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1. Fault rock heterogeneity can produce fault weakness and reduce fault stability

   https://doi.org/10.1038/s41467-022-27998-2  

   Bedford, John D.; Faulkner, Daniel R.; Lapusta, Nadia (December 2022, Nature Communications)

   Abstract Geological heterogeneity is abundant in crustal fault zones; however, its role in controlling the mechanical behaviour of faults is poorly constrained. Here, we present laboratory friction experiments on laterally heterogeneous faults, with patches of strong, rate-weakening quartz gouge and weak, rate-strengthening clay gouge. The experiments show that the heterogeneity leads to a significant reduction in strength and frictional stability in comparison to compositionally identical faults with homogeneously mixed gouges. We identify a combination of weakening effects, including smearing of the weak clay; differential compaction of the two gouges redistributing normal stress; and shear localization producing stress concentrations in the strong quartz patches. The results demonstrate that geological heterogeneity and its evolution can have pronounced effects on fault strength and stability and, by extension, on the occurrence of slow-slip transients versus earthquake ruptures and the characteristics of the resulting events, and should be further studied in lab experiments and earthquake source modelling. 

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2. The spectrum of fault slip in elastoplastic fault zones

   https://doi.org/10.1016/j.epsl.2023.118310  

   Mia, Md Shumon; Abdelmeguid, Mohamed; Elbanna, Ahmed E (October 2023, Earth and Planetary Science Letters)
3. Fault-tip damage zone development during normal fault propagation within the Sevier Fault Zone, southern Utah

   https://doi.org/10.18277/AKRSG.2025.37.06  

   Ryter, Lila (July 2025, Keck Geology Consortium)
4. Hematite fault rock thermochronometry and textures inform fault zone processes

   https://doi.org/10.1016/j.jsg.2020.104002  

   Ault, Alexis K. (April 2020, Journal of Structural Geology)
5. DeepFL: integrating multiple fault diagnosis dimensions for deep fault localization

   https://doi.org/10.1145/3293882.3330574  

   Li, Xia; Li, Wei; Zhang, Yuqun; Zhang, Lingming (January 2019, ACM SIGSOFT International Symposium on Software Testing and Analysis)

   Learning-based fault localization has been intensively studied recently. Prior studies have shown that traditional Learning-to-Rank techniques can help precisely diagnose fault locations using various dimensions of fault-diagnosis features, such as suspiciousness values computed by various off-the-shelf fault localization techniques. However, with the increasing dimensions of features considered by advanced fault localization techniques, it can be quite challenging for the traditional Learning-to-Rank algorithms to automatically identify effective existing/latent features. In this work, we propose DeepFL, a deep learning approach to automatically learn the most effective existing/latent features for precise fault localization. Although the approach is general, in this work, we collect various suspiciousness-value-based, fault-proneness-based and textual-similarity-based features from the fault localization, defect prediction and information retrieval areas, respectively. DeepFL has been studied on 395 real bugs from the widely used Defects4J benchmark. The experimental results show DeepFL can significantly outperform state-of-the-art TraPT/FLUCCS (e.g., localizing 50+ more faults within Top-1). We also investigate the impacts of deep model configurations (e.g., loss functions and epoch settings) and features. Furthermore, DeepFL is also surprisingly effective for cross-project prediction. 

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