关键词: 机器学习, K邻近域算法, 全域地质特征预测, 地质建模, 钻孔数据

Abstract: The original drilling data collected cannot be deeply excavated and utilized by traditional modeling methods. Therefore, an improved Knearest neighbor(KNN) algorithm is improved. This is a spatial adaptive interpolation fitting algorithm that is developed based on the original KNN algorithm. It incorporates automatic selection of 〖WT5BX〗k〖WT〗〖WT５《TNR》〗values based on different geological layers and utilizes the original geological data for further analysis. The data from a railway investigation project is selected as a data source to put into the improved KNN algorithm model, successfully obtaining the characteristic 〖WT5BX〗k〖WT〗〖WT5《TNR》〗values of each stratum in the site and accomplishing the geological modeling. By comparing the actual borehole and the borehole predicted by original KNN and improved KNN, it is found that the improved KNN algorithm is more accurate in predicting the thin layers, and the overall accuracy is higher. Compared with the original KNN algorithm and other common classification algorithms, the improved algorithm can obtain better stratum prediction results, especially for the thin stratum, and obtain higher precision and accuracy, which can better guide the prediction of underground 3D space.

Key words: machine learning, Knearest neighbor, prediction of fullarea geological features, geological modeling, drilling data