Artificial neural networks (ANN) and stochastic techniques to estimate earthquake occurrences in Northeast region of India

Amit Zarola, Arjun Sil

Abstract


The paper presents the probability of earthquake occurrences and forecasting of earthquake magnitudes size in northeast India, using four stochastic models (Gamma, Lognormal, Weilbull and Log-logistic) and artificial neural networks, respectively considering updated earthquake catalogue of magnitude Mw ≥ 6.0 that occurred from year 1737 to 2015 in the study area. On the basis of past seismicity of the region, the conditional probabilities for the identified seismic source zones (12 sources) have been estimated using their best fit model and respective model parameters for various combinations of elapsed time (T) and time interval (t). It is observed that for elapsed time T=0 years, EBT & Kabaw zone shows highest conditional probability and it reaches 0.7 to 0.91 after about small time interval of 3-6 years (2014-2017; since last earthquake of Mw ≥ 6.0 occurred in the year 2011) for an earthquake magnitude Mw ≥ 6.0.Whereas, Sylhet zone shows lowest value of conditional probability among all twelve seismic source zones and it reaches 0.7 after about large time interval of 48 years (year 2045, since last event of Mw ≥ 6.0 occurred in the year 1999). While for elapsed time up to 2016 from the occurrence of the last earthquake of magnitude Mw ≥ 6.0, the MBT & MCT region shows highest conditional probability among all twelve seismic source zones and it reaches 0.88 to 0.91 after about 6-7 (2022-2023) years and in the same year (2022-2023) Sylhet zone shows lowest conditional probability and it reaches 0.14-0.17. However, we proposed Artificial Neural Network (ANN) technique used to predict the possible magnitude of future earthquake in the identified seismic source zones is based on feedforward backpropagation neural network model with single hidden layer. For conditional probability of earthquake occurrence above 0.8, the neural network gives the magnitude of future earthquake as Mw 6.6 in Churachandpur-Mao fault (CMF) region in the years 2014 to 2017 and for Myanmar Central Basin (MCB) region it gives magnitude of future earthquake as Mw 7.0 in the years 2013 to 2016 and for Eastern Boundary Thrust (EBT) & Kabaw region it gives magnitude of future earthquake as Mw 6.4 in the years 2015-2018. The epicentre of recently occurred 4 January 2016 Manipur earthquake (M 6.7), 13 April 2016 Myanmar earthquake (M 6.9) and the 24 August 2016 Myanmar earthquake (M 6.8) are located in Churachandpur-Mao fault (CMF) region Myanmar Central Basin (MCB) region and EBT & Kabaw region, respectively and that are the identified seismic source zones in the study area which show that the ANN model yields good prediction accuracy.


Keywords


Forecasting, Seismicity, ANN, Seismic source, Hazard, Probability

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References


DOI: http://dx.doi.org/10.4401/ag-7353


 

Published by INGV, Istituto Nazionale di Geofisica e Vulcanologia - ISSN:  2037-416X