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

Abstract

Objectives and Scope: Deep learning's efficacy in seismic interpretation, including denoising, horizon or fault detection, and lithology prediction, hinges on the quality of the training dataset. Acquiring high-quality seismic data is challenging due to confidentiality, and alternative approaches like using synthetic or augmented data often fail to adequately capture realistic wavefield variations, ambient noise, and complex multipathing effects such as multiples. We introduce an innovative seismic data augmentation method that incorporates realistic geostatistical features and synthetic multiples, enhancing the training and transferability of deep neural networks in multi seismic applications. Methods and Procedures: Our method comprises two primary steps: (1) Creating augmented impedance models from existing seismic images and well logs, and (2) Simulating seismic data from these models. The first step merges Image-Guided Interpolation (IGI, Hale et al., 2010) and Sequential Gaussian Simulation (SGS) to generate models that retain original structural features of the input seismic image and introduce random small-scale features aligned with the geostatistical properties of the input seismic data. The second step employs reflectivity forward modeling method (Kennett, 1984) to simulate both primary and multiple seismic data trace-by-trace. This approach, summing up infinite order internal multiples, effectively reproduces the full properties of reflection wavefields, which is a good approximation in areas without rapidly changing structures. Results and Observations: Our numerical tests validate the method's effectiveness. The IGI technique interpolates well log data into gridded velocity models, maintaining seismic horizons and smoothing fault features. The SGS method then generates stochastic velocity model implementations preserving the geostatistical distribution of the input seismic data. The resulting reflectivity forward modeling successfully distinguishes between multiples and primaries, facilitating the creation of nuanced training datasets and labels. Further tests involve training two Transformer-based seismic fault detection neural networks: one with conventional data lacking multiples and another with our augmented data incorporating multiples. While both networks exhibit similar validation performance, their generalization capabilities differ markedly. The network trained with conventional data shows reduced accuracy and fault detection reliability on synthetic field data. In contrast, the network trained with our augmented data demonstrates better precision, accuracy, and recall on the same dataset. Significance and Novelty: Our approach generates augmented seismic data that retains the original seismic cubes' and well logs' geostatistical features and multiples, crucial for training deep learning models with high transferability for various seismological tasks. This method's novelty lies in its consideration of geostatistical characteristics, wavelet fluctuations, and multiples. The resulting data is more complex, varied, and realistic compared to conventional augmentation methods. Neural networks trained on this data exhibit enhanced transferability over those trained with traditional synthetic data incorporating only random noise. This advancement represents a significant leap in seismic data processing and interpretation, particularly for deep learning applications in geophysics.