# Three-Dimensional Modeling of the Xichang Crust in Sichuan, China by Machine Learning
## 7 Discussions and Conclusions
### Conclusions  
Our main aim is to explore a new approach to automatically build a more realistic crustal medium structure model based on the previous results, which can be used in the later geophysical field simulation and forecast. It is a comprehensive application of previous research results, and its main data sources rely on velocity structure data, geological structure data, and relocated earthquake catalog. Therefore, modeling uncertainty is also limited by the used prior research results. With further research, the accuracy of various input data may be improved, and the crustal medium structure model will be continuously updated to reflect the real seismogenic structure.  
Moreover, the efficient automatic modeling technique can more efficiently make the conversion from the numerous geoscience datasets to the comprehensive model, which can not only be used for mutual verification of the reliability of research results, but also better serve the earthquake engineering, earthquake emergency response, and operational earthquake prediction.  
Finally, we need to select the model scale and to combine this with the actual scenario. In this case of the Xichang crustal medium we show clearly that the accuracy of the kilometer level is able to satisfy the description of the structural characteristics of large-scale seismotectonic units. If there is a higher precision model scenario, it may bring higher data redundancy and increase the burden of model calculation. Therefore, the accuracy of datasets and modeling purpose is mutually dependent. The reasonable trade-off can provide a good balance between the modeling difficulty and scenario application.  
**Author Contributions:** Conceptualization, H.Z. and S.C.; methodology, L.-W.G., and S.C.; software, S.C.; validation, S.C., and L.-J.C.; formal analysis, S.C., and L.-J.C.; investigation, L.-J.C.; resources, S.C.; data curation, L.-J.C.; writing--original draft preparation, L.-W.G.; writing--review and editing, H.Z. and S.C.; visualization, L.-W.G.; supervision, H.Z.; project administration, S.C.; funding acquisition, S.C. and H.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** National Natural Science Foundation of China (U1939205) and the National Key R&D Program of China (2017 YFC1500503).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Initial fault data and precise location data provided by the China Earthquake Scientific Experimental Site ([[http://www.cse.ac.cn](http://www.cse.ac.cn)]([http://www.cse.ac.cn](http://www.cse.ac.cn)), accessed on 30 January 2022), and velocity structure model data from China Geophysical Reference Model ([[http://chinageoeffmodel.org](http://chinageoeffmodel.org)]([http://chinageoeffmodel.org](http://chinageoeffmodel.org)), accessed on 30 January 2022).  
**Acknowledgments:** We acknowledge the initial fault data and precise location data provided by the China Earthquake Scientific Experimental Site ([[http://www.csees.ac.cn](http://www.csees.ac.cn)]([http://www.csees.ac.cn](http://www.csees.ac.cn)), accessed on 30 January 2022). I am also grateful to [PERSON] for providing earthquake catalog and [PERSON] for providing the focal mechanism solution results and [PERSON] from Columbia University and [PERSON] from University of Lousiana for constructive suggestions and comments.  
**Conflicts of Interest:** The authors declare no conflict of interest.