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

机译：基于时空数据挖掘的地震预测：LSTM网络方法

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摘要

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.
机译：地震预测是地震学中的一个非常重要的问题，其成功可能会拯救许多人类生活。已经提出了各种技术来解决这个问题，例如数学分析，机器学习算法，如决策树和支持向量机，以及前体信号研究。不幸的是，由于地震的看似动态和不可预测的性质，他们通常没有很好的结果。相比之下，我们注意到由于地壳运动，地震在空间上和时间上的相关性。因此，不应基于该位置中的历史数据，而是不应基于该位置的历史数据来进行特定位置的地震预测，而是根据较大区域中的历史数据。在本文中，我们采用了一个被称为长短期内存（LSTM）网络的深度学习技术，以了解不同地区地震之间的时空关系，并通过利用这种关系来预测。仿真结果表明，本文开发的二维输入的LSTM网络能够发现和利用地震之间的时空相关性，以提高预测。

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