Thomas Mejer Hansen
Thomas Mejer Hansen
Department of Geoscience, Aarhus University
Verificeret mail på geo.au.dk - Startside
Citeret af
Citeret af
Linear inverse Gaussian theory and geostatistics
TM Hansen, AG Journel, A Tarantola, K Mosegaard
Geophysics 71 (6), R101, 2006
Inverse problems with non-trivial priors: Efficient solution through Sequential Gibbs Sampling
TM Hansen, KS Cordua, K Mosegaard
Computational Geosciences, 1-19, 2012
Identifying unsaturated hydraulic parameters using an integrated data fusion approach on cross-borehole geophysical data
MC Looms, A Binley, KH Jensen, L Nielsen, TM Hansen
Vadose Zone Journal 7 (1), 238-248, 2008
Monte Carlo full-waveform inversion of crosshole GPR data using multiple-point geostatistical a priori information
KS Cordua, TM Hansen, K Mosegaard
Geophysics 77 (2), H19-H31, 2012
SIPPI: A Matlab toolbox for sampling the solution to inverse problems with complex prior information: Part 2—Application to crosshole GPR tomography
TM Hansen, KS Cordua, MC Looms, K Mosegaard
Computers & Geosciences 52, 481-492, 2013
Accounting for imperfect forward modeling in geophysical inverse problems—exemplified for crosshole tomography
TM Hansen, KS Cordua, BH Jacobsen, K Mosegaard
Geophysics 79 (3), H1-H21, 2014
An unsupervised deep-learning method for porosity estimation based on poststack seismic data
R Feng, T Mejer Hansen, D Grana, N Balling
Geophysics 85 (6), M97-M105, 2020
Efficient Monte Carlo sampling of inverse problems using a neural network-based forward—Applied to GPR crosshole traveltime inversion
TM Hansen, KS Cordua
Geophysical Journal International 211 (3), 1524-1533, 2017
Gravity inversion predicts the nature of the Amundsen Basin and its continental borderlands near Greenland
A Døssing, TM Hansen, AV Olesen, JR Hopper, T Funck
Earth and Planetary Science Letters 408, 132-145, 2014
VISIM: Sequential simulation for linear inverse problems
TM Hansen, K Mosegaard
Computers & Geosciences 34 (1), 53-76, 2008
Multiple-point statistical simulation for hydrogeological models: 3-D training image development and conditioning strategies
AS Høyer, G Vignoli, TM Hansen, LT Vu, DA Keefer, F Jørgensen
Hydrology and Earth System Sciences 21 (12), 6069-6089, 2017
Bayesian convolutional neural networks for seismic facies classification
R Feng, N Balling, D Grana, JS Dramsch, TM Hansen
IEEE Transactions on Geoscience and Remote Sensing 59 (10), 8933-8940, 2021
Monte Carlo reservoir analysis combining seismic reflection data and informed priors
A Zunino, K Mosegaard, K Lange, Y Melnikova, T Mejer Hansen
Geophysics 80 (1), R31-R41, 2015
Using geostatistics to describe complex a priori information for inverse problems
TM Hansen, K Mosegaard, KS Cordua
VIII International Geostatistics Congress 1, 329-338, 2008
Attribute-guided well-log interpolation applied to low-frequency impedance estimation
TM Hansen, K Mosegaard, R Pedersen-Tatalovic, A Uldall, NL Jacobsen
Geophysics 73 (6), R83, 2008
A frequency matching method: solving inverse problems by use of geologically realistic prior information
K Lange, J Frydendall, KS Cordua, TM Hansen, Y Melnikova, ...
Mathematical geosciences 44, 783-803, 2012
Event-based low-frequency impedance modeling using well logs and seismic attributes
R Pedersen-Tatalovic, A Uldall, N Lange Jacobsen, T Mejer Hansen, ...
The Leading Edge 27 (5), 592-603, 2008
Probabilistic integration of geo‐information
TM Hansen, KS Cordua, A Zunino, K Mosegaard
Integrated imaging of the earth: Theory and applications, 93-116, 2016
MPSLIB: A C++ class for sequential simulation of multiple-point statistical models
TM Hansen, LT Vu, T Bach
SoftwareX 5, 127-133, 2016
mgstat: A geostatistical matlab toolbox
TM Hansen
Online web resource. URL http://mgstat. sourceforge. net, 2004
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