[SCG62-P07] LSTM and CNN Applications to Forecast Earthquake Magnitude Probability Distribution
Keywords:machine learning , earthquake forecasting, Recurrent Neural Network
With the development of computer science, several new technologies have been created: liner regression, nonlinear regression, and classification based on machine learning. According to updates of computer hardware, not only original machine learning such as Random Forest (RF), Support Vector Machines (SVM), and Neural Network (NN) has been used for the earthquake data mining but also several Neural Network deep learning methods has been abundantly used for these forecasting problems. Nowadays imagine identification technology based on Convolutional Neural Network (CNN) and text generation technology based on Recurrent Neural Network (RNN) are the most representative technologies. These two kinds of most useful Neural Network models are also widely used for spatial data mining as well as temporal data mining. We consider to combine these two Neural Networks to find spatio-temporal relationship among earthquakes at different positions and to perform earthquake event forecasting by taking advantage of this relationship.
At 14:46 on 11 March 2011, the Tohoku earthquake occurred. It is well-known that predicting this kind of a giant earthquake is most helpful to us for disaster mitigation. In this study, we use earthquake event data within 100 km depth in 2000-2010 from the JMA (Japan Meteorological Agency) unified catalog, and sort out one-day JMA magnitude probability distribution values (events number, magnitude average value, maximum magnitude value, and standard value) as one dataset per spatial grid to cover the catalog over Japan. Through supervised learning, we forecast earthquake magnitude probability distribution based on LSTM RNN (Long-Short Term Memory Recurrent Neural Network) and CNN (Convolutional Neural Network) prior to the 2011 Tohoku earthquake.