Evaluation of initial geostress field of underground powerhouse in the complex alteration area of western Sichuan based on back-propagation neural network method
文献类型：期刊论文
| 作者 | Zheng, Hong3; Cao, Shiqi2,3; Jiang, Quan3; Li, Shaojun3; Xu, Dingping3; Li, Zhiwei1 |
| 刊名 | GEOMECHANICS AND GEOPHYSICS FOR GEO-ENERGY AND GEO-RESOURCES
 |
| 出版日期 | 2025-12-01 |
| 卷号 | 11期号:1页码:25 |
| 关键词 | Alteration development features Classification of altered rocks Back-propagation neural network Geostress inversion |
| ISSN号 | 2363-8419 |
| DOI | 10.1007/s40948-025-00949-z |
| 英文摘要 | Rock alteration leads to the degradation of the physical and mechanical properties of original rock. The spatial variance in the degree of rock alteration significantly influences regional ground stress distribution characteristics. The key problem in the process of ground stress inversion of 3-d geological model of underground powerhouse is that the geological model is difficult to divide reasonable alteration region, and the mechanical parameters of altered rock mass have strong non-uniformity. To solve these problems, a rapid and efficient classification criterion based on exploration-derived geological information was proposed. Additionally, this classification was used to analyze the distribution function of mechanical parameters in surrounding rocks across different alteration grades. The geological data obtained through exploration tunnels reveal that the spatial distribution characteristics of joint lineament density and altered rock development features are closely related to the fault. Based on the development mechanism of altered rocks, the distribution law of alteration is revealed from the perspective of engineering geology. And with the application of the back-propagation neural network method, a new three-dimensional ground stress inversion model that considers the distribution of altered rock was proposed. The inversion results indicate that the ground stress values obtained from the model, which considers the distribution of various alteration grades, correspond closely with the observed ground stress distribution characteristics. Moreover, this result proves that this inversion method is reasonable when obtaining the geostress field at engineering scale across the alteration region as evinced by a comparative study. This inversion method has good applicability to obtain the initial ground stress distribution in the area of structural alteration where the lithology difference is large but there is no obvious lithology boundary. |
| 资助项目 | The National Natural Science Foundation of China[52479112] ; The National Natural Science Foundation of China[52325905] ; The Young Top-notch Talent Cultivation Program of Hubei Province and Public Bidding and Leadership Project of Power China[DJ-HXGG-2023-04] |
| WOS研究方向 | Energy & Fuels ; Engineering ; Geology |
| 语种 | 英语 |
| WOS记录号 | WOS:001439665700003 |
| 出版者 | SPRINGER HEIDELBERG |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/36888]  |
| 专题 | 中科院武汉岩土力学所 |
| 通讯作者 | Zheng, Hong |
| 作者单位 | 1.CCCC Highway Consultants CO Ltd, Beijing 100010, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn Safety, Wuhan 430071, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zheng, Hong,Cao, Shiqi,Jiang, Quan,et al. Evaluation of initial geostress field of underground powerhouse in the complex alteration area of western Sichuan based on back-propagation neural network method[J]. GEOMECHANICS AND GEOPHYSICS FOR GEO-ENERGY AND GEO-RESOURCES,2025,11(1):25. |
| APA | Zheng, Hong,Cao, Shiqi,Jiang, Quan,Li, Shaojun,Xu, Dingping,&Li, Zhiwei.(2025).Evaluation of initial geostress field of underground powerhouse in the complex alteration area of western Sichuan based on back-propagation neural network method.GEOMECHANICS AND GEOPHYSICS FOR GEO-ENERGY AND GEO-RESOURCES,11(1),25. |
| MLA | Zheng, Hong,et al."Evaluation of initial geostress field of underground powerhouse in the complex alteration area of western Sichuan based on back-propagation neural network method".GEOMECHANICS AND GEOPHYSICS FOR GEO-ENERGY AND GEO-RESOURCES 11.1(2025):25. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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