Parameter-Efficient Adaptation of Pre-Trained Vision Foundation Models for Active and Passive Seismic Data Denoising

arXiv:2605.10953 · physics.geo-ph, cs.CV, cs.LG · Submitted 2026-04-30 · Read on arXiv

physics.geo-ph, cs.CV, cs.LG

Submitted: 2026-04-30

Updated: 2026-09-22

Comments: 34 pages, 8 figures, 6 tables. Preprint

Code: https://github.com/shenghanlin/SeismicFoundationModel

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

Adaptation — 40× in this paper · explained in 1 episode
Fine-Tuning — 25× in this paper · explained in 5 episodes
Generalization — 19× in this paper · explained in 13 episodes
Foundation Model — 18× in this paper · explained in 3 episodes
Foundation Models — 15× in this paper · explained in 7 episodes
Domain Shift — 13× in this paper · explained in 4 episodes
Feature Extraction — 9× in this paper · explained in 2 episodes
Fidelity — 9× in this paper · explained in 2 episodes
Robustness — 7× in this paper · explained in 5 episodes
Transformer — 7× in this paper · explained in 2 episodes
Benchmark — 5× in this paper · explained in 2 episodes
Parameter-Efficient Fine-Tuning — 4× in this paper · explained in 2 episodes
Ablation — 3× in this paper · explained in 1 episode
Consistency — 3× in this paper · explained in 3 episodes
Inverse Problem — 3× in this paper · explained in 2 episodes
Low-Rank Adaptation (LoRA) — 3× in this paper · explained in 9 episodes
Ablation study — 2× in this paper · explained in 1 episode
Attention-Based Deep Image Prior — 2× in this paper
Catastrophic Forgetting — 2× in this paper · explained in 5 episodes
Core — 2× in this paper · explained in 2 episodes
Distillation — 2× in this paper · explained in 2 episodes
Embeddings — 2× in this paper · explained in 2 episodes
Local Similarity — 2× in this paper
Objective Function — 2× in this paper · explained in 2 episodes
Structural Integrity — 2× in this paper · explained in 3 episodes
Trace — 2× in this paper · explained in 2 episodes
Transfer Learning — 2× in this paper · explained in 10 episodes
Activation Function — 1× in this paper · explained in 1 episode
AdamW Optimizer — 1× in this paper · explained in 1 episode
Adaptability — 1× in this paper · explained in 1 episode
Attention Mechanism — 1× in this paper · explained in 2 episodes
Benchmarking — 1× in this paper · explained in 3 episodes
Composite Loss Function — 1× in this paper · explained in 2 episodes
Computational overhead — 1× in this paper · explained in 2 episodes
Domain Adaptation — 1× in this paper · explained in 5 episodes
Downstream Tasks — 1× in this paper · explained in 2 episodes
Forward modeling — 1× in this paper · explained in 2 episodes
International Conference on Learning Representations — 1× in this paper
Knowledge Distillation — 1× in this paper · explained in 11 episodes
Latent Representation — 1× in this paper · explained in 2 episodes
Latent Representations — 1× in this paper · explained in 3 episodes
LoRA Fine-Tuning — 1× in this paper · explained in 3 episodes
Loss Function — 1× in this paper · explained in 2 episodes
Non-Gaussianity — 1× in this paper · explained in 2 episodes
Over-smoothing — 1× in this paper · explained in 4 episodes
Regularization — 1× in this paper · explained in 3 episodes
Representation Learning — 1× in this paper · explained in 2 episodes
Self-Supervised Learning — 1× in this paper · explained in 7 episodes
Self-distillation — 1× in this paper · explained in 6 episodes
Sparsity — 1× in this paper · explained in 4 episodes
Test-time Adaptation (TTA) — 1× in this paper · explained in 3 episodes
Transferability — 1× in this paper · explained in 4 episodes
U-net architecture — 1× in this paper · explained in 3 episodes
Wavelet Transform — 1× in this paper · explained in 2 episodes

Parameter-Efficient Adaptation of Pre-Trained Vision Foundation Models for Active and Passive Seismic Data Denoising

Terminology

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