no code implementations • 28 Jul 2023 • 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 to help solve the full waveform inversion (FWI) problem.
no code implementations • 21 Jun 2023 • Shihang Feng, Hanchen Wang, Chengyuan Deng, Yinan Feng, Yanhua Liu, Min Zhu, Peng Jin, Yinpeng Chen, Youzuo Lin
We conduct comprehensive numerical experiments to explore the relationship between P-wave and S-wave velocities in seismic data.
no code implementations • 27 Apr 2023 • Yinan Feng, Yinpeng Chen, Peng Jin, Shihang Feng, Zicheng Liu, Youzuo Lin
Geophysics has witnessed success in applying deep learning to one of its core problems: full waveform inversion (FWI) to predict subsurface velocity maps from seismic data.
no code implementations • 28 Apr 2022 • Yinan Feng, Yinpeng Chen, Shihang Feng, Peng Jin, Zicheng Liu, Youzuo Lin
In particular, when dealing with the inversion from seismic data to subsurface velocity governed by a wave equation, the integral results of velocity with Gaussian kernels are linearly correlated to the integral of seismic data with sine kernels.
2 code implementations • 4 Nov 2021 • Chengyuan Deng, Shihang Feng, Hanchen Wang, Xitong Zhang, Peng Jin, Yinan Feng, Qili Zeng, Yinpeng Chen, Youzuo Lin
The recent success of data-driven FWI methods results in a rapidly increasing demand for open datasets to serve the geophysics community.