A U-Net-based denoising method for semi-airborne transient electromagnetic data and its application
Objective and Methods The semi-airborne transient electromagnetic (SATEM) method, an efficient geophysical exploration technique, has been extensively applied to mineral resource exploration, groundwater surveys, and geothermal resource surveys. However, the collected data are frequently susceptible...
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Editorial Office of Coal Geology & Exploration
2025-01-01
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| Series: | Meitian dizhi yu kantan |
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| Online Access: | http://www.mtdzykt.com/article/doi/10.12363/issn.1001-1986.24.05.0303 |
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| author | Dong LIU Hao FENG Yongxin WANG Xiaosheng ZHOU Yuhong YAO Huaifeng SUN |
| author_facet | Dong LIU Hao FENG Yongxin WANG Xiaosheng ZHOU Yuhong YAO Huaifeng SUN |
| author_sort | Dong LIU |
| collection | DOAJ |
| description | Objective and Methods The semi-airborne transient electromagnetic (SATEM) method, an efficient geophysical exploration technique, has been extensively applied to mineral resource exploration, groundwater surveys, and geothermal resource surveys. However, the collected data are frequently susceptible to noise interference, significantly affecting the accuracy of subsequent data processing and interpretation. To address issues such as residual noise and the loss of effective signals, enhance denoising effects, and reduce the influence of subjective factors, this study proposed a denoising method for SATEM data based on the U-Net deep learning architecture (also referred to as the U-Net-based method) by applying U-Net to SATEM data denoising. In this method, a U-shaped encoder-decoder architecture is employed to automatically learn and extract noise features from the data through an end-to-end training approach. The encoder learns and extracts noise features from data, while the decoder reconstructs the noise features and restores denoised data. By introducing skip connections to the symmetric layers in the encoder and the decoder, the U-Net-based method effectively integrates the low-level features bearing rich spatial information with the high-level features containing semantic information, thus achieving accurate denoising. Results and Conclusions Practical calculation cases indicate that the U-Net-based method can improve the signal-to-noise ratio (SNR) of data by approximately 10 dB after denoising, proving significant advantages of denoising SATEM data compared to traditional denoising methods. This method has been employed to denoise the measured data of the No.2 Fenghuang tunnel in the Laibin-Du'an section of the Hezhou-Bama expressway in Guangxi, significantly enhancing the interpretability of the multi-channel diagrams and apparent resistivity images after data denoising. Therefore, the U-Net-based method holds great practical significance for SATEM data denoising, thus providing effective technical support for future geophysical exploration. |
| format | Article |
| id | doaj-art-96723c69fca44c778f1d016176446745 |
| institution | Kabale University |
| issn | 1001-1986 |
| language | zho |
| publishDate | 2025-01-01 |
| publisher | Editorial Office of Coal Geology & Exploration |
| record_format | Article |
| series | Meitian dizhi yu kantan |
| spelling | doaj-art-96723c69fca44c778f1d0161764467452025-02-12T07:20:18ZzhoEditorial Office of Coal Geology & ExplorationMeitian dizhi yu kantan1001-19862025-01-0153122623410.12363/issn.1001-1986.24.05.030324-05-0303liudongA U-Net-based denoising method for semi-airborne transient electromagnetic data and its applicationDong LIU0Hao FENG1Yongxin WANG2Xiaosheng ZHOU3Yuhong YAO4Huaifeng SUN5Institute of Geotechnical and Underground Engineering, Shandong University, Jinan 250061, ChinaInstitute of Geotechnical and Underground Engineering, Shandong University, Jinan 250061, ChinaInstitute of Geotechnical and Underground Engineering, Shandong University, Jinan 250061, ChinaInstitute of Geotechnical and Underground Engineering, Shandong University, Jinan 250061, ChinaGuangxi Communications Investment Group CO., Ltd., Nanning 530022, ChinaInstitute of Geotechnical and Underground Engineering, Shandong University, Jinan 250061, ChinaObjective and Methods The semi-airborne transient electromagnetic (SATEM) method, an efficient geophysical exploration technique, has been extensively applied to mineral resource exploration, groundwater surveys, and geothermal resource surveys. However, the collected data are frequently susceptible to noise interference, significantly affecting the accuracy of subsequent data processing and interpretation. To address issues such as residual noise and the loss of effective signals, enhance denoising effects, and reduce the influence of subjective factors, this study proposed a denoising method for SATEM data based on the U-Net deep learning architecture (also referred to as the U-Net-based method) by applying U-Net to SATEM data denoising. In this method, a U-shaped encoder-decoder architecture is employed to automatically learn and extract noise features from the data through an end-to-end training approach. The encoder learns and extracts noise features from data, while the decoder reconstructs the noise features and restores denoised data. By introducing skip connections to the symmetric layers in the encoder and the decoder, the U-Net-based method effectively integrates the low-level features bearing rich spatial information with the high-level features containing semantic information, thus achieving accurate denoising. Results and Conclusions Practical calculation cases indicate that the U-Net-based method can improve the signal-to-noise ratio (SNR) of data by approximately 10 dB after denoising, proving significant advantages of denoising SATEM data compared to traditional denoising methods. This method has been employed to denoise the measured data of the No.2 Fenghuang tunnel in the Laibin-Du'an section of the Hezhou-Bama expressway in Guangxi, significantly enhancing the interpretability of the multi-channel diagrams and apparent resistivity images after data denoising. Therefore, the U-Net-based method holds great practical significance for SATEM data denoising, thus providing effective technical support for future geophysical exploration.http://www.mtdzykt.com/article/doi/10.12363/issn.1001-1986.24.05.0303semi-airborne transient electromagnetic (satem) methoddeep learningu-netdenoisingcomplex noise |
| spellingShingle | Dong LIU Hao FENG Yongxin WANG Xiaosheng ZHOU Yuhong YAO Huaifeng SUN A U-Net-based denoising method for semi-airborne transient electromagnetic data and its application Meitian dizhi yu kantan semi-airborne transient electromagnetic (satem) method deep learning u-net denoising complex noise |
| title | A U-Net-based denoising method for semi-airborne transient electromagnetic data and its application |
| title_full | A U-Net-based denoising method for semi-airborne transient electromagnetic data and its application |
| title_fullStr | A U-Net-based denoising method for semi-airborne transient electromagnetic data and its application |
| title_full_unstemmed | A U-Net-based denoising method for semi-airborne transient electromagnetic data and its application |
| title_short | A U-Net-based denoising method for semi-airborne transient electromagnetic data and its application |
| title_sort | u net based denoising method for semi airborne transient electromagnetic data and its application |
| topic | semi-airborne transient electromagnetic (satem) method deep learning u-net denoising complex noise |
| url | http://www.mtdzykt.com/article/doi/10.12363/issn.1001-1986.24.05.0303 |
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