Articles | Volume 14, issue 10
https://doi.org/10.5194/gmd-14-6197-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-6197-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
15 Oct 2021
Development and technical paper |
| 15 Oct 2021
| 15 Oct 2021
Modelling of faults in LoopStructural 1.0
Lachlan Grose
CORRESPONDING AUTHOR
School of Earth, Atmosphere and Environment, Monash University, P.O. Box 28E, Victoria, Australia
Laurent Ailleres
School of Earth, Atmosphere and Environment, Monash University, P.O. Box 28E, Victoria, Australia
Gautier Laurent
Université d'Orléans, CNRS, BRGM, ISTO, UMR 7327, Orleans, France
Guillaume Caumon
Université de Lorraine, CNRS, GeoRessources, 54000 Nancy, France
Mark Jessell
Centre for Exploration Targeting, School of Earth and Environment, The University of Western Australia, Perth, Western Australia, Australia
Robin Armit
School of Earth, Atmosphere and Environment, Monash University, P.O. Box 28E, Victoria, Australia
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Cited articles
Allmendinger, R. W.: Propagation Folds, Tectonics, 17, 640–656,
https://doi.org/10.1029/98TC01907, 1998. a
Blaikie, T., Ailleres, L., Betts, P. G., and Cas, R. A.: Interpreting
subsurface volcanic structures using geologically constrained 3-D gravity
inversions: Examples of maar-diatremes, Newer Volcanics Province,
southeastern Australia, J. Geophys. Res.-Sol. Ea., 119,
3857–3878, https://doi.org/10.1002/2013JB010751, 2014. a
Calcagno, P. P., Chilès, J., Courrioux, G., Guillen, A., Calcagno, P. P.,
Courrioux, G., Joly, A., Ledru, P., Courrioux, G., Calcagno, P. P.,
Chilès, J., Courrioux, G., and Guillen, A.: Geological modelling from
field data and geological knowledge, Phys. Earth Planet.
In., 171, 147–157, https://doi.org/10.1016/j.pepi.2008.06.013, 2008. a, b, c, d, e, f, g
Cardozo, N.: Trishear in 3D. Algorithms, implementation, and limitations,
J. Struct. Geol., 30, 327–340, https://doi.org/10.1016/j.jsg.2007.12.003,
2008. a
Caumon, G., Gray, G., Antoine, C., and Titeux, M.-O.: Three-Dimensional
Implicit Stratigraphic Model Building From Remote Sensing Data on Tetrahedral
Meshes: Theory and Application to a Regional Model of La Popa Basin, NE
Mexico, IEEE T. Geosci. Remote S., 51, 1613–1621,
https://doi.org/10.1109/TGRS.2012.2207727, 2013. a, b, c, d, e, f
Cherpeau, N. and Caumon, G.: Stochastic structural modelling in sparse data
situations, Petrol. Geosci., 21, 233–247,
https://doi.org/10.1144/petgeo2013-030, 2015. a, b
Cherpeau, N., Caumon, G., and Lévy, B.: Stochastic simulations of fault
networks in 3D structural modeling, C. R. Geosci., 342,
687–694, https://doi.org/10.1016/j.crte.2010.04.008, 2010. a
Cherpeau, N., Caumon, G., Caers, J., and Lévy, B.: Method for Stochastic
Inverse Modeling of Fault Geometry and Connectivity Using Flow Data,
Math. Geosci., 44, 147–168, https://doi.org/10.1007/s11004-012-9389-2,
2012. a
Chilès, J. P.: Geostatistics: modeling spatial uncertainty, Wiley, New York, USA, 1999. a
Cox, S., Wall, V., Etheridge, M., and Potter, T.: Deformational and
metamorphic processes in the formation of mesothermal vein-hosted gold
deposits – examples from the Lachlan Fold Belt in central Victoria,
Australia, Ore Geol. Rev., 6, 391–423,
https://doi.org/10.1016/0169-1368(91)90038-9, 1991. a
Cristallini, E. O. and Allmendinger, R. W.: Pseudo 3-D modeling of trishear
fault-propagation folding, J. Struct. Geol., 23, 1883–1899,
https://doi.org/10.1016/S0191-8141(01)00034-7, 2001. a
de Kemp, E. A.: Visualization of complex geological structures using 3-D
Bézier construction tools, Comput. Geosci., 25, 581–597,
https://doi.org/10.1016/S0098-3004(98)00159-9, 1999. a
de la Varga, M. and Wellmann, J. F.: Structural geologic modeling as an
inference problem: A Bayesian perspective, Interpretation, 4, 1–16,
https://doi.org/10.1190/INT-2015-0188.1, 2016. a, b, c, d
de la Varga, M., Schaaf, A., and Wellmann, F.: GemPy 1.0: open-source stochastic geological modeling and inversion, Geosci. Model Dev., 12, 1–32, https://doi.org/10.5194/gmd-12-1-2019, 2019. a, b, c
Erslev, E. A.: Trishear fault-propagation folding, Geology, 19,
617–620, https://doi.org/10.1130/0091-7613(1991)019<0617:TFPF>2.3.CO;2, 1991. a
Georgsen, F., Røe, P., Syversveen, A. R., and Lia, O.: Fault displacement
modelling using 3D vector fields, Comput. Geosci., 16, 247–259,
https://doi.org/10.1007/s10596-011-9257-z, 2012. a
Godefroy, G., Laurent, G., Caumon, G., and Walter, B.: A parametric
unfault-and-refault method for chronological structural modeling, 79th EAGE
Conference and Exhibition 2017, 12–15,
https://doi.org/10.3997/2214-4609.201701146, 2017. a
Godefroy, G., Caumon, G., Ford, M., Laurent, G., Jackson, C. A.-L., Laurent,
G., Ford, M., Caumon, G., Ford, M., Laurent, G., Jackson, C. A.-L., Laurent,
G., Ford, M., and Caumon, G.: A parametric fault displacement model to
introduce kinematic control into modeling faults from sparse data,
Interpretation, 6, B1–B13, https://doi.org/10.1190/INT-2017-0059.1, 2018. a, b, c, d, e, f, g, h, i, j, k, l, m
Gonçalves, Í. G., Kumaira, S., and Guadagnin, F.: A machine
learning approach to the potential-field method for implicit modeling of
geological structures, Comput. Geosci., 103, 173–182,
https://doi.org/10.1016/j.cageo.2017.03.015, 2017. a, b
Grose, L., Ailleres, L., Laurent, G., Armit, R., and Jessell, M.: Inversion of
geological knowledge for fold geometry, J. Struct. Geol., 119,
1–14, https://doi.org/10.1016/j.jsg.2018.11.010, 2019. a, b, c
Grose, L., Ailleres, L., Laurent, G., and Jessell, M.: Loop3D/LoopStructural (Version 1.2.0), Zenodo [code], https://doi.org/10.5281/zenodo.5234619, 2021b. a, b, c
Hale, D.: Methods to compute fault images, extract fault surfaces, and
estimate fault throws from 3D seismic images, Geophysics, 78, O33–O43,
https://doi.org/10.1190/GEO2012-0331.1, 2013. a
Henrion, V., Caumon, G., and Cherpeau, N.: ODSIM: An Object-Distance
Simulation Method for Conditioning Complex Natural Structures, Math.
Geosci., 42, 911–924, https://doi.org/10.1007/s11004-010-9299-0, 2010. a
Hillier, M. J., Schetselaar, E. M., de Kemp, E. A., and Perron, G.:
Three-Dimensional Modelling of Geological Surfaces Using Generalized
Interpolation with Radial Basis Functions, Math. Geosci., 46,
931–953, https://doi.org/10.1007/s11004-014-9540-3, 2014. a, b
Huggins, P., Watterson, J., Walsh, J. J., and Childs, C.: Relay zone geometry
and displacement transfer between normal faults recorded in coal-mine plans,
J. Struct. Geol., 17, 1741–1755,
https://doi.org/10.1016/0191-8141(95)00071-K, 1995. a
Irakarama, M., Laurent, G., Renaudeau, J., and Caumon, G.: Finite Difference
Implicit Structural Modeling of Geological Structures, Math.
Geosci., 53, 785–808, https://doi.org/10.1007/s11004-020-09887-w, 2020. a, b, c
Jessell, M., Aillères, L., Kemp, E. D., Lindsay, M., Wellmann, F.,
Hillier, M., Laurent, G., Carmichael, T., and Martin, R.: Next generation 3D
geological modelling and inversion, Society of Economic Geologists Special
Publication, 18, 261–272, 2014. a
Jessell, M. W.: Noddy: an interactive map creation package, Unpublished MSc
Thesis, University of London, 1981. a
Jessell, M. W. and Valenta, R. K.: Structural geophysics: Integrated
structural and geophysical modelling, in: Computer Methods in the
Geosciences, 15, 303–324,
https://doi.org/10.1016/S1874-561X(96)80027-7, 1996. a, b
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. a, b
Lindsay, M., Aillères, L., Jessell, M., de Kemp, E., and Betts, P.:
Locating and quantifying geological uncertainty in three-dimensional models:
Analysis of the Gippsland Basin, southeastern Australia, Tectonophysics,
546–547, 10–27, https://doi.org/10.1016/j.tecto.2012.04.007, 2012. a
Maerten, F. and Maerten, L.: On a method for reducing interpretation
uncertainty of poorly imaged seismic horizons and faults using
geomechanically based restoration technique, Interpretation, 3,
SAA105–SA116, https://doi.org/10.1190/INT-2015-0009.1, 2015. a
Mallet, J.-L.: Elements of Mathematical Sedimentary Geology: the GeoChron
Model, EAGE Publications, 1–4, https://doi.org/10.3997/9789073834811, 2014. a
Mallet, J.-L. L.: Discrete smooth interpolation in geometric modelling,
Computer-Aided Design, 24, 178–191, https://doi.org/10.1016/0010-4485(92)90054-E,
1992. a, b
Manchuk, J. G. and Deutsch, C. V.: Boundary modeling with moving least
squares, Comput. Geosci., 126, 96–106,
https://doi.org/10.1016/j.cageo.2019.02.006, 2019. a, b
Marchal, D., Guiraud, M., and Rives, T.: Geometric and morphologic evolution
of normal fault planes and traces from 2D to 4D data, J. Struct.
Geol., 25, 135–158, https://doi.org/10.1016/S0191-8141(02)00011-1, 2003. a
Marechal, A.: Kriging Seismic Data in Presence of Faults, in: Geostatistics for Natural Resources Characterization, edited by: Verly, G., David, M., Journel, A. G., and Marechal, A., Springer, Dordrecht, the Netherlands, https://doi.org/10.1007/978-94-009-3699-7_17, 1984. a, b
Maxelon, M., Renard, P., Courrioux, G., Braendli, M., Mancktelow, N. S.,
Brändli, M., Mancktelow, N. S., Braendli, M., and Mancktelow, N. S.: A
workflow to facilitate three-dimensional geometrical modelling of complex
poly-deformed geological units, Comput. Geosci., 35, 644–658,
https://doi.org/10.1016/j.cageo.2008.06.005, 2009. a
Mueller, A. G., Harris, L. B., and Lungan, A.: Structural control of
greenstone-hosted Gold mineralization by transcurrent shearing: A new
interpretation of the Kalgoorlie mining district, Western Australia, Ore
Geol. Rev., 3, 359–387, https://doi.org/10.1016/0169-1368(88)90027-3, 1988. a
Putz, M., Stuwe, K., Jessell, M., and Calcagno, P.: Three-dimensional model
and late stage warping of the Plattengneis shear zone in the Eastern Alps,
Tectonophysics, 412, 87–103, 2006. a
Renaudeau, J., Malvesin, E., Maerten, F., and Caumon, G.: Implicit Structural
Modeling by Minimization of the Bending Energy with Moving Least Squares
Functions, Math. Geosci., 51, 693–724,
https://doi.org/10.1007/s11004-019-09789-6, 2019. a, b
Thibaut, M., Gratier, J. P., Léger, M., and Morvan, J. M.: An inverse
method for determining three-dimensional fault geometry with thread
criterion: Application to strike-slip and thrust faults (Western Alps and
California), J. Struct. Geol., 18, 1127–1138,
https://doi.org/10.1016/0191-8141(96)00035-1, 1996. a
Vollgger, S. A., Cruden, A. R., Ailleres, L., and Cowan, E. J.: Regional dome
evolution and its control on ore-grade distribution: Insights from 3D
implicit modelling of the Navachab gold deposit, Namibia, Ore Geol.
Rev., 69, 268–284, https://doi.org/10.1016/j.oregeorev.2015.02.020, 2015.
a
Walsh, J. J. and Watterson, J.: Distributions of cumulative displacement and
seismic slip on a single normal fault surface, J. Struct.
Geol., 9, 1039–1046, 1987. a
Wellmann, F. and Caumon, G.: 3-D Structural geological models: Concepts,
methods, and uncertainties, Elsevier Inc., 1st Edn., vol. 59,
https://doi.org/10.1016/bs.agph.2018.09.001, 2018. a, b
Wellmann, J. F., Horowitz, F. G., Schill, E., and Regenauer-Lieb, K.: Towards
incorporating uncertainty of structural data in 3D geological inversion,
Tectonophysics, 490, 141–151, https://doi.org/10.1016/j.tecto.2010.04.022, 2010. a
Wellmann, J. F., Jessell, M., de la Varga, M., Murdie, R. E., Gessner, K.,
Varga, M. D. E. L. A., Murdie, R. E., Gessner, K., Jessell, M., de la Varga,
M., Murdie, R. E., Gessner, K., and Jessell, M.: Uncertainty estimation for
a geological model of the Sandstone greenstone belt, Western Australia
– insights from integrated geological and
geophysical inversion in a Bayesian inference framework, Geological Society,
London, Special Publications, 453, 41–56, https://doi.org/10.1144/SP453.12, 2017. a
Wu, X., Luo, S., and Hale, D.: Moving faults while unfaulting 3D seismic
images, SEG Technical Program Expanded Abstracts, 34, 1692–1697,
https://doi.org/10.1190/segam2015-5881428.1, 2015. a, b
Yang, L., Hyde, D., Grujic, O., Scheidt, C., and Caers, J.: Assessing and
visualizing uncertainty of 3D geological surfaces using level sets with
stochastic motion, Comput. Geosci., 122, 54–67,
https://doi.org/10.1016/j.cageo.2018.10.006, 2019. a, b
Short summary
Fault discontinuities in rock packages represent the plane where two blocks of rock have moved. They are challenging to incorporate into geological models because the geometry of the faulted rock units are defined by not only the location of the discontinuity but also the kinematics of the fault. In this paper, we outline a structural geology framework for incorporating faults into geological models by directly incorporating kinematics into the mathematical framework of the model.
Fault discontinuities in rock packages represent the plane where two blocks of rock have moved....
