arXiv subjects

Zheng Cong

Publications and source records attributed to Zheng Cong.

3 recordsLinked to original sources

arXiv2026

Horizontal-Component Prior-based Framework for Adaptive Shear-wave Leakage Suppression in OBC Data

Shear-wave leakage in the vertical (Z) component of ocean-bottom cable (OBC) seismic data commonly results from the receiver tilt and poor seafloor coupling, introducing unwanted coherent noise that impacts the subsequent data processing and imaging. Traditional denoising methods are limited by manual parameter tuning and idealized model assumptions, while deep-learning (DL) approaches have shown significant potential in suppressing shear-wave leakage. However, supervised learning requires clean primary waves (P waves) as the label, which is generally impractical to obtain for field data. To address these challenges, we propose a framework based on horizontal-component priors for adaptive shear-wave leakage suppression (HPAS). Instead of relying on clean primary-wave (P-wave) data, HPAS generates input-label pairs directly from raw multi-component field data using an additive-subtractive noise strategy. Specifically, we extract shear-wave (S-wave) noise from the horizontal components and apply a linear transformation to match its first and second order moments with the S-wave leakage in the Z-component, and the statistically matched noise is then added to and subtracted from the original Z-component to create the input and label pairs. By allowing the denoising model to learn the S-wave features present in the differences between the input and the label, the adaptive denoising process approximates supervised learning. Evaluations on both synthetic and field data demonstrate that the proposed HPAS framework effectively and adaptively suppresses S-wave leakage while preserving the amplitude of the P-wave signals in the Z-component, offering a robust solution with strong generalization capabilities.

physics.geo-ph↗

arXiv2026

Transcending Classical Neural Network Boundaries: A Quantum-Classical Synergistic Paradigm for Seismic Data Processing

In recent years, a number of neural-network (NN) methods have exhibited good performance in seismic data processing, such as denoising, interpolation, and frequency-band extension. However, these methods rely on stacked perceptrons and standard activation functions, which imposes a bottleneck on the representational capacity of deep-learning models, making it difficult to capture the complex and non-stationary dynamics of seismic wavefields. Different from the classical perceptron-stacked NNs which are fundamentally confined to real-valued Euclidean spaces, the quantum NNs leverage the exponential state space of quantum mechanics to map the features into high-dimensional Hilbert spaces, transcending the representational boundary of classical NNs. Based on this insight, we propose a quantum-classical synergistic generative adversarial network (QC-GAN) for seismic data processing, serving as the first application of quantum NNs in seismic exploration. In QC-GAN, a quantum pathway is used to exploit the high-order feature correlations, while the convolutional pathway specializes in extracting the waveform structures of seismic wavefields. Furthermore, we design a QC feature complementarity loss to enforce the feature orthogonality in the proposed QC-GAN. This novel loss function can ensure that the two pathways encode non-overlapping information to enrich the capacity of feature representation. On the whole, by synergistically integrating the quantum and convolutional pathways, the proposed QC-GAN breaks the representational bottleneck inherent in classical GAN. Experimental results on denoising and interpolation tasks demonstrate that QC-GAN preserves wavefield continuity and amplitude-phase information under complex noise conditions.

cs.LG↗

arXiv2024

Transformer For Low-frequency Extrapolating of Seismic Data

Full waveform inversion (FWI) is used to reconstruct the physical properties of subsurface media which plays an important role in seismic exploration. However, the precision of FWI is seriously affected by the absence or inaccuracy of low-frequency information. Therefore, reconstructing the low-frequency signals accurately is highly significant in seismic data processing. Low-frequency extrapolation of seismic records can be approached as a deep learning regression problem. Thus, to obtain low-frequency information from band-limited seismic records, a novel network structure called low-frequency extrapolation transformer (LFET) is proposed to construct the nonlinear mapping relationship between the data missing low-frequency and low-frequency data in a supervised learning approach, which is inspired by the transformer model widely used in natural language processing (NLP). We apply multi-head self-attention (MSA) modules to model the remote dependencies of seismic data. Based on this, we introduce a shifted window partitioning approach to reduce the calculating amount. Due to the field data are not suitable for supervised learning, we generate synthetic seismic records using submodels selected from the benchmark Marmousi model as training data whose characteristics are similar to that of the field data. A single trace of synthetic band-limited seismic data in the time domain is used as the input data, and the parameters of LFET are updated based on the errors between the predicted trace and the corresponding label. The experimental results on the data generated by different models, different wavelets, and different kinds of field marine data demonstrate the feasibility and generalization of the proposed method. Furthermore, the proposed method achieves higher accuracy with lower computational expense than the traditional CNN method.

physics.geo-ph↗