## 6 Conclusions  
In this study, we have developed a dynamic adaptive rock physics model (DARPM) and an adaptive rock physics inversion method leveraging the anisotropic characteristics of shale gas reservoirs. We integrate rock physics information into deep learning to establish the RPM-FCNN (rock physics model--fully connected neural network) framework for the quantitative seismic characterization of shale gas reservoirs, enabling the extraction of the spatial distribution of reservoir properties and seismic anisotropy parameters. The DARPM is constructed based on the intricate microstructural and macroscopic anisotropic features of shale gas reservoirs. This model serves as the cornerstone of quantitative seismic characterization, accurately describing the physical characteristics and seismic elastic anisotropy features of shale gas reservoirs with varying macroscopic anisotropy. The model facilitates a precise depiction of shale gas reservoir characteristics and the dynamic establishment of nonlinear relationships between reservoir properties and seismic elastic features across different intervals. An adaptive rock physics inversion method is proposed based on the established DARPM. This method processes well log data and employs differentiated rock physics inversion algorithms for intervals with varying anisotropic features, predicting corresponding reservoir properties and seismic anisotropy parameters. By incorporating the DARPM and its inversion results into an FCNN, the RPM-FCNN framework is established for quantitative seismic characterization of shale gas reservoirs. In this framework, shale gas reservoir rock physics information is utilized, and the inversion results serve as input and output data for learning and training to obtain nonlinear quantitative relationships between reservoir properties and elastic parameters. The trained RPM-FCNN framework is applied to pre-stack seismic inversion data to calculate reservoir properties and seismic elastic anisotropy parameters. The predicted organic matter mixture aspect ratio \(\alpha_{k}\) reflects the connectivity of organic matter. The predicted organic matter porosity \(\Phi_{k}\) is associated with shale maturity and gas saturation. The seismic anisotropy parameters \(\varepsilon\) and \(\gamma\) provide insights into the intrinsic anisotropic and layering structure of reservoirs. The total porosity \(\Phi\), clay content _Vclay_, and kerogen content _Vkerogen_ are related to shale brittleness and the potential for the production of shale gas reservoirs. These results contribute to the quantitative characterization of shale gas reservoirs, providing valuable information for identifying prospective gas zones in the study area. The proposed dynamic adaptive rock physics modeling approach can be extended to other types of oil and gas reservoirs for fine-grained descriptions and quantitative characterizations of hydrocarbon resources. Furthermore, the quantitative seismic characterization RPM-FCNN framework can inspire further research by combining specific RPMs, inversion methods, and other complex artificial intelligence algorithms tailored to different oil and gas resources. Moreover, the RPM-FCNN framework can be applied to other geological surveys, but certain application prerequisites are necessary. Before using this framework, a detailed exploration of the stratigraphic characteristics, microstructure features, and microphysical properties of the study area is required. Additionally, it is essential to construct corresponding rock physics models to describe the subsurface structures. Conceptualization, X.D. ; data curation, X.D. and X.K. ; formal analysis, W.F. and T.L. ; funding acquisition, X.D. ; investigation, X.D. ; methodology, X.D., W.F. and T.L. ; project administration, X.D. ; resources, X.D. and D.Y. ; software, X.D. and X.K. ; supervision, X.D. ; validation, X.D., D.Y. and W.F. ; visualization, X.D. ; writing--original draft, X.D. ; writing--review and editing, X.D. All authors have read and agreed to the published version of the manuscript.