The industry treats the distributed acoustic sensing (DAS) system, which uses an optical fiber cable in vertical seismic profiling (VSP) data acquisition, as a new and fast-growing technology. The high-quality data set acquired from the DAS acquisition system can produce high-precision VSP images and obtain more detailed checkshots. However, in field data, the acquired VSP data set suffers from strong coherent DAS coupling noise. Many factors may cause coherent DAS coupling noise, such as the cable slapping and ringing due to the physical placement, the regular swing of the wireline in the well, and the uncoupling between the fiber cable and the casing. This DAS coupling noise reduces the signal-to-noise ratio and affects the subsequent processing and interpretation. Removing the DAS coupling noise can help to improve the quality of the VSP data set acquired with the DAS system. We have developed a sparse-optimization-based DAS coupling noise removing method. In the DAS-based VSP data set, the effective signal and the coupling noise have distinct morphological characteristics. The effective VSP signal has a wide bandwidth, whereas the DAS coupling noise appears in some narrow frequency bands in the frequency domain. The continuous wavelet transform and the discrete cosine transform can sparsely represent the effective VSP signal and DAS coupling noise, respectively. Therefore, we choose these two transforms as two sparse dictionaries and combine them to form an overcomplete dictionary. The morphological component analysis (MCA) can use the morphological difference between different components and the overcomplete dictionary to sparsely represent all components in the complicated signal. Based on the MCA theory, we use the block coordinate relaxation algorithm to separate the effective VSP signal and DAS coupling noise. Applications on a synthetic data set and two field data sets have validated the effectiveness of our method.

Low-frequency oscillatory ground-roll is regarded as one of the main regular interference waves, which obscures primary reflections in land seismic data. Suppressing the ground-roll can reasonably improve the signal-to-noise ratio of seismic data. Conventional suppression methods, such as high-pass and various f-k filtering, usually cause waveform distortions and loss of body wave information because of their simple cut-off operation. In this study, a sparsity-optimised separation of body waves and ground-roll, which is based on morphological component analysis (MCA) theory, is realized by constructing dictionaries using tunable Q-factor wavelet transforms (TQWT) with different Q-factors. Our separation model is grounded on the fact that the input seismic data are composed of low-oscillatory body waves and high-oscillatory ground-roll. Two different waveform dictionaries using a low Q-factor and a high Q-factor, respectively, are confirmed as able to sparsely represent each component based on their diverse morphologies. Thus, seismic data including body waves and ground-roll can be nonlinearly decomposed into low-oscillatory and high-oscillatory components. This is a new noise attenuation approach according to the oscillatory behaviour of the signal rather than the scale or frequency. We illustrate the method using both synthetic and field shot data. Compared with results from conventional high-pass and f-k filtering, the results of the proposed method prove this method to be effective and advantageous in preserving the waveform and bandwidth of reflections.

Because the seismic wave propagates through the subsurface, part of the elastic energy eventually ends up as heat energy. This phenomenon is known as absorption (or anelastic attenuation). The factors causing anelastic attenuation include fluid movement and grain boundary friction. The seismic quality factor ($Q$) quantifies the anelastic attenuation and is commonly used in assisting reservoir characterization. However, current $Q$-estimation approaches are mainly implemented on a poststack seismic volume. The $Q$-estimation approaches applied to poststack seismic data assume that the seismic data are normal incident reflections, and they do not consider the effect of the travel path on seismic attenuation. In theory, the attenuation degree of the low-frequency component should differ from the attenuation degree of the high-frequency component for large-offset seismic data. We have developed a method to qualitatively estimate seismic attenuation in the prestack seismic domain. A continuous wavelet transform is used to extract the low- and high-frequency components for the common-reflection point gathers. The difference between the amplitude of the low-frequency component and the amplitude of the high-frequency component is used to measure the seismic attenuation factor. We have determined the effectiveness of our method by applying it to synthetic and real seismic data.