Abstract
One of the most destructive natural hazard is an earthquake. Both the built environment and significant lives are lost. Due to presence of subduction zone and continuous continent to continent collision, major earthquakes in India are concentrated along the seismo-tectonically active Himalayan arc. Seismicity along the Alpine Himalayan Belt varies in well-defined patterns which are based on different parameters such as distance from tectonic source, presence of other historic earthquakes, intersections of tectonic features (in numbers), river length, presence of water body, meandering of rivers, soil conditions. An earthquake catalogue consisting of 540 earthquakes from year 1552 to 2020 is prepared for the consideration of data set. 117 tectonic features are considered for the study. For extracting feature a buffer of radius 25 km is considered along each earthquake of the catalogue. Four features namely distance from nearest tectonic feature, magnitude of earthquake, number of earthquakes within the buffer zone and presence of rivers (length) is extracted. A combination of these features allows identifying patterns based on training and classification of the entire data set.
This paper is an attempt to compare four classification algorithms namely Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), K nearest neighbors (KNN) and Artificial Neural Networks (ANN) and present an integrated predictive model for identifying seismically susceptible zones (SSZ) around the Kangra Region. The seismicity in the study area is divided into two classes: class A and Class B based on the magnitude of earthquakes and are called as training data set for the classification algorithms. The training data set provides a basis for classifying a set of data into class A and Class B based on the extracted features. After classification the study area is the classified into two classes: Class A: Significant seismically susceptible area, Class B: less Susceptible zone. A comparative analysis is done in terms of accuracy of classifying an earthquake into the correct class as per training data set. Of the four classification methodologies, ANN showed the best performance by reducing the error in misclassifying the epicenters from either class. The errors accuracy of classifying an earthquake in the correct group were found to be 24.74%, 6.45%, 7.78%, 2.9% for LDA, SVM, KNN and ANN respectively.
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References
Middlemiss, C.S.: The Kangra earthquake of 4th April, 1905. Geological survey of India (1910)
Mridula, Sinvhal, A., Wason, H.R.: A review on pattern recognition techniques for seismic hazard analysis. In: Proceedings of International Conference on Emerging Trends in Engineering and Technology, pp. 854–858 (2013)
King, G., Yielding, G.: The evolution of a thrust fault system: processes of rupture initiation, propagation and termination in the 1980 El Asnam (Algeria) earthquake. Geophys. J. Int. 77(3), 915–933 (1984)
Ikeda, M., Toda, S., Kobayashi, S., Ohno, Y., Nishizaka, N., Ohno, I.: Tectonic model and fault segmentation of the Median Tectonic Line active fault system on Shikoku, Japan 28(5) Tectonics, (2009)
Mark, R.K.: Application of linear statistical models of earthquake magnitude versus fault length in estimating maximum expectable earthquakes. Geology 5(8), 464–466 (1977)
Gelfand, I.M., Guberman, S.l., Izvekova, M.L., Kelis-Borok, V.I., Ranz Man, E.J.A.: Criteria of high seismicity, determined by pattern recognition. Tectonophysics 13, 415–422 (1972)
Gelfand, I.M., Guberman, Sh.A., Izvekova, M.L., Keilis-Borok, V.I., Ranzman, E.: Recognition of places where strong earthquake may occur, I. Pamir and Tien Shan, 6, (In Russian) Computational Seismology (1973)
Oday, V., Gaur, V.K., Wason, H.R.: Spatial prediction of earthquakes in the Kumaon Himalaya by pattern recognition, 30(2 & 3), pp. 253–264, Mausam (1979)
Sinvhal, A., Sinvhal, H., Joshi, G., Singh, V.N.: A valid pattern of microzonation. In: Proceedings of 4th International Conference on Seismic Zonation, vol. 3, pp. 641–648 (1991)
Sinvhal, A.: Seismic modelling and pattern recognition in oil exploration. Springer Science & Business Media; 2012 Dec 6
Bhatia, S.C., Chetty, T.R.K., Filimonov, M.B., Gorshkov, A.I., Rantsman, E.Y., Rao, M.N.: Identification of potential areas for the occurrence of strong earthquakes in Himalayan arc region. Proc. Indian Acad. Sci.-earth Planetary Sci. 101(4), 369–385 (1992)
Peresan, A., Zuccolo, E., Vaccari, F., Gorshkov, A., Panza, G.F.: Neo-deterministic seismic hazard and pattern recognition techniques: time-dependent scenarios for North-Eastern Italy. Pure Appl. Geophys. 168(3), 583–607 (2011)
Mridula, Sinvhal, A., Wason, H.R.: Identification of seismically susceptible areas in western Himalaya using pattern recognition. J. Earth Syst. Sci. 125(4), 855–871 (2016)
Sinvhal, A., Khattri, K.: Application of seismic reflection data to discriminate subsurface lithostratigraphy. Geophysics 48(11), 1498–1513 (1983)
Sinvhal, A., Khattri, K.N.: Sinvhal, H., Awasthi, A.K.: Seismic indicators of stratigraphy. Geophysics 49(8), 1196–1212 (1984)
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Mridula, Gola, K.K. (2023). Comparative Study of Various Pattern Recognition Techniques for Identifying Seismo-Tectonically Susceptible Areas. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 717. Springer, Cham. https://doi.org/10.1007/978-3-031-35510-3_53
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