Probabilistic Inversion of Multiconfiguration Electromagnetic Induction Data Using Dimensionality Reduction Technique: A Numerical Study

Autor: Davood Moghadas

Publikováno v: Vadose Zone Journal, Vol 18, Iss 1 (2019)

Low-frequency loop–loop electromagnetic induction (EMI) offers several key advantages over many other geophysical techniques for proximal soil sensing. Yet, because of problems with the inversion of measured apparent electrical conductivity (EC) da

Externí odkaz: https://doaj.org/article/005f163e39e9437688dfa9c9d9d85b23

The Influence of Geostatistical Prior Modeling on the Solution of DCT-Based Bayesian Inversion: A Case Study from Chicken Creek Catchment

Autor: Davood Moghadas, Jasper A. Vrugt

Publikováno v: Remote Sensing, Vol 11, Iss 13, p 1549 (2019)

Low frequency loop-loop electromagnetic induction (EMI) is a widely-used geophysical measurement method to rapidly measure in situ the apparent electrical conductivity (ECa) of variably-saturated soils. Here, we couple Bayesian inversion of a quasi-t

Externí odkaz: https://doaj.org/article/b335a45890154054a3632fa601b1232f

Spatiotemporal monitoring of soil moisture from EMI data using DCT-based Bayesian inference and neural network

Autor: Khan Zaib Jadoon, Davood Moghadas, Matthew F. McCabe

Publikováno v: Journal of Applied Geophysics. 169:226-238

Loop-loop electromagnetic induction (EMI) has proven to be efficient for fast and real-time soil apparent electrical conductivity (ECa) measurements. It is important to develop robust and accurate inversion strategies to obtain soil electromagnetic c

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::e18368b75ad175c5fb2e1a77c670976f
https://doi.org/10.1016/j.jappgeo.2019.07.004

Machine learning to estimate soil moisture from geophysical measurements of electrical conductivity

Autor: Annika Badorreck, Davood Moghadas

Publikováno v: Near Surface Geophysics. 17:181-195

Soil water content (θ) is a key variable in different earth science disciplines since it mediates the water and energy exchange between the surface and atmosphere. Electrical and electromagnetic geophysical techniques have been widely used to estima

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::3330c4408354be1ac40029a64d7eedca
https://doi.org/10.1002/nsg.12036

Soil electrical conductivity imaging using a neural network-based forward solver:Applied to large-scale Bayesian electromagnetic inversion

Autor: Anders Vest Christiansen, Ahmad A. Behroozmand, Davood Moghadas

Publikováno v: Moghadas, D, Behroozmand, A A & Christiansen, A V 2020, ' Soil electrical conductivity imaging using a neural network-based forward solver : Applied to large-scale Bayesian electromagnetic inversion ', Journal of Applied Geophysics, vol. 176, 104012 . https://doi.org/10.1016/j.jappgeo.2020.104012

In recent years, probabilistic solution to the inversion of electromagnetic induction (EMI) data has been progressively developed for non-invasive subsurface characterization. However, Bayesian inversion of EMI data using forward solvers based on ful

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e92d2af650cc3fdd1f10bf04ce527b5e
https://pure.au.dk/portal/da/publications/soil-electrical-conductivity-imaging-using-a-neural-networkbased-forward-solver(96698957-1309-462a-82d4-b94e17fd9a9b).html