Possibility of Land Movement Prediction for Creep or before Earthquake Using Lidar Geodetic Data in a Machine Learning Scheme

Abstract

Earthquake prediction is one of the most pursued problems in geoscience. Different geological and seismological approaches exist for the prediction of the earthquake and its subsequent land change. However, in many cases, they fail in their mission. In this paper, we address the well-established earthquake prediction problem by a novel approach. We use a four-dimensional location-time machine learning scheme to estimate the time of earthquake and its land change. We present a study for the Ridgecrest, California 2019 earthquake prediction. We show the accuracy of our method is around 14 centimeters for the land change, and around 2 days for the time of the earthquake, predicted from data more than 3 years before the earthquake.

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