Seismic Imaging
13 papers with code • 0 benchmarks • 0 datasets
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Libraries
Use these libraries to find Seismic Imaging models and implementationsMost implemented papers
Semi-Supervised Segmentation of Salt Bodies in Seismic Images using an Ensemble of Convolutional Neural Networks
ybabakhin/kaggle_salt_bes_phalanx
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Seismic image analysis plays a crucial role in a wide range of industrial applications and has been receiving significant attention.
Learning by example: fast reliability-aware seismic imaging with normalizing flows
To arrive at this result, we train the NF on pairs of low- and high-fidelity migrated images.
Data-driven Estimation of Sinusoid Frequencies
sreyas-mohan/DeepFreq
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NeurIPS 2019
Frequency estimation is a fundamental problem in signal processing, with applications in radar imaging, underwater acoustics, seismic imaging, and spectroscopy.
A deep-learning based Bayesian approach to seismic imaging and uncertainty quantification
alisiahkoohi/seismic-imaging-with-SGLD
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Uncertainty quantification is essential when dealing with ill-conditioned inverse problems due to the inherent nonuniqueness of the solution.
Reliable amortized variational inference with physics-based latent distribution correction
While generic and applicable to other inverse problems, by means of a linearized seismic imaging example, we show that our correction step improves the robustness of amortized variational inference with respect to changes in the number of seismic sources, noise variance, and shifts in the prior distribution.
Deep-learning inversion: a next generation seismic velocity-model building method
YangFangShu/FCNVMB-Deep-learning-based-seismic-velocity-model-building
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Seismic velocity is one of the most important parameters used in seismic exploration.
Acoustic Non-Line-Of-Sight Imaging
Non-line-of-sight (NLOS) imaging enables unprecedented capabilities in a wide range of applications, including robotic and machine vision, remote sensing, autonomous vehicle navigation, and medical imaging.
Uncertainty quantification in imaging and automatic horizon tracking: a Bayesian deep-prior based approach
slimgroup/Software.SEG2020
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In this paper, we focus on how UQ trickles down to horizon tracking for the determination of stratigraphic models and investigate its sensitivity with respect to the imaging result.
Direct Velocity Inversion of Ground Penetrating Radar Data Using GPRNet
We simulate numerous GPR data from a range of pseudo‐random velocity models and feed the datasets into GPRNet for training.
Deep Bayesian inference for seismic imaging with tasks
slimgroup/deep_inference_with_tasks
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We propose to use techniques from Bayesian inference and deep neural networks to translate uncertainty in seismic imaging to uncertainty in tasks performed on the image, such as horizon tracking.
