The Jiangnan Orogenic Belt (JOB) evolved from the Yangtze and Cathaysia blocks through multi-stage oceanic-continental subduction, collisional orogeny, and intracontinental deformation, which is an important region to study the formation and evolution of the South China Continent (SCC). Magnetotelluric soundings were collected along a 520-km-long northwest (NW)-trending profile across the middle segment of the JOB to explore the possible remnants of ancient tectonic–magmatic processes beneath the central SCC by combining with the satellite gravity and magnetic data. The resistivity model reveals that the crust in the middle segment of the JOB and its adjacent area is characterized by high resistivity anomalies, while the uppermost mantle is characterized as medium resistivity anomalies and separated by several subvertical, lithospheric-scale conductors. Two trans-crust anomalies of high conductivity and low density beneath the Jiujiang–Shitai Buried fault (JSBF) and Jiangshan–Shaoxing fault (JSF) extend south-eastward to the lithosphere, which are attributed to the NW and southeast boundaries of the middle segment of the JOB. The imaged NW-trending of JSF reflects the tectonic process of the JOB subducting under the Cathaysia Block. Two lower-crustal conductors also imaged beneath the Jiuling area are interpreted as the partial melting of the lower crust, which may be related to the deep southeast subduction of the Paleo-south China Ocean during 970 to 860 Ma. In addition, the trans-lithosphere high conductivity adjacent to the ancient collisional zone of the Jinning period II (ACZII) is probably related to the asthenosphere upwelling caused by the soft collision between the Yangtze and Cathaysia Blocks, which triggered the contemporaneous magmatism in the Jiuling area. This work provides a new insight into the lithospheric evolution in SCC during the Neoproterozoic.

Seismic data processing plays a pivotal role in extracting valuable subsurface information for various geophysical applications. However, seismic records often suffer from inherent random noise, which obscures meaningful geological features and reduces the reliability of interpretations. In recent years, deep learning methodologies have shown promising results in performing noise attenuation tasks on seismic data. In this research, we propose modifications to the standard U-Net structure by integrating dense and residual connections, which serve as the foundation of our approach named the dense and residual (DARE U-Net) network. Dense connections enhance the receptive field and ensure that information from different scales is considered during the denoising process. Our model implements local residual connections between layers within the encoder, which allows earlier layers to directly connect with deep layers. This promotes the flow of information, allowing the network to utilize filtered and unfiltered input. The combined network mechanisms preserve the spatial information loss during the contraction process so that the decoder can locate the features more accurately by retaining the high-resolution features, enabling precise location in seismic image denoising. We evaluate this adapted architecture by applying synthetic and real data sets and calculating the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). The effectiveness of this method is well noted.

The traditional three-dimensional (3D) magnetotelluric (MT) forward modeling using Krylov subspace algorithms has the problem of low modeling efficiency. To improve the computational efficiency of 3D MT forward modeling, we present a novel geometric multigrid algorithm for the finite element method. We use the vector finite element to discretize Maxwell’s equations in the frequency domain and apply the Dirichlet boundary conditions to obtain large sparse complex linear equations for the solution of EM responses. To improve the convergence of the solution at low frequencies we use the divergence correction to correct the electric field. Then, we develop a V-cycle geometric multigrid algorithm to solve the linear equations system. To demonstrate the efficiency and effectiveness of our geometric multigrid method, we take three synthetic models (COMMEMI 3D-2 model, Dublin test model 1, modified SEG/EAEG salt dome model) and compare our results with the published ones. Numerical results show that the geometric multigrid algorithm proposed in this paper is much better than the commonly used Krylov subspace algorithms (such as SOR-GMRES, ILU-BICGSTAB, SOR-BICGSTAB) in terms of the iteration number, the solution time, and the stability, and thus is more suitable for large-scale 3D MT forward modeling.

To improve the signal-to-noise ratio (SNR) of controlled-source electromagnetic (CSEM) data observed in strong interference environments, a new deep learning network is proposed and named gated temporal convolutional network (GTCN) to map noisy sequences to high-quality sequences. This network is an improvement of two state-of-the-art (SOTA) networks specifically designed for time series processing, temporal convolutional network (TCN) and gated recurrent units (GRU). A carefully crafted sample set is created by utilizing shift-invariant sparse coding (SISC) methods and used to train the newly proposed network and six other SOTA deep learning networks. Experimental results of the synthetic data indicate that the new network not only outperforms SISC in accuracy and efficiency, but also is significantly superior to the other six SOTA deep learning methods. The proposed GTCN method can improve the 0 dB noisy signals to 32.6749 dB and improve the average SNR from -5 dB to 23.5999dB. The effectiveness and reliability of the proposed method are also verified through measured data from Sichuan and Yunnan provinces, China. The time series processed by the new approach exhibits more pronounced periodic characteristics, resulting in smoother and more continuous apparent resistivity curves. All these experiments demonstrate that the new scheme is an effective method to improve the quality of CSEM data and contribute to the reliability of CSEM exploration.

Magnetotelluric (MT) and magnetovariational (MV) sounding are two principal geophysical methods used to determine the electrical structure of the earth using natural electromagnetic signals. The complex relationship between the alternating electromagnetic fields can be defined by transfer functions, and their proper selection is crucial in a 3-D inversion. A synthetic case was studied to assess the capacity of these transfer functions to recover the electrical resistivity distribution of the subsurface and to evaluate the advantages and disadvantages of using the tipper vector W to complement the impedance tensor Z and the phase tensor Φ. The analysis started with two sensitivity tests to appraise the sensitivity of each type of transfer function, which is calculated for an oblique conductor model, showing that the resistivity perturbation of the same model will produce distinct perturbations to different transfer functions; the transfer function sensitivity is significantly different. A 3-D inversion utilizing the quasi-Newton method based on the L-BFGS formula was performed to invert different transfer functions and their combinations, along with quantifying their accuracy. The synthetic case study illustrates that a 3-D inversion of either the Z or Φ responses presents a superior ability to recover the subsurface electrical resistivity; joint inversions of the Z or Φ responses with the W responses possess superior imaging of the horizontal continuity of the conductive block. The appraisal of the 3-D inversion results of different transfer functions can facilitate assessing the advantages of different transfer functions and acquiring a more reasonable interpretation.