Earthquake Magnitude Prediction Using Support Vector Machine and Convolutional Neural Network

Esther Kesewa Amfo, University of Texas at El Paso

Abstract

A deep learning-based method Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for earthquake prediction is proposed. Large-magnitude earthquakes triggered by earthquakes can kill thousands of people and cause millions of dollars worth of economic losses. The accurate prediction of large-magnitude earthquakes is a worldwide problem. In recent years, deep learning technology that can automatically extract features from mass data has been applied in image recognition, natural language processing, object recognition, etc., with great success. We explore to apply deep learning technology to earthquake prediction ,we propose a deep learning method for continuous earthquake prediction using historical seismic events. Also our project includes the Support vector machine (SVM). The modeling is a machine learning-based method. It involves a training phase with associated input and a predicting phase with target output decision values. In recent years, these two method has become increasingly popular for prediction of earthquake magnitudes. Taking new Mexico as an example, we train our deep leaning network model, using the images of the dataset. Finally, we make earthquake predictions, using the trained network model. The result shows that we can get the best result, when we predict earthquakes . The proposed method performs well without manually designing feature vectors, as in the traditional neural network method. This method can be applied to earthquake prediction in other seismic zones.

Subject Area

Mathematics|Applied Mathematics

Recommended Citation

Amfo, Esther Kesewa, "Earthquake Magnitude Prediction Using Support Vector Machine and Convolutional Neural Network" (2019). ETD Collection for University of Texas, El Paso. AAI22582986.
https://digitalcommons.utep.edu/dissertations/AAI22582986

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