Forecasting maximum magnitude of fluid-induced earthquakes: Bridging statistical extrapolation and physics-based forecasting
Year: 2025
Type: article
Abstract: The forecasing of maximum magnitude ( M max ) for fluid-induced seismicity is a critical challenge in risk management for geothermal energy development, unconventional hydrocarbon extraction, and wastewater disposal. This review systematically examines eight forecasting models spanning three categories: statistical models (G-R relation, extreme value theory, order statistics), semi-physical models (injection volume/time correlations), and physics-based models (rupture mechanics, rate-state friction, pressure diffusion). Through tripartite analysis of parameter sensitivity, scientific interpretability, and engineering validation, we reveal fundamental constraints: (1) Model performance is strictly governed by parameter coverage. Statistical models relying solely on seismic catalogs neglect fault mechanics parameters. Semi-physical models establish empirical Δ V / T - M max relationships but fail to explain dynamic slip processes and post-injection "trailing effects". Physics-based models integrate fault stress, friction evolution, and fluid diffusion, offering unparalleled mechanistic insights, yet their computational demands pose challenges for real-time field applications (only 7.5% of 228 global cases). (2) Scientific interpretability exhibits hierarchical deficiencies. While physics-based models quantitatively resolve stress transfer and friction evolution (e.g., rate-state models distinguishing seismic/aseismic slip), forecasting of far-field triggering and multi-fault interactions remain reliant on immature theories like poroelastic coupling. (3) Systemic validation biases emerge: 57.5% of cases concentrate in stable North American terrains with underrepresentation of Pacific Rim high-stress zones; 79.8 % involve geothermal/shale gas operations lacking CO 2 sequestration validations; M ≥ 4.0 events constitute merely 12.7% of datasets, leaving M ≥ 5.0 predictive capacity unverified. We propose transformative pathways including hybrid “physics-core + machine learning” architectures, standardized multi-scenario validation protocols, and dynamic traffic-light systems, aiming to advance cross-disciplinary forecasting paradigms for engineering risk governance.
Source: Rock Mechanics Bulletin
Authors: Shaohua Zeng, Changsheng Jiang, Hongyu Zhai, Lingbin Meng, Ziang Wang
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
Awards: G2096646839, G6420602319, G8372742706
OpenAlex ID: W4409838832