Papers with Code 아카이브

Seismic Imaging

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

0

평가표

0

보존된 평가 기록

0

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/Vahid-Negahdari/Elastic-Full-Waveforrm-Inversion

공식 코드

Integrating Physics of the Problem into Data-Driven Methods to Enhance Elastic Full-Waveform Inversion with Uncertainty Quantification

https://github.com/alisiahkoohi/seismic-imaging-with-SGLD

공식 코드

A deep-learning based Bayesian approach to seismic imaging and uncertainty quantification

https://github.com/nadja1611/noisier2inverse-joint-denoising-and-reconstruction-of-correlated-noise

공식 코드

Noisier2Inverse: Self-Supervised Learning for Image Reconstruction with Correlated Noise

https://github.com/slimgroup/InvertibleNetworks.jl

공식 코드

Learning by example: fast reliability-aware seismic imaging with normalizing flows

https://github.com/slimgroup/Software.SEG2020

공식 코드

Uncertainty quantification in imaging and automatic horizon tracking: a Bayesian deep-prior based approach

https://github.com/slimgroup/Software.SEG2021

공식 코드

Learning by example: fast reliability-aware seismic imaging with normalizing flows

https://github.com/slimgroup/deep_inference_with_tasks

공식 코드

Deep Bayesian inference for seismic imaging with tasks

https://github.com/slimgroup/gcs-cam

공식 코드

De-risking geological carbon storage from high resolution time-lapse seismic to explainable leakage detection

https://github.com/slimgroup/reliableavi.jl

공식 코드

Reliable amortized variational inference with physics-based latent distribution correction

https://github.com/slimgroup/velocity_continuation_with_fnos

공식 코드

Velocity continuation with Fourier neural operators for accelerated uncertainty quantification

https://github.com/sreyas-mohan/DeepFreq

공식 코드

Data-driven Estimation of Sinusoid Frequencies

https://github.com/ybabakhin/kaggle_salt_bes_phalanx

공식 코드

Semi-Supervised Segmentation of Salt Bodies in Seismic Images using an Ensemble of Convolutional Neural Networks

https://github.com/MS-Mind/MS-Code-08/tree/main/NFNet

Learning by example: fast reliability-aware seismic imaging with normalizing flows

https://github.com/YangFangShu/FCNVMB-Deep-learning-based-seismic-velocity-model-building

Deep-learning inversion: a next generation seismic velocity-model building method

https://github.com/ivboh/3d_image_segmentation

Semi-Supervised Segmentation of Salt Bodies in Seismic Images using an Ensemble of Convolutional Neural Networks

https://github.com/lRomul/argus-tgs-salt

Semi-Supervised Segmentation of Salt Bodies in Seismic Images using an Ensemble of Convolutional Neural Networks

https://github.com/panpp-git/cResFreq

Data-driven Estimation of Sinusoid Frequencies

https://github.com/slimgroup/Software.siahkoohi2020EAGEdlb

A deep-learning based Bayesian approach to seismic imaging and uncertainty quantification

https://github.com/stanleyjzheng/PyData

Semi-Supervised Segmentation of Salt Bodies in Seismic Images using an Ensemble of Convolutional Neural Networks

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.