**3.4. Statistical analysis based on lineament data**

from middle phase representing 4 days before earthquake scenario, and two major trends ESE-

The unusual behavior of lineaments clearly seen from these three phases of rose diagrams which shows an anomaly prior to earthquake strike. Whereas, lineament direction was trying to reach its original state but failed to adjust its original position due to internal geodynamic activities that occurred by this high magnitude (7.8 Mw) earthquake. The mean strike position was in E-W and along with two other directions NNE-SSW and SSE-WNW were observed in the post-earthquake phase (**Figure 7e**), though still there exist anomaly compare to normal phase in the absence of earthquake event. Subsequently, all these lineaments directional change were correlated and related within the regional context of the Gorkha-Nepal and its adjoining areas which is a great indication of any structural change and considered as a vital

On the other hand, **Figure 8** illustrates the lineament directions movement around Imphal, Manipur regions from 19 March 2015 to 17 January 2016 (**Figure 8(a–e)**). In order to analyze the lineaments directional change, the present analysis has been performed in the absence

**Figure 8.** Directional change measurement through rose diagrams for Imphal-Manipur earthquake: (a) 19 March 2015, (b) 30 November 2015, (c) 16 December 2015, (d) 1 January 2016, (e) 17 January 2016 (all diagrams based on temporal

WNW and N-S directions clearly be interpreted from this rose diagram (**Figure 7d**).

clue to know that impending earthquake.

164 Multi-purposeful Application of Geospatial Data

lineament data).

After fluctuations analysis of lineament data as shown in Section 3.2, few statistical test were performed in this section against number of lineament and length change. This statistical analyses were done based on the method discussed in Section 3.2.4 under Section 3.2, by using box-whisker for number of lineament and line trend by considering mean and SD value (**Figures 9** and **10**) in the absence and the presence of earthquake event.

The automatic extraction of lineament data values of both tables (**Tables 2** and **3**) suggests anomaly presence over the two study areas prior to earthquake strike, which was also observed even after the earthquake. On the other hand, the scenario was quite normal in the absence of earthquake event. The derived result illustrates different number of lineaments as observed through box plot and whiskers line chart (**Figure 9a**: Gorkha of Nepal; **Figure 10a**: Imphal of Manipur). However, line length value also differs in both cases (**Figure 9b**: Gorkha and **Figure 10b**: Imphal). These changes were noticed in our two cases, and probably due to

**Figure 9.** Results of number of lineament and lineament length change observed in the absence and the presence of Gorkha-Nepal earthquake (25 April 2015), from 31 January 2015 (85 days before) to 7 May 2015 (12 days after). (a) Number of lineament variation is represented by box-whisker with black line and (b) lineament mean length change (measured in kilometers) is represented by black dash line and solid black line with dot and light black arrow headed lines showing standard deviation (SD) value, respectively based on number of days observation.

overlay analyses were performed to observe the abnormal and normal behavior of those lineaments, next directional change measurement by creating rose diagrams considering the lineament number and length. Finally, statistical comparisons were performed and presented under the three phases of lineament behavior changes in respect of earthquake day. The exact epicenter, magnitude, depth, and time of occurrence of earthquake on particular strike day are quite challenging to predict through this present study, but observing lineament data anomalies from the two case studies (prior to earthquake) suggests that pre-earthquake anomaly detection is possible. Landsat 8 OLI satellite sensors-based time series data show its credit to extract lineament data through most popular traditional automatic LINE algorithm techniques found suitable for this research and help to identify the pre-earthquake anomaly of lineaments in two earthquake prone areas. Though few lineament extractions were obstructed due to the presence of cloud around the epicenter and its adjoining areas, but the outcome showed that it has less

Pre-earthquake Anomaly Detection and Assessment through Lineament Changes Observation…

http://dx.doi.org/10.5772/intechopen.72735

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The research results from both study areas suggest that, as the time progresses, the lineament behavior also changed, which identified and confirmed through the experimental results based on the theoretical model and related methods. However, this change was obvious and probably occurred during that time due to movement of the underlain structure and several unknown internal activities. Through the present analyses method, this study assessed successfully of the two earthquakes in two different locations, that is, Gorkha of Nepal (7.8 M<sup>w</sup> with 15-m depth, major category) and Imphal of Manipur, eastern India (6.7 Mw with 56-m depth, strong category) earthquakes, respectively. The existing unusual lineament anomalies appeared all over the images in the pre-earthquake stage, especially highly observed close to

In this study, based on the Landsat 8 OLI satellite-derived lineament data of the two earthquake regions from 2015 to 2016, the spatial fluctuations of lineaments data and their behavioral changes were analyzed in the presence and absence of earthquake event, which categorized into three phases, that is, in the absence of earthquake (no anomaly), the presence

The Gorkha earthquake of Nepal was a result of thrusting along the Main Himalayan Thrust (MHT) [27], and the analysis of the SAR interferograms led to the interpretations that the event was a blind thrust and seismogenic fault [28–30]. However, for Imphal the existing literature suggest that the regional plate boundary in eastern India-the Indo-Burmese Arc is oriented approximately south-southwest-north-northeast directions, see [31], matching the

Present research is the first kind of study conducted and applied in both the earthquake prone areas based on the theoretical model concept. This study creates a breeze in between all four softwares, which were deployed from preprocessing to final stage output performed through geointegration techniques of ENVI—PCI Geomatica—ArcGIS-RockWorks software's, respectively. These combined techniques were successfully applied on Landsat 8 OLI optical imageries, which

the epicenter area in both cases (epicenter marked in red asterisks).

of earthquake event (anomaly) and post-earthquake phase, respectively.

effect on the extracted data.

**4. Conclusions**

orientation of extracted lineaments.

**Figure 10.** Results of number of lineament and lineament length change observation in the absence and the presence of Imphal-Manipur earthquake (4 January 2016) from 19 March 2015 (292 days before) to 17 January 2016 (12 days after). The internal details of data representations of both figures are the same as **Figure 9**.

different geologic condition, structural arrangements, depth, and magnitude variations of the mentioned two earthquakes.

The earthquake occurrence day is represented as a vertical solid black line. The left black vertical dotted line represents the absence of earthquake event (no anomaly) at this stage, and the second black point dash line represents extreme anomaly of 4 days prior to strike and black dash line line indicates the anomaly still present representing post-earthquake scenario. In both cases, the X-axis represents the days, which considered for lineament change observation during the corresponding earthquake, and Y-axis represents the number of extracted lineaments (**Figures 9a** and **10a**), and lineament length (km) represents with the SD and mean value (**Figures 9b** and **10b**).

**Figures 9** and **10** represent data anomaly of two study areas in the presence of earthquake event (prior to earthquake). However, the number of lineament was found stable (in the absence of earthquake event) when the days observed 85 days before the earthquake event (case 1: Gorkha-Nepal) compared to the highest anomalous behavior present prior to earthquake strike (4 days before) and recorded approx. three times higher number of lineaments (**Figure 9a**). However, lineament anomaly was observed 2.5 times higher than stable condition (20 days before strike).

In **Figure 10** (Imphal-Manipur case), anomaly exists in the absence of earthquake event, and when the observation day's progresses from initial anomaly stage, lineaments were increased (4 days before: highest abnormality presence in the anomaly stage) than two other anomaly phases (**Figure 10a**), whereas other two phases were also showing anomaly.

On the other hand, lineament length (km) mean value was recorded as higher (**Figure 9b**). However, the mean length can be shorter or longer and it can be varied due to different geological settings and underlying geological activities. Furthermore, the SD value of lineament length of the two study areas represents (solid black line with dot symbol) different trend, which is decreasing-increasing-decreasing trend in strong earthquake case (Gorkha of Nepal: 7.8 Mw) compared to the absence of earthquake (**Figure 9b**). Whereas, in major earthquake case (Imphal of Manipur: 6.7 Mw), it follows increasing-decreasing-increasing trend (**Figure 10b**).

The results, which observed in each stage based on different analyses method, have individual credit, but all these data are integrated with each other in a sense, like that, first it generated number of lineaments, then measured the lineament length and its overall statistical values. Thereafter, overlay analyses were performed to observe the abnormal and normal behavior of those lineaments, next directional change measurement by creating rose diagrams considering the lineament number and length. Finally, statistical comparisons were performed and presented under the three phases of lineament behavior changes in respect of earthquake day. The exact epicenter, magnitude, depth, and time of occurrence of earthquake on particular strike day are quite challenging to predict through this present study, but observing lineament data anomalies from the two case studies (prior to earthquake) suggests that pre-earthquake anomaly detection is possible.

Landsat 8 OLI satellite sensors-based time series data show its credit to extract lineament data through most popular traditional automatic LINE algorithm techniques found suitable for this research and help to identify the pre-earthquake anomaly of lineaments in two earthquake prone areas. Though few lineament extractions were obstructed due to the presence of cloud around the epicenter and its adjoining areas, but the outcome showed that it has less effect on the extracted data.

The research results from both study areas suggest that, as the time progresses, the lineament behavior also changed, which identified and confirmed through the experimental results based on the theoretical model and related methods. However, this change was obvious and probably occurred during that time due to movement of the underlain structure and several unknown internal activities. Through the present analyses method, this study assessed successfully of the two earthquakes in two different locations, that is, Gorkha of Nepal (7.8 M<sup>w</sup> with 15-m depth, major category) and Imphal of Manipur, eastern India (6.7 Mw with 56-m depth, strong category) earthquakes, respectively. The existing unusual lineament anomalies appeared all over the images in the pre-earthquake stage, especially highly observed close to the epicenter area in both cases (epicenter marked in red asterisks).
