Search arXiv⌕ Search

arXiv · physics/0312088

Reverse Detection of Short-Term Earthquake Precursors

Abstract

We introduce a new approach to short-term earthquake prediction based on the concept of selforganization of seismically active fault networks. That approach is named "Reverse Detection of Precursors" (RDP), since it considers precursors in reverse order of their appearance. This makes it possible to detect precursors undetectable by direct analysis. Possible mechanisms underlying RDP are outlined. RDP is described with a concrete example: we consider as short-term precursors the newly introduced chains of earthquakes reflecting the rise of an earthquake correlation range; and detect (retrospectively) such chains a few months before two prominent Californian earthquakes - Landers, 1992, M = 7.6, and Hector Mine, 1999, M = 7.3, with one false alarm. Similar results (described elsewhere) are obtained by RDP for 21 more strong earthquakes in California (M >= 6.4), Japan (M >= 7.0) and the Eastern Mediterranean (M >= 6.5). Validation of the RDP approach requires, as always, prediction in advance for which this study sets up a base. We have the first case of advance prediction; it was reported before Tokachi-oki earthquake (near Hokkaido island, Japan), Sept. 25, 2003, M = 8.1. RDP has potentially important applications to other precursors and to prediction of other critical phenomena besides earthquakes. In particular, it might vindicate some short-term precursors, previously rejected as giving too many false alarms.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

V. Keilis-Borok, P. Shebalin, A. Gabrielov, D. Turcotte. 2003-12-13. Reverse Detection of Short-Term Earthquake Precursors. https://doi.org/10.1016/j.pepi.2004.02.010

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Scaling-law-informed neural point processes for earthquake sequence forecasting

Earthquake sequence forecasting requires models that can learn nonlinear history dependence while retaining robust statistical structure. We develop a scaling-law-informed neural marked point process, termed Fusion, that combines neural representations of catalog history with temporal features derived from the Epidemic-Type Aftershock Sequence model and magnitude information derived from the Gutenberg--Richter law. The model separates the magnitude cutoff applied to the input catalog from the fixed target-event threshold, allowing lower-magnitude earthquakes to inform forecasts without changing the target-event set. For the 2016--2017 Amatrice--Visso--Norcia sequence, Fusion achieves the highest target-event temporal likelihood when lower-magnitude events are retained, outperforming both ETAS and a purely neural point-process baseline. Event-wise and cumulative analyses show sustained timing gains through substantial portions of the Visso and Norcia sequences. Across five benchmark catalogs, catalog-specific neural training with a fixed ETAS prior yields the highest temporal likelihood at the minimum evaluated magnitude cutoff. Magnitude likelihood shows no consistent predictive gain beyond the Gutenberg--Richter-based ETAS reference, indicating that the additional information captured by Fusion is primarily temporal. These results show that lower-magnitude catalog histories and empirical scaling-law information complement neural sequence learning for target-event timing.

physics.geo-ph↗

FA-Agent: An extensible multimodal agent framework for first-arrival picking in mountainous seismic data

First-arrival traveltimes underpin near-surface velocity modeling and static corrections, but in low signal-to-noise mountainous land data the first arrival is often weak, intermittent, and masked by strong direct waves and noise. Energy-based pickers then pick later arrivals or noise, and deep-learning pickers usually require retraining for a new survey. Because erroneous picks bias first-arrival tomography, many difficult zones are still inspected and corrected manually. We present FA-Agent, an agent framework for first-arrival picking built on a multimodal large language model. It combines a model that performs visual recognition and makes decisions, tools that process the data, and skills that carry experience of processing mountainous data. The framework is open and extensible: new pickers, denoising methods, and quality-control tools can be added, and new experience can be written into the skills. It is restricted neither to mountainous data nor to a particular model. Emulating a seismic data processor, the agent first surveys the whole gather to find zones needing attention, then examines them in enlarged views, and finally keeps, corrects, or removes the initial picks. On semi-synthetic gathers with known first arrivals and real mountainous noise, the agent picked the first arrival with high accuracy where it was clearly visible and removed the picks where it was invisible and could not be judged. Applied to randomly sampled gathers from a mountainous survey, it corrected picks onto weak first-arrival branches and removed picks where no first arrival could be confirmed, and it performed well on the most difficult data. Cases reviewed by experts are added to the skills automatically, so the agent improves with use, and a locally deployed model keeps confidential data on site. FA-Agent can thus become an essential component of future automated seismic data processing workflows.

physics.geo-ph↗

Supershear Rupture Indicator in Near-fault Particle Motion

Earthquake rupture propagation speed is an essential source parameter that fundamentally controls hazard and risk. In this paper, we develop and demonstrate the capability of a single near-fault seismic record in delineating earthquake rupture speed by inspecting displacement particle motion. We apply this method to near-fault strong-motion data in M7+ strike-slip earthquakes and identify diverse particle-motion signatures associated with subshear rupture, supershear rupture, supershear transition, initial rupture expansion, and potential multiple rupture fronts. This study highlights the value of near-fault observations for determining rupture speed.

physics.geo-ph↗