Seismic data analysis using machine learning techniques for earthquake prediction

Abstract

Earthquakes are one of the most destructive costly natural hazards faced by the nation in which they occur without an explicit warning and may cause serious injuries or loss of human lives as a result of damages and destroyed a lot of properties and buildings or other rigid structures. The aim of this study is to evaluate the performance of Artificial Intelligence techniques in predicting earthquakes occurring in the region of Himalayan belt (with the use of different types of input data). These parameters are extracted from Himalayan Earthquake catalogue comprised of all minor, major events and their aftershock sequences in the Himalayan basin for the past 128 years from 1887 to 2015. This Data warehouse contains event data, event time with seconds, latitude, longitude, depth, Standard deviation and Magnitude. These field data converted into eight mathematically computed parameters known as seismicity indicators. These seismicity indicators have been used to train the Artificial Intelligence techniques for better decision making and predicting the magnitude of the pre-defined future time period. These mathematically computed indicators consider are the clustered based on every events above 2.5 magnitude, Total number of events from past years to 2014, frequency-magnitude distribution b-values, Gutenberg-Richter inverse power law curve for the n events, the rate of square root of seismic energy released during the n events, Energy released from the event, the mean square deviation about the regression line based on the Gutenberg-Richer inverse power law for the n events, coefficient of variation of mean time and average value of the magnitude for last n events. We propose a three-layer feed forward BP neural network model to identify factors, with the actual occurrence of the earthquake magnitude M and other seven mathematically computed parameters seismicity indicators as input and target vectors in Himalayan basin area. newline newline newline