## 5 Conclusions  
We have predicted the spatial distribution of shale gas reservoirs in Longmaxi Formation of the study area in Southern China using a frequency-dependent seismic fluid mobility attribute, extracted from the post-stack seismic data through a novel spectral decomposition technique (i.e., MP-EEMD scheme), and we also have demonstrated its applicability for the shale reservoirs in complex structural area lacking well control. There are some significant deviations between the TF locations estimated by using the traditional MP algorithm and the true ones due to the interference of multi-frequency components in seismic signal. The synthetic data test indicates that the larger the difference between interfering frequencies, the larger the deviation between the estimated and true TF locations, and the low-amplitude component cannot be recognized on the TF spectrum. These deviations in the TF spectrum will affect the computational accuracy of frequency-dependent seismic attribute. Based on the EEMD algorithm, we propose a novel MP-EEMD scheme to minimizes the effect of frequency interference. The synthetic data test indicates that the MP-EEMD scheme provides a TF spectrum with high accuracy and resolution, which also recognize the weak component. The real data test indicates that the MP-EEMD scheme not only achieves a more focused time-frequency spectrum, but also adaptively removes noise from real seismic data. By introducing the asymptotic low-frequency reflection coefficient, the seismic fluid mobility attribute can be extracted from the low-frequency post-stack seismic data through the spectral decomposition technique. The 1D attributes near wells show that the seismic fluid mobility based on the MP-EEMD algorithm exhibits less interference at the gas-free zone and better indication at the gas zone than that based on the MP algorithm. Thus, the MP-EEMD-based seismic fluid mobility attribute technology is applied to characterize the shale reservoirs with complex subsurface structure and low exploration level in Southern China. The spatial distribution of shale gas reservoirs predicted from the seismic fluid mobility attribute section is consistent with actual well data. Based on the predicted result, we have learned that the shale gas enrich areas in the study area are located near the bottom of the Longmaxi Formation. Referring to the predicted spatial distribution pattern of shale gas reservoirs in the area can help engineers design the directions and trajectories of directional drilling during the development stage. By combining the seismic fluid mobility attribute with the inverted velocity data, the thickness map of shale gas reservoirs in target formation is obtained, which is consistent  
Figure 14: Predicted thickness map of the shale gas reservoirs in Longmaxi Formation. with sedimentary background of marine shale. Referring to the thickness map of shale gas reservoirs in the area can help researchers to evaluate shale gas resources in the early stage of exploration. We have also given two potential area of future research by analyzing the thickness, structure, and burial depth. This work can provide some references for the subsequent exploration and research of shale gas reservoirs. Conceptualization, J.Z. ; methodology, J.Z., A.S., and H.H. ; software, J.Z. and H.H. ; validation, J.Z. and H.H. ; formal analysis, J.Z. and H.H. ; investigation, J.Z. and H.H. ; resources, H.H. ; data curation, J.Z. ; writing--original draft preparation, J.Z. ; writing--review and editing, A.S., L.R., and T.T. ; visualization, J.Z. A.S., L.R., and T.T. ; supervision, H.H. ; project administration, H.H. ; funding acquisition, H.H. All authors have read and agreed to the published version of the manuscript. This research was funded by National Natural Science Foundation of China with grant number 41974124. Data Availability Statement: Data sharing not applicable. No new data were created or analyzed in this study. Data sharing is not applicable to this article.