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The Bayesian method is a powerful tool to estimate the resistivity distribution and associated uncertainty from time-domain electromagnetic (TDEM) data. Because the forward simulation of the TDEM method is computationally expensive and a large number of samples are needed to globally explore the model space, the full Bayesian inversion of the TDEM data is limited to layered models. To make the high-dimensional Bayesian inversion tractable, we have adopted a divide-and-conquer strategy to speed up the Bayesian inversion of TDEM data. First, the full data sets and model spaces are divided into disjoint batches based on the coverage of the sources so that the independent and highly efficient Bayesian subsampling can be conducted. Then, the samples from each subsampling procedure are combined to get the full posterior. To obtain an asymptotically unbiased approximation to the full posterior, a kernel density product method is used to reintegrate samples from each subposterior. The model parameters and their uncertainty are estimated from the full posterior. Our method is tested on synthetic examples and applied to a field data set acquired with a large fixed-loop configuration. The 2D section from the Bayesian inversion revealed several mineralized zones, one of which matches well with the information from a nearby drillhole. The field example indicates the ability of the Bayesian inversion to infer reliable resistivity and uncertainty.

The separation of P- and S-wavefields is considered to be an effective approach for eliminating wave-mode crosstalk in elastic reverse time migration (RTM). At present, the Helmholtz decomposition method is widely used for isotropic media. However, it tends to change the amplitudes and phases of the separated wavefields compared with the original wavefields. Other methods used to obtain pure P- and S-wavefields include the application of the elastic wave equations of the decoupled wavefields. To achieve high computational accuracy, staggered-grid finite-difference (FD) schemes are usually used to numerically solve the equations by introducing an additional stress variable. However, the computational cost of this method is high because a conventional hybrid wavefield (the P- and S-wavefields are mixed together) simulation must be created before the P- and S-wavefields can be calculated. We have developed the first-order particle velocity equations to reduce the computational cost. The equations can describe four types of particle-velocity wavefields: the vector P-wavefield, the scalar P-wavefield, the vector S-wavefield, and the vector S-wavefield rotated in the direction of the curl factor. Without introducing the stress variable, only the four types of particle velocity variables are used to construct the staggered-grid FD schemes; therefore, the computational cost is reduced. We also develop an algorithm to calculate the P and S propagation vectors using the four particle velocities, which is simpler than the Poynting vector. Finally, we apply the velocity equations and propagation vectors to elastic RTM and angle-domain common-image gather computations. These numerical examples illustrate the efficiency of our methods.

Existing noise removal processes for airborne electromagnetic (AEM) data generally consist of several steps, with each using a specific method to remove a specific type of noise. To improve the efficiency of AEM denoising and reduce the impact of the subjective judgment of the operators on the processing results, we have adopted a deep learning method based on a denoising autoencoder (DAE), which enables in one single processing step the removal of multisource noise. The most common noise sources in AEM data, including motion-induced noise, nearby or moderately distant sferics noise, power-line noise, and background electromagnetic noise, will be combined with a large number of simulation responses to build a training set. The data in the training set will be used to train the deep learning DAE neural network so that the neural network could fully learn the respective characteristics of the signal and noise and further effectively distinguish the AEM response signal (useful signal) from the above noise. The field data were processed using this method, and the processing results were compared with those obtained using traditional methods. The comparison test revealed that this method is helpful to reduce the influence of subjective factors on the quality of data results and compress the entire AEM data processing time.

Transient electromagnetic (TEM) data can be seriously distorted by induced polarization (IP) phenomena when a polarizable body is present. The TEM field generated by a grounded-wire source contains transverse electric (TE) and transverse magnetic (TM) modes. The IP effect is most commonly studied with the TEM total field, rather than considering the difference between TE and TM fields. To investigate the effect of IP phenomena on the TE and TM fields, we have performed a detailed analysis on IP-distorted TEM data based on numerical and field examples. We first compare the IP effect on the TE and TM fields when polarizable bodies with different polarizable parameters are present. The TM field is more severely affected by the IP effect than the TE field. Compared to a single grounded-wire source, a double-line grounded-wire source can generate a larger TM field in the horizontal electric field. We compare the IP effect on TEM data from single- and double-line grounded-wire TEM configurations, and find that the data from the double-line configuration have a higher TM/TE ratio and are more severely affected by IP phenomena than in the single-line case. Thus, it would be easier to identify and extract the IP response from field data acquired with a double-line grounded-wire source configuration. These results have been verified by a field survey of the Kalatongke copper-nickel ore district, which has predominantly layered geology, in Xinjiang, China.

We have developed a scheme for decoupling the induced polarization (IP) effect from time-domain electromagnetic (TDEM) data. This scheme is achieved by simultaneously sampling the resistivity and pseudochargeability in a Bayesian framework. The TDEM and IP responses are simulated separately with the sampled model parameters and then are stacked to fit the IP-affected TDEM data. Thus, the influence of the IP phenomenon is eliminated in the process of recovering the resistivity. To reduce the computational cost brought by the Bayesian sampling, we use a 2D parametrization instead of sampling the full 3D space and we use a linear perturbation approximation for calculating the IP response. The linearized inversion results are used as the initial model, and a multiple proposed points algorithm is used to accelerate the sampling. We validate the proposed method with synthetic and field examples showing that it restores accurate estimates of electrical structures from the TDEM data that are significantly affected by the IP phenomenon. Our method could advance the application of the TDEM method to the scenario in which the IP may affect the TDEM data and mask the underlying geologic targets.

In helicopter-borne transient electromagnetic (HTEM) signal processing, removal of motion-induced noise is one of the most important steps. A special type of short-term noise, which could be classified as high-frequency motion-induced noise (HFM noise) based on its cause and time-frequency features, was observed in the field data of the Chinese Academy of Sciences-HTEM system. Because the HFM noise is an in-band noise for the HTEM response, it usually remains after the normal denoising procedure developed for the conventional motion-induced noise. To solve this problem, we have developed a three-stage workflow to remove the HFM noise using the wavelet neural network (WNN). In the first stage, the WNN training is performed, and the data segment in which the HFM noise is dominant is selected as the sample set. In the second stage, the HFM noise corresponding to the data segment in which the earth’s response coexisted with the HFM noise is predicted using the well-trained WNN. In the last stage, the predicted HFM noise is removed from the corresponding original data. As an example, we applied our workflow in the field data observed in Inner Mongolia, the HFM noise is removed effectively, and the results provide a strong data foundation for the subsequent processing procedures.

Increases in the mining depth of coal pose a significant challenge to the conventional loop source time-domain electromagnetic (TEM) method that requires significant enlargement of the loop size and transmitting current to realize the deeper sounding results required. As an alternative, TEM devices based on a grounded wire source are generally used to solve detections deeper than several hundred meters. To map the water-enriched zones buried underneath approximately 1000 m at a coal mine in Shaanxi, China, loop TEM and short-offset transient electromagnetic (SOTEM) measurements were conducted. We carried out 1D forward modeling and inversion constrained by drilling informa-tion, and the results reveal that the resolution capability of loop TEM and SOTEM is almost the same in detecting a conductive layer in the absence of any noise. However, for a given noise level and decay time, the SOTEM method provides a deeper investigation than loop TEM without compromising sensitivity. The field examples validated the synthetic results. The loop TEM with dimensions of [Formula: see text] realized a maximum depth of 1000 m, whereas the reliable detection depth of 1500 m was achieved by using a 723 m long grounded wire source using the SOTEM method. Moreover, the labor required is significantly reduced, and the efficiency is dramatically raised using the SOTEM method. Our results predict that the SOTEM method should play a more important role in deep hydrogeophysical investigations.