Remote Sensing-based Synchronized Seismic Precursor Detection using SARIMAX model Forecasting: A Case Study of the Mw 7.4 Taiwan Earthquake of April 2024

Syed Faizan Haider¹ Munawar Shah² Rasim Shahzad³

¹ Global Navigation Satellite System Research Lab, National Center of GIS and Space Applications, Institute of Space Technology, Islamabad 44000, Pakistan; Faizanhaider92110@gmail.com

Abstract

Earthquakes are among the most destructive natural phenomena, demanding innovative approaches to prediction and monitoring. The Mw 7.4 Taiwan earthquake exemplifies the need for robust methodologies to detect and analyze seismic precursors in vulnerable regions. This study utilizes Remote Sensing (RS) and Global Navigation Satellite System (GNSS) technologies to investigate possible seismic precursors, including Total Electron Content (TEC), Sea Surface Temperature (SST), Land Surface Temperature (LST), Air Pressure (AP), Relative Humidity (RH), Outgoing Longwave Radiation (OLR), and Air Temperature (AT). Employing statistical methods such as Standard Deviation (STDEV) and the Seasonal AutoRegressive Integrated Moving Average with Exogenous Variables (SARIMAX) model, this study identified synchronized anomalies 5–6 days before the earthquake. Additionally, geomagnetic disturbances were detected 9 days prior, coinciding with an active geomagnetic storm (Kp > 5; Dst < -75 nT; ap > 70 nT). A confutation analysis of atmospheric parameters from the same region and time period over the previous five years further validated the findings, enhancing confidence in the results. By combining statistical, spatial, and forecasting methodologies, this research advances the understanding of seismic precursors and underscores the potential of integrating multi-parameter analysis for improved earthquake prediction and disaster management strategies.

Keywords: earthquake precursors; geomagnetic storm; GNSS TEC; remote sensing; SARIMAX