  Seismic fault detection holds significant geographical and practical
application value, aiding experts in subsurface structure interpretation and
resource exploration. Despite some progress made by automated methods based on
deep learning, research in the seismic domain faces significant challenges,
particularly because it is difficult to obtain high-quality, large-scale,
open-source, and diverse datasets, which hinders the development of general
foundation models. Therefore, this paper proposes Seismic Fault SAM, which, for
the first time, applies the general pre-training foundation model-Segment
Anything Model (SAM)-to seismic fault interpretation. This method aligns the
universal knowledge learned from a vast amount of images with the seismic
domain tasks through an Adapter design. Specifically, our innovative points
include designing lightweight Adapter modules, freezing most of the
pre-training weights, and only updating a small number of parameters to allow
the model to converge quickly and effectively learn fault features; combining
2.5D input strategy to capture 3D spatial patterns with 2D models; integrating
geological constraints into the model through prior-based data augmentation
techniques to enhance the model's generalization capability. Experimental
results on the largest publicly available seismic dataset, Thebe, show that our
method surpasses existing 3D models on both OIS and ODS metrics, achieving
state-of-the-art performance and providing an effective extension scheme for
other seismic domain downstream tasks that lack labeled data.
