## 5 Conclusions  
The geologically guided sparse criteria-constrained multitrace reflectivity inversion method proposed in this study effectively addresses the resolution limitations in imaging subtle reservoirs obscured by strong coal-seam reflection shielding. Through systematic theoretical analysis and practical applications, the following key conclusions are drawn.  
Theoretical analysis reveals an intrinsic correlation between the sparse matrix in the inversion algorithm and stratigraphic interface positions: minima in the sparse vector \(\mathbf{q}\) systematically correspond to reflection interfaces, while the composite matrix \(\mathbf{Q}\) quantitatively characterizes subsurface structural patterns through vertical impedance contrasts and lateral depositional continuity. This provides the theoretical foundation for integrating geological prior information into inversion operators.  
The innovatively constructed geologically guided sparse matrix integrates three geological elements: (a) a macrostructural framework extracted from seismic data attributes, (b) interface spatial positions constrained by stratigraphic boundaries, and (c) internal distributions of geological bodies calibrated with well-log data. This comprehensive constraint framework achieves simultaneous optimization of data fidelity and geological plausibility.  
Field application in a western carbonate fracture-cavity reservoir demonstrates significant improvements: outperforming conventional sparse inversion, this method notably enhances vertical resolution, thereby successfully restoring reservoir responses shielded by strong coal-seam reflections and achieving clear imaging of fracture-cavity geometries in inverted sections. Integrated analysis of drilling production data and inversion results confirms strong consistency between interpreted fracture-cavity spatial configurations and actual development dynamics, providing reliable support for precise characterization of subtle reservoirs.  
This methodology establishes a new paradigm for interpretable geological model-driven multitrace seismic inversion. Future research should focus on three directions: (1) developing a multi-physics coupled inversion framework for anisotropic media, (2) integrating multivariate information and deep learning algorithms for the intelligent extraction and dynamic constraint of geological features, and (3) creating geological evolution inversion modules for time-lapse seismic data. Therefore, through continuous algorithmic optimization, this approach holds promising potential for broader applications in complex hydrocarbon reservoir exploration and development.  
**Author Contributions:** S.C.: writing--original draft; Y.X.: methodology; J.F.: data curation; Y.Y.: formal analysis; S.Y.: supervision. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the National Key Research and Development Program of China (2018 YFA0702504), and in part by the R&D Department of China National Petroleum Corporation (2022 DQ0604-01).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** All authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest. The funder was not involved in the study design, collection, analysis, interpretation of data, writing of this article, or decision to submit it for publication.