Tunneling route prediction of shield machine based on random forest P-wave generation
Shi Su-zhen*, Gu Jian-ying, Liu Zui-liang, Duan Pei-fei, Han Qi, Qi You-chao, Zhang Xin
1. State Key Laboratory of Coal Resources and Safety Mining, Beijing 100083, P.R. China;
2. Yangquan Coal Industry (Group) Co. LTD, Yangquan 37000.
3. Bei Fang Investigation, Design & Research CO.LTD, Tianjin 300222;
4. College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, P.R. China.
Abstract：
In recent years, coal mine has applied shield tunneling machines to roadway excavation to improve production efficiency. Geological condition is an important factor that determines the efficiency of shield machines. The shield machine is most favorable for medium to hard surrounding rocks such as limestone and sandstone. Therefore, the lithology prediction of the location of a planned excavation roadway becomes the core issue in improving the efficiency of the shield machine. At present, seismic inversion is an essential method for lithology prediction. However, in Yangquan Xinjing area, missing P-wave logging curves affects the impedance inversion. Therefore, using existing logging curves to generate missing P-wave logging curves and using sandstone exposure data to continuously update lithology distribution prediction results are of great interest. In this study, logging curves were first pretreated by standardization to ensure the inversion effect. Because of the missing acoustic logging curves, the random forest regression algorithm was introduced using density, natural gamma, apparent resistivity, and spontaneous potential curves as characteristic variables to establish a curve regression prediction model. Then, P-wave logging curves were acquired. After a full analysis of the principles of acoustic and gamma curves, a quasi-acoustic curve is constructed, and a quasi-acoustic inversion was performed. The top and bottom interfaces of the K7 sandstone were interpreted on the inversion data body. The interpreted horizon information was converted from the time domain to the depth domain. The predicted results agreed well with the exposed data. At the same time, combined with the lithology exposure data from the shield tunneling machine, the distribution prediction of the K7 sand body in the target roadway section was updated and iterated many times, which provided effective guidance for the optimization of the tunneling route of the shield tunneling machine.
基金资助:This work was supported by the Joint Fund of the State Key Laboratory of Coal Resources and Safe Mining-Beijing University Outstanding Young Scientists Program Project (BJJWZYJH01201911413037); State Key Laboratory of “Coal Resources and Safe Mining” Open Fund (SKLCRSM19ZZ02)
通讯作者: Shi Su-zhen (Email: ssz@cumtb.edu.cn)
E-mail: ssz@cumtb.edu.cn
作者简介: Shi Suzhen is an associate professor and master supervisor at the China University of Mining and Technology (Beijing). Her main work is seismic data interpretation and inversion. Her contact information is State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology (Beijing), 11 Ding Xueyuan Road, Haidian District, Beijing 100083, China. Her email address is ssz@cumtb.edu.cn.
2024, Vol. 21 
