Where Does Adaptation Matter?

Abstract

Seismic trace denoising is a long-standing problem in geophysical data processing, and recent work has shown that vision foundation models pre-trained on natural images can be adapted to it parameter-efficiently rather than trained from scratch. Such adaptation is typically applied uniformly across all transformer layers, but it is not known where along the network the adaptation effort is actually needed --- that is, where the representation gap between natural images and seismic data is concentrated. We investigate this question for two structurally distinct families of parameter-efficient fine-tuning (PEFT): Low-Rank Adaptation (LoRA), which injects a low-rank update into the attention projections, and Pfeiffer bottleneck adapters, which insert a residual MLP module after the feed-forward sub-layer. Using a DINOv3 ViT-S/16 backbone on active-source seismic image denoising and holding the per-layer parameter budget fixed across both mechanisms, we sweep adaptation placement across restricted subsets of the twelve transformer layers within the DINOv3 architecture and measure denoising quality at each placement. We find that adaptation is strongly concentrated in the early layers: placing modules on only the first four layers recovers most of the denoising quality of full adaptation at one third of the adapter parameters, and even a single early layer is already competitive with full adaptation, while a roughly monotonic early-to-late importance ordering holds across placements. Crucially, this profile is near-identical for the two mechanisms at matched budget, indicating that the effect is a property of where adaptation is applied rather than of the particular PEFT design. These results suggest that, for this task and backbone, the natural-image-to-seismic gap is primarily a low-level, input-stage shift, and that early-layer-heavy placement is an effective and economical default for PEFT-based adaptation of vision foundation models to seismic data.