The advantages of the long-offset transient electromagnetic method include deep detection and sensitive response to resistivity anomalies. It is widely used in underground mineral resources exploration, fluid identification in petroleum reservoirs, hydraulic fracturing, and dynamic residual oil and gas monitoring. After the primary field signal is turned off, grounded electrodes or coils are used to observe the secondary eddy field. The secondary field signal decays quickly and has a large dynamic range and a wide frequency band but is easily affected by various natural and human electromagnetic interferences. Therefore, noise reduction and distortion correction are important issues in the processing of transient electromagnetic data. This paper proposes a systematic noise interference suppression process. Multi-period and positive–negative bipolar signal stackings were used to remove random noise and suppress DC offset signals. Then, a time-domain inverse digital recursive method was applied to remove characteristic frequency signals, e.g., power frequency signals and their harmonic interference. A standard noise-free signal was constructed through forward modeling simulation and verified by adding different types of noise. Finally, high-quality transient electromagnetic secondary field attenuation signals were obtained through overlapping windowing technology. We applied this algorithm to obtain electromagnetic data from dynamic monitoring of hydraulic fracturing in Fuling shale gas and from a copper–iron metal mine in Daye City, demonstrating its effectiveness.

The presence of remanent magnetization introduces uncertainties in the processing and interpretation of magnetic data. In the literature, a variety of methods have been proposed to extract the intensity and direction of remanent magnetization. However, the existing methods still have some limitations, such as biases in results due to the use of inaccurate prior information and the complex computational process of extracting remanent magnetization information, especially from superimposed anomalies by multiple field sources. In this study, we develop an effective method to extract the intensity and direction of the remanent magnetization based on deep learning. We first use an improved U-Net as the backbone network to obtain the feature of spatial location and remanent magnetization parameters of anomalies and fuse the extracted multiscale feature information. At the same time, residual connections are added between the convolution layers to alleviate the loss of information and reduce gradient disappearance. The network, through continuous training, can directly learn the nonlinear mapping relationship between anomalies and the remanent magnetization intensity and direction, without the need for a prior information and complex calculations. Subsequently, we test the proposed method on synthetic examples and field data example in Yeshan region. All the outcomes demonstrate the capability in accurately extracting intensity and direction of remanent magnetization.

Magnetotelluric (MT) is an electromagnetic exploration method using natural field sources, which are unstable, irregular, and vulnerable to interference from electromagnetic signals. The total power spectrum of the magnetotelluric signal is superimposed by a sub-power spectrum of segmented time series. To reduce the influence of electromagnetic interference, the pre-processing of magnetotelluric data will delete the severely interfered sub-power spectrum. The selection of power spectrum often requires human-computer interaction, which is time-consuming and laborious, heavily dependent on personal experience, and has great randomness. This article innovation is put forward based on the classification of Rhoplus correction and deep learning automatic power spectrum method, first of all, the MT power spectrum impedance estimation results are Rhoplus one dimensional inversion and obtain fitting, using neural network learning fitting differential characteristics and classification, and then for the power spectrum of MT again after low-quality quantum power spectrum to eliminate overlay, Impedance estimation is performed, and the above process is repeated to find the optimal power spectrum combination. Through the comparison and verification of measured MT data, the automatic power spectrum selection method based on Rhoplus correction and deep learning classification achieve similar results to manual editing.

The inherent limitations of individual geophysical methods and the sparsity of observational data often render inversion results unstable and non-unique. Joint inversion of multiphysics data exploits the complementary sensitivities of different physical fields regarding depth, resolution, and boundary features, thereby significantly mitigating the ambiguity of single-method inversion and enhancing interpretation reliability. Traditional joint inversion approaches primarily fall into two categories: spatial structure-based and physical parameter-based constraints. The former relies on the similarity of property distribution patterns, which struggles to decouple non-homologous anomalies, while the latter is often constrained by the unreliability of empirical relationships under complex geological conditions. Recently, deep learning methods based on the U-Net architecture have achieved joint inversion by establishing constraints based solely on spatial structural similarity (Hu et al., 2025) or physical parameter correlations (Guo et al., 2021). Although promising, these methods often fail to accurately characterize non-homologous anomalies in complex geological environments.This study proposes a dual-stream 3D U-Net architecture incorporating a hybrid attention-gating mechanism. In terms of methodology, we first construct a training dataset based on rock physics data that encompasses both statistical correlations and structural discrepancies. Regarding the network architecture, independent encoders are employed to extract 3D features from gravity and magnetic data, respectively. A cross-attention module is then utilized to capture deep structural correlations, thereby enhancing cooperative inversion in homologous regions. Subsequently, a gated fusion module is introduced as an adaptive feature selector to effectively disentangle inconsistent features in non-homologous regions. Finally, the prediction models are generated through independent decoders.During the joint inversion implementation phase, the network takes preliminary independent inversion results as input to predict high-fidelity models that integrate physical and geological priors. We incorporate these predicted models as reference models into the regularization term of the joint inversion objective function, constructing a deep-prior-based constraint. During iterative optimization, this constraint guides the inversion trajectory toward the fine geological structures predicted by deep learning by minimizing the discrepancy between the inverted and reference models, while ensuring the fit to observational data. This mechanism achieves an organic integration of data-driven and physics-driven approaches.ReferencesHu, Y. Su, X. Wu, Y. Huang and J. Chen, "Successive Deep Perceptual Constraints for Multiphysics Joint Inversion," in IEEE Transactions on Geoscience and Remote Sensing, vol. 63, pp. 1-14, 2025, Art no. 5907114. Guo, H. M. Yao, M. Li, M. K. P. Ng, L. Jiang and A. Abubakar, "Joint Inversion of Audio-Magnetotelluric and Seismic Travel Time Data With Deep Learning Constraint," in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 9, pp. 7982-7995, Sept. 2021.