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Abstract Understanding the connection between seismic activity and the earthquake nucleation process is a fundamental goal in earthquake seismology with important implications for earthquake early warning systems and forecasting. We use high-resolution acoustic emission (AE) waveform measurements from laboratory stick-slip experiments that span a spectrum of slow to fast slip rates to probe spatiotemporal properties of laboratory foreshocks and nucleation processes. We measure waveform similarity and pairwise differential travel-times (DTT) between AEs throughout the seismic cycle. AEs broadcasted prior to slow labquakes have small DTT and high waveform similarity relative to fast labquakes. We show that during slow stick-slip, the fault never fully locks, and waveform similarity and pairwise differential travel times do not evolve throughout the seismic cycle. In contrast, fast laboratory earthquakes are preceded by a rapid increase in waveform similarity late in the seismic cycle and a reduction in differential travel times, indicating that AEs begin to coalesce as the fault slip velocity increases leading up to failure. These observations point to key differences in the nucleation process of slow and fast labquakes and suggest that the spatiotemporal evolution of laboratory foreshocks is linked to fault slip velocity.
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A Python Code for Detecting True Repeating Earthquakes from Self‐Similar Waveforms (FINDRES)
Seismic data are generally scrutinized for repeating earthquakes (REs) to evaluate slip rates, changes in the mechanical properties of a fault zone, and accelerating nucleation processes in foreshock and aftershock sequences. They are also used to study velocity changes in the medium, earthquake physics and prediction, and for constraining creep rate models at depth. For a robust detection of repeaters, multiple constraints and different parameter configurations related to waveform similarity have been proposed to measure cross‐correlation values at a local seismic network and evaluate the location of overlapping sources. In this work, we developed a Python code to identify REs (FINDRES), inspired by previous literature, which combines both seismic waveform similarity and differential S‐P travel time measured at each seismic station. A cross‐spectral method is applied to evaluate precise differential arrival travel times between earthquake pairs, allowing a subsample precision and increasing the capacity to resolve an overlapping common source radius. FINDRES is versatile and works with and without P‐ and S‐wave phase pickings, and has been validated using synthetic and real data, and provides reliable results. It would contribute to the implementation of open‐source Python packages in seismology, supporting the activities of researchers and the reproducibility of scientific results.
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- Award ID(s):
- 2103976
- PAR ID:
- 10353605
- Editor(s):
- Allison Bent, Editor-in-Chief
- Date Published:
- Journal Name:
- Seismological research letters
- Volume:
- 93
- Issue:
- 5
- ISSN:
- 0895-0695
- Page Range / eLocation ID:
- 2847–2857.
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/https://doi.org/10.1785/0220220048
