Analyzing the Performance of GPS Data for Earthquake Prediction

Valeri Gitis,Konstantin Petrov,Alexander Derendyaev

doi:10.3390/rs13091842

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’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.

Highlights

Danang Sri Hadmoko, ChristopherIn the immediate vicinity of the source of a future earthquake, anomalous changes in a number of processes occur
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 Global Positioning System (GPS) data, based on seismological data, and based on combined GPS data and seismological data
We tried to answer two questions: (1) Are space geodesy data effective for systematic earthquake prediction?; (2) is the earthquake forecast improved if seismological data are supplemented with space geodesy data? Obviously, the answers to these questions depend on the spatial density of the network of receiving stations, on the parameters of the time series of GPS measurements, on the method of preprocessing of GPS data, and on the method for forecasting earthquakes

Summary

Introduction

Danang Sri Hadmoko, ChristopherIn the immediate vicinity of the source of a future earthquake, anomalous changes in a number of processes occur. Seismological monitoring data are currently the most widely available. Systematic earthquake prediction systems use only seismological data [5,6]. The use of additional information on spatial and temporal changes in a geological environment can provide more accurate predictions of earthquakes. The monitoring data of the Earth’s surface displacement obtained by Global Positioning System (GPS) have been published in real time for a number of seismically active regions. These data are used to study block models of the Earth’s crust and in earthquake prediction studies. In the articles [7,8,9,10], the displacements of the

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