Deep-learning tomography

By Mauricio Araya-Polo, Joseph Jennings, Amir Adler, T. Dahlke. 2018

509 citations recorded by the source corpus.

Subfields: Geophysics, Mechanical Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/4cdf4cbeed7f55953850410ebd9da8104033876e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Geophysics

In Mechanical Engineering

In Ocean Engineering

Citations in the corpus

Deep Learning for Geophysics: Current and Future Trends (2021, background): “raphy (Araya-Polo et al., 2018) and FWI (Yang & Ma, 2019) show promising results on synthetic 2D data.”
A Multiscale Neural Network Based on Hierarchical Matrices (2018, methodology): “Deep neural networks have been recently used to solve PDEs [7, 8, 12, 16, 26, 40, 43, 54, 56] and classical inverse problems [3, 49].”
Machine Learning in Seismology: Turning Data into Insights (2018, methodology): “Araya-Polo et al. (2018) develop a formulation for FWI that replaces the iterative inversion scheme for velocity features with a deep NN. Lewis and Vigh (2017) use a deep NN FWI method to better detect salt domes.”
Deep-learning inversion: a next generation seismic velocity-model building method (2019, methodology): “Inspired by this concept, Araya-Polo et al. (2018) proposed an approach for VMB.”
Tackling Climate Change with Machine Learning (2019, background): “questration. First, ML can help identify and characterize potential storage locations. Oil and gas companies have had promising results using ML for subsurface imaging based on raw seismograph traces [462]. These models and the data behind them could likely be repurposed to help trap CO 2 rather tha”
Deep-learning seismology (2022, methodology): “Among these, incorporating the domain knowledge into training data through data generation and augmentation by using numerical simulations is the most commonly used approach in seismology for active seismic data processing ( 47 , 150 ), wave simulation ( 68 ), and velocity estimation ( 91 ).”
Deep Learning Techniques for Inverse Problems in Imaging (2020, methodology): “Deep learning techniques have been recently proposed to tackle these problems [41], [42], including methods that rely on generative models [43] constrained by partial differential”
Deep-Learning Inversion of Seismic Data (2019, methodology): “[39] used convolutional neural networks (CNNs) to reconstruct velocity model from a semblance cube calculated from raw data.”