The crustal stress state is a key physical parameter for understanding lithospheric dynamics, elucidating earthquake preparation mechanisms, and assessing regional seismic hazards. It also provides essential data for the optimal design, safe construction, and operation of major underground energy and geotechnical engineering projects. Systematically reviewing the research context in this field and clarifying the current progress and challenges can provide guidance for future research. [Methods] Through a systematic review and synthesis, the technical methodologies and the evolution of development paradigms are summarized in three interconnected domains: acquisition of crustal stress data, analysis and modeling of stress fields, and stress processes associated with earthquakes . [Results] (1) Progress in stress information acquisition: Observation techniques have advanced from shallow to deep levels and from single-site measurements to network-based monitoring. Traditional methods have been continuously refined, while deep borehole in-situ techniques such as anelastic strain recovery (ASR) and differential strain curve analysis (DSCA) have extended observation depths beyond 5 km. Integration of multidisciplinary data has become a prominent trend. (2) Advances in stress field analysis and modeling: Methodologies have evolved from analytical and numerical approaches to an intelligent framework integrating mechanism, data, and knowledge. Numerical models have developed from two-dimensional elastic formulations to three-dimensional visco-elastoplastic representations, enabling dynamic characterization of regional four-dimensional stress fields. (3) Developments in earthquake-related stress processes: In-situ stress measurements, Coulomb stress modeling, and combined physical–numerical experiments jointly reveal the cyclic pattern of “quiescence–accumulation–release–adjustment” during earthquake preparation, as well as stress triggering and shadow effects, and the physical mechanisms underlying fault instability nucleation. [Conclusions] Current research still faces challenges such as the scarcity of deep stress data, the complexity of multi-source data integration, and high uncertainty in initial stress field determination. Future studies should focus on developing intelligent, multi-method technologies for deep stress observation; constructing machine learning–based inversion and four-dimensional dynamic stress field models constrained by physical principles; advancing research on thermo–chemical–mechanical coupling rheology; and promoting a new paradigm for seismic prediction that integrates stress mechanisms, big data, and expert knowledge, thereby providing a more robust scientific foundation for seismic risk assessment and disaster prevention and mitigation. [Significance] By reviewing the advancements and prospects in crustal stress and earthquake research, references and insights are provided for the observation and analysis of seismic stress processes and for research on seismic dynamic prediction methods.
DownLoad: