214 citations · 672 across the 36 of their papers we have counts for
14 papers · 1 filter
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…
Fourier-DeepONet: Fourier-enhanced deep operator networks for full waveform inversion with improved accuracy, generalizability, and robustness
Min Zhu, Shihang Feng, Youzuo Lin +1
Full waveform inversion (FWI) infers the subsurface structure information from seismic waveform data by solving a non-convex optimization problem. Data-driven FWI has been increasi…
Solving Seismic Wave Equations on Variable Velocity Models with Fourier Neural Operator
Bian Li, Hanchen Wang, Shihang Feng +2
In the study of subsurface seismic imaging, solving the acoustic wave equation is a pivotal component in existing models. The advancement of deep learning enables solving partial d…
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…
On the Robustness and Generalization of Deep Learning Driven Full Waveform Inversion
Chengyuan Deng, Youzuo Lin
The data-driven approach has been demonstrated as a promising technique to solve complicated scientific problems. Full Waveform Inversion (FWI) is commonly epitomized as an image-t…
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…
Making Invisible Visible: Data-Driven Seismic Inversion with Spatio-temporally Constrained Data Augmentation
Yuxin Yang, Xitong Zhang, Qiang Guan +1
Deep learning and data-driven approaches have shown great potential in scientific domains. The promise of data-driven techniques relies on the availability of a large volume of hig…
InversionNet3D: Efficient and Scalable Learning for 3D Full Waveform Inversion
Qili Zeng, Shihang Feng, Brendt Wohlberg +1
Seismic full-waveform inversion (FWI) techniques aim to find a high-resolution subsurface geophysical model provided with waveform data. Some recent effort in data-driven FWI has s…
Physics-Consistent Data-driven Waveform Inversion with Adaptive Data Augmentation
Renán Rojas-Gómez, Jihyun Yang, Youzuo Lin +2
Seismic full-waveform inversion (FWI) is a nonlinear computational imaging technique that can provide detailed estimates of subsurface geophysical properties. Solving the FWI probl…
SeismoGen: Seismic Waveform Synthesis Using Generative Adversarial Networks
Tiantong Wang, Daniel Trugman, Youzuo Lin
Detecting earthquake events from seismic time series has proved itself a challenging task. Manual detection can be expensive and tedious due to the intensive labor and large scale…
Deep Learning Approach in Automatic Iceberg - Ship Detection with SAR Remote Sensing Data
Cheng Zhan, Licheng Zhang, Zhenzhen Zhong +5
Deep Learning is gaining traction with geophysics community to understand subsurface structures, such as fault detection or salt body in seismic data. This study describes using de…
Efficient Data-Driven Geologic Feature Detection from Pre-stack Seismic Measurements using Randomized Machine-Learning Algorithm
Youzuo Lin, Shusen Wang, Jayaraman Thiagarajan +2
Conventional seismic techniques for detecting the subsurface geologic features are challenged by limited data coverage, computational inefficiency, and subjective human factors. We…
Cascaded Region-based Densely Connected Network for Event Detection: A Seismic Application
Yue Wu, Youzuo Lin, Zheng Zhou +3
Automatic event detection from time series signals has wide applications, such as abnormal event detection in video surveillance and event detection in geophysical data. Traditiona…