The Controlling Factors and Prediction of Deep-Water Mass Transport Deposits in the Pliocene Qiongdongnan Basin, South China Sea
Large-scaled submarine slides or mass transport deposits (MTDs) widely occurred in the Pliocene Qiongdongnan Basin, South China Sea. The good seismic mapping and distinctive topography, as well as the along-striking variation in sediment supply, make it an ideal object to explore the linkage of cont...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
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| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/12/12/2115 |
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| Summary: | Large-scaled submarine slides or mass transport deposits (MTDs) widely occurred in the Pliocene Qiongdongnan Basin, South China Sea. The good seismic mapping and distinctive topography, as well as the along-striking variation in sediment supply, make it an ideal object to explore the linkage of controlling factors and MTD distribution. The evaluation of the main controlling factors of mass transport deposits utilizes the analysis of terrestrial catastrophes as a reference based on the GIS-10.2 software. The steepened topography is assumed to be an external influence on triggering MTDs; therefore, the MTDs are mapped to the bottom interface of the corresponding topography strata. Based on detailed seismic and well-based observations from multiple phases of MTDs in the Pliocene Qiongdongnan Basin (QDNB), the interpreted controlling factors are summarized. Topographic, sedimentary, and climatic factors are assigned to the smallest grid cell of this study. Detailed procedures, including correlation analysis, significance check, and recursive feature elimination, are conducted. A random forest artificial intelligence algorithm was established. The mean value of the squared residuals of the model was 0.043, and the fitting degree was 82.52. To test the stability and accuracy of this model, the training model was used to calibrate the test set, and five times 2-fold cross-validation was performed. The area under the curve mean value is 0.9849, indicating that the model was effective and stable. The most related factors are correlated to the elevation, flow direction, and slope gradient. The predicted results were consistent with the seismic interpretation results. Our study indicates that a random forest artificial intelligence algorithm could be useful in predicting the susceptibility of deep-water MTDs and can be applied to other study areas to predict and avoid submarine disasters caused by wasting processes. |
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| ISSN: | 2077-1312 |