214 citations · 672 across the 36 of their papers we have counts for
36 papers
Learned Full Waveform Inversion Incorporating Task Information for Ultrasound Computed Tomography
Luke Lozenski, Hanchen Wang, Fu Li +4
Ultrasound computed tomography (USCT) is an emerging imaging modality that holds great promise for breast imaging. Full-waveform inversion (FWI)-based image reconstruction methods…
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…
HOSSnet: an Efficient Physics-Guided Neural Network for Simulating Crack Propagation
Shengyu Chen, Shihang Feng, Yao Huang +4
Hybrid Optimization Software Suite (HOSS), which is a combined finite-discrete element method (FDEM), is one of the advanced approaches to simulating high-fidelity fracture and fra…
: 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.,…
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…
Image as First-Order Norm+Linear Autoregression: Unveiling Mathematical Invariance
Yinpeng Chen, Xiyang Dai, Dongdong Chen +4
This paper introduces a novel mathematical property applicable to diverse images, referred to as FINOLA (First-Order Norm+Linear Autoregressive). FINOLA represents each image in th…
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…
Battle Against Fluctuating Quantum Noise: Compression-Aided Framework to Enable Robust Quantum Neural Network
Zhirui Hu, Youzuo Lin, Qiang Guan +1
Recently, we have been witnessing the scale-up of superconducting quantum computers; however, the noise of quantum bits (qubits) is still an obstacle for real-world applications to…
Enhanced prediction accuracy with uncertainty quantification in monitoring CO2 sequestration using convolutional neural networks
Yanhua Liu, Xitong Zhang, Ilya Tsvankin +1
Monitoring changes inside a reservoir in real time is crucial for the success of CO2 injection and long-term storage. Machine learning (ML) is well-suited for real-time CO2 monitor…
Self-Supervised Learning based on Heat Equation
Yinpeng Chen, Xiyang Dai, Dongdong Chen +4
This paper presents a new perspective of self-supervised learning based on extending heat equation into high dimensional feature space. In particular, we remove time dependence by…
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…
Quantum Neural Network Compression
Zhirui Hu, Peiyan Dong, Zhepeng Wang +3
Model compression, such as pruning and quantization, has been widely applied to optimize neural networks on resource-limited classical devices. Recently, there are growing interest…
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…
Extremely Weak Supervision Inversion of Multi-physical Properties
Shihang Feng, Peng Jin, Xitong Zhang +4
Multi-physical inversion plays a critical role in geophysics. It has been widely used to infer various physical properties~(such as velocity and conductivity). Among those inversio…
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…
Connect the Dots: In Situ 4D Seismic Monitoring of CO2 Storage with Spatio-temporal CNNs
Shihang Feng, Xitong Zhang, Brendt Wohlberg +2
4D seismic imaging has been widely used in CO sequestration projects to monitor the fluid flow in the volumetric subsurface region that is not sampled by wells. Ideally, real-t…
PreMevE Update: Forecasting Ultra-relativistic Electrons inside Earth's Outer Radiation Belt
Saurabh Sinha, Yue Chen, Youzuo Lin +1
Energetic electrons inside Earth's outer Van Allen belt pose a major radiation threat to space-borne electronics that often play vital roles in our modern society. Ultra-relativist…
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…
Multiscale Data-driven Seismic Full-waveform Inversion with Field Data Study
Shihang Feng, Youzuo Lin, Brendt Wohlberg
Seismic full-waveform inversion (FWI), which uses iterative methods to estimate high-resolution subsurface models from seismograms, is a powerful imaging technique in exploration g…
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…
Forecasting Megaelectron-Volt Electrons inside Earth's Outer Radiation Belt: PreMevE 2.0 Based on Supervised Machine Learning Algorithms
Rafael Pires de Lima, Yue Chen, Youzuo Lin
Here we present the recent progress in upgrading a predictive model for Megaelectron-Volt (MeV) electrons inside the Earth's outer Van Allen belt. This updated model, called PreMev…