Journal article
Self-potential inversion based on Attention U-Net deep learning network
Journal of Central South University, Vol.31(9), pp.3156-3167
09/01/2024
DOI: 10.1007/s11771-024-5755-8
Abstract
Landfill leaks pose a serious threat to environmental health, risking the contamination of both groundwater and soil resources. Accurate investigation of these sites is essential for implementing effective prevention and control measures. The self-potential (SP) stands out for its sensitivity to contamination plumes, offering a solution for monitoring and detecting the movement and seepage of subsurface pollutants. However, traditional SP inversion techniques heavily rely on precise subsurface resistivity information. In this study, we propose the Attention U-Net deep learning network for rapid SP inversion. By incorporating an attention mechanism, this algorithm effectively learns the relationship between array-style SP data and the location and extent of subsurface contaminated sources. We designed a synthetic landfill model with a heterogeneous resistivity structure to assess the performance of Attention U-Net deep learning network. Additionally, we conducted further validation using a laboratory model to assess its practical applicability. The results demonstrate that the algorithm is not solely dependent on resistivity information, enabling effective locating of the source distribution, even in models with intricate subsurface structures. Our work provides a promising tool for SP data processing, enhancing the applicability of this method in the field of near-subsurface environmental monitoring.
Details
- Title: Subtitle
- Self-potential inversion based on Attention U-Net deep learning network
- Creators
- You-jun Guo - Central South UniversityYi-an Cui - Central South UniversityHang Chen - Boise State UniversityJing Xie - Central South UniversityChi Zhang - University of ViennaJian-xin Liu - Central South University
- Resource Type
- Journal article
- Publication Details
- Journal of Central South University, Vol.31(9), pp.3156-3167
- DOI
- 10.1007/s11771-024-5755-8
- ISSN
- 2095-2899
- eISSN
- 2227-5223
- Publisher
- Journal Of Central South Univ
- Number of pages
- 12
- Grant note
- CX20200228 / Hunan Provincial Innovation Foundation for Postgraduate, China 42174170; 41874145; 72088101 / National Natural Science Foundation of China; National Natural Science Foundation of China (NSFC)
- Language
- English
- Date published
- 09/01/2024
- Academic Unit
- Earth and Environmental Sciences
- Record Identifier
- 9984962529802771
Metrics
1 Record Views