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

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.

Seismotectonics Considered Artificial Neural Network Earthquake Prediction in Northeast Seismic Region of China

“…What is remarkable about their work is that they compared the prediction accuracies with a recurrent neural network, a radial basis function neural network and a Levenberg-Marquardt back-propagation neural network. The recurrent neural network yielded the best prediction result so they tried to predict the time and location of an earthquake in southern California by recurrent neural network [12]. Probabilistic neural network, also a type of ANN, was used to forecast the earthquake in southern California and presented good prediction accuracies for earthquakes of magnitudes between 4.5 and 6.0 [12].…”

“…The recurrent neural network yielded the best prediction result so they tried to predict the time and location of an earthquake in southern California by recurrent neural network [12]. Probabilistic neural network, also a type of ANN, was used to forecast the earthquake in southern California and presented good prediction accuracies for earthquakes of magnitudes between 4.5 and 6.0 [12]. The radial basis function neural network yielded more accurate and effective prediction re-sults than adaptive neural fuzzy, a kind of artificially intelligent method, in South Iran [13].…”

“…Many kinds of ANN methods were compared to search for the rational method to build the earthquake model [5,12,13,19]. Seismicity indicators were analyzed to choose the rational input parameters [11,16] and to consider the monitoring data [18,14,20].…”

“…The best set of seismicity indicators to predict earthquakes were also discussed [16]. The prediction range and period were considered [12]. These studies indicated that there are two methods to improve earthquake prediction: (1) reducing the prediction time frame from one month to twenty two days and (2) dividing the seismic regions into smaller areas and performing a parametric study.…”

Neurocomputing in Civil Infrastructure

“…Panakkat and Adeli [102] presented neural network models for earthquake magnitude prediction using multiple seismicity indicators. They [103] also presented an RNN for approximate earthquake time and location prediction. Adeli and Panakka [104] presented a probabilistic neural network for earthquake magnitude prediction.…”