Seismic data augmentation with realistic geostatistical features and synthetic multiples for deep learning and seismic interpretation tasks

ABSTRACT Seismic data acquisition is inherently constrained by budget and operational challenges, which often result in datasets that fail to adequately capture geological complexity. These limitations, coupled with the subjectivity from interpreters, lead to inconsistencies in seismic interpretation. Although machine learning (ML) has shown potential for automating interpretation tasks, its generalization across diverse geological settings remains limited by the scarcity of high-fidelity training datasets. Effective seismic data augmentation is critical to addressing this gap by enhancing dataset diversity and realism. However, traditional augmentation methods often fail to replicate key geological features, such as stochastic scattering effects caused by small-scale heterogeneities, limiting their real-world applicability. We propose a novel seismic data augmentation technique that integrates sequential Gaussian simulation with synthetic internal multiples. This approach generates realistic and diverse datasets by accurately modeling stochastic scattering while preserving critical geological structures. The augmented datasets improve manual interpretation and ML model performance in tasks such as fault detection, stratigraphy truncation, and horizon picking. By enhancing data fidelity and variability, our method offers a robust solution to challenges in seismic interpretation, advancing the accuracy and efficiency of workflows across complex geological settings.