Predicting the Accuracy of Early-est Earthquake Magnitude Estimates with an LSTM Neural Network: A Preliminary Analysis

TL;DR

This paper explores using an LSTM neural network to predict the accuracy of early earthquake magnitude estimates shortly after an event, aiming to improve rapid assessment methods.

Contribution

It introduces a novel application of LSTM networks for predicting the reliability of early earthquake magnitude estimates, which is a new approach in seismology.

Findings

LSTM can effectively predict magnitude estimate accuracy.

Preliminary results show promising prediction performance.

This approach may enhance early earthquake response strategies.

Abstract

This report presents a preliminary analysis of an LSTM neural network designed to predict the accuracy of magnitude estimates computed by Early-est during the first minutes after an earthquake occurs.