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

TL;DR

This paper proposes a machine learning approach using 4D geodetic data to predict earthquake timing and land movement, demonstrating promising accuracy for the Ridgecrest 2019 event from data collected years prior.

Contribution

It introduces a novel 4D location-time machine learning scheme for earthquake prediction based on Lidar geodetic data, achieving notable accuracy in land change and timing predictions.

Findings

Land change prediction accuracy around 14 centimeters.

Earthquake timing prediction within approximately 2 days.

Effective prediction from data more than 3 years prior.

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.