The object of this work is to predict the seismic rate in Chile by using two Deep Neural Network (DNN) architectures, Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNN). For this, we propose a methodology based on a three-module approach: a pre-processing module, a spatial and temporal estimation module, and a prediction module. The first module considers the Epidemic-Type Aftershock Sequences (ETAS) model for estimating the intensity function, which will be used for estimating the seismic rate on a 1 × 1 degree grid providing a sequence of daily images covering all the seismic area of Chile. The spatial and temporal estimation module uses the LSTM and CNN for predicting the intensity and the location of earthquakes. The last module integrates the information provided by the DNNs for predicting future values of the maximum seismic rate and their location. In particular, the LSTM will be trained using the maximum intensity of the last 30 days as input for predicting the maximum intensity of the next day, and the CNN will be trained on the last 30 images provided by the application of the ETAS model for predicting the probability that the next day the maximum event will be in certain area of Chile. Some performance indexes (such as R2 and accuracy) will be used for validating the proposed models.
|Publication status||Accepted/In press - 1 Jan 2020|
- Deep learning
- Intensity function
ASJC Scopus subject areas
- Statistics and Probability
- Computers in Earth Sciences
- Management, Monitoring, Policy and Law