*2.3.3. Lineament direction analysis method*

pixels (30), FTHR-Threshold for Line fitting error in pixels (3), ATHR-Threshold for Angular

In the first stage, RADI parameter (filter radius) specifies the size of the Gaussian kernel used as a filter during edge detection. The edge strength image was threshold to obtain a binary image. Therefore, the choice of RADI value depends on the condition, like as, the greater the value, the less noise and detail appear in the edge detection image. In the second stage, this image was defined by the GTHR parameter (edge gradient threshold) value after testing with different values, and the suitable one was considered. In the third stage, curves are extracted from binary edge image, which have several substeps. First, a thinning algorithm was applied to the binary edge image to generate pixel-wise skeleton curves, then sequence of pixels for each curve was extracted from that corresponding image. Any curve with the number of pixels less than parameter value LTHR was discarded automatically from further processing. Thereafter, extracted pixel curve was converted to vector form by fitting piece wise line segments to it. The resulting polyline was an approximation to the original pixel curve where the maximum fitting error distance between the two was specified by the FTHR parameter. Finally, the algorithm links pairs of polylines, which satisfy the last two parameters, where the angle between the two segments was less than the parameter ATHR and the distance between end points was less than the parameter DTHR. The lineament extraction algorithm takes these problems into account to extract linear features from the corresponding image.

The main geometric characteristics of a single linear line are orientation and length (continuity) and in case of curved line, curvature [26]. For line split generation, ArcGIS 10.5 Model builder was used to automate GIS processes by linking data input, tools/functions, and data output, which saved into shapefile format. These lineament features extracted as a compound line, which splitted into a single line at their vertices and recorded the polylines in a vector layer.

Thereafter, lineament line length analysis was performed using the ArcGIS 10.5 software through conversion of meters into kilometer unit. The most important factor was that the lineaments in an automated one were shorter in length, so that a few of them could be combined to form one long lineament. In this stage, we are getting lineament length of all the attribute

To observe the lineament fluctuation change over the two study areas, satellite image-derived vector output, that is, lineaments were considered to perform overlay technique on each temporal data, helps to prepare corresponding lineament fluctuations maps. However, the criteria have the following conditions, if fluctuations of lineament persist over the study areas in the presence of earthquake, those are considered as "anomaly." These anomalous changes of lineament data represent fluctuations over the two study areas in three different phases, that is, initial, middle, and strong phase. On the other hand, if lineament observed less in number along with other statistical information in the absence of earthquake event, it is considered as normal behavior and categorized as "no anomaly" and finally, to know lineament situation after the earthquake, it is indicated as post-earthquake phase. However, the lineament increases or decreases at this phase does not matter, and this has been done only for comparison.

values based on the derived products of lineaments of the two study areas.

*2.3.2. Lineament fluctuations change observation method*

difference in degrees (30), and DTHR-Threshold for linking distance in pixels (20).

156 Multi-purposeful Application of Geospatial Data

The processing of the orientation of lineaments simply produces a directional diagram, which shows the distribution of lineament features. For lineament direction trend analysis, previously saved lineament data as dxf format was used in the RockWorks 16v software environment, where lineament computation was performed to measure bearing (unidirectional: 0–360°), length (m), line start and ending values, respectively, ultimately helps to create rose diagram. The directional diagram that depicts the orientation of the linear features finally saved it in the required format as a tiff file. Later, following the same process, remaining rose diagrams were prepared for the two study areas to figure out the directional change of lineaments based on three different stages (i.e., in the absence, presence, and after earthquake event, respectively).

#### *2.3.4. Statistical analysis method*

The statistical approach of the geometric parameters (number of lineaments and lengths variation) of lineaments is required to describe the structure of a region. The length parameters (i.e., total number of lineament, minimum, maximum, mean, total sum, and standard deviation) are generated based on all attributes of corresponding temporal lineament data of the two impending earthquakes in the absence and the presence of earthquake event. As, the lineament data variations observed in different places, the total number of lineament and length variations, that is, mean and standard deviation values were considered for anomalies identification of the study areas, which also further compare with post-earthquake data.

For statistical comparison, the demarcated line has been drawn over the line graph to represent the change behavior of lineament in three different situations with respect to earthquake occurrence day. The left black vertical dotted line used to represent the absence of earthquake marked as "no anomaly" and the second black point dash line considered to represent "extreme anomaly" prior to strike, and black solid line indicates the earthquake occurrence day in the corresponding study areas, whereas black dash line plotted in both the graphs to indicate the anomaly still present representing post-earthquake scenario. The X-axis represents the days which considered for earthquake observation and Y-axis represents the number of extracted lineaments and lineament length (mean and standard deviation value in km), respectively. However, number of lineament and mean value of lineament length were further used to justify the lineament change, observed through scatter diagram.
