Unsupervised ground-roll attenuation via implicit neural representations

Abstract

ABSTRACT Coherent noise attenuation in land seismic data is particularly challenging, especially when dealing with ground roll. Unlike incoherent noise, ground roll overlaps with reflections in time-space and frequency-wavenumber domains, making it difficult to separate them without distorting the signal. Traditional attenuation methods often struggle with this overlap, leading to a trade-off between preserving the reflections and effectively reducing noise. Recent advances in deep learning offer promising alternatives, but many rely on supervised learning, which requires a substantial amount of paired training data, which is often unavailable in real-world scenarios. Unsupervised approaches, although avoiding the need for labeled data, frequently face issues such as convergence instability and extensive parameter tuning. We develop an unsupervised deep-learning framework for separating reflections from ground roll to address these challenges. Our method leverages the inherent low-frequency bias of implicit neural representations, which emphasizes self-similarity features during training. The network initially learns to represent smoother, flattened events in seismic data before focusing on features with deeper dips and incoherent noise. To enhance the network’s ability to capture the self-similarity of reflections, we apply a normal moveout (NMO) correction to flatten the reflections before using the network to extract these features from the NMO-corrected data. We further incorporate a horizontal derivative regularization term into the loss function. This term penalizes horizontal variations, ensuring a more stable convergence and reducing the burden of parameter tuning, thereby eliminating the need for early stopping. Our approach is validated with synthetic and real land data examples and compared against traditional f-k filtering methods. The results demonstrate its power in effectively attenuating noise while preserving the integrity of seismic reflections.

Topics & Concepts