Rasmus Bødker Madsen
Rasmus Bødker Madsen
Researcher, GEUS
Verificeret mail på geus.dk - Startside
Citeret af
Citeret af
Estimation and accounting for the modeling error in probabilistic linearized amplitude variation with offset inversion
RB Madsen, TM Hansen
Geophysics 83 (2), N15-N30, 2018
Assessment of complex subsurface redox structures for sustainable development of agriculture and the environment
B Hansen, DD Voutchkova, PBE Sandersen, A Kallesøe, L Thorling, ...
Environmental Research Letters 16 (2), 025007, 2021
3D multiple-point geostatistical simulation of joint subsurface redox and geological architectures
RB Madsen, H Kim, AJ Kallesøe, PBE Sandersen, TN Vilhelmsen, ...
Hydrology and Earth System Sciences 25 (5), 2759-2787, 2021
On inferring the noise in probabilistic seismic AVO inversion using hierarchical Bayes
RB Madsen, A Zunino, TM Hansen
SEG Technical Program Expanded Abstracts 2017, 601-606, 2017
Choosing between Gaussian and MPS simulation: the role of data information content—a case study using uncertain interpretation data points
RB Madsen, I Møller, TM Hansen
Stochastic Environmental Research and Risk Assessment 35 (8), 1563-1583, 2021
Geology-driven modeling: A new probabilistic approach for incorporating uncertain geological interpretations in 3D geological modeling
RB Madsen, AS Høyer, LT Andersen, I Møller, TM Hansen
Engineering Geology 309, 106833, 2022
Estimation of a non‐stationary prior covariance from seismic data
RB Madsen, TM Hansen, H Omre
Geophysical Prospecting 68 (2), 393-410, 2020
Single-step probabilistic inversion of 3D seismic data of a carbonate reservoir in Southwest Iran
H Heidari, TM Hansen, H Amini, ME Niri, RB Madsen, N Amini
Geophysics 87 (2), B159-B177, 2022
Accounting for processing errors in AVO/AVA data
RB Madsen, E Nørmark, TM Hansen
80th EAGE Conference and Exhibition 2018 2018 (1), 1-5, 2018
A method to construct statistical prior models of geology for probabilistic inversion of geophysical data
RB Madsen, AS Høyer, PBE Sandersen, I Møller, TM Hansen
Engineering Geology 324, 107252, 2023
Incorporating interpretation uncertainties from deterministic 3D hydrostratigraphic models in groundwater models
T Enemark, RB Madsen, TO Sonnenborg, LT Andersen, PBE Sandersen, ...
Hydrology and Earth System Sciences Discussions 2023, 1-29, 2023
Why probabilistic models are often true, but can be either useful or useless.
T Mejer Hansen, R Bødker Madsen
EGU General Assembly Conference Abstracts, EGU-11807, 2023
Moving away from deterministic solutions: A probabilistic machine learning approach to account for geological model uncertainty in groundwater modelling
M Busk Dahl, T Norvin Vilhelmsen, R Bødker Madsen, T Mejer Hansen
EGU General Assembly Conference Abstracts, EGU-6313, 2023
Why probabilistic models are often true, but can be either useful or useless.
TM Hansen, RB Madsen
EGU23, 2023
Assessment of the impact of noise magnitude and bandwidth variations on a probabilistic inversion of reflection seismic data
H Heidari, RB Madsen, H Amini, TM Hansen, M Emami‐Niri, N Amini
Geophysical Prospecting 71 (3), 443-455, 2023
Examining Different Noise Scenarios for the Probabilistic Inversion of Seismic Data
A Heidari, TM Hansen, N Amini, H Amini, RB Madsen, ME Niri
82nd EAGE Annual Conference & Exhibition 2021 (1), 1-5, 2021
A recipe for launching a diamond open-access journal with a century of geological knowledge in the pantry: Lessons learned from GEUS Bulletin
C Jex, W Colgan, M Bryld Wessel Fyhn, AA Garde, JR Ineson, A Hambly, ...
EGU General Assembly Conference Abstracts, EGU21-7168, 2021
When Is Mcmc Feasible for AVO Inversion?
S Kuppens, R Madsen, T Hansen
80th EAGE Conference and Exhibition 2018 2018 (1), 1-3, 2018
Probabilistic Linear Inversion of Reflection Seismic Data
RB Madsen
University of Copenhagen, Faculty of Science, Niels Bohr Institute, 2018
Uncertainty Assessments in 3d Geological Modelling of Complex Unconsolidated Sediments–with Emphasis on Handling Subjectivity in the Entire Workflow
AS Høyer, PBE Sandersen, LT Andersen, RB Madsen, MH Mortensen, ...
Available at SSRN 4444386, 0
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