Recurrent Neural Network for Approximate Earthquake Time and Location Prediction Using Multiple Seismicity Indicators

Abstract

Abstract: A computational approach is presented for predicting the location and time of occurrence of future moderate‐to‐large earthquakes in an approximate sense based on neural network modeling and using a vector of eight seismicity indicators as input. Two different methods are explored. In the first method, a large seismic region is subdivided into several small subregions and the temporal historical earthquake record is divided into a number of small equal time periods. Seismicity indicators are computed for each subregion for each time period and their relationship to the magnitude of the largest earthquake occurring in that subregion during the following time‐period is studied using a recurrent neural network. In the second more direct approach, the temporal historical earthquake record is divided into a number of unequal time periods where each period is defined as the time between large earthquakes. Seismicity indicators are computed for each time‐period and their relationship to the latitude and longitude of the epicentral location, and time of occurrence of the following major earthquake is studied using a recurrent neural network.