Geological structure recognition model based on improved random forest algorithm

Seismic attributes are often used for structural interpretation and prediction. In order to overcome the problems of multiple solutions and uncertainty caused by single seismic attribute prediction, seismic multi-attribute fusion technology is used to interpret and predict geological structures. Based on the classical machine learning random forest algorithm model, an improved random forest algorithm is proposed to fuse and classify multiple seismic attributes. Combining the seismic multi-attribute fusion technology with the improved random forest algorithm, a geological structure recognition model based on the improved random forest algorithm is established. Taking the second mining area of the second belt of Shanxi Xinyuan Coal Co., Ltd. as the research area, based on the twelve seismic attributes extracted from the three-dimensional seismic exploration results, through the attribute correlation analysis and feature importance analysis of the twelve attributes, according to the results, all twelve attributes are retained for subsequent attribute fusion. Using the exposed and verified geological structure faults and collapse columns as sample labels, an improved grid search optimization algorithm is proposed. The number of classifiers and the maximum feature number of a single decision tree are combined to search the grid. The algorithm model is established based on Python language platform. The experimental results show that the prediction accuracy of the improved algorithm model reaches 97%, After subsequent model verification, it is proved that compared with several algorithms such as logistic regression, gradient lifting and decision tree, the improved random forest algorithm can more effectively identify abnormal bodies such as faults and collapse columns in geological structures, with higher recognition accuracy and wider applicability

Collapsed column identification model based on K-means SMOTE and random forest algorithm

In order to overcome the problem of multiple solutions and uncertainties in the identification of collapsed columns with a single seismic attribute and the problem of identification accuracy shift caused by unbalanced sample data, a binary classification collapsed column based on K-means SMOTE and random forest was constructed. The model can identify collapse columns by joint analysis of multiple seismic attributes. Taking the southern mining area of the east wing of the first mining area of Shanxi Xinyuan Coal Company as the research area, 12 seismic attributes extracted by the front interpreters through 3D seismic exploration technology are used as sample features, and the actually revealed collapse column information is used as sample labels to build a seismic multi-attribute attribute dataset; seismic attribute selection is carried out through correlation analysis, cluster analysis evaluation and random forest importance analysis, and 6 relatively independent seismic attributes are finally selected as sample features; the K-means SMOTE algorithm is used to balance the data set, and 8 992 data are obtained, of which 6 294 data are selected as the training set and 2 698 data are used as the test set; the random forest binary classification model is built based on the python language platform, and the final accuracy of predicting the collapsed column can reach 87%. By comparing three common machine learning classification algorithms, the model identified collapsed columns with higher accuracy

Seismic-Geological Integrated Study on Sedimentary Evolution and Peat Accumulation Regularity of the Shanxi Formation in Xinjing Mining Area, Qinshui Basin

Accurate identification of the lithofacies and sedimentary facies of coal-bearing series is significant in the study of peat accumulation, coal thickness variation and coal-measured unconventional gas. This research integrated core, logging and 3D seismic data to conduct a comprehensive seismic–geological study on the sedimentary evolution characteristics and peat accumulation regularity of the Shanxi Formation in the Xinjing mining area of the Qinshui Basin. Firstly, the high-resolution sequence interface was identified, and the isochronous stratigraphic framework of the coal-bearing series was constructed. Then, the temporal and spatial evolution of sedimentary filling and sedimentary facies was dynamically analyzed using waveform clustering, phase rotation, stratal slice and frequency–division amplitude fusion methods. The results show that the Shanxi Formation in the study area can be divided into one third-order sequence and two fourth-order sequences. It developed a river-dominated deltaic system, mainly with delta plain deposits, and underwent a constructive–abandoned–constructive development stage. The locally distributed No. 6 coal seam was formed in a backswamp environment with distribution constrained by the distributary channels. The delta was abandoned at the later stage of the SS1 sequence, and the peat accumulation rate was balanced with the growth rate of the accommodation, forming a large-area distributed No. 3 thick coal seam. During the formation of the SS2 sequence, the No. 3 coal seam was locally thinned by epigenetic erosion of the river, and the thin coal belt caused by erosion is controlled by the location of the distributary channels and their extension direction. This study can provide a reference for the research on the distribution of thin sand bodies, sedimentary evolution and peat accumulation regularity in the coal-bearing series under the marine–continental transitional environment

Seismic-Geological Integrated Study on Sedimentary Evolution and Peat Accumulation Regularity of the Shanxi Formation in Xinjing Mining Area, Qinshui Basin

Accurate identification of the lithofacies and sedimentary facies of coal-bearing series is significant in the study of peat accumulation, coal thickness variation and coal-measured unconventional gas. This research integrated core, logging and 3D seismic data to conduct a comprehensive seismic–geological study on the sedimentary evolution characteristics and peat accumulation regularity of the Shanxi Formation in the Xinjing mining area of the Qinshui Basin. Firstly, the high-resolution sequence interface was identified, and the isochronous stratigraphic framework of the coal-bearing series was constructed. Then, the temporal and spatial evolution of sedimentary filling and sedimentary facies was dynamically analyzed using waveform clustering, phase rotation, stratal slice and frequency–division amplitude fusion methods. The results show that the Shanxi Formation in the study area can be divided into one third-order sequence and two fourth-order sequences. It developed a river-dominated deltaic system, mainly with delta plain deposits, and underwent a constructive–abandoned–constructive development stage. The locally distributed No. 6 coal seam was formed in a backswamp environment with distribution constrained by the distributary channels. The delta was abandoned at the later stage of the SS1 sequence, and the peat accumulation rate was balanced with the growth rate of the accommodation, forming a large-area distributed No. 3 thick coal seam. During the formation of the SS2 sequence, the No. 3 coal seam was locally thinned by epigenetic erosion of the river, and the thin coal belt caused by erosion is controlled by the location of the distributary channels and their extension direction. This study can provide a reference for the research on the distribution of thin sand bodies, sedimentary evolution and peat accumulation regularity in the coal-bearing series under the marine–continental transitional environment