miscellaneous-4

Seismic Imaging

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in seismic-imaging-4

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i

Use these libraries to find seismic-imaging-4 models and implementations

slimgroup/InvertibleNetworks.jl

2 papers 149

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Most implemented papers

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

Artsiom Sanakoyeu, Yauhen Babakhin, Hirotoshi Kitamura•Mon Apr 08 2019

A semi-supervised method for segmentation (delineation) of salt bodies in seismic images which utilizes unlabeled data for multi-round self-training and outperforms state-of-the-art on the TGS Salt Identification Challenge dataset and is ranked the first among the 3234 competing methods.

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

Ali Siahkoohi, F. Herrmann•Mon Apr 12 2021

This is the first attempt to train a conditional network on what the authors know from neighboring images to improve the current image and assess its reliability, and is a data-driven variational inference approach where a normalizing flow is training a type of invertible neural net capable of cheaply sampling the posterior distribution given previously unseen seismic data from neighboring surveys.

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

Ali Siahkoohi, F. Herrmann, G. Rizzuti•Sun Jan 12 2020

This work proposes to use the functional form of a randomly initialized convolutional neural network as an implicit structured prior, which is shown to promote natural images and excludes images with unnatural noise.

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Data-driven Estimation of Sinusoid Frequencies

Gautier Izacard, S. Mohan, Carlos Fernandez-Granda•Sun Jun 02 2019

This work proposes a novel neural-network architecture that produces a significantly more accurate representation, and combines it with an additional neural- network module trained to detect the number of frequencies, which yields a fast, fully-automatic method for frequency estimation that achieves state-of-the-art results.

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Reliable amortized variational inference with physics-based latent distribution correction

Ali Siahkoohi, F. Herrmann, G. Rizzuti, Rafael Orozco•Sat Jul 23 2022

This work aims to increase the resilience of amortized variational inference in the presence of moderate data distribution shifts via a correction to the conditional normalizing flow’s latent distribution that improves the approximation to the posterior distribution for the data at hand.

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Acoustic Non-Line-Of-Sight Imaging

V. Koltun, Gordon Wetzstein, David B. Lindell•Fri May 31 2019

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. Recent approaches to solving this challenging problem employ optical time-of-flight imaging systems with highly sensitive time-resolved photodetectors and ultra-fast pulsed lasers. However, despite recent successes in NLOS imaging using these systems, widespread implementation and adoption of the technology remains a challenge because of the requirement for specialized, expensive hardware. We introduce acoustic NLOS imaging, which is orders of magnitude less expensive than most optical systems and captures hidden 3D geometry at longer ranges with shorter acquisition times compared to state-of-the-art optical methods. Inspired by hardware setups used in radar and algorithmic approaches to model and invert wave-based image formation models developed in the seismic imaging community, we demonstrate a new approach to seeing around corners.

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

Jianwei Ma, Fangshu Yang•Sat Feb 16 2019

A novel method based on the supervised deep fully convolutional neural network (FCN) for velocity-model building (VMB) directly from raw seismograms is investigated, showing promising performances in comparison with conventional FWI even when the input data are in more realistic scenarios.

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

Ali Siahkoohi, F. Herrmann, G. Rizzuti•Tue Mar 31 2020

This work is fundamentally based on a special reparameterization of reflectivity, known as "deep prior", and verified that the estimated confidence intervals for the horizon tracking coincide with geologically complex regions, such as faults.

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Direct Velocity Inversion of Ground Penetrating Radar Data Using GPRNet

Zi Xian Leong, T. Zhu•Mon May 31 2021

Ground penetrating radar (GPR) is used to image the shallow subsurface as evident in earth and planetary exploration. Electromagnetic (EM) velocity (permittivity) models are inverted from GPR data for accurate migration. While conventional velocity analysis methods are designed for multioffset GPR data, to our knowledge, the velocity analysis for zero‐offset GPR has been underexplored. Inspired by recent deep learning seismic impedance inversion, we propose a deep learning guided technique, GPRNet, that is based on convolutional neural networks to directly learn the intrinsic relationship between GPR data and EM velocity. GPRNet takes in GPR data and outputs the corresponding EM velocity. We simulate numerous GPR data from a range of pseudo‐random velocity models and feed the datasets into GPRNet for training. Each training data set comprises of a pair of one‐dimensional GPR data and EM velocity. During training phase, the neural network's weights are updated iteratively until convergence. This process is analogous to full‐waveform inversion in which the best model is found by iterative optimization until simulated data matches observed data. We test GPRNet on synthetic testing datasets and the predicted velocity models are accurate. A case study is presented where this method is applied on a GPR data collected at the former Wurtsmith Air Force Base in Michigan. The inversion results agree with velocity models established by previous GPR inversion studies of the similar area. We expect the GPRNet open‐source software to be useful in imaging the subsurface for earth and planetary exploration.

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Deep Bayesian inference for seismic imaging with tasks

Ali Siahkoohi, F. Herrmann, G. Rizzuti•Sat Oct 09 2021

It is proposed 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 and stochastic gradient Langevin dynamics is employed to sample from the posterior distribution.

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