| Citation: | ZHAO Jun，WANG Junyu，LAI Qiang，et al. Fine-grained identification of internal characteristic zones within strike-slip faults via the XGBoost algorithm[J]. Bulletin of Geological Science and Technology，2025，44（2）：1-11 doi: 10.19509/j.cnki.dzkq.tb20230584
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Because of the strong heterogeneity of fault zones, diverse reservoir types, and complex fluid distributions, the logging responses between damage, fault breccias , and dissolution zones within strike-slip faults are complex and variable, making it difficult to identify the three characteristic zones effectively inside strike-slip faults using imaging and conventional logging data.
The extreme gradient boosting (XGBoost) algorithm is introduced to establish a model to improve the identification accuracy of the three characteristic zones within strike-slip faults.
The logging response characteristics of the three characteristic zones within strike-slip faults are analyzed, and the sensitive logging curves are selected to construct a feature vector space set based on the mean and variance. The XGBoost algorithm is applied to establish XGBoost regression prediction models for the dissolution, breccias, and damage zones of strike-slip faults. The key parameters of the XGBoost model are optimized through multiclass evaluation indicators to improve the identification accuracy of the characteristic zones within strike-slip faults.
The constructed XGBoost model was used to identify the internal characteristic zones of strike-slip faults in the study area, with a total of 234 samples; 208 samples were correctly identified, resulting in an identification accuracy of 88.89%. The prediction results reveal that, within the internal characteristic zones of strike-slip faults, the damage zone has the widest distribution, followed by the breccias zone, and the dissolution zone is the narrowest, which is consistent with the actual distribution of the internal characteristic zones of strike-slip faults.
The identification model of internal characteristic zones within strike-slip faults based on the XGBoost algorithm can be used to effectively identify the damage, breccias, and dissolution zones, thereby supporting more effective analysis of the distribution of small-scale dissolution cavities and fracture reservoir spaces inside strike-slip faults, and providing reference information for the accurate characterization of the internal structure of strike-slip faults.
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