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 pij3 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 pij3 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.

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.