查看更多>>摘要：Distributed acoustic sensing (DAS), a new geophone for effective acquisition of vertical seismic profile, has several advantages, including low-cost, high-precision, and high temperature-resistance. However, it is susceptible to various noises that can contaminate the desired weak signals, thus denoising is an essential and independent procedure in DAS processing. Therefore, we propose the transform learning method to train a denoising model in the temporal-frequency domain considering that DAS noise is multiple and performs complexly in the time domain. Initially, synchrosqueezing transform is used to generate DAS data effective sparse representation in the frequency domain where the data remains more centered and uniform. Further, a redesigned convolutional neural network based on residual learning is built to extract different features between the noise and signal components, thus separating them. Finally, the denoised results are obtained by inversely transforming the denoised components into the time domain. Three factors improve the denoising performance of our method: 1. It can directly provide high-dimensional features for training, reducing data-dependency to an extent. 2. The designed network calculates a powerful nonlinear mapping between the additive noise and input noisy component, significantly reducing the training difficulties. 3. A new objective function in the synchrosqueezing frequency domain is designed for nonlinear mapping optimization. Both synthetic and real examples can demonstrate the method's effectiveness in denoising and improving the signal-to-noise ratio of DAS. Further, the denoised results can contribute to a more accurate velocity analysis.
查看更多>>摘要：Field data from the semi-airborne transient electromagnetic method (SATEM) contain various noises. Therefore, performing denoising before data inversion and interpretation is important. Currently, different types of noise are suppressed separately, not considering the variance in the signal-to-noise ratio at different time ranges. The signal details after denoising are lost and the residual noise is large. We propose a denoising scheme that uses wavelet thresholding (WT) and adaptive singular-value decomposition (SVD) filtering. We first intercepted the decay curves into several segments according to the statistical characteristics of the noise. Subsequently, a WT denoising method was used to remove the Gaussian noise and suppress noise in different signal segments. Finally, the denoised datasets were decomposed and filtered using the SVD method. The number of singular values was determined based on the optimal difference spectrum peak value, which was used to adaptively restore the responses in each segment. The proposed denoising scheme can effectively suppress multi-source noise and provide solid datasets for inversion, which was confirmed through a field survey in Shanxi Province.
NSTL
Elsevier