OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Bayesian inversion of magnetotelluric data considering dimensionality discrepancies
Hoël Seillé, Gerhard Visser
Geophysical Journal International (2020) Vol. 223, Iss. 3, pp. 1565-1583
Open Access | Times Cited: 20

Showing 20 citing articles:

Trans-dimensional Bayesian joint inversion of magnetotelluric and geomagnetic depth sounding responses to constrain mantle electrical discontinuities
Hongbo Yao, Zhengyong Ren, Jingtian Tang, et al.
Geophysical Journal International (2023) Vol. 233, Iss. 3, pp. 1821-1846
Closed Access | Times Cited: 10

Utilisation of probabilistic magnetotelluric modelling to constrain magnetic data inversion: proof-of-concept and field application
Jérémie Giraud, Hoël Seillé, Mark Lindsay, et al.
Solid Earth (2023) Vol. 14, Iss. 1, pp. 43-68
Open Access | Times Cited: 5

Probabilistic Cover‐Basement Interface Map in Cloncurry, Australia, Using Magnetotelluric Soundings
Hoël Seillé, Gerhard Visser, Jelena Markov, et al.
Journal of Geophysical Research Solid Earth (2021) Vol. 126, Iss. 7
Closed Access | Times Cited: 10

Memetic Strategy of Particle Swarm Optimization for One-Dimensional Magnetotelluric Inversions
Ruiheng Li, Lei Gao, Nian Yu, et al.
Mathematics (2021) Vol. 9, Iss. 5, pp. 519-519
Open Access | Times Cited: 9

Using deep generative neural networks to account for model errors in Markov chain Monte Carlo inversion
Shiran Levy, Jürg Hunziker, Eric Laloy, et al.
Geophysical Journal International (2021) Vol. 228, Iss. 2, pp. 1098-1118
Open Access | Times Cited: 7

Bayesian fusion of MT and AEM probabilistic models with geological data: examples from the eastern Gawler Craton, South Australia
Hoël Seillé, Stephan Thiel, Kate Brand, et al.
Exploration Geophysics (2023) Vol. 55, Iss. 5, pp. 486-505
Open Access | Times Cited: 1

An information theoretic Bayesian uncertainty analysis of AEM systems over Menindee Lake, Australia
Anandaroop Ray, Yusen Ley‐Cooper, Ross C. Brodie, et al.
Geophysical Journal International (2023) Vol. 235, Iss. 2, pp. 1888-1911
Closed Access | Times Cited: 1

Deep basin conductor characterization using machine learning-assisted magnetotelluric Bayesian inversion in the SW Barents Sea
Romain Corseri, Hoël Seillé, Jan Inge Faleide, et al.
Geophysical Journal International (2024) Vol. 238, Iss. 1, pp. 420-432
Open Access

Transdimensional Inversion of Flow Data with a Cascaded Reversible Jump Algorithm on a Layer-Cake Model
Julien Herrero, Guillaume Caumon, Thomas Bodin, et al.
(2024)
Closed Access

Transdimensional geometrical inversion: Application to undercover imaging using gravity data
Jérémie Giraud, Mahtab Rashidifard, Vitaliy Ogarko, et al.
(2024), pp. 167-170
Closed Access

Non-linear Optimization by Generalized Neighborhood Algorithm (GNA) and its Application for Magnetotellurics (MT) Layered-Earth Modeling
Hendra Grandis, Prihadi Sumintadireja, Sungkono Sungkono
Heliyon (2024) Vol. 10, Iss. 22, pp. e40220-e40220
Open Access

Transdimensional joint inversion of flow and well log data using a cascaded Metropolis sampler on a layer-cake model
Julien Herrero, Guillaume Caumon, Thomas Bodin, et al.
Geoenergy Science and Engineering (2024) Vol. 246, pp. 213605-213605
Open Access

Two-dimensional interpretation of audio-magnetotelluric data around the epicenter distribution of the 2016 Gyeongju earthquake (ML 5.8), Korea
Kiyeon Kim, Seokhoon Oh, Hyoung-Seok Kwon, et al.
Geosciences Journal (2023) Vol. 27, Iss. 5, pp. 563-580
Closed Access

Detailed answer on RC2
Jérémie Giraud
(2022)
Open Access

Reply on RC1
Jérémie Giraud
(2022)
Open Access

Reply on RC2
Jérémie Giraud
(2022)
Open Access

Using deep generative neural networks to account for model errors in Markov chain Monte Carlo inversion.
Shiran Levy, Jürg Hunziker, Eric Laloy, et al.
arXiv (Cornell University) (2021)
Closed Access

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