Revealing Hidden Precursors to Earthquakes via a Stress-Sensitive Transformation of Seismic Noise

Abstract: Earthquake prediction has long been one of the most elusive challenges in science. Laboratory experiments and simulations suggest that failure precursors should exist, yet reliable signals have remained unobserved in real-world seismic records, leaving open the question of whether they are absent in nature or simply hidden within noise. Here we introduce a stress-sensitive frequency-domain transformation that tracks energy differences between adjacent frequency bands, isolating subtle spectral changes linked to evolving shear and normal stress. Applied to both laboratory acoustic emission data and seismic records from seven major earthquakes (Mw 5.9-9.0), including the 2011 Tohoku and 2023 Turkey-Syria events, the transform consistently reveals precursory signatures, arc-like trajectories and accelerations toward extrema, emerging hours to days before rupture. These features are robust across diverse tectonic settings, from induced seismicity and volcanic collapse to continental strike-slip and subduction megathrust earthquakes. Our findings demonstrate that hidden precursors are indeed encoded in ambient seismic noise, offering a pathway toward real-time fault monitoring and actionable short-term earthquake forecasting.