# 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…

## 22 Citations

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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.

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