Inversion of Two-Dimensional In Situ Stress Field Constrained by Multisource Data: A Case Study of Logging-Seismic Integrated Fault Identification

In situ stress field inversion is a fundamental challenge in geothermal resource development, oil and gas exploration, and mine safety assessment. To address the non-uniqueness and limited accuracy of traditional single-data-source inversion approaches, this study proposes a two-dimensional in situ stress field inversion method constrained by multi-source data, based on integrated well-seismic fault identification. By incorporating dynamic and static mechanical parameters from well logs and employing both a combined spring model and an anisotropic model, a fault-constrained stress field inversion framework is established. Deep learning and optimization algorithms are utilized to integrate the vertical constraints from well logging data with the lateral continuity characteristics of seismic data, enabling high-resolution reconstruction of the in situ stress field. Taking the complex fault-developed geothermal field in the Xiong’an New Area of the Jizhong Depression, Bohai Bay Basin, as a case study, the proposed method demonstrates a marked reduction in inversion error and a substantial improvement in both fault localization accuracy and stress characterization reliability.