Articles | Volume 14, issue 1
https://doi.org/10.5194/se-14-43-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/se-14-43-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Method article
| 
18 Jan 2023
Method article |
| 18 Jan 2023
| 18 Jan 2023
Utilisation of probabilistic magnetotelluric modelling to constrain magnetic data inversion: proof-of-concept and field application
Centre for Exploration Targeting (School of Earth Sciences), University of Western Australia, 35 Stirling Highway, Crawley, Australia
Mineral Exploration Cooperative Research Centre, School of Earth Sciences, University of Western Australia, 35 Stirling Highway, Crawley, Australia
RING, GeoRessources, Université de Lorraine, 2 rue du doyen Marcel Roubault, Vandoeuvre-lès-Nancy, France
now at: RING, GeoRessources, Université de Lorraine, 2 rue du doyen Marcel Roubault, Vandoeuvre-lès-Nancy, France
Hoël Seillé
CSIRO Deep Earth Imaging Future Science Platform,
Australian Resources Research Centre, Kensington, Australia
CSIRO Mineral Resources,
Australian Resources Research Centre, Kensington, Australia
Mark D. Lindsay
Centre for Exploration Targeting (School of Earth Sciences), University of Western Australia, 35 Stirling Highway, Crawley, Australia
CSIRO Mineral Resources,
Australian Resources Research Centre, Kensington, Australia
ARC Industrial Transformation Training Centre in Data Analytics for Resources and Environment (DARE), Sydney, Australia
Gerhard Visser
CSIRO Mineral Resources,
Australian Resources Research Centre, Kensington, Australia
Vitaliy Ogarko
Centre for Exploration Targeting (School of Earth Sciences), University of Western Australia, 35 Stirling Highway, Crawley, Australia
Mineral Exploration Cooperative Research Centre, School of Earth Sciences, University of Western Australia, 35 Stirling Highway, Crawley, Australia
Mark W. Jessell
Centre for Exploration Targeting (School of Earth Sciences), University of Western Australia, 35 Stirling Highway, Crawley, Australia
Mineral Exploration Cooperative Research Centre, School of Earth Sciences, University of Western Australia, 35 Stirling Highway, Crawley, Australia
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Cited articles
Agostinetti, N. P. and Bodin, T.:
Flexible Coupling in Joint Inversions: A Bayesian Structure Decoupling Algorithm, J. Geophys. Res.-Sol. Ea., 123, 8798–8826, https://doi.org/10.1029/2018JB016079, 2018.
Anderson, C. G. and Logan, K. J.:
The history and current status of geophysical exploration at the Osborne Cu & Au deposit, Mt. Isa, Explor. Geophys., 23, 1–7, https://doi.org/10.1071/EG992001, 1992.
Astic, T. and Oldenburg, D. W.:
A framework for petrophysically and geologically guided geophysical inversion using a dynamic Gaussian mixture model prior, Geophys. J. Int., 219, 1989–2012, https://doi.org/10.1093/gji/ggz389, 2019.
Austin, J. and Blenkinsop, T.:
The Cloncurry Lineament: Geophysical and geological evidence for a deep crustal structure in the Eastern Succession of the Mount Isa Inlier, Precambrian Res., 163, 50–68, https://doi.org/10.1016/j.precamres.2007.08.012, 2008.
Austin, J. R., Schmidt, P. W., and Foss, C. A.:
Magnetic modeling of iron oxide copper-gold mineralization constrained by 3D multiscale integration of petrophysical and geochemical data: Cloncurry District, Australia, Interpretation, 1, T63–T84, https://doi.org/10.1190/INT-2013-0005.1, 2013.
Bhattacharyya, B. K.:
Magnetic anomalies due to prism-shaped bodies with arbitrary polarization, GEOPHYSICS, 29, 517–531, 1964.
Bijani, R., Lelièvre, P. G., Ponte-Neto, C. F., and Farquharson, C. G.:
Physical-property-, lithology- and surface-geometry-based joint inversion using Pareto Multi-Objective Global Optimization, Geophys. J. Int., 209, 730–748, https://doi.org/10.1093/gji/ggx046, 2017.
Blenkinsop, T.:
Mount Isa inlier, Precambrian Res., 163, 1–6, https://doi.org/10.1016/j.precamres.2007.08.009, 2008.
Bodin, T. and Sambridge, M.: Seismic tomography with the reversible jump algorithm, Geophys. J. Int., 178, 1411–1436, https://doi.org/10.1111/j.1365-246X, 2009.
Boyd, S.:
Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers, Foundations and Trends® in Machine Learning, 3, 1–122, https://doi.org/10.1561/2200000016, 2010.
Calcagno, P., Chilès, J. P., Courrioux, G., and Guillen, A.:
Geological modelling from field data and geological knowledge. Part I. Modelling method coupling 3D potential-field interpolation and geological rules, Phys. Earth Planet. In., 171, 147–157, https://doi.org/10.1016/j.pepi.2008.06.013, 2008.
Caldwell, T. G., Bibby, H. M., and Brown, C.: The magnetotelluric phase tensor. Geophysical Journal International, 58, 457–469. https://doi.org/10.1111/j.1365-246X, 2004.
Case, G., Blenkinsop, T., Chang, Z., Huizenga, J. M., Lilly, R., and McLellan, J.:
Delineating the structural controls on the genesis of iron oxide–Cu–Au deposits through implicit modelling: a case study from the E1 Group, Cloncurry District, Australia, Geol. Soc. London, Spec. Publ., 453, 349–384, https://doi.org/10.1144/SP453.4, 2018.
Chave, A. D., Jones, A. G., Mackie, R., and Rodi, W.:
The Magnetotelluric Method, Cambridge University Press, Cambridge, https://doi.org/10.1017/CBO9781139020138, 2012.
Clark, D. A.:
Magnetic effects of hydrothermal alteration in porphyry copper and iron-oxide copper–gold systems: A review, Tectonophysics, 624–625, 46–65, https://doi.org/10.1016/j.tecto.2013.12.011, 2014.
Conway, D., Simpson, J., Didana, Y., Rugari, J., and Heinson, G.:
Probabilistic Magnetotelluric Inversion with Adaptive Regularisation Using the No-U-Turns Sampler, Pure Appl. Geophys., 175, 2881–2894, https://doi.org/10.1007/s00024-018-1870-5, 2018.
Èuma, M. and Zhdanov, M. S.:
Massively parallel regularized 3D inversion of potential fields on CPUs and GPUs, Comput. Geosci., 62, 80–87, https://doi.org/10.1016/j.cageo.2013.10.004, 2014.
Èuma, M., Wilson, G. A., and Zhdanov, M. S.:
Large-scale 3D inversion of potential field data, Geophys. Prospect., 60, 1186–1199, https://doi.org/10.1111/j.1365-2478.2011.01052.x, 2012.
Dentith, M. and Mudge, S. T.:
Geophysics for the mineral exploration geologist, 438 pp., https://doi.org/10.1007/s00126-014-0557-9, 2014.
Dentith, M., Enkin, R. J., Morris, W., Adams, C., and Bourne, B.:
Petrophysics and mineral exploration: a workflow for data analysis and a new interpretation framework, Geophys. Prospect., 68, 178–199, https://doi.org/10.1111/1365-2478.12882, 2020.
Dhnaram, C. and Greenwood, M.:
3D mineral potential of the Quamby area, 2013.
Egbert, G. D. and Kelbert, A.:
Computational recipes for electromagnetic inverse problems, Geophys. J. Int., 189, 251–267, https://doi.org/10.1111/j.1365-246X.2011.05347.x, 2012.
Evans, R. L., Chave, A. D., Jones, A. G., Mackie, R., and Rodi, W.:
Conductivity of Earth materials, in: The Magnetotelluric Method, Cambridge University Press, Cambridge, 50–95, https://doi.org/10.1017/CBO9781139020138.004, 2012.
Farquharson, C. G. and Oldenburg, D. W.:
A comparison of automatic techniques for estimating the regularization parameter in non-linear inverse problems, Geophys. J. Int., 156, 411–425, https://doi.org/10.1111/j.1365-246X.2004.02190.x, 2004.
Gallardo, L. A. and Meju, M. A.:
Characterization of heterogeneous near-surface materials by joint 2D inversion of dc resistivity and seismic data, Geophys. Res. Lett., 30, 1658, https://doi.org/10.1029/2003GL017370, 2003.
Gallardo, L. A., Fontes, S. L., Meju, M. A., Buonora, M. P., and de Lugao, P. P.:
Robust geophysical integration through structure-coupled joint inversion and multispectral fusion of seismic reflection, magnetotelluric, magnetic, and gravity images: Example from Santos Basin, offshore Brazil, GEOPHYSICS, 77, B237–B251, https://doi.org/10.1190/geo2011-0394.1, 2012.
Giraud, J. and Seillé, H.:
Datasets for the integration of MT data with magnetic inversion: proof-of-concept using synthetic data and field application, Zenodo [data set], https://doi.org/10.5281/zenodo.6340159, 2022.
Giraud, J., Pakyuz-Charrier, E., Jessell, M., Lindsay, M., Martin, R., and Ogarko, V.:
Uncertainty reduction through geologically conditioned petrophysical constraints in joint inversion, GEOPHYSICS, 82, ID19–ID34, https://doi.org/10.1190/geo2016-0615.1, 2017.
Giraud, J., Lindsay, M., Ogarko, V., Jessell, M., Martin, R., and Pakyuz-Charrier, E.:
Integration of geoscientific uncertainty into geophysical inversion by means of local gradient regularization, Solid Earth, 10, 193–210, https://doi.org/10.5194/se-10-193-2019, 2019a.
Giraud, J., Ogarko, V., Lindsay, M., Pakyuz-Charrier, E., Jessell, M., and Martin, R.:
Sensitivity of constrained joint inversions to geological and petrophysical input data uncertainties with posterior geological analysis, Geophys. J. Int., 218, 666–688, https://doi.org/10.1093/gji/ggz152, 2019b.
Giraud, J., Lindsay, M., and Jessell, M.:
Generalization of level-set inversion to an arbitrary number of geologic units in a regularized least-squares framework, GEOPHYSICS, 86, R623–R637, https://doi.org/10.1190/geo2020-0263.1, 2021a.
Giraud, J., Ogarko, V., Martin, R., Jessell, M., and Lindsay, M.:
Structural, petrophysical, and geological constraints in potential field inversion using the Tomofast-x v1.0 open-source code, Geosci. Model Dev., 14, 6681–6709, https://doi.org/10.5194/gmd-14-6681-2021, 2021b.
Giraud, J., Seillé, H., Visser, G., Ogarko, V., Lindsay, M., and Jessell, M.:
Utilisation of stochastic MT inversion results to constrain gravity inversion, in: 82nd EAGE Annual Conference & Exhibition, 1–5, https://doi.org/10.3997/2214-4609.202113105, 2021c.
Grose, L., Ailleres, L., Laurent, G., and Jessell, M.:
LoopStructural 1.0: time-aware geological modelling, Geosci. Model Dev., 14, 3915–3937, https://doi.org/10.5194/gmd-14-3915-2021, 2021.
Guillen, A., Calcagno, P., Courrioux, G., Joly, A., and Ledru, P.:
Geological modelling from field data and geological knowledge. Part II. Modelling validation using gravity and magnetic data inversion, Phys. Earth Planet. In., 171, 158–169, https://doi.org/10.1016/j.pepi.2008.06.014, 2008.
Haber, E. and Oldenburg, D.:
Joint inversion: a structural approach, Inverse Probl., 13, 63–77, https://doi.org/10.1088/0266-5611/13/1/006, 1997.
Hansen, P. C. and Johnston, P. R.:
The L-Curve and its Use in the Numerical Treatment of Inverse Problems, in: Computational Inverse Problems in Electrocardiography, edited by: Johnson, P., 119–142, 2001.
Heincke, B., Jegen, M., Moorkamp, M., Hobbs, R. W., and Chen, J.:
An adaptive coupling strategy for joint inversions that use petrophysical information as constraints, J. Appl. Geophys., 136, 279–297, https://doi.org/10.1016/j.jappgeo.2016.10.028, 2017.
Jaccard, P.:
Étude comparative de la distribution florale dans une portion des Alpes et du Jura, Bull. la Société Vaudoise des Sci. Nat., 37, 547–579, https://doi.org/10.5169/seals-266450, 1901.
Kelbert, A., Meqbel, N., Egbert, G. D., and Tandon, K.:
ModEM: A modular system for inversion of electromagnetic geophysical data, Comput. Geosci., 66, 40–53, https://doi.org/10.1016/j.cageo.2014.01.010, 2014.
Lajaunie, C., Courrioux, G., and Manuel, L.:
Foliation fields and 3D cartography .in geology: Principles of a method based on potential interpolation, Math. Geol., 29, 571–584, https://doi.org/10.1007/BF02775087, 1997.
Lampinen, H., Occhipinti, S., Lindsay, M., and Laukamp, C.:
Magnetic susceptibility of Edmund Basin, Capricorn Orogen, WA, ASEG Ext. Abstr., 2016, 1–8, https://doi.org/10.1071/aseg2016ab254, 2016.
Le, C. V. A., Harris, B. D., Pethick, A. M., Takam Takougang, E. M., and Howe, B.:
Semiautomatic and Automatic Cooperative Inversion of Seismic and Magnetotelluric Data, Surv. Geophys., 37, 845–896, https://doi.org/10.1007/s10712-016-9377-z, 2016.
Le Pape, F., Jones, A. G., Jessell, M. W., Perrouty, S., Gallardo, L. A., Baratoux, L., Hogg, C., Siebenaller, L., Touré, A., Ouiya, P., and Boren, G.:
Crustal structure of southern Burkina Faso inferred from magnetotelluric, gravity and magnetic data, Precambrian Res., 300, 261–272, https://doi.org/10.1016/j.precamres.2017.08.013, 2017.
Lelièvre, P., Farquharson, C., and Hurich, C.:
Joint inversion of seismic traveltimes and gravity data on unstructured grids with application to mineral exploration, GEOPHYSICS, 77, K1–K15, https://doi.org/10.1190/geo2011-0154.1, 2012.
Lelièvre, P. G. and Farquharson, C. G.:
Integrated Imaging for Mineral Exploration, in: Integrated Imaging of the Earth: Theory and Applications, edited by: Moorkamp, M., Lelièvre, P. G., Linde, N. and Khan, A., 137–166, https://doi.org/10.1002/9781118929063.ch8, 2016.
Li, Y. and Oldenburg, D. W.:
Fast inversion of large-scale magnetic data using wavelet transforms, Geophys. J. Int., 152, 251–265, https://doi.org/10.1046/j.1365-246X.2003.01766.x, 2003.
Lines, L. R., Schultz, A. K., and Treitel, S.:
Cooperative inversion of geophysical data, GEOPHYSICS, 53, 8–20, https://doi.org/10.1190/1.1442403, 1988.
Malinverno, A.: Parsimonious Bayesian Markov chain Monte Carlo inversion in a nonlineargeophysical problem, Geophys. J. Int., 151, 675–688, https://doi.org/10.1046/j.1365-246X, 2002.
Manassero, M. C., Afonso, J. C., Zyserman, F., Zlotnik, S., and Fomin, I.:
A reduced order approach for probabilistic inversions of 3-D magnetotelluric data I: general formulation, Geophys. J. Int., 223, 1837–1863, https://doi.org/10.1093/gji/ggaa415, 2020.
Martin, R., Monteiller, V., Komatitsch, D., Perrouty, S., Jessell, M., Bonvalot, S., and Lindsay, M. D.:
Gravity inversion using wavelet-based compression on parallel hybrid CPU/GPU systems: application to southwest Ghana, Geophys. J. Int., 195, 1594–1619, https://doi.org/10.1093/gji/ggt334, 2013.
Martin, R., Giraud, J., Ogarko, V., Chevrot, S., Beller, S., Gégout, P., and Jessell, M.:
Three-dimensional gravity anomaly inversion in the Pyrenees using compressional seismic velocity model as structural similarity constraints, Geophys. J. Int., 225, 1063–1085, https://doi.org/10.1093/gji/ggaa414, 2020.
Martin, R., Giraud, J., Ogarko, V., Chevrot, S., Beller, S., Gégout, P., and Jessell, M.:
Three-dimensional gravity anomaly data inversion in the Pyrenees using compressional seismic velocity model as structural similarity constraints, Geophys. J. Int., 225, 1063–1085, https://doi.org/10.1093/gji/ggaa414, 2021.
Moorkamp, M.:
Integrating Electromagnetic Data with Other Geophysical Observations for Enhanced Imaging of the Earth: A Tutorial and Review, Surv. Geophys., 1–28, https://doi.org/10.1007/s10712-017-9413-7, 2017.
Moorkamp, M.:
Joint inversion of gravity and magnetotelluric data from the Ernest-Henry IOCG deposit with a variation of information constraint, in: First International Meeting for Applied Geoscience & Energy Expanded Abstracts, 1711–1715, https://doi.org/10.1190/segam2021-3582000.1, 2021.
Moorkamp, M., Heincke, B., Jegen, M., Hobbs, R. W., and Roberts, A. W.:
Joint Inversion in Hydrocarbon Exploration, in: Integrated Imaging of the Earth: Theory and Applications, 167–189, https://doi.org/10.1002/9781118929063.ch9, 2016.
Ogarko, V., Giraud, J., Martin, R., and Jessell, M.:
Disjoint interval bound constraints using the alternating direction method of multipliers for geologically constrained inversion: Application to gravity data, GEOPHYSICS, 86, G1–G11, https://doi.org/10.1190/geo2019-0633.1, 2021a.
Ogarko, V., Giraud, J., and Martin, R.:
Tomofast-x v1.0 source code, Zenodo [code], https://doi.org/10.5281/zenodo.4454220, 2021b.
Oliver-Ocaño, F. M., Gallardo, L. A., Romo-Jones, J. M., and Pérez-Flores, M. A.:
Structure of the Cerro Prieto Pull-apart basin from joint inversion of gravity, magnetic and magnetotelluric data, J. Appl. Geophys., 170, 103835, https://doi.org/10.1016/j.jappgeo.2019.103835, 2019.
Paige, C. C. and Saunders, M. A.:
LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares, ACM T. Math. Software, 8, 43–71, https://doi.org/10.1145/355984.355989, 1982.
Pakyuz-Charrier, E., Lindsay, M., Ogarko, V., Giraud, J., and Jessell, M.:
Monte Carlo simulation for uncertainty estimation on structural data in implicit 3-D geological modeling, a guide for disturbance distribution selection and parameterization, Solid Earth, 9, 385–402, https://doi.org/10.5194/se-9-385-2018, 2018.
Pakyuz-Charrier, E., Jessell, M., Giraud, J., Lindsay, M., and Ogarko, V.:
Topological analysis in Monte Carlo simulation for uncertainty propagation, Solid Earth, 10, 1663–1684, https://doi.org/10.5194/se-10-1663-2019, 2019.
Pakyuz-Charrier, E. I. G.:
Mansfield (Victoria, Australia) area original GeoModeller model and relevant MCUE outputs, Zenodo [data set], https://doi.org/10.5281/zenodo.848225, 2018.
Peng, M., Tan, H., and Moorkamp, M.:
Structure-Coupled 3-D Imaging of Magnetotelluric and Wide-Angle Seismic Reflection/Refraction Data With Interfaces, J. Geophys. Res.-Sol. Ea., 124, 10309–10330, https://doi.org/10.1029/2019JB018194, 2019.
Portniaguine, O. and Zhdanov, M. S.:
3-D magnetic inversion with data compression and image focusing, GEOPHYSICS, 67, 1532–1541, https://doi.org/10.1190/1.1512749, 2002.
Ren, Z. and Kalscheuer, T.:
Uncertainty and Resolution Analysis of 2D and 3D Inversion Models Computed from Geophysical Electromagnetic Data, Surv. Geophys., 41, 47–112, https://doi.org/10.1007/s10712-019-09567-3, 2019.
Rodi, W. L., Mackie, R. L., Chave, A. D., Jones, A. G., Mackie, R., and Rodi, W.:
The inverse problem, in: The Magnetotelluric Method, edited by: Chave, A. D. and Jones, A. G., Cambridge University Press, Cambridge, 347–420, https://doi.org/10.1017/CBO9781139020138.010, 2012.
Sambridge, M., Gallagher, K., Jackson, A., and Rickwood, P.: Trans-dimensional inverse problems,model comparison and the evidence, Geophys. J. Int., 167, 528–542, https://doi.org/10.1111/j.1365-246X, 2006.
Scalzo, R., Kohn, D., Olierook, H., Houseman, G., Chandra, R., Girolami, M., and Cripps, S.:
Efficiency and robustness in Monte Carlo sampling for 3-D geophysical inversions with Obsidian v0.1.2: setting up for success, Geosci. Model Dev., 12, 2941–2960, https://doi.org/10.5194/gmd-12-2941-2019, 2019.
Schweizer, D., Blum, P., and Butscher, C.:
Uncertainty assessment in 3-D geological models of increasing complexity, Solid Earth, 8, 515–530, https://doi.org/10.5194/se-8-515-2017, 2017.
Seillé, H. and Visser, G.:
Bayesian inversion of magnetotelluric data considering dimensionality discrepancies, Geophys. J. Int., 223, 1565–1583, https://doi.org/10.1093/gji/ggaa391, 2020.
Seillé, H., Visser, G., Markov, J., and Simpson, J.:
Probabilistic Cover-Basement Interface Map in Cloncurry, Australia, Using Magnetotelluric Soundings, J. Geophys. Res.-Sol. Ea., 126, e2021JB021883, https://doi.org/10.1029/2021JB021883, 2021.
Shannon, C. E. E.:
A Mathematical Theory of Communication, Bell Syst. Tech. J., 27, 379–423, https://doi.org/10.1002/j.1538-7305.1948.tb01338.x, 1948.
Sun, J. and Li, Y.:
Multidomain petrophysically constrained inversion and geology differentiation using guided fuzzy c-means clustering, Geophysics, 80, ID1–ID18, https://doi.org/10.1190/geo2014-0049.1, 2015.
Tarantola, A.: Inverse Problem Theory and Methods for Model Parameter Estimation, Society for Industrial and Applied Mathematics, Philadelphia, https://doi.org/10.1137/1.9780898717921, 2005.
Tikhonov, A. N. and Arsenin, V. Y.:
Solution of Ill-Posed Problems, Math. Comput., 32, 491, https://doi.org/10.2307/2006360, 1978.
Tveit, S., Mannseth, T., Park, J., Sauvin, G., and Agersborg, R.:
Combining CSEM or gravity inversion with seismic AVO inversion, with application to monitoring of large-scale CO2 injection, Comput. Geosci., 24, 1201–1220, https://doi.org/10.1007/s10596-020-09934-9, 2020.
Visser, G.:
Smart stitching: adding lateral priors to ensemble inversions as a post-processing step, ASEG Ext. Abstr., 2019, 1–4, https://doi.org/10.1080/22020586.2019.12073075, 2019.
Visser, G. and Markov, J.:
Cover thickness uncertainty mapping using Bayesian estimate fusion: leveraging domain knowledge, Geophys. J. Int., 219, 1474–1490, https://doi.org/10.1093/gji/ggz358, 2019.
Wellmann, F. and Caumon, G.:
3-D Structural geological models: Concepts, methods, and uncertainties, edited by: Schmelzback, C., Cambridge, Massachusetts, 1–121, https://doi.org/10.1016/bs.agph.2018.09.001, 2018.
Wellmann, J. F. and Regenauer-Lieb, K.:
Uncertainties have a meaning: Information entropy as a quality measure for 3-D geological models, Tectonophysics, 526–529, 207–216, https://doi.org/10.1016/j.tecto.2011.05.001, 2012.
Xiang, E., Guo, R., Dosso, S. E., Liu, J., Dong, H., and Ren, Z.: Efficient hierarchical trans-dimensionalBayesian inversion of magnetotelluric data, Geophys. J. Int., 213, 1751–1767, 2018.
Zhang, R., Li, T., Deng, X., Huang, X., and Pak, Y.:
Two-dimensional data-space joint inversion of magnetotelluric, gravity, magnetic and seismic data with cross-gradient constraints, Geophys. Prospect., 68, 721–731, https://doi.org/10.1111/1365-2478.12858, 2020.
Short summary
We propose and apply a workflow to combine the modelling and interpretation of magnetic anomalies and resistivity anomalies to better image the basement. We test the method on a synthetic case study and apply it to real world data from the Cloncurry area (Queensland, Australia), which is prospective for economic minerals. Results suggest a new interpretation of the composition and structure towards to east of the profile that we modelled.
We propose and apply a workflow to combine the modelling and interpretation of magnetic...
