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Seismic noise suppression plays a crucial role in seismic data processing and geological structure interpretation. Distributed acoustic sensing (DAS) is widely applied in acquisition of vertical seismic profiling (VSP) data, but the collected seismic data often contains various complex noise with strong energy such as background noise, single-trace low-frequency interference, single-trace random interference, and coupled noise. Therefore, how to effectively suppress the diverse noise for DAS VSP data is important in subsequent seismic data processing. At present, deep learning methods based on convolutional neural network (CNN) have been extensively employed in seismic denoising and have achieved remarkable results, but they often exhibit poor ability to capture global information. Subsequently, deep learning methods based on Transformer are developed, which excel at capturing global features but exhibit high computational complexity and weak ability to capture local information. However, extracting both global and local features is crucial for seismic noise suppression. Global features provide insight into the overall structure of seismic data, while local features capture detail features containing rich geological structure information. Therefore, we propose a deep learning approach based on selective kernel feature fusion Uformer (SKUformer), which combines the advantages of Transformer and CNN to simultaneously capture global and local information. First, we replace the convolutional layer in U-net with the parallel dilated convolution locally enhanced shifted window (PDC-LeSwin) Transformer block, enhancing the capability of network to capture global information and acquiring multiscale information for VSP data. Moreover, we introduce the PDC module and locally enhanced feed-forward (LeFF) module to extract rich detailed information in VSP data. Additionally, we change the skip connection to the selective kernel feature fusion (SKFF) module to selectively fuse multiscale features, including low-frequency features and detailed features in VSP data. Finally, we validate the effectiveness of our method by applying it to the synthetic and the field DAS VSP data, comparing it with three other methods, thus demonstrating superiority in removing complex noise while preserving effective seismic signals.

The moving high-speed train (HST) generates strong and repeatable vibrations in the railway roadbed, resulting in the propagation of complicated seismic waves into the subsurface medium. Therefore, the moving HST could be considered as a novel seismic source to detect the subsurface structure near high-speed railways (HSRs). An HST consists of several carriages. Therefore, the moving HST is one typical combined moving source that stimulates complex interference wavefields. Seismic interferometry (SI) is one commonly used method to generate virtual shot gather. However, the HST-induced seismic signal has one typical equidistant narrowband discrete (ENBD) spectrum feature, which means many frequency components are missing. Thus, the virtual shot gather based on an HST-induced seismic signal does not have sufficient bandwidth. In this study, we propose a processing scheme to construct broadband virtual shot gathers by distinguishing the moving direction of the HST and the propagation direction of seismic waves and stacking the HST events with different speeds. The feasibility of our proposed method is validated using both synthetic and real HST data, providing a broadband virtual shot gathers from HST-induced seismic signals.

Ground-penetrating radar (GPR) is a pivotal noninvasive tool that yields subsurface images critical to archeology, near-surface characterization, geotechnical studies, and disaster response. The antenna central frequency of the GPR system has a significant impact on penetration depth and resolution. Lower antenna frequencies penetrate deeper but at lower resolutions, while higher frequencies offer detailed images at reduced depths. Therefore, improving the resolution of low-frequency radar with increased detection depth is an essential research focus. Inspired by image super-resolution advancements, supervised deep learning methods that rely on strictly paired training data have achieved remarkable success. However, acquiring such paired samples in practical scenarios is often a formidable challenge. To tackle this, we propose a novel resolution enhancement technique through weakly supervised learning, effectively addressing the scarcity of strictly paired samples in real-world situations. We utilize two sets of antennas with different central frequencies to construct our training data, with a low-frequency antenna as input and a high-frequency antenna as the learning target. A cycle-consistent generative adversarial network (Cycle-GAN) is trained to discern the mapping between low-resolution inputs and unpaired high-resolution data. The refined network is then employed to improve low-frequency GPR data resolution. Our work is validated on synthetic and real-world datasets. The proposed method effectively strengthens critical high-frequency details for finer imaging and broadens the frequency bandwidth. Significantly, it enhances resolution without compromising the detection depth of low-resolution GPR data, marking a substantial advancement in subsurface imaging technology.

The high-speed train (HST), as a special kind of seismic source, can be simplified as a mobile combination source. The HST-induced seismic data (also known as HST seismic data) contains rich geological structural information. However, HST seismic data interfere highly in space and time and their spectra have narrow-band separation spectral characteristics in frequency domain. It is very difficult to effectively extract the geological structural information contained in HST seismic data. In this work, we try to use the elastic wave equation traveltime inversion (EWTI) method to explore the subsurface velocity information using the seismic data induced by HSTs running on viaducts, which is conducive to providing a more reliable initial model to serve full waveform inversion (FWI). However, as mentioned previously, HST seismic data are highly complex, which makes the EWTI method based on first-break picking no longer applicable to them. Therefore, we apply dynamic time warping (DTW) to obtain the time shifts between observed HST-induced seismic data and synthetic HST-induced seismic data. Based on the time shifts obtained by DTW, the subsurface velocity model can be updated by establishing the misfit function and calculating the gradient. Numerical experiments using the simple velocity anomaly model and the Marmousi2 model show that the DTW-based EWTI method is able to obtain the large-scale background model and provide a reliable initial model to serve FWI. The results in this work also demonstrate the considerable potential of underground exploration using HST-induced seismic data.

How to represent a seismic wavefield? Traditionally, while seismic wavefields are conceptualized continuously, acquisition geometries capture seismic data discretely using 2-D spatial coordinates. Motivated by recent advances in neural radiance fields for 3-D reconstruction through implicit neural representation, we introduce implicit seismic representation (ISR) for 5-D seismic data interpolation. This approach processes seismic data coordinates as inputs and outputs amplitude values at those coordinates with multilayer perceptrons (MLPs). Due to the continuous nature of the coordinates, ISR can achieve representations at any desired resolution and is easily scalable to a 5-D representation. To achieve a continuous representation of seismic data, we employ a self-supervised learning strategy to train the ISR on observed data. The trained network is then capable of interpolating missing seismic traces by querying every coordinate of the missing data. Our approach’s effectiveness is validated through synthetic and field data experiments, showcasing superior reconstruction abilities. Our findings highlight the potential of the implicit neural representation framework to achieve precise parametrization of continuous seismic wavefields, marking a significant advancement in seismic data processing and analysis.

During seismic acquisition, the received seismic data typically comprise many components, such as effective reflections and various interferences. Some components, such as industrial electrical interference and traffic vibrations, manifest as the equidistant narrowband discrete spectra (ENBD-spectra) in the frequency domain. Morphological component analysis (MCA) is widely used for separating different component from complicated seismic data. Therefore, it has been successfully used to extract the narrowband components from seismic data. However, the conventional MCA method overlooks equidistant feature of ENBD-spectra component in seismic data separation. In this study, we propose an improved MCA method that uses the interval between neighboring spectrum peaks as a constraint to separating the data with ENBD-spectra component. Two types of seismic datasets are used to show the proposed MCA’s effectiveness. The first type of dataset contains industrial electrical interference, while another type of dataset contains high-speed train (HST)-induced seismic signals. Both synthetic data examples and real data examples show that the proposed method has better performance in separating the seismic data with ENBD-spectra component and keeping the fidelity of separation compared with the conventional MCA method.

Time–frequency (TF) analysis represents a potent tool for processing and interpreting seismic data. Synchrosqueezing transforms (SSTs) remarkably enhance frequency resolution by accumulating coefficients along the frequency axis. However, their TF resolution is related to their mother’s TF transforms. The high-order Fourier-based SST (FSST) with a long window exhibits improved frequency resolution, albeit at the cost of mixing detailed frequency variations. Conversely, a high-order FSST with a short window provides enhanced time resolution but suffers from low-frequency resolution and component interference between multiple components of a complex signal. To ameliorate this, our study proposes a cascaded high-order FSST. Our proposed approach commences with a long-window high-order FSST to decompose a complicated signal into multiple components. Subsequently, a short-window high-order FSST is applied to each component. By summing the squeezed TF representations of all components, we generate a TF representation that boasts the improved TF resolution with the cost of involving multiple high-order FSSTs. The visual evaluation and sparsity measure are used to show our method’s efficacy and TF resolution over common high-order FSST through a synthetic multicomponent signal (MCS) with two components. The wavelets interference would make the seismic signal’s frequency component change. Therefore, further substantiation comes from two wavelet-interference-related examples: the field data example about cycle interbeds and an HST-induced seismic data example, wherein our proposed transform demonstrates its superior ability in precisely tracking subtle frequency variations with time and its advantages over common high-order FSST in extracting cycle thin-interbeds’ thickness variation along depth and characterizing the HST speed.

The ground vibration generated by running high-speed trains (HSTs) can excite seismic wavefields with high stability and good repeatability. When the seismic waves generated by the HST source propagate in the subsurface medium, information about the geological structure of the subsurface is embedded in the wavefields, which can be used for imaging and inversion. To make better use of the HST seismic data, it is necessary to focus on the propagation characteristic and carry out a forward modeling study of the seismic wavefield generated by the HST. Since the seismic waves generated by running HST propagate through 3-D space in practice, it is vital to explore the forward modeling of the HST seismic data in 3-D space, which can obtain more accurate simulation results of the seismic wavefield and helps for a better understanding of the wavefield characteristic of the HST seismic data. In this work, we conduct full-wave forward modeling of the wavefield excited by HSTs running on viaducts in 3-D space. The moving combination source function of the HST seismic data is provided. We use the 3-D isotropic elastic wave equation with constant density in this work and employ the free surface boundary condition to ensure the simulation for both body waves and surface waves. The 3-D finite-difference method with a staggered-grid technique is applied for the forward modeling. By comparing the simulated data with the real HST seismic data, we found that there is a good similarity between them. The 3-D forward modeling of the HST seismic data in this article lays the foundation for wavefield characterization and subsurface medium inversion in the future.