arXiv subjects

Anxiang Di

Publications and source records attributed to Anxiang Di.

3 recordsLinked to original sources

arXiv2026

MetaPOCS: Meta-Learned Multi-Exit Unfolding for Budget-Aware Seismic Data Interpolation

Seismic data interpolation must reconcile reconstruction fidelity with the computational budgets of acquisition quality control and subsequent imaging. Fixed-depth unfolding networks are optimized for a single operating point, whereas their intermediate estimates may be insufficiently trained for early deployment. We present MetaPOCS, a meta-learned multi-exit unfolding framework for budget-aware seismic interpolation. Building on a spatial--spectral POCS reconstruction backbone, MetaPOCS supervises intermediate stages and preserves the acquired traces at every exit. A Meta-GF-based optimization procedure constructs exit-oriented parameter displacements from a common initialization and learns their fusion weights through an equal-exit outer objective. The weights vary across parameter tensors and respect the nested support of the reconstruction stages. We derive the resulting fusion hypergradient to characterize how the outer objective reallocates competing updates, and distinguish this mechanism from scalar loss reweighting. At inference, one checkpoint provides a set of reconstruction budgets through a latency-calibrated exit policy. The evaluation framework couples matched-cost interpolation comparisons with finite-update diagnostics and a fixed reverse-time-migration operator, connecting multi-exit optimization to waveform and imaging fidelity. MetaPOCS thus provides a unified formulation for studying the reconstruction--computation trade-off of seismic unfolding across deployment budgets.

cs.LG↗

arXiv2026

Spatial-Spectral Interactive POCS Network for Large-Gap Seismic Data Interpolation

Large gaps in seismic acquisition disrupt reflection events and hinder spectral recovery. Existing seismic interpolation deep unfolding networks primarily rely on localized spatial priors, limiting their ability to aggregate the global context necessary for reconstructing these extensive missing regions. We propose a spatial-spectral interactive projection-onto-convex-sets network (SSI-POCS) to tackle this severely ill-posed problem. By unfolding the classical iterative solver, our network integrates physical consistency with dual-domain deep priors via learned transforms, soft thresholding, and exact data reinsertion. Within the learned prior, a spatial Restormer branch captures local waveform structures, while a fast Fourier convolution (FFC) branch extracts global context, effectively suppressing incoherent components and enhancing spectral energy concentration in the $f-k$ domain. A dual-domain fusion module exchanges these complementary features via bidirectional channel attention and gated modulation, yielding substantial interpolation improvements. To overcome the severe loss of lateral continuity caused by large gaps, we introduce structural similarity (SSIM) supervision, complemented by focal frequency loss (FFL) and transform consistency constraints to suppress incoherent high-frequency components and regularize training. Experiments on a synthetic salt dome and a North Sea field dataset under 50% random and consecutive trace removal demonstrate the effectiveness of SSI-POCS. It significantly enhances event continuity across large gaps and mitigates artifacts, achieving a 3.285~dB SNR improvement over the best competing method while reducing parameters by 75.66%.

cs.CV↗

arXiv2025

Efficient Seismic Data Interpolation via Sparse Attention Transformer and Diffusion Model

Seismic data interpolation is a critical pre-processing step for improving seismic imaging quality and remains a focus of academic innovation. To address the computational inefficiencies caused by extensive iterative resampling in current plug-and-play diffusion interpolation methods, we propose the diffusion-enhanced sparse attention transformer (Diff-spaformer), a novel deep learning framework. Our model integrates transformer architectures and diffusion models via a Seismic Prior Extraction Network (SPEN), which serves as a bridge module. Full-layer sparse multi-head attention and feed-forward propagation capture global information distributions, while the diffusion model provides robust prior guidance. To mitigate the computational burden of high-dimensional representations, self-attention is computed along the channel rather than the spatial dimension. We show that using negative squared Euclidean distance to compute sparse affinity matrices better suits seismic data modeling, enabling broader contribution from amplitude feature nodes. An adaptive ReLU function further discards low or irrelevant self-attention values. We conduct training within a single-stage optimization framework, requiring only a few reverse diffusion sampling steps during inference. Extensive experiments demonstrate improved interpolation fidelity and computational efficiency for both random and continuous missing data, offering a new paradigm for high-efficiency seismic data reconstruction under complex geological conditions.

physics.geo-ph↗