APMEG: Quadratic Time-Frequency Distribution Analysis of Energy Concentration Features for Unveiling Reliable Diagnostic Precursors in Global Major Earthquakes Short-Term Prediction

Earthquake prediction remains a significant challenge in seismology, and advancements in signal processing techniques have opened new avenues for improving prediction accuracy. This paper explores the application of Time-Frequency Distributions (TFDs) to seismic signals for earthquake prediction. TFDs provide a comprehensive analysis of the non-stationary nature of seismic data, allowing for identification of precursory patterns based on energy concentration features. Current earthquake prediction models primarily focus on long-term forecasts, predicting events by identifying a cycle in historical data, or on nowcasting, providing alerts seconds after a quake has begun. However, both approaches offer limited utility for disaster management, compared to short-term earthquake prediction methods. This paper proposes a new method for short-term earthquake prediction, tested through analysis of recent major earthquakes and their respective prior minor earthquakes for five earthquake-prone countries, namely Türkiye, Indonesia, Philippines, New Zealand, and Japan. Precursors in the time-frequency domain have been consistently identified in all datasets within several hours or a few days before the major earthquakes occurred, which were not present in the observation and analysis of the earthquake catalogs in time domain. This research contributes towards the ongoing efforts in earthquake prediction, highlighting the potential of quadratic non-linear TFDs as a significant tool for non-stationary seismic signal analysis and timely earthquake prediction. To the best of the authors’ knowledge, no similar approach for consistently detecting earthquake diagnostics precursors has been proposed, and therefore, we propose a novel approach in reliable earthquake prediction using TFDs analysis.