Deep residual network-based magnetic compensation for a towed, CPT-based magnetometer detection system
XU Qiang-Feng1,2(), WANG Xue-Feng1,2(), DENG Yi-Cheng1,2, HE Huan-Xue1,2, ZHANG Hui-Song1,2, LU Xiang-Dong1,2
1. Beijing Institute of Aerospace Control Devices, Beijing 100854, China 2. Quantum Engineering Research Center, China Aerospace Science and Technology Corporation, Beijing 100094, China
The traditional Tolles-Lawson (TL) linear model suffers limitations of compensation precision in processing complex magnetic disturbance data obtained using a towed coherent population trapping (CPT)-based atomic magnetometer detection system. This study introduced neural network technology to achieve more precise nonlinear compensation. Given that conventional neural network models encounter issues such as gradient vanishment or overfitting during parameter updates, this study proposed a deep residual network-based magnetic compensation model (MCRNet) to further enhance the compensation effects of magnetic interference within a towed pod. Experimental results demonstrate that compared to the best traditional method—The Residual Backpropagation Network (ResBP), the proposed MCRNet model increased the improvement ratio (IR) to 4.826 from 4.251, increasing by 13.53%, and reduced the root mean square error (RMSE) of compensated residual magnetism to 0.171 from 0.2, representing a reduction of 14.5%. The proposed model enhances the magnetic survey accuracy of towed magnetic anomaly detection systems.
徐强锋, 王学锋, 邓意成, 和焕雪, 张慧松, 卢向东. CPT磁力仪拖曳式探测系统的深度残差网络磁补偿方法[J]. 物探与化探, 2025, 49(5): 1212-1220.
XU Qiang-Feng, WANG Xue-Feng, DENG Yi-Cheng, HE Huan-Xue, ZHANG Hui-Song, LU Xiang-Dong. Deep residual network-based magnetic compensation for a towed, CPT-based magnetometer detection system. Geophysical and Geochemical Exploration, 2025, 49(5): 1212-1220.
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