Does Full Waveform Inversion Benefit from Big Data?
Peng Jin Yinan Feng Shihang Feng Hanchen Wang Yinpeng Chen Benjamin Consolvo Zicheng Liu Youzuo Lin
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.