This paper investigates the impact of big data on deep learning models for full waveform inversion (FWI). While it is well known that big data can boost the performance of deep learning models in many tasks, its effectiveness has not been validated for FWI. To address this gap, we present an empirical study that investigates how deep learning models in FWI behave when trained on OpenFWI, a collection of large-scale, multi-structural datasets published recently. Particularly, we train and evaluate the FWI models on a combination of 10 2D subsets in OpenFWI that contain 470K data pairs in total. Our experiments demonstrate that larger datasets lead to better performance and generalization of deep learning models for FWI. We further demonstrate that model capacity needs to scale in accordance with data size for optimal improvement.

翻译：本文研究了大数据对全波形反演（FWI）深度学习模型的影响。尽管众所周知大数据能够在许多任务中提升深度学习模型的性能，但其对FWI的有效性尚未得到验证。为填补这一空白，我们开展了一项实证研究，探究基于近期发布的大规模多结构数据集集合OpenFWI训练的FWI深度学习模型的行为特征。具体而言，我们在OpenFWI的10个二维子数据集组合（总计包含47万组数据对）上训练并评估FWI模型。实验表明，更大规模的数据集能够提升FWI深度学习模型的性能与泛化能力。我们进一步证明，为获得最优性能提升，模型容量需随数据规模同步扩展。