3 papers
physics.geo-ph2026
SeisDiff-intp: a unified prompt-guided flow matching framework for multi-tasks seismic interpretation
Donglin Zhu, Peiyao Li, Ge Jin
The increasing demand for deep learning in seismic interpretation has highlighted significant challenges, particularly the reliance on massive, labeled datasets and the inefficienc…
physics.geo-ph2026
Noise is All You Need: rethinking the value of noise on seismic denoising via diffusion models
Donglin Zhu, Peiyao Li, Ge Jin
We introduce SeisDiff-denoNIA, a diffusion-based seismic denoising framework that trains directly on noise extracted from field data, eliminating the dependence on synthetic datase…
physics.geo-ph2025
SeisDiff-deno: A Diffusion-Based Denoising Framework for Tube Wave Attenuation in VSP Data
Donglin Zhu, Peiyao Li, Ge Jin
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 Se…