Unsupervised Seismic Footprint Removal With Physical Prior Augmented Deep Autoencoder
This work addresses a domain-specific challenge in seismic data processing for geophysics, offering an incremental improvement by integrating physical priors into an unsupervised deep learning approach.
The paper tackles the problem of removing seismic acquisition footprints, which are coherent noise structures that damage useful signals in seismic data, by proposing an unsupervised footprint removal network (FR-Net) that combines a deep convolutional autoencoder with a unidirectional total variation model, achieving superior performance over previous state-of-the-art methods on synthetic and field datasets.
Seismic acquisition footprints appear as stably faint and dim structures and emerge fully spatially coherent, causing inevitable damage to useful signals during the suppression process. Various footprint removal methods, including filtering and sparse representation (SR), have been reported to attain promising results for surmounting this challenge. However, these methods, e.g., SR, rely solely on the handcrafted image priors of useful signals, which is sometimes an unreasonable demand if complex geological structures are contained in the given seismic data. As an alternative, this article proposes a footprint removal network (dubbed FR-Net) for the unsupervised suppression of acquired footprints without any assumptions regarding valuable signals. The key to the FR-Net is to design a unidirectional total variation (UTV) model for footprint acquisition according to the intrinsically directional property of noise. By strongly regularizing a deep convolutional autoencoder (DCAE) using the UTV model, our FR-Net transforms the DCAE from an entirely data-driven model to a \textcolor{black}{prior-augmented} approach, inheriting the superiority of the DCAE and our footprint model. Subsequently, the complete separation of the footprint noise and useful signals is projected in an unsupervised manner, specifically by optimizing the FR-Net via the backpropagation (BP) algorithm. We provide qualitative and quantitative evaluations conducted on three synthetic and field datasets, demonstrating that our FR-Net surpasses the previous state-of-the-art (SOTA) methods.