Publications (56)
Edge-InversionNet: Enabling Efficient Inference of InversionNet on Edge Devices
Zhepeng Wang, Isaacshubhanand Putla, Weiwen Jiang +1
Seismic full waveform inversion (FWI) is a widely used technique in geophysics for inferring subsurface structures from seismic data. And InversionNet is one of the most successful…
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…
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…
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…
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…
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…
Proceedings of the Workshop on Data Mining for Geophysics and Geology
Youzuo Lin, Weichang Li, Alipio Jorge +3
Modern geosciences have to deal with large quantities and a wide variety of data, including 2-D, 3-D and 4-D seismic surveys, well logs generated by sensors, detailed lithological…
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…
Escaping Barren Plateau: Co-Exploration of Quantum Circuit Parameters and Architectures
Yipei Liu, Yuhong Song, Jinyang Li +4
Barren plateaus (BP), characterized by exponentially vanishing gradients that hinder the training of variational quantum circuits (VQC), present a pervasive and critical challenge…
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…
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…
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…
QuGeo: An End-to-end Quantum Learning Framework for Geoscience -- A Case Study on Full-Waveform Inversion
Weiwen Jiang, Youzuo Lin
The rapid advancement of quantum computing has generated considerable anticipation for its transformative potential. However, harnessing its full potential relies on identifying "k…
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…
A Novel Diffusion Model for Pairwise Geoscience Data Generation with Unbalanced Training Dataset
Junhuan Yang, Yuzhou Zhang, Yi Sheng +2
Recently, the advent of generative AI technologies has made transformational impacts on our daily lives, yet its application in scientific applications remains in its early stages.…
A Data-Driven CO2 Leakage Detection Using Seismic Data and Spatial-Temporal Densely Connected Convolutional Neural Networks
Zheng Zhou, Youzuo Lin, Zhongping Zhang +4
In carbon capture and sequestration, developing effective monitoring methods is needed to detect and respond to CO2 leakage. CO2 leakage detection methods rely on geophysical obser…
Contextual Hourglass Network for Semantic Segmentation of High Resolution Aerial Imagery
Panfeng Li, Youzuo Lin, Emily Schultz-Fellenz
Semantic segmentation for aerial imagery is a challenging and important problem in remotely sensed imagery analysis. In recent years, with the success of deep learning, various con…
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…
Exploring Invariance in Images through One-way Wave Equations
Yinpeng Chen, Dongdong Chen, Xiyang Dai +5
In this paper, we empirically reveal an invariance over images-images share a set of one-way wave equations with latent speeds. Each image is uniquely associated with a solution to…
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…
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…
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…
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…
Data-driven Seismic Waveform Inversion: A Study on the Robustness and Generalization
Zhongping Zhang, Youzuo Lin
Acoustic- and elastic-waveform inversion is an important and widely used method to reconstruct subsurface velocity image. Waveform inversion is a typical non-linear and ill-posed i…
BrainPuzzle: Hybrid Physics and Data-Driven Reconstruction for Transcranial Ultrasound Tomography
Shengyu Chen, Shihang Feng, Yi Luo +2
Ultrasound brain imaging remains challenging due to the large difference in sound speed between the skull and brain tissues and the difficulty of coupling large probes to the skull…
Spatial-Temporal Densely Connected Convolutional Networks: An Application to CO2 Leakage Detection
Zheng Zhou, Youzuo Lin, Yue Wu +3
In carbon capture and sequestration, building an effective monitoring method is a crucial step to detect and respond to CO2 leakage. CO2 leakage detection methods rely on geophysic…
RED-DiffEq: Regularization by denoising diffusion models for solving inverse PDE problems with application to full waveform inversion
Siming Shan, Min Zhu, Youzuo Lin +1
Partial differential equation (PDE)-governed inverse problems are fundamental across various scientific and engineering applications; yet they face significant challenges due to no…
PreMevE-MEO: Predicting Ultra-relativistic Electrons Using Observations from GPS Satellites
Yinan Feng, Yue Chen, Youzuo Lin
Ultra-relativistic electrons with energies greater than or equal to two megaelectron-volt (MeV) pose a major radiation threat to spaceborne electronics, and thus specifying those h…
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-…
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…
APS-USCT: Ultrasound Computed Tomography on Sparse Data via AI-Physic Synergy
Yi Sheng, Hanchen Wang, Yipei Liu +4
Ultrasound computed tomography (USCT) is a promising technique that achieves superior medical imaging reconstruction resolution by fully leveraging waveform information, outperform…
A Physics-guided Generative AI Toolkit for Geophysical Monitoring
Junhuan Yang, Hanchen Wang, Yi Sheng +2
Full-waveform inversion (FWI) plays a vital role in geoscience to explore the subsurface. It utilizes the seismic wave to image the subsurface velocity map. As the machine learning…
: 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.,…
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…
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…
InversionNet: A Real-Time and Accurate Full Waveform Inversion with CNNs and continuous CRFs
Yue Wu, Youzuo Lin
Full-waveform inversion problems are usually formulated as optimization problems, where the forward-wave propagation operator maps the subsurface velocity structures to seismic…
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…
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…
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…
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…
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…
A Unified Framework for Forward and Inverse Problems in Subsurface Imaging using Latent Space Translations
Naveen Gupta, Medha Sawhney, Arka Daw +2
In subsurface imaging, learning the mapping from velocity maps to seismic waveforms (forward problem) and waveforms to velocity (inverse problem) is important for several applicati…
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…
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…
Learned Correction Methods for Ultrasound Computed Tomography Imaging Using Simplified Physics Models
Luke Lozenski, Hanchen Wang, Fu Li +4
Ultrasound computed tomography (USCT) is an emerging modality for breast imaging. Image reconstruction methods that incorporate accurate wave physics produce high resolution quanti…
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…
Earthquake Detection in 1-D Time Series Data with Feature Selection and Dictionary Learning
Zheng Zhou, Youzuo Lin, Zhongping Zhang +2
Earthquakes can be detected by matching spatial patterns or phase properties from 1-D seismic waves. Current earthquake detection methods, such as waveform correlation and template…
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…
MUX-USCT: A Noise-Robust Neural Network for Ultrasound Computed Tomography
Yuchen Yuan, Hanhan Wu, Jinyang Li +4
Deep neural networks (DNNs) have shown strong potential for ultrasound computed tomography (USCT) reconstruction in ideal noise-free environments, yet existing DNNs are vulnerable…
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…
Two Teachers Better Than One: Hardware-Physics Co-Guided Distributed Scientific Machine Learning
Yuchen Yuan, Junhuan Yang, Hao Wan +4
Scientific machine learning (SciML) is increasingly applied to in-field processing, controlling, and monitoring; however, wide-area sensing, real-time demands, and strict energy an…
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…
Seismic-Net: A Deep Densely Connected Neural Network to Detect Seismic Events
Yue Wu, Youzuo Lin, Zheng Zhou +1
One of the risks of large-scale geologic carbon sequestration is the potential migration of fluids out of the storage formations. Accurate and fast detection of this fluids migrati…