Distributed acoustic sensing (DAS) has emerged as a transformative technology for high-resolution seismic exploration. However, compared to conventional geophysics, the vertical seismic profile (VSP) data obtained by DAS has a relatively low signal-to-noise ratio (SNR). This limitation stems primarily from the transient fiber deployment in casing operations, which leads to suboptimal fiber-ground coupling and creates characteristic zigzag noise patterns that degrade signal fidelity. This study presents a feature picking principal component analysis (FPPCA) framework for adaptive zigzag noise suppression. The method includes four stages: power spectral density estimation with multilevel spectral smoothing to preserve critical mid-low frequency components, design of frequency-adaptive hanning windows targeting harmonic sidelobe suppression, bandwidth parameter optimization guided by dominant frequency localization to prevent spectral aliasing during feature extraction, and PCA based noise separation using cumulative variance thresholds derived from localized zigzag features. Validation dataset comprising one synthetic and one field DAS-VSP datasets demonstrates the framework’s ability to maintain broadband signal integrity while achieving spectrally consistent noise attenuation.

Due to finite acquisition aperture, limited frequency bands, and unbalanced illumination, conventional migration often fails to generate high-quality reflectivity images. In contrast, least-squares migration (LSM) can produce high-resolution and amplitude-preserved images by solving a linear inverse problem. Although LSM has been implemented by ray-based and wave-equation propagators, its application has primarily been limited to isotropic media, thereby restricting its ability to handle anisotropy. To mitigate this issue, we propose a least-squares Gaussian beam migration (LSGBM) method for vertical transverse isotropic (VTI) media. Based on an efficient VTI ray tracing, we first derive the Born modeling and adjoint migration operators, and then iteratively update the reflectivity model. To suppress data overfitting artifacts, a multiplicative Cauchy constraint is introduced in the LSGBM to promote a sparse inversion result. Additionally, an approximate diagonal Hessian is employed as a preconditioner to accelerate convergence. Numerical experiments demonstrate that the proposed VTI LSGBM can correct anisotropic effects, enhance spatial resolution, and improve amplitude fidelity, producing high-quality images.

Land seismic data are often obscured by noise, severely affecting the accuracy of subsequent seismic imaging and interpretation. Dictionary learning (DL) is an effective method for noise suppression. However, finding a fast DL method that is suitable for weak signals and can suppress multi-scale strong noise is still a hot topic. In this paper, we introduce a noise suppression method that combines DL with fast adaptive empirical mode decomposition (FABEMD). We leverage the advantages of FABEMD in multi-scale signal decomposition, along with the efficient sparse representation capabilities of DL, to achieve noise suppression for low signal-to-noise ratio seismic signals. We group bi-dimensional intrinsic mode functions based on their cross-correlation coefficients and train dictionaries for components using the sequential generalization K-means method, enhancing computational efficiency and adaptability. Numerical examples using both synthetic and field data validate the practicality and versatility of the proposed method, indicating its improved performance in denoising compared tof-xEMD, BEMD, and traditional DL methods.

As seismic exploration focuses on deep and ultra-deep hydrocarbon targets, seismic data are characterized by weak reflection signals and extremely low signal-to-noise ratio (SNR). Although weak reflections help delineate deep geological structures, the low SNR presents challenges for traditional denoising methods. We propose a high-dimensional adaptive progressive seismic denoising (APSD) method to enhance the SNR of deep weak reflection signals. Instead of using a global noise variance, we estimate local noise variances using a 3-D Laplacian mask based on the local characteristics of seismic data at different locations for nonstationary seismic signals. It employs local noise variance to calculate a Gaussian bilateral kernel function to estimate high-amplitude noise in the time domain and low-amplitude noise in the frequency domain. We further extend this algorithm to three dimensions by adjusting the parameters of the 3-D kernel function during the iterative process. Numerical examples of synthetic and field data demonstrate the feasibility and adaptability of the proposed method. Its comparison with the dictionary learning method and optimal damped rank reduction methods shows that the proposed method can significantly improve the SNR of deep reflection signals and is a good tool for processing deep and ultra-deep seismic data.

The presence of source-related noise, characterized by exceptionally large amplitudes, poses a significant challenge in seismic data processing, impeding the accurate recovery of the effective signal within overlapping regions. In response, we introduce an adaptive time-frequency denoising method tailored to mitigate this issue. First, the seismic data is horizontally sorted, and exponential function fittings are applied to exploit amplitude difference information embedded in the dataset. Then, the curvatures of the exponential functions are computed, and their maximal values are chosen to establish a dynamic threshold curve, offering an estimate for the range of abnormal traces from shallow to deep regions. Next, further refinement of the dynamic threshold curve involves iterative adjustments based on the standard deviation of the curvature functions, resulting in a more precise separation between signal and noise traces. Finally, leveraging the optimized dynamic threshold curve, adaptive threshold value computations are performed at each frequency slice and subsequently applied through soft thresholding to effectively reduce the impact of strong amplitude noise. Numerical examples involving synthetic and field data demonstrate the effectiveness of the proposed method in attenuating strong source-related noise, surpassing the performance of traditional f-k filter, non-convex (NC) threshold and static threshold methods.

Conventional full-waveform seismic inversion (FWI) tries to estimate a subsurface model that can accurately predict surface records by minimizing an${L} _{\mathbf {2}}$-norm misfit between observed and synthetic data. If the initial model is far away from the true model, the cycle-skipping issue might occur and the${L} _{\mathbf {2}}$-norm-based FWI produces a spurious model. To mitigate this problem, we present a novel FWI scheme using a high-dimensional local coherence misfit function. A 2-D/3-D window is first used to extract local seismic waveform from the common-shot gathers. Then, we apply a normalized cross-correlation to measure the coherence of local synthetic and observed records, which is used as the misfit to iteratively update the subsurface velocity model. The new misfit function enhances the contribution of phase fitting while reducing the amplitude contribution, which helps to increase the tolerance of FWI to an inaccurate initial velocity model. In addition, the computation of local waveform coherence along the temporal and spatial axes can adaptively balance the adjoint source amplitudes for strong near-offset reflections and weak far-offset refractions, which improves the low- wavenumber updates. Numerical experiments for synthetic and field data demonstrate that the proposed FWI scheme has a better tolerance to inaccurate starting models and is less sensitive to cycle-skipping issues compared with the conventional FWI method.