Density estimation of weak periodic signals in pre-earthquake seismic waves

TL;DR

This paper presents a nonlinear chaos-based method to detect weak periodic signals in seismic data, potentially useful for earthquake forecasting and other noisy, chaotic systems.

Contribution

It introduces a novel approach using a driven Duffing oscillator to identify subtle deterministic features in noisy seismic records, aiding earthquake prediction.

Findings

Detected systematic frequency shifts in seismic signals before earthquakes.

Applied the method to real seismic data from the Marmara fault segment.

Showed potential for real-time seismic monitoring and forecasting.

Abstract

We introduce a method for identifying weak periodic components in pre-earthquake seismic waveforms by examining the scale-index response of a driven Duffing chaotic oscillator. This nonlinear setup helps detect and classify subtle deterministic features buried in low-amplitude, noisy seismic records. We apply this approach to seismic data collected before three moderate-to-strong earthquakes, and compare the results with a quiescent control period. The weak periodic signals detected with the approach exhibit clear, systematic shifts in frequency. Kernel density estimates highlight these changes in the dynamics of the Marmara fault segment south of Istanbul, the likely location of a future large Istanbul earthquake. The results indicate also that chaos-based detection methods can reveal possible precursory patterns that point to the potential of such approaches for real-time seismic monitoring and forecasting. More generally, this study adds to the growing interaction between nonlinear dynamics and geophysics by providing another perspective on the complex behaviour of active fault systems. The density-based analysis of weak periodic signals proposed here may also be useful in other fields that involve large, noisy datasets with chaotic characteristics, such as physiology or financial systems.