Earthquake Prediction Based on Spatio-Temporal Data Mining: An LSTM Network Approach

By Qianlong Wang, Y. Guo, Lixing Yu, Pan Li. 2020

185 citations recorded by the source corpus.

Earthquake prediction is a very important problem in seismology, the success of which can potentially save many human lives. Various kinds of technologies have been proposed to address this problem, such as mathematical analysis, machine learning algorithms like decision trees and support vector machines, and precursors signal study. Unfortunately, they usually do not have very good results due to the seemingly dynamic and unpredictable nature of earthquakes. In contrast, we notice that earthquakes are spatially and temporally correlated because of the crust movement. Therefore, earthquake prediction for a particular location should not be conducted only based on the history data in that location, but according to the history data in a larger area. In this paper, we employ a deep learning technique called long short-term memory (LSTM) networks to learn the spatio-temporal relationship among earthquakes in different locations and make predictions by taking advantage of that relationship. Simulation results show that the LSTM network with two-dimensional input developed in this paper is able to discover and exploit the spatio-temporal correlations among earthquakes to make better predictions than before.

Subfields: Artificial Intelligence, Geophysics

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Citations in the corpus

Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, methodology): “In another study [151], an LSTM network is used for the prediction of earthquakes.”
A Survey on Federated Learning: The Journey From Centralized to Distributed On-Site Learning and Beyond (2021, background): “DL applications are ranging from as simple as Netflix is following in Google and Facebook footsteps to improve its services, to as sophisticated as self-driving cars [22], smart healthcare [23], fraud detection [24], earthquake prediction [25], and many more.”
Deep-learning seismology (2022, background): “Early attempts relied on hand-engineered features, but the emphasis has shifted to neural network – based forecasting approaches that more directly incorporate the complete spatio-temporal structure of seismic catalogs ( 82 , 83 ).”