Search arXiv⌕ Search

arXiv · physics/0604053

Ergodicity in Natural Earthquake Fault Networks

Abstract

Numerical simulations have shown that certain driven nonlinear systems can be characterized by mean-field statistical properties often associated with ergodic dynamics [C.D. Ferguson, W. Klein, and J.B. Rundle, Phys. Rev. E 60, 1359 (1999); D. Egolf, Science 287, 101 (2000)]. These driven mean-field threshold systems feature long-range interactions and can be treated as equilibrium-like systems with dynamics that are statistically stationary over long time intervals. Recently the equilibrium property of ergodicity was identified in an earthquake fault system, a natural driven threshold system, by means of the Thirumalai-Mountain (TM) fluctuation metric developed in the study of diffusive systems [K.F. Tiampo, J.B. Rundle, W. Klein, J.S. Sa Martins, and C. D. Ferguson, Phys. Rev. Lett. 91, 238501 (2003)]. In this work we analyze the seismicity of three naturally-occurring earthquake faults networks from a variety of tectonic settings in an attempt to investigate the range of applicability of effective ergodicity, using the TM metric and other, related statistics. Results suggest that, once variations in the catalog data resulting from technical and network issues are accounted for, all of these natural earthquake systems display stationary periods of metastable equilibrium and effective ergodicity that are disrupted by large events. We conclude that a constant rate of events is an important prerequisite for these periods of punctuated ergodicity, and that while the level of temporal variability in the spatial statistics is the controlling factor in the ergodic behavior of seismic networks, no single statistic is sufficient to ensure quantification of ergodicity. Specifically, we demonstrate that stationarity, while a necessary condition, is not sufficient to ensure ergodicity in fault systems.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

K. F. Tiampo, J. B. Rundle, W. Klein, J. Holliday, J. S. Sa Martins, C. D. Ferguson. 2006-10-24. Ergodicity in Natural Earthquake Fault Networks. https://doi.org/10.1103/physreve.75.066107

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↗