Deep learning-based near-field effect correction method for Controlled Source Electromagnetic Method and application

• Published in: PloS one Vol. 19; no. 11; p. e0308875
• Main Authors: Luo, Wei, Chen, Xianjie, Wang, Shixing, Zhao, Siwei, Yin, Xiaokang, Lan, Xing, Jiang, Peifan, Wang, Shaojun
• Format: Journal Article
• Language: English
• Published: San Francisco Public Library of Science 11-11-2024; Public Library of Science (PLoS)
• Subjects: Algorithms; Computer and Information Sciences; Datasets; Deep learning; Earth Sciences; Effectiveness; Electromagnetism; Engineering and Technology; Geological structures; Geology; Geophysical exploration; Geophysical methods; Machine learning; Magnetic fields; Methods; Near fields; Physical Sciences; Research and Analysis Methods; Simulation; Simulation methods; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0308875

Addressing the impact of near-field effects in the Controlled Source Electromagnetic Method(CSEM) has long been a focal point in the realm of geophysical exploration. Therefore, we propose a deep learning-based near-field correction method for controlled-source electromagnetic methods. Initially, diverse datasets for a layered geologic model are generated through forward simulation. Building upon the characteristics of near-field effects, a deep learning network utilizing LSTM-CNN is meticulously constructed. Multiple experiments are executed to scrutinize the network’s effectiveness in mitigating near-field effects and its resilience against noise. Following this, the proposed method is applied to actual CSEM data to validate its applicability in practice. The method is subsequently tested on measured CSEM data, confirming its practical efficacy. Results from experiments indicate that, for theoretical data, the LSTM-CNN network-trained data closely aligns with simulated data, showcasing a significant improvement. Moreover, when applied to measured data, the method eradicates false high-resistance anomalies at lower frequencies. In conclusion, this deep learning-based correction method proficiently eliminates the influence of near-field effects in the CSEM, delivering practical application benefits that more accurately reflect the authentic geologic structure.