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.

Highlights

  • The earthquake forecasting gives the probability of time, location and magnitude of occurrence of earthquake, which is necessary to understand the seismic hazard of any region [Parvez and Ram, 1997]

  • It is observed that for elapsed time T=0 year, Eastern boundary thrust (EBT) and Kabaw zone shows highest conditional probability among all twelve seismic source zones showing chances of occurrences 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, see Table 1) for an earthquake magnitude Mw ≥ 6.0

  • The present study describes the probabilistic assessment and forecasting of magnitude of future earthquakes in the northeast India using four models (Gamma, Lognormal, Weibull and Log-logistic) and artificial neural networks, respectively

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Summary

Introduction

The earthquake forecasting gives the probability of time, location and magnitude of occurrence of earthquake, which is necessary to understand the seismic hazard of any region [Parvez and Ram, 1997]. The statistical approach based on trends of earthquake such as seismicity patterns, seismic gaps, and characteristics of earthquake is the most appropriate and widely used method for estimation of the seismic hazard in any region. Utsu [1972], Hagiwara [1974], and Rikitake [1974] have proposed a statistical probabilistic approach for forecasting of future earthquake for a particular region. Their models are based on the concept that the earthquake is a renewal process, in which just after an earthquake event the probability of occurrences of earthquake is initially low.

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