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Found 29 papers, 4 papers with code
A Physics-guided Generative AI Toolkit for Geophysical Monitoring
Full-waveform inversion (FWI) plays a vital role in geoscience to explore the subsurface.
On the Hidden Waves of Image
We term this phenomenon hidden waves, as it reveals that, although the speeds of the set of wave equations and autoregressive coefficient matrices are latent, they are both learnable and shared across images.
Edge-InversionNet: Enabling Efficient Inference of InversionNet on Edge Devices
Seismic full waveform inversion (FWI) is a widely used technique in geophysics for inferring subsurface structures from seismic data.
Learned Full Waveform Inversion Incorporating Task Information for Ultrasound Computed Tomography
Once trained, the CNN can perform real-time FWI image reconstruction from USCT waveform data.
Does Full Waveform Inversion Benefit from Big Data?
This paper investigates the impact of big data on deep learning models for full waveform inversion (FWI).
$\mathbf{\mathbb{E}^{FWI}}$: Multi-parameter Benchmark Datasets for Elastic Full Waveform Inversion of Geophysical Properties
We conduct comprehensive numerical experiments to explore the relationship between P-wave and S-wave velocities in seismic data.
HOSSnet: an Efficient Physics-Guided Neural Network for Simulating Crack Propagation
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 fragmentation processes but the application of pure HOSS simulation is computationally expensive.
Fourier-DeepONet: Fourier-enhanced deep operator networks for full waveform inversion with improved accuracy, generalizability, and robustness
Here, we develop a Fourier-enhanced deep operator network (Fourier-DeepONet) for FWI with the generalization of seismic sources, including the frequencies and locations of sources.
Image as First-Order Norm+Linear Autoregression: Unveiling Mathematical Invariance
This paper introduces a novel mathematical property applicable to diverse images, referred to as FINOLA (First-Order Norm+Linear Autoregressive).
Simplifying Full Waveform Inversion via Domain-Independent Self-Supervised Learning
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.
Enhanced prediction accuracy with uncertainty quantification in monitoring CO2 sequestration using convolutional neural networks
The method remains robust even if the testing data are distorted due to problems in the field data acquisition.
Self-Supervised Learning based on Heat Equation
When transferring to object detection with frozen backbone, QB-Heat outperforms MoCo-v2 and supervised pre-training on ImageNet by 7. 9 and 4. 5 AP respectively.
Solving Seismic Wave Equations on Variable Velocity Models with Fourier Neural Operator
Previous works that concentrate on solving the wave equation by neural networks consider either a single velocity model or multiple simple velocity models, which is restricted in practice.
Quantum Neural Network Compression
Model compression, such as pruning and quantization, has been widely applied to optimize neural networks on resource-limited classical devices.
An Intriguing Property of Geophysics Inversion
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.
Extremely Weak Supervision Inversion of Multi-physical Properties
We explore a multi-physics inversion problem from two distinct measurements~(seismic and EM data) to three geophysical properties~(velocity, conductivity, and CO$_2$ saturation).
On the Robustness and Generalization of Deep Learning Driven Full Waveform Inversion
For robustness, we prove the upper bounds of the deviation between the predictions from clean and noisy data.
OpenFWI: Large-Scale Multi-Structural Benchmark Datasets for Seismic Full Waveform Inversion
The recent success of data-driven FWI methods results in a rapidly increasing demand for open datasets to serve the geophysics community.
Unsupervised Learning of Full-Waveform Inversion: Connecting CNN and Partial Differential Equation in a Loop
In particular, we use finite difference to approximate the forward modeling of PDE as a differentiable operator (from velocity map to seismic data) and model its inversion by CNN (from seismic data to velocity map).
Making Invisible Visible: Data-Driven Seismic Inversion with Spatio-temporally Constrained Data Augmentation
To validate the effectiveness of our data augmentation techniques, we apply them to solve a subsurface seismic full-waveform inversion using simulated CO$_2$ leakage data.
Connect the Dots: In Situ 4D Seismic Monitoring of CO2 Storage with Spatio-temporal CNNs
Via both numerical and expert evaluation, we conclude that our models can produce high-quality 2D/3D seismic imaging data at a reasonable cost, offering the possibility of real-time monitoring or even near-future forecasting of the CO$_2$ storage reservoir.
InversionNet3D: Efficient and Scalable Learning for 3D Full Waveform Inversion
Seismic full-waveform inversion (FWI) techniques aim to find a high-resolution subsurface geophysical model provided with waveform data.
Physics-Consistent Data-driven Waveform Inversion with Adaptive Data Augmentation
Seismic full-waveform inversion (FWI) is a nonlinear computational imaging technique that can provide detailed estimates of subsurface geophysical properties.
SeismoGen: Seismic Waveform Synthesis Using Generative Adversarial Networks
However, the accuracy of those methods relies on a sufficient amount of high-quality training data, which itself can be expensive to obtain due to the requirement of domain knowledge and subject matter expertise.
Contextual Hourglass Network for Semantic Segmentation of High Resolution Aerial Imagery
Semantic segmentation for aerial imagery is a challenging and important problem in remotely sensed imagery analysis.
VelocityGAN: Data-Driven Full-Waveform Inversion Using Conditional Adversarial Networks
Acoustic- and elastic-waveform inversion is an important and widely used method to reconstruct subsurface velocity image.
Seismic-Net: A Deep Densely Connected Neural Network to Detect Seismic Events
In particular, we demonstrate the efficacy of our Seismic-Net by formulating our detection problem as an event detection problem with time series data.
Efficient Data-Driven Geologic Feature Detection from Pre-stack Seismic Measurements using Randomized Machine-Learning Algorithm
We employ a data reduction technique in combination with the conventional kernel ridge regression method to improve the computational efficiency and reduce memory usage.
Cascaded Region-based Densely Connected Network for Event Detection: A Seismic Application
Because of the fact that some positive events are not correctly annotated, we further formulate the detection problem as a learning-from-noise problem.
