The results of earthquake prediction largely depend on the quality of data and the methods of their joint processing. At present, for a number of regions, it is possible, in addition to data from earthquake catalogs, to use space geodesy data obtained with the help of GPS. The purpose of our study is to evaluate the efficiency of using the time series of displacements of the Earth’s surface according to GPS data for the systematic prediction of earthquakes. The criterion of efficiency is the probability of successful prediction of an earthquake with a limited size of the alarm zone. We use a machine learning method, namely the method of the minimum area of alarm, to predict earthquakes with a magnitude greater than 6.0 and a hypocenter depth of up to 60 km, which occurred from 2016 to 2020 in Japan, and earthquakes with a magnitude greater than 5.5. and a hypocenter depth of up to 60 km, which happened from 2013 to 2020 in California. For each region, we compare the following results: random forecast of earthquakes, forecast obtained with the field of spatial density of earthquake epicenters, forecast obtained with spatio-temporal fields based on GPS data, based on seismological data, and based on combined GPS data and seismological data. The results confirm the effectiveness of using GPS data for the systematic prediction of earthquakes.

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分析GPS数据的地震预报性能

地震预测的结果很大程度上取决于数据的质量及其联合处理的方法。目前，对于许多地区，除了地震目录中的数据外，还可以使用借助GPS获得的空间大地测量数据。我们研究的目的是评估根据GPS数据将地表位移的时间序列用于地震的系统预测的效率。效率标准是在警报区域有限的情况下成功预测地震的概率。我们使用机器学习方法（即最小警报区域）来预测2016年至2020年在日本发生的大于6.0级地震和震源深度最大为60 km的地震以及大于5。5.震中深度达60 km，这发生在2013年至2020年的加利福尼亚州。对于每个区域，我们将比较以下结果：地震的随机预测，通过地震震中的空间密度场获得的预测，基于GPS数据，地震数据以及基于组合GPS数据的时空场获得的预测和地震数据。结果证实了使用GPS数据进行地震系统预测的有效性。并基于GPS数据和地震数据的组合。结果证实了使用GPS数据进行地震系统预测的有效性。并基于GPS数据和地震数据的组合。结果证实了使用GPS数据进行地震系统预测的有效性。