基于改进XGBoost的地震多属性地质构造识别方法

地震属性可以用来解释与预测地质构造，因此地震属性被广泛地运用在煤矿地质构造的识别。但一般情况下，勘探区中无构造区域与有构造区域分布不均衡，无构造区域远远多于有构造区域。机器学习中，传统的分类器更习惯于偏向多数类，这使得如何有效地识别出构造体成为一个难题。为了解决这一问题，提出了一种针对不平衡数据集的改进极限梯度提升(extreme gradient boosting, XGBoost)构造识别方法。该方法的原理是，首先，以基于三维地震勘探成果数据体提取的12种地震属性为数据集特征，以实际揭露后的地质构造为数据集标签构建多属性数据集，然后以特征对标签的相关性为标准，过滤掉冗余的特征；其次，将边界样本分类算法(boundary sample classification, BSC)与合成少数类过采样技术(synthetic minority over-sampling technique, SMOTE)相结合形成BSC-SMOTE算法。用BSC-SMOTE算法对原始数据集进行平衡，再利用平衡后的数据集训练XGBoost分类器，并用贝叶斯优化(Bayesian optimization, B...

Seismic multi-attribute geological structure identification method based on improved XGBoost

Seismic attributes can be used to interpret and predict geological structures, and therefore are widely used in the identification of coal mine geological structures. However, in general, the distribution of regions without structures and regions with structures in the exploration area is unbalanced, with many more regions without structures than with structures. In machine learning, traditional classifiers tend to be biased towards the majority class, making it difficult to effectively identify structures. To solve this problem, an improved extreme gradient boosting (XGBoost) construction recognition method for imbalanced datasets was proposed. Firstly, twelve seismic attributes extracted from a three-dimensional seismic exploration dataset were used as dataset features and actual disclosed geological structures as dataset labels to construct a multi-attribute dataset. Then, redundant features were filtered based on the correlation between features and labels. Next, the boundary sample classification (BSC) algorithm was combined with the synthetic minority over-sampling technique (SMOTE) to form the BSC-SMOTE algorithm. The original dataset was balanced using the BSC-SMOTE algorithm, and the balanced dataset was then used to train the XGBoost classifier. The classifier was further optimized using Bayesian optimization (BO) to search for hyperparameters. Finally, the classifier was used to predict structures. Taking the east wing mining area of Shanxi Xinyuan Coal Mine Co., Ltd. as the research area, the experimental results show that the prediction accuracy of the improved XGBoost algorithm model is 0.95, which is 0.16 higher than the original XGBoost algorithm, and more than 0.15 higher than the traditional algorithms such as KNN, random forest and SVM. The prediction results of the improved XGBoost model are basically consistent with the actual exposed structure after visualization, which shows that the model can effectively identify geological structures.