# Probabilistic neural network-based 2D travel-time tomography

@article{Earp2020ProbabilisticNN, title={Probabilistic neural network-based 2D travel-time tomography}, author={Stephanie Earp and Andrew Curtis}, journal={Neural Computing and Applications}, year={2020}, pages={1 - 19} }

Travel-time tomography for the velocity structure of a medium is a highly nonlinear and nonunique inverse problem. Monte Carlo methods are becoming increasingly common choices to provide probabilistic solutions to tomographic problems but those methods are computationally expensive. Neural networks can often be used to solve highly nonlinear problems at a much lower computational cost when multiple inversions are needed from similar data types. We present the first method to perform fully…

## 21 Citations

Bayesian Geophysical Inversion Using Invertible Neural Networks

- GeologyJournal of Geophysical Research: Solid Earth
- 2021

Invertible neural networks (INNs) are introduced and shown to provide comparable posterior pdfs to those obtained using Monte Carlo, including correlations between parameters, and provide more accurate marginal distributions than MDNs.

Bayesian Seismic Tomography using Normalizing Flows

- Mathematics, GeologyGeophysical Journal International
- 2020

We test a fully non-linear method to solve Bayesian seismic tomographic problems using data consisting of observed travel times of first-arriving waves. Rather than using Monte Carlo methods to…

Polynomial surrogates for Bayesian traveltime tomography

- GeologyGEM - International Journal on Geomathematics
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This paper tackles the issue of the computational load encountered in seismic imaging by Bayesian traveltime inversion. In Bayesian inference, the exploration of the posterior distribution of the…

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- Geology, MathematicsJournal of Geophysical Research: Solid Earth
- 2020

The results show that variational inference methods can produce accurate approximations to the results of Monte Carlo sampling methods at significantly lower computational cost, provided that gradients of parameters with respect to data can be calculated efficiently.

Machine Learning Enabled Traveltime Inversion Based on the Horizontal Source Location Perturbation

- GeologyGEOPHYSICS
- 2021

Gradient based traveltime tomography, which aims to minimize the difference between modeled and observed first arrival times, is a highly non-linear optimization problem. Stabilization of this…

Approaching geoscientific inverse problems with adversarial vector-to-image domain transfer networks

- Geology
- 2019

We present vec2pix, a deep neural network designed to predict categorical or continuous 2D subsurface property fields from one-dimensional measurement data (e.g., time series), thereby, offering a…

Direct multi-modal inversion of geophysical logs using deep learning

- GeologyArXiv
- 2022

This work presents a proof-of-concept approach to multimodal probabilistic inversion of logs using a single evaluation of an artificial deep neural network (DNN) trained using the ”multiple-trajectoryprediction” (MTP) loss functions, which avoids mode collapse typical for traditional MDNs, and allows multi-modal prediction ahead of data.

Interrogating Subsurface Structures Using Probabilistic Tomography: An Example Assessing the Volume of Irish Sea Basins

- GeologyJournal of Geophysical Research: Solid Earth
- 2022

The ultimate goal of a scientific investigation is usually to find answers to specific, often low‐dimensional questions: what is the size of a subsurface body? Does a hypothesized subsurface feature…

Real-time super-resolution mapping of locally anisotropic grain orientations for ultrasonic non-destructive evaluation of crystalline material

- Materials ScienceNeural Comput. Appl.
- 2022

Estimating the spatially varying microstructures of heterogeneous and locally anisotropic media non-destructively is necessary for the accurate detection of flaws and reliable monitoring of…

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