JIANG X F, ZHUANG P Z,QIAN Y S, et al. Research on intelligent analysis of stratigraphic information based on sparse geological survey data [J]. Journal of Henan Polytechnic University (Natural Science), doi: 10.16186/j.cnki.1673-9787.2024110035.

Received：2024-11-19

Revised：2025-02-24

Online：2025-10-23

Research on intelligent analysis of stratigraphic information based on sparse geological survey data

Jiang Xiufeng¹,Zhuang Peizhi²,Qian Yuanshun²,Li Chaoji²,Shi Guang³,Zhang Mingjing³

(1. Shandong Hi-Speed Construction Management Group Co., Ltd., Jinan 250098，Shandong，China；2. School of  Qilu Transportation, Shandong University, Jinan 250002，Shandong，China;3. Shandong Provincial Communication Planning and Design Institute Group Co.， Ltd.， Jinan 250101，Shandong，China)

Abstract: Objectives In response to the difficulties in accurately and efficiently obtaining the spatial distribution of stratum using traditional geological exploration data, data processing methods, and conventional deep learning techniques, this study develops a method for predicting the spatial distribution of stratum based on sparse exploration data. Methods First, the known sparse exploration data is transformed using matrix methods. Subsequently, a convolutional integration algorithm is used to extract stratum distribution features from reference maps, and the extracted features are used to train a classifier. Then, based on the trained classifier, multi-scale cyclic interpolation calculations are performed on the stratum maps to be predicted, ultimately forming a two-dimensional spatial distribution of the predicted stratum. Furthermore, based on two reference stratum maps that are perpendicular to each other, the method proposed in this study is used to extract features of the three-dimensional spatial distribution of strata, and the extracted features are cyclically interpolated with the sparse exploration data to ultimately achieve the prediction and construction of a three-dimensional stratum model. Results Taking the actual stratum distribution map as the standard for calculating prediction accuracy, in the two-dimensional verification example using a single stratum reference map and 6 sparse data points, the prediction accuracy of this method is 96%. In the three-dimensional verification example based on 2 stratum reference maps and 36 sparse exploration data points, the prediction accuracy is 92.3%. The prediction accuracy of this method is higher than that of current geological exploration processing methods and traditional machine learning methods. Conclusions The method proposed in this study can effectively extract the spatial distribution features of strata and can predict the spatial distribution of strata efficiently and accurately using only sparse exploration data. The prediction accuracy and computational efficiency meet the actual exploration accuracy requirements and engineering needs. This method can be further extended to geological disaster prevention and mineral development.

Key words：sparse data; convolutional neural network; integrated learning algorithm; stratigraphic reconstruction

CLC: U412.22