Generating Datasets for Deep Learning Seismic Inversion: Constructing Subsurface Parameters Models Using Statistical Information

Abstract Deep learning shows great promise for seismic parameter inversion, but its success hinges on large, high-quality datasets, which are often scarce in this field. To address this, we propose a novel method for constructing a subsurface parameter model, which can be used to build a training dataset. Our approach leverages kernel density estimation to capture the distribution characteristics and inherent relationships between P-, S-wave velocity, and density. We then use the Metropolis–Hastings sampling algorithm to generate reference points and apply transformations to create realistic parameter models. By constructing parameter models based on actual data distributions, this method inherently avoids the inclusion of abnormal samples, making it particularly beneficial for training inversion neural networks. Unlike conventional approaches that focus on single-velocity influences, our method considers the interplay between multiple parameters, resulting in a more realistic representation. This approach effectively addresses data scarcity, mitigates data leakage, and provides a robust foundation for deep-learning-based inversion.