Airborne transient electromagnetic (ATEM) technology is a technique often used in mineral exploration and geological mapping. Due to inductive polarization (IP) phenomena, the ATEM response curve often shows a negative response or declines rapidly to the attenuation curve. Traditional resistivity inversion techniques do not explain the IP response of a signal well, so the negative response is usually removed during data processing, resulting in a reduced correctness and authenticity of the findings. In this paper, in the parameter inversion based on the Cole–Cole model, the Jacobian matrix chain analysis method is used to calculate, and the current waveform calculation is also considered in the inversion. The results show that compared with the perturbation method, the analysis technique can greatly reduce the calculation time and improve the inversion efficiency. In the single-point one-dimensional inversion and lateral constraint quasi-two-dimensional inversion, the Cole–Cole four-parameter forward response has strong inversion accuracy, which can successfully invert the actual exploration content and the Cole–Cole four-parameter response. Some measured sounding data in the Qingchengzi survey area of Liaoning Province, China have a negative response to IP, and the resistivity scheme cannot be used alone for inversion, but the real underground resistivity structure can be obtained through the method studied in this paper, and good exploration results can be obtained.

An active clamp, three-phase, six-switch power factor correction rectifier with a one-cycle controller is proposed, which can effectively suppress the reverse recovery of the reverse parallel diode of the bridge arm switch and reduce the reverse recovery loss. The main switches and auxiliary switch are both zero-voltage switches. It works in a fixed switching frequency and low power stress during the switching period. The specific working process and control circuit are analyzed in detail, and the model simulation is carried out. The experimental platform of a 2.5 KW prototype, with a complete test and verification of the soft switch technology proposed in this paper, was set up in the laboratory.

Electromagnetic (EM) telemetry is one of the key research subjects that has increased application in transmitting real-time data between the borehole and surface during drilling. There are several methods that can be used to model an EM telemetry system, including the method of moments (MoM) and the electric field integral equation method. We have developed a pulse basis function for the MoM, which assists in the analytical estimation of thin wire kernel integrations and avoids singular problems when simulating current distributions. The scattering field is solved using the current distribution. We examine the method using a simple example of vertical drilling. In the model, a vertically placed drill string is driven through a layered earth model. The drill string represents a long, thin wire antenna with a source located near the drill bit. The validity of the method is confirmed by comparing our results to the analytical solution in a half-space model as well as the current distributions along the drill string derived from the proposed method with those derived from a commercial finite-element solver, COMSOL-Multiphysics. Furthermore, the efficiency of this method is demonstrated by comparing its computational costs with those of COMSOL. The simulated EM telemetry in a known geologic setting is then compared with a field data example. These comparisons show that the proposed method is accurate and efficient. Moreover, we develop insights into how the parameters needed for calculations alter current distributions along the drill string and the signal received at the surface. The proposed method is therefore useful for obtaining the optimal design parameters for EM telemetry systems.

This paper deals with the study of multi-channel adaptive noise cancellation with a focus on its application in electromagnetic (EM) telemetry. We presented new variable step-size least mean square (LMS) techniques: regularized variable step-size LMS and regularized sigmoid variable size LMS, for electromagnetic telemetry data processing. Considering the complexity and spatial distribution of environmental noise, algorithms with multiple reference signals were used to retrieve transmitted EM signals. The feasibility of the regularized variable step size LMS algorithms with numerical simulation was analyzed and presented. The adaptive processing techniques were applied in the recovery of frequency and binary phase shift key modulated signal. The proposed multi-channel adaptive technique achieves fast convergence speed, low mean squared error and is shown to have good convergence characteristics compared to conventional methods. In addition to attaining good results from the multi-channel adaptive filter and performing the signal analysis in real-time, we implemented combined fast effective impulse noise removal techniques. The combination of median and mean filters was effective in removing a wide range of impulsive noises without distorting any other data points. Further, electromagnetic telemetry data were acquired during a drilling operation in Sichuan province, China, for real field application. Data processing workflow was designed for EM telemetry data processing based on the noise characteristics, simulation results and expected result for demodulation. To establish a comprehensive overview, a performance comparison of the acquisition array system is also provided. Conclusively, the introduced multichannel adaptive noise canceling techniques are very effective in recovering transmitted EM telemetry signals.

The full-waveform inversion (FWI) of ground penetrating radar data can recover the permittivity and conductivity variations in a Debye medium. Numerical tests verify the performance of the FWI method in recovering permittivity and conductivity anomalies and medium boundaries. It further reveals that the process of water and carbon dioxide flooding oil could be successfully monitored with the FWI method by referring to the variations of medium conductivity and permittivity. In terms of the FWI method developed in this study, the forward modeling is run by the auxiliary finite difference method, the model correction in inversions is estimated by minimizing the difference between the energy signals of observed and synthetic data with the L-BFGS method, and the corresponding calculations are running on the GPUs in this study, which are effective in reaching an optimal inversion result and reducing the computational consumptions.

Data telemetry is a critical element of successful unconventional well drilling operations, involving the transmission of information about the well-surrounding geology to the surface in real-time to serve as the basis for geosteering and well planning. However, the data extraction and code recovery (demodulation) process can be a complicated system due to the non-linear and time-varying characteristics of high amplitude surface noise. In this work, a novel model fuzzy wavelet neural network (FWNN) that combines the advantages of the sigmoidal logistic function, fuzzy logic, a neural network, and wavelet transform was established for the prediction of the transmitted signal code from borehole to surface with effluent quality. Moreover, the complete workflow involved the pre-processing of the dataset via an adaptive processing technique before training the network and a logistic response algorithm for acquiring the optimal parameters for the prediction of signal codes. A data reduction and subtractive scheme are employed as a pre-processing technique to better characterize the signals as eight attributes and, ultimately, reduce the computation cost. Furthermore, the frequency-time characteristics of the predicted signal are controlled by selecting an appropriate number of wavelet bases “N” and the pre-selected range for $p_{ij}^3$ to be used prior to the training of the FWNN system. The results, leading to the prediction of the BPSK characteristics, indicate that the pre-selection of the N value and $p_{ij}^3$ range provides a significantly accurate prediction. We validate its prediction on both synthetic and pseudo-synthetic datasets. The results indicated that the fuzzy wavelet neural network with logistic response had a high operation speed and good quality prediction, and the correspondingly trained model was more advantageous than the traditional backward propagation network in prediction accuracy. The proposed model can be used for analyzing signals with a signal-to-noise ratio lower than 1 dB effectively, which plays an important role in the electromagnetic telemetry system.