqrt{\frac{t}{\mu}} - \sqrt{\frac{\mu}{t}} \right) \right\} + \exp^{\left( \frac{t}{\mu} \right)} \Phi \left\{ \frac{1}{a} \left( \sqrt{\frac{t}{\mu}} + \sqrt{\frac{\mu}{t}} \right) \right\} \end{split} \tag{9}$$

Using the log-likelihood function is given by

$$L(s; \lambda; \sigma/t\_i) = \prod\_{i=1}^{n} \left\{ \frac{s}{\sqrt{2\pi\sigma^2 t\_i^3}} \ \exp\left(-\frac{(s - \lambda t\_i)^2}{2\sigma^2 t\_i}\right) \right\} \tag{10}$$

we find that expectation of *<sup>T</sup>*, *E T*ð Þ¼ *<sup>μ</sup>* and variance of *<sup>T</sup>*, *var T*ð Þ¼ *<sup>σ</sup>*<sup>2</sup> <sup>¼</sup> ð Þ *μα* 2 where α is the ratio of the standard deviation to the mean, that is, the coefficient of variation or the aperiodicity of the failure-time distribution. This is also called the ratio of the sample standard deviation to the sample mean. α is a measure of irregularity in the seismic event sequence and determines the shape of the Brownian Passage-Time Distributions.

#### *1.4.1 Time dependence*

According to Matthews et al. [5], the Brownian passage-time distributions quantify occurrence-time probabilities for a steadily loaded system subject to random perturbations. The distribution is used to answer questions like "what is the effect of past time since the last event on conditional probabilities for the next event?"

#### *1.4.2 Hazard rate*

The hazard rate of Brownian passage-time distributions is given by

$$h(t) = \frac{f(t)}{1 - F(t)}\tag{11}$$

#### *1.4.3 Residual life distribution*

To examine the effect of elapsed time on occurrence probabilities, we may consider the residual life conditioned on > *t*. The conditional probability of a seismic event at any given time, *t* given that time *P* has passed after an event is given by

$$R(P) = P(P \le t + P/P > t) \tag{12}$$

$$\dot{x} = \frac{F(t+P) - F(t)}{1 - F(t)}\tag{13}$$

residual life density

$$=r(p) = \frac{\partial \, R(P)}{\partial P} \tag{14}$$

$$\dot{\lambda} = \frac{f(t+P)}{1 - F(t)}\tag{15}$$

mean residual life is

$$m(p) = \int\_0^\infty p \, r(p) \partial p \tag{16}$$

The properties of the Brownian passage-time distributions that makes it appropriate for seismic forecasting is the shape of the hazard-rate function and behavior of the residual life as time increases, asymptotic mean residual life, and hazard rate. The value of *h*ð Þ 0 is also important, as it governs the likelihood of immediate rerupture after an event. All BPT hazard-rate functions share a general shape, The hazard-rate function, *h t*ð Þ, of all models in the Brownian passage-time distribution family always starts at 0 at *t* ¼ 0, then goes upward a highest value at some time after the probability distribution's mode, and then goes down approaching a asymptotically a fixed value *h*<sup>∞</sup> as time approaches infinity. The value can be found by taking the limit as *t* ! ∞ as,

$$h\_{\infty} = \frac{1}{2\mu a^2} \tag{17}$$

Brownian failure process eventually attains a quasi-stationary state in which residual time to failure becomes independent of passed time an applicable property in seismic forecasting. The Brownian passage-time distribution describes the failure probability of a Brownian relaxation oscillator as a function of elapsed time and the statistical properties of the failure time series.

Brownian relaxation oscillator can be used to estimate the probability of a seismic event in the next time interval in a given region. Seismic data from the past events can be utilized to estimate the parameters *s*; λ; σ, which differs from region to region. Once the parameter has been estimated a good approximation of *t*, the time until the next event can then be calculated. This is the proposed model for seismic forecasting.

#### **1.5 Model parameter estimation**

#### *1.5.1 Maximum-likelihood estimates*

Suppose we have a consistent, complete, and homogeneous past seismic data of time between seismic events, *t*1; *t*2; *t*3; … *::tn* of a given region. The estimates of model parameters *s*; λ and σ can be found by looking for values that maximize the parameters using the sample data. Such values can be found by equating to zero the first derivatives of *f*(*t*;*s*;λ;σ) with respect to each parameter.

$$\frac{\partial f(t; \mathbf{s}; \lambda; \sigma)}{\partial \mathbf{s}} = \prod\_{i=1}^{n} \frac{\partial \frac{s}{\sqrt{2\pi \sigma^2 t\_i^{\beta}}} \cdot \exp\left(-\frac{\left(\mathbf{s} - \lambda t\_i\right)^2}{2\sigma^2 t\_i}\right)}{\partial \mathbf{s}} = \mathbf{0} \tag{18}$$

$$\frac{\partial f(t;s;\lambda;\sigma)}{\partial \lambda} = \prod\_{i=1}^{n} \frac{\partial \frac{s}{\sqrt{2\pi\sigma^2 t\_i^{\lambda}}} \exp\left(-\frac{(s-\lambda t\_i)^2}{2\sigma^2 t\_i}\right)}{\partial \lambda} = \mathbf{0} \tag{19}$$

$$\frac{\partial f(t;s;\lambda;\sigma)}{\partial \sigma} = \prod\_{i=1}^{n} \frac{\partial \frac{s}{\sqrt{2\pi\sigma^2 t\_i^{\beta}}} \exp\left(-\frac{\left(s - \lambda t\_i\right)^2}{2\sigma^2 t\_i}\right)}{\partial \sigma} = 0 \tag{20}$$

The aforesaid equations will give maximum-likelihood estimates of *s*;λ and σ that can be used in the probability density function *f t*ð Þ ; *s*; λ; σ to estimate the variable time *t* of future events.

#### *1.5.2 Magnitude forecast*

The time of a seismic event is estimated from the above model *f t*ð Þ ; *s*; λ; σ , but every seismic event must have a forecasted value of the expected magnitude. Suppose we have consistent, complete and homogeneous past seismic data of magnitudes *m*1; *m*2; *m*3; … *::mn* of a given region.

The unbiased estimate of the future expected magnitude is the mean of past magnitude

$$m\_{\mu} = \frac{\sum\_{i=1}^{n} m\_i}{n} \tag{21}$$

From 1.5.1 and 1.5.2 above, the time and magnitude of future events can be forecasted.

#### **2. Conclusion**

The Brownian relaxation oscillator with random term for seismic random perturbation modeled above represents a model that can be used for seismic forecasting. The inclusion of the error term in the model gives allowance of a deviation of the forecast from the actual event. Since the error term is known to be normally distributed with mean zero and constant variance, the expectation of the deviation is zero. The error term also represents other seismic formation variables and errors in model parameter estimation.

For one to accurately use the model, a consistent, complete, and homogeneous with unified magnitude, past seismic data are needed. Such data are always unavailable, because a complete catalog of the full population of events begins from the start of the earth planet formation. Statistical method utilizes a very small sample beginning "yesterday" when seismic monitoring started to infer population parameters. If such model parameters can be accurately estimated, seismic events can be accurately forecasted using the Brownian relaxation oscillator with random perturbation.

#### **Conflict of Interest**

The author declares no conflict of interest.

*Earth's Crust and Its Evolution - From Pangea to the Present Continents*

#### **Author details**

Edmore Utete National University of Science and Technology, Bulawayo, Zimbabwe

\*Address all correspondence to: eddieutete@yahoo.com

© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Seismic Forecasting Using a Brownian Passage Time Distribution DOI: http://dx.doi.org/10.5772/intechopen.101454*

#### **References**

[1] Reid HF. On mass-movements in tectonic earthquakes. In: The California Earthquake of April 18, 1906: Report of the State Earthquake Investigation Commission. Washington, D.C: Carnegie Institution of Washington; 1910

[2] Nishenko S, Buland R. A generic recurrence interval distribution for earthquake forecasting. Bulletin of the Seismological Society of America. 1987; **77**(1382):1389

[3] Hagiwara Y. Probability of earthquake occurrence as obtained from a Weibull distribution analysis of crustal strain. Tectonophysics. 1974;**23**:313-318

[4] Kagan YY, Knopoff L. Statistical short-term earthquake prediction. Science. 1987;**236**(4808):1563-1567

[5] Matthews MV, Ellsworth WL, Reasenberg PA. A Brownian model for recurrent earthquakes. Bulletin of the Seismological Society of America. 2001; **92**(6):2233-2250

[6] Kagan YY, Jackson DD. Worldwide doublets of large shallow earthquakes. Bulletin of the Seismological Society of America. 1999;**89**:1147-1155

[7] Davis PM, Jackson DD, Kagan YY. The longer it has been since the last earthquake the longer the expected time till the next? Bulletin of the Seismological Society of America. 1989; **79**:1439-1456

#### **Chapter 9**

## Revelation of Potentially Seismic Dangerous Tectonic Structures in a View of Modern Geodynamics of the Eastern Caucasus (Azerbaijan)

*Talat Kangarli, Tahir Mammadli, Fuad Aliyev, Rafig Safarov and Sabina Kazimova*

#### **Abstract**

The stress state of the earth's crust in the Eastern Caucasus, located in the zone of collision junction of the North Caucasian, South Caucasian, and Central Iranian continental massifs, is a consequence of the inclusion of the Arabian indenter into the buffer structures of the southern framing of Eurasia at the continental stage of alpine tectogenesis. This evidenced from the results of geophysical observations of the structure and seismic-geodynamic activity of the region's crust. The latter, at the neotectonic stage, was presented as underthrust of the South Caucasian microplate under the southern structures of Eurasia. The analysis and correlation of historical and recent seismic events indicate the confinement of most earthquake foci to the nodes of intersection of active faults with various orientations or to the planes of deep tectonic ruptures and lateral displacements along unstable contacts of material complexes of various competencies. The focal mechanisms of seismic events reveal various rupture types, but in general, the earthquake foci are confined to the nodes of intersection of faults of the general Caucasian and anti-Caucasian directions. Based on the observed weak seismicity, active areas of deep faults were identified, which are accepted as potential source zones.

**Keywords:** earthquake, seismotectonics, focal mechanism, geodynamics, accretionary prism

#### **1. Introduction**

The territory of the Middle East, the northern periphery of which corresponds to the South Caucasus, is a collage of different-scale tectonic blocks—Anatolian-Taurus, Central Iranian, South Caucasian microplates, and smaller blocks (**Figure 1**), located between the Arabian continental plate (in the south) and the southern edge of the Eurasian continent (in the north). The latter at the neotectonic stage of tectogenesis (from the end of the Miocene) exist in the regime of collision convergence, which in turn causes exceptional tectonic activity in the region [2–15]. This feature is evidenced by often occurrence of strong and destructive earthquakes in Turkey, Iran, and the Caucasus Isthmus in the present time. The seismicity of these territories is explained by intensive restructuring of the structural plan with significant amplitudes of recent movements.

#### *Earth's Crust and Its Evolution - From Pangea to the Present Continents*

#### **Figure 1.**

*Allocation of accretion prism within structure of the Greater Caucasus of the Caucasus isthmus (modified from [1]).*

#### **Figure 2.**

*Map of earthquakes epicenters М* ≥ *3.0 of the territory of Azerbaijan for the period 2004–2020.*

In this regard, the eastern part of the South Caucasus, where Azerbaijan is located, characterized by highly seismic activity with periodic occurrence of seismic events with *M* > 5, is no exception (**Figure 2**).

The stress state of the earth's crust in the region located in the collision junction zone of the North Caucasian, South Caucasian, and Central Iranian continental massifs (tectonic microplates) is a consequence of the intrusion of the Arabian indenter into the buffer structures of the southern framing of Eurasia at the continental stage of alpine tectogenesis (from the end of the Miocene). This is evidenced by the results of geophysical observations of the structure and seismicgeodynamic activity of the region's earth crust. The latter, at the neotectonic stage,

#### *Revelation of Potentially Seismic Dangerous Tectonic Structures in a View of Modern… DOI: http://dx.doi.org/10.5772/intechopen.101274*

was an underthrust (S-subduction—continental subduction or pseudosubduction) region of the South Caucasian microplate under the southern underbelly of Eurasia (Scythian-Turanian epigercynian platform) in the northern wing and active terrestrial volcanism with the formation of volcano-plutonic complexes. Namely, the process of lateral compression, which continues at the current stage of tectogenesis under the influence of the collision approach of the Arabian and Eurasian continents, determines the high level of seismic and geodynamic activity in the study area.

Seismological and paleoseismotectonic studies, and seismic and seismotectonic zoning works carried out in various seismic regions of the Caucasus (including territory of Azerbaijan) confirm the controllability of earthquake focal areas by a network of faults of general Caucasian and anti-Caucasian direction with various types of prolongation. However, in general, the reason of current seismic activity is the horizontal movements of different-scale tectonic blocks of the earth's crust, located in the zone of collision interaction of the Afro-Arabian and Eurasian continental plates.

We carried out the analysis and interpretation of seismological data, as well as the results from GPS monitoring of modern geodynamic activity with the identification of their correlations with the features of the deep structure. GPS monitoring data in the Eastern Caucasus indicate an intensive advancement of the South Caucasus block in the northern points. The analysis and correlation of historical and recent (2012–2020) seismic events indicate the confinement of earthquake sources mainly to the nodes of intersection of active faults of various strikes or to the planes of deep tectonic disruptions and lateral displacements along unstable contacts of material complexes of various competencies.

#### **2. Recent geodynamic processes**

The observed seismic activity is generally confined with the rates of horizontal movements that took place for the period of GPS monitoring of the modern geodynamics of the region since 1998 [6, 16–21]. In comparison with the data for 2004, the rates of horizontal movements for the absolute majority of observation points according to the data of 2020 increased by 2–8 mm/year (**Figure 3**). At the same time, transverse zoning is traced in the distribution of velocities, similar to the seismic one: to the west of Samur-Agdash velocity, disturbances are on average 8–10 mm/year, and to the east of it, they exceed 13 mm/year (13–15 mm/year).

At the same time, longitudinal zoning is observed in the distribution of the rates of horizontal movements, which correlates with the main Caucasian tectonic zoning of the territory.

Review of the distribution data of the velocity vectors of horizontal displacements of GPS geodetic points on the territory of Azerbaijan and the neighboring areas of Iran for the period 1998–2020 leads us to conclude about a significant (up to 15 mm/year) rate of movement in the north-north-east direction of the southwestern flank and the central strip of the South Caucasian microplate, including the territory of the south-eastern segment of the Lesser Caucasus, Kura depression, and Talysh. At the same time, within the northeastern flank of the microplate corresponding to the Vandam-Gobustan megazone of the Greater Caucasus, the velocity vectors are reduced to 6–13 mm/year, and even further north, in the hanging wing of the Kbaad-Zanginsky deep underthrust, that is, directly within the accretionary prism, completely decrease to 0–6 mm/year (data from 2010 to 2014). In general, the tangential contraction of the earth's crust in the region is estimated at 4–10 mm/year.

#### **Figure 3.**

*GPS velocities of horizontal movements of the earth's surface in Azerbaijan and adjacent regions in 2004 (a) and 2020 (b). Compiled by R.T. Safarov. (1) main structural zones (longitudinal tectonic blocks): (I) Gusar-Davachinskaya, (II) the Lateral Ridge of the Greater Caucasus, (III) Southern slope of the Greater Caucasus, (IV) Kakheti-Vandam-Gobustan, (V) Kurinskaya, (VI) Artvin-Garabagh, (VII) Talysh, and (VIII) Araz; (2) deep faults at the boundaries of structural zones; and (3) Samur-Agdash fault.*

This is confirmed by the observed directions and velocities of the earth's surface movement within territory of Azerbaijan and adjacent areas according to the results of measurements of GPS points in 2015–2020 (**Figure 4**). The velocity field clearly illustrates the movement of the earth's surface in the N-NE direction. At the same time, the plots clearly show a specific feature of the velocity field, namely a contrasting decrease in velocity at observation points located in the southern wing of the Zangin thrust fault, in comparison with the velocities recorded within the Kura and more southern zones (see **Figures 3** and **4**).

#### **Figure 4.**

*GPS velocities of horizontal movements in Azerbaijan and adjacent regions (2020) and graphs of parallel and transverse components of GPS velocities along sections AA /, BB /, CC /, DD /, and EE / [5, 6, 16].*

*Revelation of Potentially Seismic Dangerous Tectonic Structures in a View of Modern… DOI: http://dx.doi.org/10.5772/intechopen.101274*

This phenomenon reflects the process of successive accumulation of elastic deformations in the pseudosubduction interaction zone of the northern flank structures of the South Caucasian microplate (Vandam-Gobustan megazone) with the accretionary prism of the Greater Caucasus.

Active faults: A well-pronounced indicator of the activity of faults (fault zones) is weak seismicity, so that, any even the smallest tectonic movements in disjunctive zones generate more or less strong seismic shakes.

The map of the earthquake epicenters that occurred on the territory of Azerbaijan over the past 20 years shows that focal zones are distributed very randomly here (**Figure 2**). At the same time, a similar peculiarity is observed within the most highly active regions, where the weaker earthquake foci clustering was observed in some areas.

One of the authors has developed a method for identifying real-time active segments of deep faults based on manifestations of weak seismicity in these zones [22]. This method is based on the idea of seismogenic structures (zones), which are known to be active faults that delimit geotectonic structures with different tectonic regimes and accumulate all strong and most of the weak and medium-strength earthquakes. According to the proposed method, the identification of seismogenic zones is carried out on the basis of the breakdown of the study area into equal areas and plotting of a map of weak seismicity. For each of these areas, within which the number of epicenters is not less than the specified threshold value, approximating lines of concentration of epicenters are constructed.

It is assumed that these lines correspond to active faults zones. These zones are actually potential source zones for strong earthquakes in specific territories. The method for determining active deep faults based on weak seismicity makes it possible to determine the location of potential source zones, as well as calculate their seismic potential and seismic effect that may occur on the earth's surface in the event of seismic activity. To assess the degree of their manifestation, the position of the sources of earthquakes and the parameters of the seismic regime are determined. At the same time, the catalogs of earthquakes are analyzed taking into account foreshock and aftershock activity, the stretch of pleistoseist zones, the character of the seismic effect decay depending on the distance, and other factors.

Coming from aforesaid, a map of potential seismic hazard for the territory of the Azerbaijan was compiled on the basis of a spatial analysis of weak seismicity (**Figure 5**), and active faults (fault zones) at the current stage of tectogenesis were identified. Based on the observed weak seismicity, active areas of these faults were identified, which are potential foci zones. At the same time, the relationship between the length of focal zones and the maximum possible magnitudes of earthquakes in them has been determined. It was found that the value of the maximum possible earthquake magnitudes (*M*MAX) in the territory of Azerbaijan is approximately equal to 7 (*M*MAX = 6.9 ÷ 7.3).

The features of the seismic activity manifestation on the territory of Azerbaijan are considered by us on the example of the southern slope of the Greater Caucasus, which at the current stage of tectogenesis is the most seismically active region of the country. Large seismic events periodically occur here, accompanied by the spontaneous release of large volumes of energy from the earth's interior. Seismic activity is associated with the ongoing intensive restructuring of the structural plan with significant amplitudes of the latest and modern movements: earthquake foci, as a rule, are confined to the boundaries of large geotectonic elements of the earth's crust and nodes of intersection of faults of various directions.

**Figure 5.** *Map of the potential seismic hazard of the territory of Azerbaijan. Compiled by T. Ya. Mammadli.*

#### **3. The dynamics of the manifestation of seismic activity**

In-depth uneven distribution of earthquakes foci, in fact, proves ongoing pseudo-subduction interaction within southern slopes of the Greater Caucasus. The hypocentral levels exist in 2–6, 8–12, 17–22, and 25–45 km. The analysis of in-depth earthquake distribution evidences about existence of structural-dynamic interrelation between along with subvertical and subhorizontal contacts in the earth crust. Spatial and in-depth earthquake clustering can be explained from the point of view of block partibility and tectonic stratification of the earth crust (**Figures 6** and **7**). Structurally, these clusters generally confine to the intersection junctions of fault zones with various directions or to the planes of tectonic ruptures and lateral displacements along weak contacts of multicomponent material complexes [21, 23–29].

Coming from temporal and spatial analysis of *M* ≥ 3 earthquakes' foci distribution for the instrumental period of monitoring (1902–2020), we delineated dynamics of seismic activity in northern slope of the Greater Caucasus (**Figures 6** and **7**). Using data of geophysical data reinterpretation, along with compiled tectonic and magmatic schemes of the study area, we divide this territory to four blocks (separated by various anti-Caucasian faults) with various levels of seismic activity [7, 29]. They are Zagatala, Sheki, Gabala-Shemakha, Gobustan zone. First, two clocks stand as eastern segments, whereas two others represent south-east segments of the Greater Caucasus. These segments are divided by Samur-Aghdash left-lateral strike-slip fault (**Figure 6**).

First, two blocks are distinguished for their lower seismic activity recorded throughout the entire period of observations (**Figure 7**):

• until 1980, 12 seismic events occurred within the Zagatala block's frontiers, confined to the consolidated crust's upper segment. Absolute majority of focuses (11) is located at depths of 12–30 km. Since 1980 until present, 66 events were recorded, with 9 events sourcing from the sedimentary cover, and 57—from 5 to 30 km depths of the consolidated crust;

*Revelation of Potentially Seismic Dangerous Tectonic Structures in a View of Modern… DOI: http://dx.doi.org/10.5772/intechopen.101274*

#### **Figure 6.**

*Schematic map of fault tectonics and earthquakes foci zones distribution on the level of Pre-Jurassic basement compiled by T.N. Kangarli, F.A. Aliyev and A.M. Aliyev [23]. (1) longitudinal blocks of the first order: (а) Tufan (T), (b) Kakheti-Vandam-Gobustan (KVG), (c) Chatma-Ajinohur (ChA), and (d) Middle Kur (MK); (2) transverse blocks of the first order: (I) Zagatala, (II) Shaki, (III) Gabala-Shamakhy, and (IV) Gobustan; (3) faults on borders of longitudinal blocks of the first range: KZ—Kbaad-Zangi, GA—Ganikh-Ayrichay-Alat, and NK—Northern Kur; (4) ruptures limiting the longitudinal blocks of the second order, including: DM—Dashaghil-Mudrisa and ShI—Shambul-Ismayilly; (5) faults on borders of transverse blocks of first level: Sl—Salavat, SA—Samur-Aghdash, and PN—Pirsaat-Neftchala; (6) other ruptures of anti-Caucasus direction, including: KK—Khimrikh-Khalatala, BV—Bulanligchay-Verkhiyan, B—Balakan, Z—Zagatala, GS—Gokhmug-Salyakhan, F—Fiy, US—Ujar-Saribash, D—Damiraparanchay, G—Girdimanchay, and S—Sighirly; and (7) earthquakes foci zones of 2012–2016 with М* ≥ *3: (a) given in a paper: (I) Balakan, (II) Zagatala, (III) Shaki, (IV) Oghuz, and (V) Gabala; and (b) other; and (8) state border.*

#### **Figure 7.**

*Synthetic seismic profile of MOVZ along the longitudinal traverse of Balakian-Shamakhi. Compiled by T.N. Kangerli, A.M. Aliyev and F.A. Aliyev. 1–3 layers of the consolidated crust: (1) sedimentary; (2) "granite"; (3) "basalt"; (4) "waveguide"; (5) upper mantle; (6) intrusives (Ш—Sheki and B—Buinuz); (7) formation velocities of seismic waves; and (8) breaking violations.*

First, two blocks differ by more pronounced seismic activity for the entire monitoring period (**Figure 7**). More detailed quantitative analysis gives us the following outcomes:

• Twelve seismic events took place within Zagatala block till 1980, which are confined to the upper part of earthquakes here. Nine of these events occurred in sedimentary layer, while 57 took place in consolidated crust within depth interval of 5–30 km;

• There occurred 14 seismic events within Shaki block till 1980. Three of these earthquakes took place in a depth of 3–5 km. which confines to alpine cover, while the rest part clustered in depths of 5–30 km, which confine with the consolidated crust. For the following period of 1981–2017, the number of earthquakes raised to 65, three of which occurred in the sedimentary layer, while 62 in consolidated crust (the distribution was 58 and 3 in the upper and lower segments respectively and 1 below Moho boundary).

Gabala-Shamakhy and Gobustan blocks have been more active throughout the entire period of observations, but there were also the leaps of seismic activity recorded in last quarter of XX century (**Figure 8**):


It can obviously be stated that the process of seismic activity was rising in the study area since 1980s of the last century. And this is despite the fact that the technical and methodological allowances for earthquake registration were not so qualitative as in the present (**Figure 9**). Within eastern segment of the study area, the upper part of the consolidated crust reveals as more seismic active, while in the south-eastern segment, earthquakes foci are scattered in the whole earth crust

#### **Figure 8.**

*Histogram of the vertical distribution of earthquake sources with М* ≥ *3 over the blocks of the earth's crust on the southern slope of the Greater Caucasus within Azerbaijan (1902–2017). Compiled by F.A. Aliyev.*

*Revelation of Potentially Seismic Dangerous Tectonic Structures in a View of Modern… DOI: http://dx.doi.org/10.5772/intechopen.101274*

**Figure 9.**

*Histogram reflecting changes in seismic activity (earthquakes with M* ≥ *3) in the Azerbaijani part of the southern slope of the Greater Caucasus in space (in depth) and in time for the period 1902–2017: (a) Gabala-Shamakhi and Gobustan blocks; and (b) Zagatala and Sheki blocks. Compiled by F.A. Aliyev.*

and also in the upper mantle. Most of deep seismic foci are located in a zone of the Western Caspian submeridianal fault. To the east of this fault zone, we can observe a stepped dipping the consolidated crust toward the Caspian hollow.

Spatial-temporal analysis of the distribution of strong seismic events in the Greater Caucasus region (within Azerbaijan borders) allows us to conclude that the northern flanks of Southern Caucasus microplate (these are structures that buried beneath accretionary wedge in the north, and the structures that revealed as a central segment or covered by a quaternary layer on the southern part of Kakheti-Vandam-Gobustan zone) are most active at the present stage of tectogenesis. These seismic active parts are divided into two zones:


• a complex tectonic knot located within the Talysh-Samur-Makhachkala submeridional seismotectonic zone in the east of the described area, corresponding to the intersection of two fault zones: the northwestern direction West Caspian (bounded from the north-east by Pirsaat and south-west by Sygyrli by elementary right lateral strike-slip faults) and Girdymanchay-Gonagkend of northeastern strike (represented by Basgal-Khashyn, Agsu-Khaltan, Sagiyan-Dibrar, Goylyardag-Nabur, and other disturbances).

Under lateral compression environment, small-scale blocks that constitute the region's earth crust trigger the emergence of transpressive deformations, which combine the shear displacements along framing transverse deformations with the compression structures such as "general-Caucasus" ruptures. Such regime leads to an emergence of multiple concentration areas of the elastic deformations confined to the mentioned dislocations and their articulation knots. It is just the exceeded ultimate strength of the rocks that causes an energy discharge and brittle destructions (according to stick-slip mechanism) in such tectonically weakened regions of the southern slope of the Azerbaijani part of Greater Caucasus (**Figure 6**).

Due to lateral compression state of the small-size blocks, into which the crust of the region is fragmented into parts, the formation of a transpressive type of deformation combines shear displacements along transverse faults. These faults confine blocks with compression structures, which include faults of the general Caucasian direction. In this tectonic regime, elastic stress is being concentrated in several zones that confined to the indicated dislocations and their junction points. Due to excess of the possible strength ability of rocks, the accumulated elastic deformations leads to energy discharge by means of earthquakes (mostly stick-slip type) in these tectonically weaker zones of the southern slope of the Greater Caucasus (see **Figure 10**).

The existence of tangential stresses in the region in real time is also indicated by the focal mechanisms of earthquakes with *M* ≥ 3 that occurred in the period

#### **Figure 10.**

*Scheme of distribution of tectonic stresses from earthquake mechanisms with M* ≥ *3 for 2003–2017—Compiled by S.E. Kazimova.*

*Revelation of Potentially Seismic Dangerous Tectonic Structures in a View of Modern… DOI: http://dx.doi.org/10.5772/intechopen.101274*

#### **Figure 11.**

*Focal mechanisms of earthquakes with М* ≥ *3 for the period 2003–2017. Compiled by S.E. Kazimova.*

2003–2017 (**Figure 11**). Analysis of the distribution of the axes of compression and expansion indicates the predominance of lateral compression oriented in the submeridional and NE-SW directions. The types of focal mechanisms generally correspond to the concepts of the geodynamics of convergent microplate boundaries, where the entire set of these mechanisms is noted (**Figure 11**)—from fault to reverse [23–25, 27, 28, 30, 31].

This is evidenced by the seismic events that took place in Northwestern Azerbaijan in the period from 2012 to the first half of 2018. As an example, the conditions and factors that determined the tangible seismic activity in the Zagatala, Balakan, and Sheki source zones are given.

One of most seismically active zones in 2012–2015 was Zagatala focal zone, where three earthquakes with *M* = 5.27–5.69 (07.05.2012) took place, along with one event with *М* = 5.02 (20.06.2012) and numerous aftershocks with *М* = 3.0–4.4. A huge number of earthquakes foci are located within depth of 5–20 km, which confines to the pre-Jurassic basement of Kakheti-Vandam-Gobustan zone's frontal part. Despite the mostly clustered focal zone, one aftershock occurred outside of this zone, within Alpine cover (07.05.2012, 05:40). This is located in vicinity of Kvemo-Kedi village (Georgia) and corresponds to a plane of Ganikh-Ayrichay-Alat thrust fault that plunges in the northern rhumbs at its intersection with northeastoriented Zagatala trans-tensional fault.

Overall, this source zone is a complex disjunctive node located in the upper part of the pre-Jurassic basement, consisting of elementary knots of intersection of tectonic faults with various orientations, where earthquake foci confined (see **Figures 12** and **13**). The volume of the rock mass, where earthquake hypocenters along with aftershocks with *М* ≥ 3 are concentrated, reaches approximately 3400 km3 .

These earthquake series are mainly associated with the activity of the Zagatala transverse fault, which in turn activated and related to the disturbances in the all-Caucasian and anti-Caucasian directions. Earthquake mechanisms here indicate the predominance of strike-slip and fault movements with the assistance of faultstrike-slip and reverse fault movements in the source zone.

#### **Figure 12.**

*The ratio of rupture dislocations and earthquake epicenters with M* ≥ *3 for the period 2012–2014—By T.N. Kangarli, F.A. Aliyev, and A.M. Aliyev [23, 28].*

#### **Figure 13.**

*Geological and geophysical section through Zagatala (III-III′ in Figure 8, 14), Balakan focal zones of earthquakes—By T.N. Kangarli, F.A. Aliyev, and A.M. Aliyev [23, 28].*

*Revelation of Potentially Seismic Dangerous Tectonic Structures in a View of Modern… DOI: http://dx.doi.org/10.5772/intechopen.101274*

### **4. Conclusions**

Analysis was performed and correlation between tectonics and modern seismic activity of the studied region leads to the following conclusions:


### **Author details**

Talat Kangarli1 \*, Tahir Mammadli<sup>2</sup> , Fuad Aliyev1 , Rafig Safarov1 and Sabina Kazimova2

1 Institute of Geology and Geophysics, ANAS, Baku, Azerbaijan

2 Republican Seismology Survey Center, ANAS, Baku, Azerbaijan

\*Address all correspondence to: tkangarli@gmail.com

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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### *Edited by Mualla Cengiz and Savaş Karabulut*

Despite decades of study, fundamental aspects of the development of the Earth's crust remain enigmatic. This book presents geophysical and geological studies obtained from different tectonic structures and geological time intervals. It contains three sections: "Crustal Evolution and Tectonic Problems", "Geophysical Methods in Geological Applications" and "Seismic Forecasting, Seismotectonics and Geodynamic Evolution of the Himalayan Belt". Chapters address such topics as the evolution of tectonic structures of Earth, how geophysical and geological data can be used for modelling this evolution, and the geodynamic processes in the Earth's crust with the present tectonic activity.

Published in London, UK © 2022 IntechOpen © Rost-9D / iStock

Earth's Crust and Its Evolution - From Pangea to the Present Continents

Earth's Crust and Its

Evolution

From Pangea to the Present Continents

*Edited by Mualla Cengiz and Savaş Karabulut*