Can GRQO based fine-tuning speed up the inference stage of the denoising pipeline by reducing the reliance on the TTA?

Abstract

Seismic denoising is essential for subsurface imaging. Deterministic methods such as Radon transforms exist but struggle to separate signal from noise. Recent solutions like vision foundation models (VFMs) offer better signal separation, but generalize poorly across geological domains. Parameter-efficient fine-tuning with LoRA and kurtosis- guided test-time adaptation (TTA) improves cross-domain generalization, yet TTA performs weight updates during inference and collapses throughput from 114.29 to 1.09 patches per second, making real-time deployment impractical. This paper inves- tigates whether group relative query optimization (GRQO), a reinforcement-learning fine-tuning method applied before deployment, can reduce reliance on TTA. A LoRA- adapted DINOv3 backbone with a U-Net decoder is fine-tuned with a GRQO objective that combines a multi-head reward, KL-divergence regularization against a frozen ref- erence, and a head-diversity penalty. On a 4,000-patch unseen synthetic data set, SFT+GRQO alone improves signal retention (MS-SSIM-R 0.638→0.604) while pre- serving a similar inference speed. GRQO does not fully replace TTA, but it lowers the optimal TTA budget for signal retention: the best configuration for MS-SSIM- R pairs GRQO with only 25 TTA epochs (MS-SSIM 0.862, MS-SSIM-R 0.452), and longer adaptation degrades performance and decreases throughput. However, GRQO alone minimally deteriorates performance when evaluated on real seismic data (LS 0.373→0.381). These results indicate that pre-deployment reinforcement learning can shorten the costly real-time adaptation and increase signal retention, but may perform worse on unseen seismic domains.