24 citations · 51 across the 12 of their papers we have counts for
12 papers
Improving Full Waveform Inversion in Large Model Era
Yinan Feng, Peng Jin, Yuzhe Guo +2
Full Waveform Inversion (FWI) is a highly nonlinear and ill-posed problem that aims to recover subsurface velocity maps from surface-recorded seismic waveforms data. Existing data-…
OpenPros: A Large-Scale Dataset for Limited View Prostate Ultrasound Computed Tomography
Hanchen Wang, Yixuan Wu, Yinan Feng +11
Prostate cancer is one of the most prevalent and deadly cancers among men, motivating the development of accurate and accessible imaging technologies for early detection. Ultrasoun…
On a Hidden Property in Computational Imaging
Yinan Feng, Yinpeng Chen, Yueh Lee +1
Computational imaging plays a vital role in various scientific and medical applications, such as Full Waveform Inversion (FWI), Computed Tomography (CT), and Electromagnetic (EM) i…
WaveDiffusion: Joint Latent Diffusion for Physically Consistent Seismic and Velocity Generation
Yinan Feng, Hanchen Wang, Yinpeng Chen +5
Full Waveform Inversion (FWI) is a critical technique in subsurface imaging, aiming to reconstruct high-resolution subsurface properties from surface measurements. Acoustic FWI inv…
Survey of Deep Learning and Physics-Based Approaches in Computational Wave Imaging
Youzuo Lin, Shihang Feng, James Theiler +7
Computational wave imaging (CWI) extracts hidden structure and physical properties of a volume of material by analyzing wave signals that traverse that volume. Applications include…
DeFault: Deep-learning-based Fault Delineation Using the IBDP Passive Seismic Data at the Decatur CO2 Storage Site
Hanchen Wang, Yinpeng Chen, Tariq Alkhalifah +3
The carbon capture, utilization, and storage (CCUS) framework is an essential component in reducing greenhouse gas emissions, with its success hinging on the comprehensive knowledg…
An Empirical Study of Large-Scale Data-Driven Full Waveform Inversion
Peng Jin, Yinan Feng, Shihang Feng +5
This paper investigates the impact of big data on deep learning models to help solve the full waveform inversion (FWI) problem. While it is well known that big data can boost the p…
: Multi-parameter Benchmark Datasets for Elastic Full Waveform Inversion of Geophysical Properties
Shihang Feng, Hanchen Wang, Chengyuan Deng +6
Elastic geophysical properties (such as P- and S-wave velocities) are of great importance to various subsurface applications like CO sequestration and energy exploration (e.g.,…
Auto-Linear Phenomenon in Subsurface Imaging
Yinan Feng, Yinpeng Chen, Peng Jin +3
Subsurface imaging involves solving full waveform inversion (FWI) to predict geophysical properties from measurements. This problem can be reframed as an image-to-image translation…
An Intriguing Property of Geophysics Inversion
Yinan Feng, Yinpeng Chen, Shihang Feng +3
Inversion techniques are widely used to reconstruct subsurface physical properties (e.g., velocity, conductivity) from surface-based geophysical measurements (e.g., seismic, electr…
OpenFWI: Large-Scale Multi-Structural Benchmark Datasets for Seismic Full Waveform Inversion
Chengyuan Deng, Shihang Feng, Hanchen Wang +6
Full waveform inversion (FWI) is widely used in geophysics to reconstruct high-resolution velocity maps from seismic data. The recent success of data-driven FWI methods results in…
Unsupervised Learning of Full-Waveform Inversion: Connecting CNN and Partial Differential Equation in a Loop
Peng Jin, Xitong Zhang, Yinpeng Chen +3
This paper investigates unsupervised learning of Full-Waveform Inversion (FWI), which has been widely used in geophysics to estimate subsurface velocity maps from seismic data. Thi…