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.

中文翻译：

---

基于时空数据挖掘的地震预测：一种 LSTM 网络方法

地震预测是地震学中一个非常重要的问题，它的成功可能挽救许多人的生命。已经提出了各种技术来解决这个问题，例如数学分析、决策树和支持向量机等机器学习算法以及前兆信号研究。不幸的是，由于地震看似动态和不可预测的性质，它们通常不会产生很好的结果。相比之下，我们注意到由于地壳运动，地震在空间和时间上是相关的。因此，对特定地点的地震预测不应仅根据该地点的历史数据进行，而应根据更大区域的历史数据进行。在本文中，我们采用一种称为长短期记忆 (LSTM) 网络的深度学习技术来学习不同地点地震之间的时空关系，并利用这种关系进行预测。仿真结果表明，本文开发的二维输入 LSTM 网络能够发现和利用地震之间的时空相关性，从而做出比以前更好的预测。