miscellaneous-11

Seismic Inversion

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

Estimation of acoustic impedance from seismic data using temporal convolutional network

G. AlRegib, Motaz Alfarraj, Ahmad Mustafa•Wed Jun 05 2019

A workflow for predicting acoustic impedance (AI) from seismic data using a network architecture based on Temporal Convolutional Network by posing the problem as that of sequence modeling overcomes some of the problems that other network architectures usually face.

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Semi-supervised Sequence Modeling for Elastic Impedance Inversion

G. Al-Regib, Motaz Alfarraj•Wed Jul 31 2019

A semi-supervised sequence modeling framework based on recurrent neural networks for elastic impedance inversion from multi-angle seismic data, which achieves an average correlation of 98% between the estimated and target EI using 10 well logs for training on a synthetic data set.

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A Deep Learning-Accelerated Data Assimilation and Forecasting Workflow for Commercial-Scale Geologic Carbon Storage

Hewei Tang, P. Fu, C. Sherman, Jize Zhang, X. Ju, Franccois P. Hamon, N. Azzolina, Matthew E. Burton-Kelly, J. Morris•Sat May 08 2021

It is proposed to leverage physical understandings of porous medium flow behavior with deep learning techniques to develop a fast data assimilation-reservoir response forecasting workflow that can complete history matching and reservoir forecasting with uncertainty quantification in less than one hour on a mainstream personal workstation.

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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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