Abstract
Constraining strain localization and the growth of shear fabrics within brittle fault zones at sub-seismic slip rates is important for understanding fault strength and frictional stability. We conducted direct shear experiments on simulated sandstone-derived fault gouges at an effective normal stress of 40 MPa, a pore pressure of 15 MPa, and a temperature of 100°C. Using a passive strain marker and X-ray Computed Tomography, we analyzed the spatial distribution of deformation in gouges deformed in the strain-hardening, subsequent strain-softening, and then steady-state regimes at displacement rates of 1, 30, and 1,000 µm/s. We developed a machine-learning-based automatic boundary detection method to recognize the shear fabrics and quantify displacement partitioning between each fabric element. Our results show fabrics oriented along R1 and Y (including boundary) shears are the two major fabric elements. At rates of 1 and 30 µm/s, the relative amount of displacement on R1 shears is displacement dependent, increasing to ∼20% of the total displacement up to the strain-softening stage, then decreasing to ∼10%–18% at the steady state. This trend is absent at the high rate where ∼18% of the displacement occurs on R1 shears throughout all investigated stages. At all rates, the relative amount of displacement on Y shears increases linearly with displacement to a total of larger than 50% at the steady state. Our study provides constraints on the development of the active slip zone, which is an important factor controlling heating and weakening associated with small-magnitude earthquakes with limited displacement (mm-dm), such as induced seismicity.
Original language | English |
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Article number | e2024JB028889 |
Number of pages | 22 |
Journal | Journal of Geophysical Research: Solid Earth |
Volume | 129 |
Issue number | 4 |
DOIs | |
Publication status | Published - Apr 2024 |
Bibliographical note
Publisher Copyright:© 2024. The Authors.
Funding
We thank Marco Scuderi, the Associate Editor, and two anonymous reviewers for their valuable comments and suggestions, which helped to improve the quality of the manuscript. We also thank Dr. Ji and Dr. Ohl for their assistance in conducting the X-ray CT analyses. This work is part of research programme DeepNL, financed by the Dutch Research Council (NWO); Grant: DEEP.NL.2018.040. We thank Marco Scuderi, the Associate Editor, and two anonymous reviewers for their valuable comments and suggestions, which helped to improve the quality of the manuscript. We also thank Dr. Ji and Dr. Ohl for their assistance in conducting the X\u2010ray CT analyses. This work is part of research programme DeepNL, financed by the Dutch Research Council (NWO); Grant: DEEP.NL.2018.040.
Funders | Funder number |
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Marco Scuderi | |
Nederlandse Organisatie voor Wetenschappelijk Onderzoek | DEEP.NL.2018.040 |
Nederlandse Organisatie voor Wetenschappelijk Onderzoek |
Keywords
- machine learning
- sandstone-derived fault gouges
- strain localization
- X-ray computed tomography