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Quantitative interpretation of the data from controlled‐source electromagnetic methods, whether via forward modelling or inversion, requires solving a considerable number of forward problems, and multigrid methods are often employed to accelerate the solving process. In this study, a new extrapolation cascadic multigrid method is employed to solve the large sparse complex linear system arising from the finite element approximation of Maxwell's equations using secondary potentials. The equations using secondary potentials are discretized by the classic nodal finite element method on nonuniform rectilinear grids. The resulting linear systems are solved by the extrapolation cascadic multigrid method with a new prolongation operator and preconditioned Stabilized bi‐conjugate gradient method smoother. High‐order interpolation and global extrapolation formulas are utilized to construct the multigrid prolongation operator. The extrapolation cascadic multigrid method with the new prolongation operator is easier to implement and more flexible in application than the original one. Finally, several synthetic examples including layered models, models with anisotropic anomalous bodies or layers, are used to validate the accuracy and efficiency of the proposed method. Numerical results show that the extrapolation cascadic multigrid method improves the efficiency of 3D controlled‐source electromagnetic forward modelling a lot, compared with traditional iterative solvers and some state‐of‐the‐art methods or software (e.g., preconditioned flexible generalized minimal residual method, emg3d) in the considered models and grid settings. The efficiency benefit is more evident as the number of unknowns increases, and the proposed method is more efficient at low frequencies. The extrapolation cascadic multigrid method can also be used to solve systems of equations arising from related applications, such as induction logging, airborne electromagnetic, etc. [ABSTRACT FROM AUTHOR]

Caves formed in hard limestone due to groundwater erosion and rainwater infiltration may cause damages of buildings. The electrical resistivity tomography (ERT) with high lateral resolution and low economic cost is a good way to detect the caves, as the electrical resistivity of water-bearing caves is much lower than that of surrounding rocks. In this study, we arranged three sets of intersected ERT profiles to detect the unknown water-bearing caves in an area of Wuhan city of China. Both two-dimensional and three-dimensional inversions were performed to obtain the underground electrical resistivity tomography images. The reliability of imaged water-bearing caves was analyzed in terms of the reliability analysis tool. The inversion results not only agree well with the drilling results, but also detect five possible locations of water-bearing caves in the limestone bedrock. The findings of these underground water-bearing caves in advance can effectively avoid potential safety hazards as this area will become the commercial center of Wuhan city. • 29 intersected ERT lines with 2663 electrodes were arranged. • Both the depth of investigation index tool and drilling verification were adopted to increase the ERT reliability. • Five new water-bearing caves were found with high confidence levels. [ABSTRACT FROM AUTHOR]

Deep Learning is an effective tool to invert the underground electrical conductivity structure. In this study, we build a new 8-layer residual neural network (ResNet1D-8) for audio magnetotelluric (AMT) data inversion based on the deep learning theory. In terms of the network structure, the degradation of the model with the increase of depth is effectively avoided by adding shortcut connections. Meanwhile, adding a batch normalization layer greatly improves the model training speed and network generalization ability. This study uses the parallel technology to quickly generate millions of samples, which effectively reduces the computational time and provides a large number of high-quality samples for deep learning model training. Compared with the simulated annealing algorithm, the neural network model in this paper has the advantages of high reliability, short inversion time, and strong model generalization ability. Moreover, we add Gaussian noise to the data of testing samples, and inversion results show that the model has good robustness. The inversion test is carried out on the field measured AMT data set collected in the Dachaidan area of Qinghai Province, China. The results show that the ResNet1D-8 residual neural network model established in this paper can effectively invert the underground electrical structure. • A new residual neural network model (ResNet1D-8) for 1-D MT inversion is constructed. • A uniform stochastic model and parallel techniques are used to rapidly generate samples. • The generalizability and robustness of ResNet1D-8 are verified through numerical experiments. [ABSTRACT FROM AUTHOR]

The multiferroic material Pb 3 Mn 7 O 15 exhibits a high resistivity arising from the orbital electrons localized around the manganese ions, which hinders its potential applications. Previous studies indicated that the resistivity of Pb 3 Mn 7 O 15 was influenced by its crystal structure and the distribution of Mn ions. Consequently, pressure-induced structural change are expected to represent a viable method for tuning the resistivity and other properties of this compound. In the present study, the pressure effect on the resistivity of Pb 3 Mn 7 O 15 was investigated up to 42 GPa. Furthermore, the X-ray diffraction and Raman spectroscopy techniques, along with the density functional theory calculations were employed to study the crystal structure evolution of Pb 3 Mn 7 O 15 under pressure. The results demonstrate an electronic semiconductor-metal phase transition in Pb 3 Mn 7 O 15 , accompanying an irreversible orthorhombic to monoclinic phase transition. Additionally, within the orthorhombic phase, an iso-structural phase transition is observed, originating from the magnetic moments collapse of manganese cations. This study provides compelling evidence that high-pressure processing can be employed to modulate the crystal structure and functional properties of multifunctional materials. • Pressure-induced metallization of Pb 3 Mn 7 O 15 (PMO). • The metallization of PMO accompanies a sequence of phase transitions. • The Pnma → P 2/ m phase transition of PMO is irreversible. [ABSTRACT FROM AUTHOR]

In order to study the spectra characteristics of audio-frequency passive electromagnetic (EM) fields under a cultural background, long-term audio magnetotelluric observation stations were deployed in two different typical ore districts in the middle and lower reaches of the Yangtze River (MLYR), east China, where it is widely regarded as in a typical natural and cultural mixed source environment. Data of orthogonal horizontal electric and magnetic fields were recorded continuously for months. Power spectra of the EM fields, signal coherences and apparent resistivities are estimated by employing the standard magnetotelluric robust data processing method. The observed data set is statistically analyzed by daily data and their medians for each field component. There are distinct band characteristics for the overall spectra. The spectra display very low power and are unstable in the high and low frequency "dead bands". There are several distinguishable extreme values, including the maxima of the fundamental power-line frequency and its harmonics, the maxima of the Schumann resonances, and a minimum at 780 Hz. In the time scale, the spectra have variations both daily and annually. The spectral amplitude is greater in the nighttime than daytime only in the high frequency "dead band", while it is greater in summer than autumn and winter in the whole observation frequency band, including the industrial power frequencies. In the spatial scale, the differences of magnetic spectrum amplitude are mainly reflected in the band of 0.35–10 Hz and the power-line frequencies between the two selected regions, which indicate the background noise level of the north is stronger than that of the south of the Yangtze River in MLYR. The results have given us a better understanding of the passive source EM fields in east China, which provide a guiding and practical value for both the natural and controlled source EM exploration methods in data collection, processing, and interpretation. • Long-term observation revealed distinct EM spectra characteristics. • "Dead bands" have a dynamic frequency range without exact boundaries. • Sources of the MLYR area have common characteristics and different local features. [ABSTRACT FROM AUTHOR]

In this study, we developed a novel 18-layers residual full convolutional neural network (18RFCN) for audio magnetotellurics (AMT) data inversion. Different from traditional inversion methods, the 18RFCN-based inversion algorithm is based on big-data training rather than prior-information assumptions, and the prediction is almost no time-consuming after network training. The problem of gradient disappearance was solved by adding shortcut connections when training deep neural networks, and we proposed two novel methods to quickly generate millions of samples for network training to improve the network generalization. We first tested on four synthetic data sets with Gaussian noises, which are entirely different from the data of sample sets, and the inversion results showed that the deep learning algorithm could accurately recover the underground conductivity structure almost no time-consuming compared to the Gauss Newton (GN) algorithm. We further tested the inversion algorithm on a field AMT data set acquired in Qinghai province, China. The inversion results of the deep learning algorithm agree well with the GN algorithm and the drilling data. • A new Convolutional Network for audio magnetotelluric inversion is presented. • Residual method is adopted to solve the problem of gradient disappearance. • A new method is adopted to generate 3 million samples for network training. • The trained network can get reliable conductivity model with no time-consuming. [ABSTRACT FROM AUTHOR]

De-noising the magnetotelluric (MT) data using the conventional time-series editing methods is at the risk of losing low-frequency signals, especially the signal below 1 Hz. To overcome this deficiency, we propose a combinatorial method based on sparse representation and mathematical morphology filtering. First, the effective low-frequency signal is reconstructed using the mathematical morphological filtering (MMF) method and protected. Then, the residual noisy signal of high frequency is sparsely decomposed using the subspace pursuit (SP) algorithm to obtain noise-free high-frequency MT signals. Finally, the effective low-frequency signal is added to the de-noised high-frequency signal to get the full-band MT data. We evaluate the proposed method using a synthetic data set and two real data sets collected in Qiadam Basin, the northeastern part of the Tibetan Plateau. Experimental results demonstrate that the presented approach can be used to remove different kinds of cultural noises while preserve the low-frequency signal below 1 Hz. The evaluation results also indicate that the proposed method is superior to the conventional methods in terms of the signal-to-noise ratio (SNR), reconstruction error (E) and normalized cross-correlation (NCC). • Noise cancellation for MT data is important but difficult. • Effective low-frequency components are protected before de-noising. • A new strategy for noise reduction of low-frequency MT data is developed. • The use of interesting sparse representation results in the most effective noise reduction. [ABSTRACT FROM AUTHOR]