arXiv subjects

Donglin Zhu

Publications and source records attributed to Donglin Zhu.

4 recordsLinked to original sources

arXiv2026

SeisDiff-intp: a unified prompt-guided flow matching framework for multi-tasks seismic interpretation

The increasing demand for deep learning in seismic interpretation has highlighted significant challenges, particularly the reliance on massive, labeled datasets and the inefficiency of training isolated models for individual tasks. To address these limitations, we introduce a unified, prompt-guided flow-matching framework (SeisDiff-intp) capable of executing multiple seismic interpretation tasks within a single model. By conditioning on varying prompts, the model dynamically switches between interpretation objectives without requiring structural modifications. Furthermore, to overcome the scarcity of labeled data for complex subsurface features, we propose an integrated generative augmentation strategy. By employing the flow matching setting, the framework can synthesize diverse and geologically realistic training pairs, specifically targeting structurally complex. Experimental results demonstrate that the proposed approach, coupled with generative augmentation, delivers high-quality, task-specific interpretations with stable and reproducible inference behavior. Ultimately, this approach provides a scalable, flexible, and robust alternative to single-task deep learning based seismic interpretation models.

physics.geo-ph↗

arXiv2026

Noise is All You Need: rethinking the value of noise on seismic denoising via diffusion models

We introduce SeisDiff-denoNIA, a diffusion-based seismic denoising framework that trains directly on noise extracted from field data, eliminating the dependence on synthetic datasets. Unlike conventional denoising methods that require clean signal labels, our approach uses field noise recorded as training targets, enabling the diffusion model to explicitly learn the true noise distribution. We further demonstrate the framework on a field DAS-VSP survey, where the model effectively suppresses multiple types of noise, including production, instrument, and environment related noise, while preserving key seismic events. By denoising shot gathers with SeisDiff-denoNIA prior to migration, the resulting images exhibit improved event continuity and enhanced reflection visibility without artificial artifacts. In addition, synthetic experiments show that the method substantially outperforms traditional signal-based diffusion models under low-SNR conditions. These results suggest that explicitly modeling noise is not only viable but advantageous for a broad class of seismic denoising tasks, particularly in challenging field environments.

physics.geo-ph↗

arXiv2026

Watch Wider and Think Deeper: Collaborative Cross-modal Chain-of-Thought for Complex Visual Reasoning

Multi-modal reasoning requires the seamless integration of visual and linguistic cues, yet existing Chain-of-Thought methods suffer from two critical limitations in cross-modal scenarios: (1) over-reliance on single coarse-grained image regions, and (2) semantic fragmentation between successive reasoning steps. To address these issues, we propose the CoCoT (Collaborative Coross-modal Thought) framework, built upon two key innovations: a) Dynamic Multi-Region Grounding to adaptively detect the most relevant image regions based on the question, and b) Relation-Aware Reasoning to enable multi-region collaboration by iteratively aligning visual cues to form a coherent and logical chain of thought. Through this approach, we construct the CoCoT-70K dataset, comprising 74,691 high-quality samples with multi-region annotations and structured reasoning chains. Extensive experiments demonstrate that CoCoT significantly enhances complex visual reasoning, achieving an average accuracy improvement of 15.4% on LLaVA-1.5 and 4.0% on Qwen2-VL across six challenging benchmarks. The data and code are available at: https://github.com/deer-echo/CoCoT.

cs.CV↗

arXiv2025

SeisDiff-deno: A Diffusion-Based Denoising Framework for Tube Wave Attenuation in VSP Data

Tube waves present a significant challenge in vertical seismic profiling data, often obscuring critical seismic signals from seismic acquisition. In this study, we introduce the Seismic Diffusion Model for Denoising, a fast diffusion model specifically designed to remove the noise from seismic shotgather effectively. Our approach balances computational efficiency with high-quality image denoising, ensuring that the method is practical and robust for real-world applications. We validate the effectiveness of the proposed method through rigorous testing on both synthetic and field data, demonstrating its capability to preserve essential seismic signals while eliminating unwanted coherent noise. The results suggest that the proposed method enhances data quality and supports continuous production during seismic acquisition, paving the way for improved subsurface monitoring and analysis.

physics.geo-ph↗