Noise Elimination for Wide Field Electromagnetic Data via Improved Dung Beetle Optimized Gated Recurrent Unit
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| Publicado en: | Geosciences vol. 15, no. 1 (2025), p. 8 |
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| Otros Autores: | , , , |
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MDPI AG
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 024 | 7 | |a 10.3390/geosciences15010008 |2 doi | |
| 035 | |a 3159470981 | ||
| 045 | 2 | |b d20250101 |b d20250131 | |
| 084 | |a 231468 |2 nlm | ||
| 100 | 1 | |a Liu, Zhongyuan |u Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment, Monitoring Ministry of Education, School of Geosciences and Info-Physics, Central South University, Changsha 410083, China; <email>205001043@csu.edu.cn</email> (Z.L.); <email>lidiquan@csu.edu.cn</email> (D.L.) | |
| 245 | 1 | |a Noise Elimination for Wide Field Electromagnetic Data via Improved Dung Beetle Optimized Gated Recurrent Unit | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Noise profoundly affects the quality of electromagnetic data, and selecting the appropriate hyperparameters for machine learning models poses a significant challenge. Consequently, the current machine learning denoising techniques fall short in delivering precise processing of Wide Field Electromagnetic Method (WFEM) data. To eliminate the noise, this paper presents an electromagnetic data denoising approach based on the improved dung beetle optimized (IDBO) gated recurrent unit (GRU) and its application. Firstly, Spatial Pyramid Matching (SPM) chaotic mapping, variable spiral strategy, Levy flight mechanism, and adaptive T-distribution variation perturbation strategy were utilized to enhance the DBO algorithm. Subsequently, the mean square error is employed as the fitness of the IDBO algorithm to achieve the hyperparameter optimization of the GRU algorithm. Finally, the IDBO-GRU method is applied to the denoising processing of WFEM data. Experiments demonstrate that the optimization capacity of the IDBO algorithm is conspicuously superior to other intelligent optimization algorithms, and the IDBO-GRU algorithm surpasses the probabilistic neural network (PNN) and the GRU algorithm in the denoising accuracy of WFEM data. Moreover, the time domain of the processed WFEM data is more in line with periodic signal characteristics, its overall data quality is significantly enhanced, and the electric field curve is more stable. Therefore, the IDBO-GRU is more adept at processing the time domain sequence, and the application results also validate that the proposed method can offer technical support for electromagnetic inversion interpretation. | |
| 653 | |a Deep learning | ||
| 653 | |a Wavelet transforms | ||
| 653 | |a Algorithms | ||
| 653 | |a Signal processing | ||
| 653 | |a Neural networks | ||
| 653 | |a Time domain analysis | ||
| 653 | |a Machine learning | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Learning algorithms | ||
| 653 | |a Efficiency | ||
| 653 | |a System theory | ||
| 653 | |a Adaptive algorithms | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Fourier transforms | ||
| 653 | |a Suspended particulate matter | ||
| 653 | |a Signal quality | ||
| 653 | |a Noise reduction | ||
| 653 | |a Optimization | ||
| 653 | |a Electric fields | ||
| 653 | |a Information processing | ||
| 653 | |a Optimization algorithms | ||
| 653 | |a Noise | ||
| 700 | 1 | |a Zhang, Xian |u Hunan Provincial Key Laboratory of Finance & Economics Big Data Science and Technology, School of Information Technology and Management, Hunan University of Finance and Economics, Changsha 410205, China | |
| 700 | 1 | |a Li, Diquan |u Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment, Monitoring Ministry of Education, School of Geosciences and Info-Physics, Central South University, Changsha 410083, China; <email>205001043@csu.edu.cn</email> (Z.L.); <email>lidiquan@csu.edu.cn</email> (D.L.) | |
| 700 | 1 | |a Liu, Shupeng |u Hunan Provincial Key Laboratory of Finance & Economics Big Data Science and Technology, School of Information Technology and Management, Hunan University of Finance and Economics, Changsha 410205, China | |
| 700 | 1 | |a Cao, Ke |u Hunan Provincial Key Laboratory of Finance & Economics Big Data Science and Technology, School of Information Technology and Management, Hunan University of Finance and Economics, Changsha 410205, China | |
| 773 | 0 | |t Geosciences |g vol. 15, no. 1 (2025), p. 8 | |
| 786 | 0 | |d ProQuest |t Publicly Available Content Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3159470981/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3159470981/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3159470981/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |