Acoustic emission dataset-1 for experimental study on fluid-driven fault nucleation, rupture processes, and permeability evolution in Oshima granit

This is first data set for key experimental data of selected experiments reported in X. Lei (2024), and the second data set is also stored at Mendeley Data (doi: 10.17632/ct25dfrns3.1). Summery of X.Lei (2024): This study investigated the fault nucleation and rupture processes driven by stress and fluid pressure in fine-grained granite by monitoring acoustic emissions (AEs). Through detailed analysis of the spatiotemporal distribution of the AE hypocenter, P-wave velocity, stress-strain, and other experimental observation data under different confining pressures for stress-driven fractures and under different water injection conditions for fluid-driven fractures, it was found that fluid has the following effects: 1) complicating the fault nucleation process, 2) exhibiting episodic AE activity corresponding to fault branching and the formation of multiple faults, 3) extending the spatiotemporal scale of nucleation processes and pre-slip, and 4) reducing the dynamic rupture velocity and stress drop. The experiments also show that 1) during the fault nucleation process, the b-value for AEs decreases from 1-1.3 to 0.5 before dynamic rupture, then rapidly recovers to around 1-1.2 during aftershock activity and 2) the hydraulic diffusivity gradually increases from an initial pre-rupture order of 0.1 m2/s to 10-100 m2/s after dynamic rupture. These results provide a reasonable fault pre-slip model, indicating that hydraulic fracturing promotes shear slip before dynamic rupture, as well as laboratory-scale insights into ensuring the safety and effectiveness of hydraulic fracturing operations related to activities such as geothermal development, evaluating the seismic risk induced by water injection, and further researching the precursory preparation process for deep fluid-driven or fluid-involved natural earthquakes. Potential uses of the data sets include but are not limited to 1) Providing training datasets for machine learning and AI-based technology development, such as developing machine learning models to predict stress accumulation and the remaining time before final fracture. 2) Developing effective methods for identifying weak or low S/N ratio AE signals. The waveform data contain numerous AE events that cannot be accurately located using conventional methods. 3) Inverting source mechanisms and moment tensors. Our research has not yet systematically analyzed the moment tensors of AE events. 4) Conducting more in-depth research on the interaction between fluid migration and rock deformation and fracture. Please cite the associated article as: X. Lei (2024), Fluid-driven fault nucleation, rupture processes, and permeability evolution in Oshima granite — Preliminary results and acoustic emission datasets, Geohazard Mechanics, https://doi.org/10.1016/j.ghm.2024.04.003本数据集为X. Lei（2024）中报道的精选实验关键实验数据的第一部分，第二部分数据集亦存储于Mendeley Data（doi: 10.17632/ct25dfrns3.1）。 X. Lei（2024）研究概述：本研究通过监测声发射（Acoustic Emission，AE），探究了应力与流体压力驱动下细粒花岗岩内的断层成核与破裂过程。针对应力驱动破裂实验，团队开展了不同围压条件下的观测；针对流体驱动破裂实验，则设置了不同注水条件开展试验。通过对声发射震源时空分布、纵波速度、应力-应变等实验观测数据的详细分析，研究发现流体具有如下作用：1）复杂化断层成核过程；2）呈现与断层分支及多断层形成对应的间歇性声发射活动；3）扩大成核过程与预滑的时空尺度；4）降低动态破裂速度与应力降。实验同时还得到两点结论：1）在断层成核过程中，声发射的b值在动态破裂前从1~1.3降至0.5，随后在余震活动期间快速回升至1~1.2左右；2）水力扩散率从破裂前初始的0.1 m²/s量级逐步升高至动态破裂后的10~100 m²/s。上述研究结果提出了合理的断层预滑模型，表明水力压裂可促进动态破裂前的剪切滑移，同时为地热开发等相关水力压裂作业的安全性与有效性评估、注水诱发地震风险评估，以及深入研究深部流体驱动或流体参与型天然地震的前兆孕育过程，提供了实验室尺度的科学认知。 本数据集的潜在用途包括但不限于： 1） 为机器学习与人工智能（AI）技术开发提供训练数据集，例如开发用于预测应力累积与最终破裂前剩余时间的机器学习模型。 2） 开发识别弱信号或低信噪比（Signal-to-Noise Ratio，S/N）声发射信号的有效方法。本数据集的波形数据包含大量常规方法无法精确定位的声发射事件。 3） 反演震源机制与矩张量。本团队目前尚未系统分析声发射事件的矩张量。 4） 开展流体运移与岩石变形、破裂之间相互作用的更深入研究。 请引用相关论文：X. Lei (2024), 《流体驱动的断层成核、破裂过程与大岛花岗岩渗透性演化——初步结果及声发射数据集》，《地质灾害力学（Geohazard Mechanics）》，https://doi.org/10.1016/j.ghm.2024.04.003