An effective estimate for selecting the regularization parameter in the 3D inversion of magnetotelluric data

Ghaedrahmati, Reza; Moradzadeh, Ali; Moradpouri, Farzad

doi:10.1007/s11600-022-00734-6

Cited by 2 publications

(1 citation statement)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(iv) Incorporate additive noise. In Xiang et al ( 2018 ), Ghaedrahmati et al ( 2022 ) and Siripunvaraporn et al ( 2005 ), they consider the admissible noise between 5 and 10 per cent for each measurement in the inverse MT problem. Therefore, for each y MT , we consider a Gaussian additive noise with a vector of mean zero and the diagonal D k of the diagonal covariance matrix k is computed using…”

Section: Data Generationmentioning

confidence: 99%

Multimodal variational autoencoder for inverse problems in geophysics: application to a 1-D magnetotelluric problem

Rodriguez,

Taylor,

Pardo

2023

Geophysical Journal International

View full text Add to dashboard Cite

Summary Estimating subsurface properties from geophysical measurements is a common inverse problem. Several Bayesian methods currently aim to find the solution to a geophysical inverse problem and quantify its uncertainty. However, most geophysical applications exhibit more than one plausible solution. Here, we propose a multimodal variational autoencoder model that employs a mixture of truncated Gaussian densities to provide multiple solutions, along with their probability of occurrence and a quantification of their uncertainty. This autoencoder is assembled with an encoder and a decoder, where the first one provides a mixture of truncated Gaussian densities from a neural network, and the second is the numerical solution of the forward problem given by the geophysical approach. The proposed method is illustrated with a one-dimensional Magnetotelluric inverse problem and recovers multiple plausible solutions with different uncertainty quantification maps and probabilities that are in agreement with known physical observations.

show abstract

Section: Data Generationmentioning

confidence: 99%