Controlled-source electromagnetic method (CSEM) signals are inevitably contaminated by man-made noises. For this reason, a new CSEM data processing method was proposed. First, the 50 Hz powerline interference was removed by FFT method, followed by the CEEMD method to remove baseline drift, and finally the correlation analysis method was used to select the high-quality signal. The method was applied to the processing of measured data in Huidong, Sichuan. As a conclusion, the presented method can effectively remove the strong cultural noise from raw CSEM data and preserve the useful signal completely; the apparent resistivity curves acquired by using the filtered data are improved significantly upon the previous.
In this paper the authors present a new neural network model, called the constrained smallest l/sub 1/-norm neural network (CSl/sub 1/ NN), for basis pursuit (BP) implementation. The BP is considered as a large-scale linear programming problem. In contrast with the simplex-BP or inferior-BP, the proposed CSl/sub 1/ NN-BP does not double the optimizing scale and can be implemented in real time via hardware. Using non-stationary artificial signals and electrogastrograms to test our simulations show that the CSl/sub 1/ NN-BP presents an excellent convergence performance for a wide range of time-frequency (TF) dictionaries and has a higher joint TF resolution not only than the traditional Wigner distribution, but also other overcomplete representation methods. Combining the high resolution with the fast implementation, the CSl/sub 1/ NN-BP can be used for online time-frequency analysis of various kinds of non-stationary signals including medical data, such as ECG, EEG and EGG.
Controlled-source electromagnetic (CSEM) data recorded in industrialized areas are inevitably contaminated by strong cultural noise. Traditional noise attenuation methods are often ineffective for intricate aperiodic noise. To address the abovementioned problem, we have developed a novel noise isolation method based on the fast Fourier transform, complementary ensemble empirical mode decomposition (CEEMD), and shift-invariant sparse coding (SISC, an unsupervised machine-learning algorithm under a data-driven framework). First, large powerline noise is accurately subtracted in the frequency domain. Then, the CEEMD-based algorithm is used to correct the large baseline drift. Finally, taking advantage of the sparsity of periodic signals, SISC is applied to autonomously learn a feature atom (the useful signal with a length of one period) from the detrended signal and recover the CSEM signal with high accuracy. We determine the performance of the SISC by comparing it with three other promising signal processing methods, such as the mathematic morphology filtering, soft-threshold wavelet filtering, and K-singular-value decomposition (another dictionary learning method) sparse decomposition. Experimental results illustrate that SISC provides the best performance. Robustness test results indicate that SISC can increase the signal-to-noise ratio of noisy signal from 0 to more than 15 dB. Case studies of synthetic and real data collected in the Chinese provinces of Sichuan and Yunnan indicate that our method is capable of effectively recovering the useful signal from the observed data contaminated with different kinds of strong ambient noise. The curves of U/I and apparent resistivity after applying our method improved greatly. Moreover, our method performs better than the robust estimation method based on correlation analysis.
When the controlled-source electromagnetic (CSEM) data are contaminated by intense cultural noise and the signal-to-noise ratio (S/N) is lower than 0 dB, the existing denoising methods can hardly achieve good results. To overcome the problem, a new strong-noise elimination method called inception-temporal convolutional network-shift-invariant sparse coding (IncepTCN-SISC) is developed based on deep learning and dictionary learning. First, a novel deep neural network model called IncepTCN is created based on the inception block and temporal convolutional network (TCN). Then, IncepTCN is used to recognize strong-noise segments in the observed signal, which are then discarded. Finally, a dictionary-learning method based on shift-invariant convolutional coding is used to denoise the remaining weak-noise segments. A series of simulated and field data experiments indicate that the new proposed IncepTCN network has obvious advantages in accuracy and efficiency compared with alternative methods. The average recognition accuracy of IncepTCN is 96.5%, which is 25.5%, 3.2%, 1.1%, and 2.0% higher than that of the fuzzy C-means clustering, convolutional neural network (CNN), residual network (ResNet), and the nonimproved TCN, respectively. In addition, the test results of unfamiliar data indicate that the generalization ability of IncepTCN is significantly better than the CNN, ResNet, and nonimproved TCN. This IncepTCN-SISC method can improve the S/N of CSEM data from −5.0 dB to 3.1 dB or from 5.0 dB to 31.9 dB and solve the denoising problem of noisy data below 0 dB to a certain extent. After IncepTCN-SISC processing, the initially distorted apparent resistivity curves become smooth, and the result is better than dictionary learning. This method is intelligent without any manual intervention and is suitable for batch processing of CSEM data.
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