Key Takeaways & Executive Findings
- •• Elevated pore pressure, modified fault-loading conditions, and aseismic slip are primary drivers of fault reactivation and induced seismicity. • Probabilistic forecasting and machine learning are increasingly used for seismic risk assessment, but face limitations from geological heterogeneity and data scarcity. • Fluid–rock interactions complicate the development of universally applicable models, constraining the generalizability of mitigation strategies. • Advancements in understanding mechanisms and improving prediction are essential for safe and sustainable deep-subsurface energy development.
Abstract
With the advancement of fracturing technologies in deeper and more geologically complex formations, fault reactivation and induced seismicity have attracted increasing attention. The increasing frequency and magnitude of these events underscore the need for a robust understanding of the governing physical mechanisms. Elevated pore pressure, modified fault-loading conditions, and aseismic slip are widely acknowledged as the primary drivers. Recent studies have explored these mechanisms under varying factors, including fluid properties, rock ductility, poroelastic responses, and evolving fault stress states, thereby offering critical insights into model refinement. Probabilistic forecasting approaches, which combine statistical analyses of historical data with real-time monitoring, are being increasingly adopted in seismic risk assessments. In parallel, machine learning techniques are employed to process large seismic datasets and identify key patterns. However, their predictive capabilities remain limited by geological heterogeneity, subsurface complexity, and scarce observational data. Moreover, fluid–rock interactions further complicate the development of universally applicable models, thereby constraining the generalizability of mitigation strategies. This review synthesizes the current understanding of induced seismicity mechanisms, evaluates the prevailing prediction and mitigation methods, and identifies major challenges and future research directions. Advancements in these areas are essential to enhancing seismic risk management and ensuring the safe, sustainable development of deep-subsurface energy resources.
1. Introduction
Fracturing technology has been widely used in the extraction of deep oil, gas, coal, and geothermal resources, notably to enhance resource development efficiency [1,2]. However, the increasing occurrence of induced seismicity associated with deep-stratum fracturing has become a major concern in energy extraction technologies. In recent years, a series of high-profile field cases have drawn global attention to the seismic risks associated with subsurface engineering activities. In the United States, regions such as Oklahoma and Texas have experienced a surge in seismicity linked to hydraulic fracturing and wastewater disposal, as documented by dense seism
and predictability of these activities [4]. Similarly, China’s Sichuan Basin has experienced moderate to large induced earthquakes (Mw>5) accompanied by large-scale shale gas hydraulic fracturing, prompting intensive studies and regulatory attention [5]. These cases highlight that induced seismicity is not only a theoretical or localized concern but also a pressing global issue with significant implications for infrastructure safety, public acceptance, and sustainable resource development [6]. A comprehensive understanding of the mechanisms driving fault reactivation is essential for developing effective risk mitigation strategies [7]. Fault slip, a fundamental process in earthquake generation, occurs when accumulated stress along a fault plane is suddenly released, causing the fault to move and generate seismic waves. Engineering activities can alter the fault normal stress, shear stress, pore pressure, and friction coefficients, thereby impacting fault stability and increasing seismicity risk [8]. Therefore, scientific understanding and accurate description of the fault slip mechanism and its influencing factors are crucial for optimizing the design and implementation of
Loading authentic research manuscript (Pages 1–5)...
JU Yang, FU Guoming, ZHOU Hongwei, GE Shirong, PENG Suping (2025). Fault reactivation and seismic risks induced by deep reservoir fracturing: Mechanisms, prediction and perspectives. SinoTechIntel Verified Research. https://doi.org/10.1016/j.ijmst.2025.06.005
Research & Educational Purpose Only:The translations, structured abstracts, analytical annotations, and data reports provided by SinoTechIntel are intended exclusively for academic research, internal corporate R&D, and educational benchmarking. They do not constitute formal engineering, chemical safety, legal, or professional advice.
Copyright & Intellectual Property Notice: Original copyright of the underlying source articles and experimental data remains with the respective authors, institutions, and original publishing journals. SinoTechIntel claims intellectual property only over its proprietary translations, analytical syntheses, and AEO structured enhancements in accordance with international fair use and academic citation principles.
Frequently Asked Questions
What are the primary mechanisms of fault reactivation induced by deep reservoir fracturing?
The primary mechanisms include elevated pore pressure, modified fault-loading conditions, and aseismic slip. These factors reduce effective normal stress and shear strength along faults, leading to slip and induced seismicity.
How is seismic risk predicted in the context of induced seismicity?
Seismic risk is predicted using probabilistic forecasting approaches that combine statistical analyses of historical data with real-time monitoring. Machine learning techniques are also employed to process large seismic datasets and identify patterns, though their predictive capabilities are limited by geological heterogeneity and data scarcity.
What are the main challenges in developing universally applicable models for induced seismicity?
Challenges include geological heterogeneity, subsurface complexity, scarce observational data, and fluid–rock interactions that complicate model development. These factors constrain the generalizability of mitigation strategies.
Why is induced seismicity a global concern?
Induced seismicity has been observed in various regions, such as Oklahoma and Texas in the United States, and the Sichuan Basin in China, with moderate to large earthquakes linked to hydraulic fracturing and wastewater disposal. These events pose risks to infrastructure safety, public acceptance, and sustainable resource development.
What future research directions are suggested for managing induced seismicity?
Future research should focus on advancing understanding of fault slip mechanisms, improving prediction models through integration of real-time data and machine learning, and developing robust mitigation strategies that account for fluid–rock interactions and geological complexity.
Related Technical Papers & Translations
A Novel Approach for Enhanced Brain Tumor Segmentation Using Multimodal MRI and Deep Learning
Brain tumor segmentation from multimodal MRI is crucial for diagnosis and treatment planning. In this study, we propose a novel deep learning framework that integrates structural and functional imaging modalities to improve segmentation accuracy. Our method employs a multi-scale attention mechanism and a hybrid loss function to handle class imbalance and boundary ambiguity. Evaluated on the BraTS benchmark, our approach achieves state-of-the-art performance, with Dice scores of 0.91, 0.87, and 0.84 for whole tumor, core, and enhancing tumor, respectively. Furthermore, we demonstrate the generalizability of our model across different scanners and protocols. Our findings suggest that the proposed method can significantly aid clinical decision-making and surgical planning.
Investigation of coupled acoustic and electrical responses and early warning approaches during re-loading of damaged coal
Initial damage from engineering disturbances in deep coal mining degrades mechanical properties and heightens dynamic-hazard risks, challenging conventional monitoring. This study probes the coupled acoustic-electrical responses of initially damaged coal under reloading and develops a multi-parameter, multi-level dynamic integrated early-warning model. Using a true-triaxial Split Hopkinson Pressure Bar (SHPB) system, we prepared specimens with graded damage by varying static deviatoric stresses and dynamic impacts. Uniaxial compression reloading was conducted with synchronous acoustic emission (AE) and resistivity monitoring. Joint time-domain responses of force, acoustics, and electricity delineated distinct loading stages. Time-frequency features were extracted via Fourier and wavelet transforms; crack architecture was quantified by 3D AE localization and fractal-dimension analysis. Initial damage markedly reduced load-bearing capacity. Resistivity decreased sharply with increasing deviatoric stress, while cumulative AE counts increased strongly. The AE spectrum evolved from bimodal to broadband with low- and high-frequency enhancement. The resistivity spectrum showed progressive bandwidth broadening, energy amplification, and high-frequency advancement. The AE spatial fractal dimension rose significantly during compaction. An integrated warning system combining multiscale entropy fusion, Temporal Convolutional Network (TCN)-Transformer forecasting, recurrence-network analysis, and a Bayesian framework yielded a 28.4 s lead time, offering a theoretical basis and technical pathway for intelligent prevention of dynamic hazards.
Influence of aggregate particle size on fracture behavior and energy evolution of cemented rockfill in the post-peak stage
Cemented rockfill (CRF) combines structural support with sustainable reuse of coal-derived solid waste. This study integrates digital image correlation, acoustic emission monitoring, and finite–discrete element simulations to investigate mechanical behavior, fracture development, and energy evolution of CRF containing 54% aggregate content with three grain-size distributions (5–10, 10–20, and 20–30 mm). Results indicate finer aggregates raise compressive strength and elastic modulus, and increase post-peak softening and residual stiffness. Fracture patterns transition from dominantly unidirectional failure in coarse specimens to pronounced X-shaped conjugate shear in fine specimens, with cracks initiating at boundaries and propagating inward. The proportion of failed joints at comparable strains decreases markedly with finer gradation, reflecting a more homogeneous crack network that enhances post-peak load retention and produces frequent minor stress fluctuations. Energy analyses reveal a coarse > medium > fine ordering in cumulative dissipation; however, finer aggregates delay rapid kinetic and dissipative energy release, promoting slower energy redistribution and improved load resistance. These findings quantify how aggregate gradation controls deformational mechanisms, crack topology, and energy partitioning, and provide design guidance for optimizing aggregate size and cementitious composition to enhance ductility, energy absorption, and structural reliability of CRF in underground engineering.