{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,13]],"date-time":"2024-08-13T05:57:09Z","timestamp":1723528629553},"reference-count":55,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2021,5,9]],"date-time":"2021-05-09T00:00:00Z","timestamp":1620518400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002261","name":"Russian Foundation for Basic Research","doi-asserted-by":"publisher","award":["20-07-00445"],"id":[{"id":"10.13039\/501100002261","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"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\u2019s 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.<\/jats:p>","DOI":"10.3390\/rs13091842","type":"journal-article","created":{"date-parts":[[2021,5,10]],"date-time":"2021-05-10T06:54:58Z","timestamp":1620629698000},"page":"1842","source":"Crossref","is-referenced-by-count":11,"title":["Analyzing the Performance of GPS Data for Earthquake Prediction"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"http:\/\/orcid.org\/0000-0003-1123-6433","authenticated-orcid":false,"given":"Valeri","family":"Gitis","sequence":"first","affiliation":[{"name":"The Institute for Information Transmission Problems, 127051 Moscow, Russia"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-7063-6176","authenticated-orcid":false,"given":"Alexander","family":"Derendyaev","sequence":"additional","affiliation":[{"name":"The Institute for Information Transmission Problems, 127051 Moscow, Russia"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-0614-3515","authenticated-orcid":false,"given":"Konstantin","family":"Petrov","sequence":"additional","affiliation":[{"name":"The Institute for Information Transmission Problems, 127051 Moscow, Russia"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,9]]},"reference":[{"key":"ref_1","unstructured":"Sobolev, G., and Ponomarev, A. 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