## 5 Conclusions  
In this study, the GR curves of 16 coring wells in the Aryskum Graben in the South Turgay Basin were used, and the automatic identification of sedimentary facies in this area was completed using the SVM algorithm. The following conclusions and achievements have been obtained.  
Figure 8: Comparison of the subfacies identification results by the SVM and geologists in well ARY15.  
Figure 9: Comparison of the subfacies identification results by the SVM and geologists in well ARY16.  
Firstly, the accurate and quantitative description of log curves is the foundation for the study. So, based on the GR curves of the 16 coring wells and previous research results, six quantitative indexes, including variance, relative gravity center, ratio amplitude, average median, average slope, and mutational amplitude difference, were selected to quantify the logging curves in the study area, and a description of the logging curves in the study area was realized.  
Secondly, correct values of hyper-parameters and an appropriate kernel function are also the key factors in determining the accuracy of the SVM classification model. The training samples and radial basis function (RBF) were selected to map the input sample points into a higher-dimensional eigenvector space. Then, the hyper-parameters \(\gamma\) and \(C\) were debugged by a cross-validation method. When the value of \(\gamma\) was 1 and \(C\) was 10, the best result of subfacies identification was obtained.  
Thirdly, the classification model was applied to the ARY14-ARY16 wells. The results show that the classification model can effectively accomplish the automatic identification task of sedimentary facies in the study area, and the accuracy is approximately 70%.  
In summary, this study provides an innovative, accurate and efficient solution for identifying depositional subfacies. The methodology shown here will provide researchers with new ideas for sedimentary facies identification. Combined with the research results of this paper, our work can provide a theoretical basis for the fine division of microfacies in the next stage of the study. However, due to the paucity of logging data in the study area, its accuracy may not be very high. Moreover, if there are too many classification objectives, the computational effort will increase significantly and the final accuracy may not be as high as the results of this study. The future work to improve this method will focus on the optimization of the algorithm.  
Formal analysis, X.A.; methodology, B.S. and X.A.; resources, H.W.; supervision, H.W. and B.S.; writing--original draft, X.A.; writing--review & editing, X.A.  
This research was funded by the National Key R&D Program of China (Grant No. 2017 YFC1500604), the Preliminary Study on Earthquake Risk Assessment Model for Typical Engineering Structures (Grant No. 2019 EEEV0103) and the Program for Innovative Research Team in China Earthquake Administration (Earthquake Disaster Simulation and Evaluation in mainland of China).  
The authors thank [PERSON] of China University of Geosciences (Beijing) and [PERSON] of Institute of Engineering Mechanics, CEA for their constructive help.  
The authors declare no conflict of interest.