Articles | Volume 14, issue 11
Geosci. Model Dev., 14, 6661–6680, 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review and perspective paper 01 Nov 2021
Review and perspective paper | 01 Nov 2021
Spatial agents for geological surface modelling
Eric A. de Kemp
Related subject area
Solid EarthRHEA v1.0: Enabling fully coupled simulations with hydro-geomechanical heterogeneityModelling of faults in LoopStructural 1.0PALEOSTRIPv1.0 – a user-friendly 3D backtracking software to reconstruct paleo-bathymetriesLoopStructural 1.0: time-aware geological modellingSub3DNet1.0: a deep-learning model for regional-scale 3D subsurface structure mappingAnalytical solutions for mantle flow in cylindrical and spherical shellsTowards a model for structured mass movements: the OpenLISEM hazard model 2.0aGO_3D_OBS: the multi-parameter benchmark geomodel for seismic imaging method assessment and next-generation 3D survey design (version 1.0)Structural, petrophysical and geological constraints in potential field inversion using the Tomofast-x v1.0 open-source codePLUME-MoM-TSM 1.0.0: a volcanic column and umbrella cloud spreading modelHydrothermalFoam v1.0: a 3-D hydrothermal transport model for natural submarine hydrothermal systemsSynthetic seismicity distribution in Guerrero–Oaxaca subduction zone, Mexico, and its implications on the role of asperities in Gutenberg–Richter lawA new open-source viscoelastic solid earth deformation module implemented in Elmer (v8.4)CobWeb 1.0: machine learning toolbox for tomographic imagingpygeodyn 1.1.0: a Python package for geomagnetic data assimilationIMEX_SfloW2D 1.0: a depth-averaged numerical flow model for pyroclastic avalanchesA multilayer approach and its application to model a local gravimetric quasi-geoid model over the North Sea: QGNSea V1.0Development of an automatic delineation of cliff top and toe on very irregular planform coastlines (CliffMetrics v1.0)Bayesian inference of earthquake rupture models using polynomial chaos expansionGeodynamic diagnostics, scientific visualisation and StagLab 3.0SaLEM (v1.0) – the Soil and Landscape Evolution Model (SaLEM) for simulation of regolith depth in periglacial environmentsSILLi 1.0: a 1-D numerical tool quantifying the thermal effects of sill intrusionsThe SPACE 1.0 model: a Landlab component for 2-D calculation of sediment transport, bedrock erosion, and landscape evolutionEllipsoids (v1.0): 3-D magnetic modelling of ellipsoidal bodiesGlobal-scale modelling of melting and isotopic evolution of Earth's mantle: melting modules for TERRApynoddy 1.0: an experimental platform for automated 3-D kinematic and potential field modellingOpen-source modular solutions for flexural isostasy: gFlex v1.0FPLUME-1.0: An integral volcanic plume model accounting for ash aggregationPyXRD v0.6.7: a free and open-source program to quantify disordered phyllosilicates using multi-specimen X-ray diffraction profile fittingr.randomwalk v1, a multi-functional conceptual tool for mass movement routingImproving the global applicability of the RUSLE model – adjustment of the topographical and rainfall erosivity factorsPLUME-MoM 1.0: A new integral model of volcanic plumes based on the method of momentsThermo-hydro-mechanical processes in fractured rock formations during a glacial advanceOn the sensitivity of 3-D thermal convection codes to numerical discretization: a model intercomparisonVerification of an ADER-DG method for complex dynamic rupture problemsA semi-implicit, second-order-accurate numerical model for multiphase underexpanded volcanic jetsNumerical model of crustal accretion and cooling rates of fast-spreading mid-ocean ridgesA hierarchical mesh refinement technique for global 3-D spherical mantle convection modelling
José M. Bastías Espejo, Andy Wilkins, Gabriel C. Rau, and Philipp Blum
Geosci. Model Dev., 14, 6257–6272,Short summary
The hydraulic and mechanical properties of the subsurface are inherently heterogeneous. RHEA is a simulator that can perform couple hydro-geomechanical processes in heterogeneous porous media with steep gradients. RHEA is able to fully integrate spatial heterogeneity, allowing allocation of distributed hydraulic and geomechanical properties at mesh element level. RHEA is a valuable tool that can simulate problems considering realistic heterogeneity inherent to geologic formations.
Lachlan Grose, Laurent Ailleres, Gautier Laurent, Guillaume Caumon, Mark Jessell, and Robin Armit
Geosci. Model Dev., 14, 6197–6213,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.
Florence Colleoni, Laura De Santis, Enrico Pochini, Edy Forlin, Riccardo Geletti, Giuseppe Brancatelli, Magdala Tesauro, Martina Busetti, and Carla Braitenberg
Geosci. Model Dev., 14, 5285–5305,Short summary
PALEOSTRIP has been developed in the framework of past Antarctic ice sheet reconstructions for periods when bathymetry around Antarctica differed substantially from today. It has been designed for users with no knowledge of numerical modelling and allows users to switch on and off the processes involved in backtracking and backstripping. Applications are broad, and it can be used to restore any continental margin bathymetry or sediment thickness and to perform basin analysis.
Lachlan Grose, Laurent Ailleres, Gautier Laurent, and Mark Jessell
Geosci. Model Dev., 14, 3915–3937,Short summary
LoopStructural is an open-source 3D geological modelling library with a model design allowing for multiple different algorithms to be used for comparison for the same geology. Geological structures are modelled using structural geology concepts and techniques, allowing for complex structures such as overprinted folds and faults to be modelled. In the paper, we demonstrate automatically generating a 3-D model from map2loop-processed geological survey data of the Flinders Ranges, South Australia.
Zhenjiao Jiang, Dirk Mallants, Lei Gao, Tim Munday, Gregoire Mariethoz, and Luk Peeters
Geosci. Model Dev., 14, 3421–3435,Short summary
Fast and reliable tools are required to extract hidden information from big geophysical and remote sensing data. A deep-learning model in 3D image construction from 2D image(s) is here developed for paleovalley mapping from globally available digital elevation data. The outstanding performance for 3D subsurface imaging gives confidence that this generic novel tool will make better use of existing geophysical and remote sensing data for improved management of limited earth resources.
Stephan C. Kramer, D. Rhodri Davies, and Cian R. Wilson
Geosci. Model Dev., 14, 1899–1919,Short summary
Computational models of Earth's mantle require rigorous verification and validation. Analytical solutions of the underlying Stokes equations provide a method to verify that these equations are accurately solved for. However, their derivation in spherical and cylindrical shell domains with physically relevant boundary conditions is involved. This paper provides a number of solutions. They are provided in a Python package (Assess) and their use is demonstrated in a convergence study with Fluidity.
Bastian van den Bout, Theo van Asch, Wei Hu, Chenxiao X. Tang, Olga Mavrouli, Victor G. Jetten, and Cees J. van Westen
Geosci. Model Dev., 14, 1841–1864,Short summary
Landslides, debris flows and other types of dense gravity-driven flows threaten livelihoods around the globe. Understanding the mechanics of these flows can be crucial for predicting their behaviour and reducing disaster risk. Numerical models assume that the solids and fluids of the flow are unstructured. The newly presented model captures the internal structure during movement. This important step can lead to more accurate predictions of landslide movement.
Andrzej Górszczyk and Stéphane Operto
Geosci. Model Dev., 14, 1773–1799,Short summary
We present the 3D multi-parameter synthetic geomodel of the subduction zone, as well as the workflow designed to implement all of its components. The model contains different geological structures of various scales and complexities. It is intended to serve as a tool for the geophysical community to validate imaging approaches, design acquisition techniques, estimate uncertainties, benchmark computing approaches, etc.
Jeremie Giraud, Vitaliy Ogarko, Roland Martin, Mark Jessell, and Mark Lindsay
Geosci. Model Dev. Discuss.,
Revised manuscript accepted for GMDShort summary
We review different techniques to model the Earth's subsurface from geophysical data (gravity field anomaly, magnetic field anomaly) using geological models and measurements of the rocks' properties. We show examples of application using idealised examples reproducing realistic features, while providing theoreitical details of the open-source algorithm we use.
Mattia de' Michieli Vitturi and Federica Pardini
Geosci. Model Dev., 14, 1345–1377,Short summary
Here, we present PLUME-MoM-TSM, a volcanic plume model that allows us to quantify the formation of aggregates during the rise of the plume, model the phase change of water, and include the possibility to simulate the initial spreading of the tephra umbrella cloud intruding from the volcanic column into the atmosphere. The model is first applied to the 2015 Calbuco eruption (Chile) and provides an analytical relationship between the upwind spreading and some characteristic of the volcanic column.
Zhikui Guo, Lars Rüpke, and Chunhui Tao
Geosci. Model Dev., 13, 6547–6565,Short summary
We present the 3-D hydro-thermo-transport model HydrothermalFoam v1.0, which we designed to provide the marine geosciences community with an easy-to-use and state-of-the-art tool for simulating mass and energy transport in submarine hydrothermal systems. HydrothermalFoam is based on the popular open-source platform OpenFOAM, comes with a number of tutorials, and is published under the GNU General Public License v3.0.
Marisol Monterrubio-Velasco, F. Ramón Zúñiga, Quetzalcoatl Rodríguez-Pérez, Otilio Rojas, Armando Aguilar-Meléndez, and Josep de la Puente
Geosci. Model Dev., 13, 6361–6381,Short summary
The Mexican subduction zone along the Pacific coast is one of the most active seismic zones in the world, where every year larger-magnitude earthquakes shake huge inland cities such as Mexico City. In this work, we use TREMOL (sThochastic Rupture Earthquake ModeL) to simulate the seismicity observed in this zone. Our numerical results reinforce the hypothesis that in some subduction regions single asperities are responsible for producing the observed seismicity.
Thomas Zwinger, Grace A. Nield, Juha Ruokolainen, and Matt A. King
Geosci. Model Dev., 13, 1155–1164,Short summary
We present a newly developed flat-earth model, Elmer/Earth, for viscoelastic treatment of solid earth deformation under ice loads. Unlike many previous approaches with proprietary software, this model is based on the open-source FEM code Elmer, with the advantage for scientists to apply and alter the model without license constraints. The new-generation full-stress ice-sheet model Elmer/Ice shares the same code base, enabling future coupled ice-sheet–glacial-isostatic-adjustment simulations.
Swarup Chauhan, Kathleen Sell, Wolfram Rühaak, Thorsten Wille, and Ingo Sass
Geosci. Model Dev., 13, 315–334,Short summary
We present CobWeb 1.0, a graphical user interface for analysing tomographic images of geomaterials. CobWeb offers different machine learning techniques for accurate multiphase image segmentation and visualizing material specific parameters such as pore size distribution, relative porosity and volume fraction. We demonstrate a novel approach of dual filtration and dual segmentation to eliminate edge enhancement artefact in synchrotron-tomographic datasets and provide the computational code.
Loïc Huder, Nicolas Gillet, and Franck Thollard
Geosci. Model Dev., 12, 3795–3803,Short summary
The pygeodyn package is a geomagnetic data assimilation tool written in Python. It gives access to the Earth's core flow dynamics, controlled by geomagnetic observations, by means of a reduced numerical model anchored to geodynamo simulation statistics. It aims to provide the community with a user-friendly and tunable data assimilation algorithm. It can be used for education, geomagnetic model production or tests in conjunction with webgeodyn, a set of visualization tools for geomagnetic models.
Mattia de' Michieli Vitturi, Tomaso Esposti Ongaro, Giacomo Lari, and Alvaro Aravena
Geosci. Model Dev., 12, 581–595,Short summary
Pyroclastic avalanches are a type of granular flow generated at active volcanoes by different mechanisms, including the collapse of steep pyroclastic deposits (e.g., scoria and ash cones) and fountaining during moderately explosive eruptions. We present IMEX_SfloW2D, a depth-averaged flow model describing the granular mixture as a single-phase granular fluid. Benchmark cases and preliminary application to the simulation of the 11 February pyroclastic avalanche at Mt. Etna (Italy) are shown.
Yihao Wu, Zhicai Luo, Bo Zhong, and Chuang Xu
Geosci. Model Dev., 11, 4797–4815,Short summary
A multilayer approach is parameterized for model development, and the multiple layers are located at different depths beneath the Earth’s surface. This method may be beneficial for gravity/manget field modeling, which may outperform the traditional single-layer approach.
Andres Payo, Bismarck Jigena Antelo, Martin Hurst, Monica Palaseanu-Lovejoy, Chris Williams, Gareth Jenkins, Kathryn Lee, David Favis-Mortlock, Andrew Barkwith, and Michael A. Ellis
Geosci. Model Dev., 11, 4317–4337,Short summary
We describe a new algorithm that automatically delineates the cliff top and toe of a cliffed coastline from a digital elevation model (DEM). The algorithm builds upon existing methods but is specifically designed to resolve very irregular planform coastlines with many bays and capes, such as parts of the coastline of Great Britain.
Hugo Cruz-Jiménez, Guotu Li, Paul Martin Mai, Ibrahim Hoteit, and Omar M. Knio
Geosci. Model Dev., 11, 3071–3088,Short summary
One of the most important challenges seismologists and earthquake engineers face is reliably estimating ground motion in an area prone to large damaging earthquakes. This study aimed at better understanding the relationship between characteristics of geological faults (e.g., hypocenter location, rupture size/location, etc.) and resulting ground motion, via statistical analysis of a rupture simulation model. This study provides important insight on ground-motion responses to geological faults.
Geosci. Model Dev., 11, 2541–2562,Short summary
Firstly, this study acts as a compilation of key geodynamic diagnostics and describes how to automatise them for a more efficient scientific procedure. Secondly, it outlines today's key pitfalls of scientific visualisation and provides means to circumvent them with, for example, a novel set of fully scientific colour maps. Thirdly, it introduces StagLab 3.0, a software that applies such fully automated diagnostics and state-of-the-art visualisation in the blink of an eye.
Michael Bock, Olaf Conrad, Andreas Günther, Ernst Gehrt, Rainer Baritz, and Jürgen Böhner
Geosci. Model Dev., 11, 1641–1652,Short summary
We introduce the Soil and Landscape Evolution Model (SaLEM) for the prediction of soil parent material evolution following a lithologically differentiated approach. The GIS tool is working within the software framework SAGA GIS. Weathering, erosion and transport functions are calibrated using extrinsic and intrinsic parameter data. First results indicate that our approach shows evidence for the spatiotemporal prediction of soil parental material properties.
Karthik Iyer, Henrik Svensen, and Daniel W. Schmid
Geosci. Model Dev., 11, 43–60,Short summary
Igneous intrusions in sedimentary basins have a profound effect on the thermal structure of the hosting sedimentary rocks. In this paper, we present a user-friendly 1-D FEM-based tool, SILLi, that calculates the thermal effects of sill intrusions on the enclosing sedimentary stratigraphy. The motivation is to make a standardized numerical toolkit openly available that can be widely used by scientists with different backgrounds to test the effects of magmatic bodies in a wide variety of settings.
Charles M. Shobe, Gregory E. Tucker, and Katherine R. Barnhart
Geosci. Model Dev., 10, 4577–4604,Short summary
Rivers control the movement of sediment and nutrients across Earth's surface. Understanding how rivers change through time is important for mitigating natural hazards and predicting Earth's response to climate change. We develop a new computer model for predicting how rivers cut through sediment and rock. Our model is designed to be joined with models of flooding, landslides, vegetation change, and other factors to provide a comprehensive toolbox for predicting changes to the landscape.
Diego Takahashi and Vanderlei C. Oliveira Jr.
Geosci. Model Dev., 10, 3591–3608,Short summary
Ellipsoids are the only bodies for which the self-demagnetization can be treated analytically. This property is useful for modelling compact orebodies having high susceptibility. We present a review of the magnetic modelling of ellipsoids, propose a way of determining the isotropic susceptibility above which the self-demagnetization must be considered, and discuss the ambiguity between confocal ellipsoids, as well as provide a set of routines to model the magnetic field produced by ellipsoids.
Hein J. van Heck, J. Huw Davies, Tim Elliott, and Don Porcelli
Geosci. Model Dev., 9, 1399–1411,Short summary
Currently, extensive geochemical databases of surface observations exist, but satisfying explanations of underlying mantle processes are lacking. We have implemented a new way to track both bulk compositions and concentrations of trace elements in a mantle convection code. In our model, chemical fractionation happens at evolving melting zones. We compare our results to a semi-analytical theory relating observed arrays of correlated Pb isotope compositions to melting age distributions.
J. Florian Wellmann, Sam T. Thiele, Mark D. Lindsay, and Mark W. Jessell
Geosci. Model Dev., 9, 1019–1035,Short summary
We often obtain knowledge about the subsurface in the form of structural geological models, as a basis for subsurface usage or resource extraction. Here, we provide a modelling code to construct such models on the basis of significant deformational events in geological history, encapsulated in kinematic equations. Our methods simplify complex dynamic processes, but enable us to evaluate how events interact, and finally how certain we are about predictions of structures in the subsurface.
A. D. Wickert
Geosci. Model Dev., 9, 997–1017,Short summary
Earth's lithosphere bends beneath surface loads, such as ice, sediments, and mountain belts. The pattern of this bending, or flexural isostatic response, is a function of both the loads and the spatially variable strength of the lithosphere. gFlex is an easy-to-use program to calculate flexural isostastic response, and may be used to better understand how ice sheets, glaciers, large lakes, sedimentary basins, volcanoes, and other surface loads interact with the solid Earth.
A. Folch, A. Costa, and G. Macedonio
Geosci. Model Dev., 9, 431–450,Short summary
We present FPLUME-1.0, a steady-state 1-D cross-section-averaged eruption column model based on the buoyant plume theory (BPT). The model accounts for plume bending by wind, entrainment of ambient moisture, effects of water phase changes, particle fallout and re-entrainment, a new parameterization for the air entrainment coefficients and a model for wet aggregation of ash particles in presence of liquid water or ice.
M. Dumon and E. Van Ranst
Geosci. Model Dev., 9, 41–57,Short summary
This paper presents a FOSS model called PyXRD used to improve the quantification of complex mixed-layer phyllosilicate assemblages using X-ray diffraction. The novelty of this model is the ab initio incorporation of the multi-specimen method, making it possible to share phases and their parameters across multiple specimens. We present results from a comparison of PyXRD with Sybilla v2.2.2 and a number of theoretical experiments illustrating the use of the multi-specimen set-up.
M. Mergili, J. Krenn, and H.-J. Chu
Geosci. Model Dev., 8, 4027–4043,Short summary
r.randomwalk is a flexible and multi-functional open-source GIS tool for simulating the propagation of mass movements. Mass points are routed from given release pixels through a digital elevation model until a defined break criterion is reached. In contrast to existing tools, r.randomwalk includes functionalities to account for parameter uncertainties, and it offers built-in functions for validation and visualization. We show the key functionalities of r.randomwalk for three test areas.
V. Naipal, C. Reick, J. Pongratz, and K. Van Oost
Geosci. Model Dev., 8, 2893–2913,Short summary
We adjusted the topographical and rainfall erosivity factors that are the triggers of erosion in the Revised Universal Soil Loss Equation (RUSLE) model to make the model better applicable at coarse resolution on a global scale. The adjusted RUSLE model compares much better to current high resolution estimates of soil erosion in the USA and Europe. It therefore provides a basis for estimating past and future global impacts of soil erosion on climate with the use of Earth system models.
M. de' Michieli Vitturi, A. Neri, and S. Barsotti
Geosci. Model Dev., 8, 2447–2463,Short summary
In this paper a new mathematical model of volcanic plume, named Plume-MoM, is presented. The model is based on the method of moments and it is able to describe the continuous variability in the grain size distribution (GSD) of the pyroclastic mixture ejected at the vent, crucial to characterize the source conditions of ash dispersal models. Results show that the GSD at the top of the plume is similar to that at the base and that plume height is weakly affected by the parameters of the GSD.
A. P. S. Selvadurai, A. P. Suvorov, and P. A. Selvadurai
Geosci. Model Dev., 8, 2167–2185,Short summary
The paper examines the coupled thermo-hydro-mechanical (THM) processes that develop in a fractured rock region within a fluid-saturated rock mass due to loads imposed by an advancing glacier. This scenario needs to be examined in order to assess the suitability of potential sites for the location of deep geologic repositories for the storage of high-level nuclear waste. The THM processes are examined using a computational multiphysics approach.
P.-A Arrial, N. Flyer, G. B. Wright, and L. H. Kellogg
Geosci. Model Dev., 7, 2065–2076,
C. Pelties, A.-A. Gabriel, and J.-P. Ampuero
Geosci. Model Dev., 7, 847–866,
S. Carcano, L. Bonaventura, T. Esposti Ongaro, and A. Neri
Geosci. Model Dev., 6, 1905–1924,
P. Machetel and C. J. Garrido
Geosci. Model Dev., 6, 1659–1672,
D. R. Davies, J. H. Davies, P. C. Bollada, O. Hassan, K. Morgan, and P. Nithiarasu
Geosci. Model Dev., 6, 1095–1107,
Adamuszek, M., Schmid, D. W., and Dabrowski, M.: Fold geometry toolbox – Automated determination of fold shape, shortening, and material properties, Jour. Struct. Geol., 33, 1406–1416, 2011.
Ailleres, L., Jessell, M., de Kemp, E. A., Caumon, G., Wellmann, F. J., and Grose, L.: Loop – Enabling 3D stochastic geological modelling, ASEG Extended Abstracts, 1–3, https://doi.org/10.1080/22020586.2019.12072955, 2019.
Amadou, M. L., Villamor, G. B., and Kyei-Baffour, N.: Simulating agricultural land-use adaptation decisions to climate change: An empirical agent-based modelling in northern Ghana, Agric. Syst., 166, 196–209, 2018.
An, G., Fitzpatrick, B. G., Christley, S., Federico, P., Kanarek, A., Miller, N. R., Oremland, M., Salinas, R., Laubenbacher, R., and Lenhart, S.: Optimization and Control of Agent-Based Models in Biology: A Perspective, Bull. Math. Biology, 79, 63–87, 2017.
Azam, F., Sharif, M., and Mohsin, S.: Multi agent-based model for earthquake intensity prediction, Jour. Comp. Theor. Nano., 12, 5765–5777, 2015.
Barrett, B. J., Hodgson, D. M., Collier, R. E., and Dorrell, R. M.: Novel 3D sequence stratigraphic numerical model for syn-rift basins: Analysing architectural responses to eustasy, sedimentation and tectonics, Mar. Petrol. Geol., 92, 270–284, https://doi.org/10.1016/j.marpetgeo.2017.10.026, 2018.
Brodaric, B.: Characterizing and representing inference histories in geologic mapping, Int. Jour. Geog. Info. Sci., 26, 265–281, https://doi.org/10.1080/13658816.2011.585992, 2012.
Brodaric, B., Gahegan, M., and Harrap, R.: The art and science of mapping: Computing geological categories from field data, Comp. Geos., 30, 719–740, https://doi.org/10.1016/j.cageo.2004.05.001, 2004.
Bürkle, A.: Collaborating miniature drones for surveillance and reconnaissance, Proceedings of SPIE – The Intern. Soc. Opt. Eng., 7480, 74800H, https://doi.org/10.1117/12.830408, 2009.
Burns, K. L.: Lithologic topology and structural vector fields applied to subsurface prediction in geology, Proceedings of International GIS/LIS’88 accessing the World, Third Annual International Conference, San Antonio, Texas, USA, 26–34, 1988.
Carmichael, T. and Ailleres, A.: Method and analysis for the upscaling of structural data, Jour. Struct. Geol., 83, 121–133, 2016.
Carrillo, J. A., Huang, Y., and Martin, S.: Nonlinear stability of flock solutions in second-order swarming models, Nonlinear Anal.-Real, 17, 332–343, 2014.
Caumon, G. and Collon-Drouaillet, P.: Editorial, Special Issue on Three-Dimensional Structural Modelling, Math. Geos., 46, 905–908, https://doi.org/10.1007/s11004-014-9571-9, 2014.
Caumon, G., Jessell, M., de Kemp, E., Nemeth, B., Peron, G., and Schetselaar, E.: Introduction to special section: Building complex and realistic geological models from sparse data, Interpretation, 4, SMi–SMi, https://doi.org/10.1190/INT-2016-0614-SPSEINTRO.1, 2016.
Caumon, G., Collon-Drouaillet, P., Carlier L., de Veslud, C., Viseur, S., and Sausse, J.: Surface-based 3D modelling of geological structures, Math. Geos., 41, 927–945, https://doi.org/10.1007/s11004-009-9244-2, 2009.
Cervelle, J. and Formenti, E.: Algorithmic Complexity and Cellular Automata, in: Encyclopedia of Complexity and Systems Science, edited by: Meyers, R., Springer, New York, NY, https://doi.org/10.1007/978-0-387-30440-3_17, 2009.
Cloetingh, S., Burov, E., Matenco, L., Beekman, F., Roure, F., and Ziegler, P. A.: The Moho in extensional tectonic settings: Insights from thermo-mechanical models, Tectonophysics, 609, 558–604, https://doi.org/10.1016/j.tecto.2013.06.010, 2013.
Courrioux, G., Nullans, S., Guillen, A., Boissonnat, J. D., Repusseau, P., Renaud, X., and Thibaut, M.: 3D volumetric modelling of Cadomian terranes (Northern Brittany, France): An automatic method using Voronoï diagrams, Tectonophysics, 331, 181–196, https://doi.org/10.1016/S0040-1951(00)00242-0, 2001.
Crooks, A. T. and Heppenstall, A. J.: Introduction to Agent-Based Modelling, in: Agent-Based Models of Geographical Systems, edited by: Heppenstall, A. J., Crooks, A. T., See, L. M., and Batty, M. Springer, Dordrecht, Netherlands, chapter 5, 85–105, https://doi.org/10.1007/978-90-481-8927-4, 2012.
Damiano, R., Lombardo, V., and Nunnari, F.: Virtual agents for the production of linear animations, Entert. Comp., 4, 187–194, 2013.
Davis, J. R. and Titus, S. J.: Modern methods of analysis for three-dimensional orientational data, Jour. Struct. Geol., 96, 65–89, 2017.
de Kemp, E. A.: 3-D visualization of structural field data: Examples from the Archean Caopatina Formation, Abitibi greenstone belt, Québec, Canada, Comp. Geos., 26, 509–530, 2000.
de Kemp, E. A.: Loop3D/GeoSwarm: GeoSwarm_R1 (GeoSwarm_r01), Zenodo [code], https://doi.org/10.5281/zenodo.4634021, 2021.
de Kemp, E. A. and Jessell, M. W.: Challenges in 3D modelling of complex geologic objects, in: Proceedings 33'rd gOcad meeting, Nancy, France, 15 September 2013.
de Kemp, E. A., Sprague, K., and Wong, W.: Interpretive Geology with Structural Constraints: An introduction to the SPARSE © plug-in, Americas GOCAD User Meeting, Houston Texas, 1–16, https://doi.org/10.5281/zenodo.4646210, 1 November 2004.
de Kemp, E. A., Jessell, M. W., Aillères, L., Schetselaar, E. M., Hillier, M., Lindsay, M. D., and Brodaric, B.: Earth model construction in challenging geologic terrain: Designing workflows and algorithms that makes sense, in: Proceedings of Exploration'17: Sixth DMEC – Decennial International Conference on Mineral Exploration, edited by: Tschirhart, V. and Thomas, M. D., Integrating the Geosciences: The Challenge of Discovery, Toronto, Canada, 21–25 October 2017, 419–439, 2017.
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.
De Paor, D. G.: Quaternions, raster shears, and the modelling of rotations in structural and tectonic studies, in: Proceedings and abstracts: Geol. Soc. Amer., New Orleans, LA, USA, 6–9 November 1995, 27, A-72, 1995.
De Paor, D. G. (Ed.): Computation of orientations for GIS – the “Roll” of Quaternions, in: Computer Methods in the Geosciences, Volume 15, Structural Geology and Personal Computers, Pergamon Press., New York, 447–456, 1996.
de Rose, Y., lachlangrose, markjessell, and Thomson, R.: Loop3D/map2loop-2: First Release (Version 1), Zenodo [code], https://doi.org/10.5281/zenodo.4288476, 2020.
de Swarte, T., Boufous, O., and Escalle, P.: Artificial intelligence, ethics and human values: the cases of military drones and companion robots, Artificial Life and Robotics, 24, 291–296, https://doi.org/10.1007/s10015-019-00525-1, 2019.
Dickinson, P., Gerling, K., Hicks, K., Murray, J., Shearer, J., and Greenwood, J.: Virtual reality crowd simulation: effects of agent density on user experience and behaviour, Virt. Real., 23, 19–32, 2019.
Fagnant, D. J. and Kockelman, K. M.: The travel and environmental implications of shared autonomous vehicles using agent-based model scenarios, Transportation Res. C-Emer., 40, 1–13, https://doi.org//10.1016/j.trc.2013.12.001, 2014.
Farin, G.: Curves and Surfaces for Computer Aided Geometric Design, 4th edn., Academic Press, San Diego, California, ISBN-13 978-0-4445-1104-1, 1997.
Frank, T., Tertois, A.-L. L., and Mallet, J.-L. L.: 3D-reconstruction of complex geological interfaces from irregularly distributed and noisy point data, Comput. Geosci., 33, 932–943, https://doi.org/10.1016/j.cageo.2006.11.014, 2007.
Gaspari, M.: Concurrency and knowledge-level communication in agent languages, Artif. Intel., 105, 1–45, 1998.
Grose, L., Laurent, G., Aillères, L., Armit, R., Jessell, M. W., and Guillaume Caumon, G.: Structural data constraints for implicit modeling of folds, J. Struct. Geol., 104, 80–92, https://doi.org/10.1016/j.jsg.2017.09.013, 2017.
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
Groshong Jr., R. H.: 3-D Structural Geology a Practical Guide to Quantitative Surface and Subsurface Map Interpretation, 2nd edn., Springer-Verlag, Berlin and Heidelberg, ISBN-13 978-3-5403-1054-9, 2006.
Guo, R. G. and Sprague, K. S.: Replication of human operators' situation assessment and decision making for simulated area reconnaissance in wargames, The Journal of Defense Modelling and Simulation, 13, 213–225, https://doi.org/10.1177/1548512915619499, 2016.
Guo, Z. and Li, B.: Evolutionary approach for spatial architecture layout design enhanced by an agent-based topology finding system, Frontiers of Architectural Research, 6, 53–62, 2017.
Hall, A. and Virrantaus, K.: Visualizing the workings of agent-based models: Diagrams as a tool for communication and knowledge acquisition, Computers, Environment and Urban Systems, 58, 1–11, https://doi.org/10.1016/j.compenvurbsys.2016.03.002, 2016.
Hamilton, W. R.: On a new species of imaginary quantities connected with a theory of quaternions, Proceedings of the Royal Irish Academy, 2, 424–434, 1844.
Hillier, M., de Kemp, E. A., and Schetselaar, E. M.: 3D Formline construction by structural field interpolation (SFI) of geologic strike and dip observations, J. Struct. Geol., 51, 167–179, 2013.
Hillier, M., Schetselaar, E. M., de Kemp, E. A., and Perron, G.: 3D modelling of geological surfaces using generalized interpolation with radial basis functions, Special Issue, Math. Geosci., 46, 931–953, 2014.
Hillier, M., de Kemp, E. A., and Schetselaar, E. M.:Implicitly modelled stratigraphic surfaces using generalized interpolation, in: AIP conference proceedings, 1738, 050004, International Conference of Numerical Analysis and Applied Mathematics, 22–28 September 2015, Rhodes, Greece, https://doi.org/10.1063/1.4951819, 2016.
Hillier, M., Wellmann, F. J., de Kemp, E. A., Brodaric, B., and Schetselaar, E. M.: Towards Topologically Constrained 3D Geological Modelling, Presentation, in: Loop3D Sponsor Review Meeting, Busselton, Western Australia, 10–13 March 2020, 2020.
Hoy, G. E. and Shalaby, A.: Use of agent-based crowd simulation to investigate the performance of large-scale intermodal facilities: case study of union station in Toronto, Ontario, Canada, Transportation Research Record: Journal of the Transportation Research Board, 2540, 20–29, https://doi.org/10.3141/2540-03, 2016.
Hu, H., Ye, G., Dong, J., Wei, W., and Jin, S.: 3-D lithospheric conductivity structures in the Cathaysia Block and the Jiangnan suture zone: implications for origins of metallogenic belts, J. Appl. Geophys., 177, 104045, https://doi.org/10.1016/j.jappgeo.2020.104045, 2020.
Jaxa-Rozen, M., Kwakkel, J. H., and Bloemendal, M.: A coupled simulation architecture for agent-based/geohydrological modelling with NetLogo and MODFLOW, Environ. Modell. Softw., 115, 19–37, https://doi.org/10.1016/j.envsoft.2019.01.020, 2019.
Jayr, S., Gringarten, E., Tertois, A.-L., Mallet, J.-L., and Dulac, J.-C.: The need for a correct geological modelling support: The advent of the UVT-transform, First Break, 26, 73–79, https://doi.org/10.3997/1365-23188.8.131.52558, 2008.
Jessell, M., Ogarko, V., de Rose, Y., Lindsay, M., Joshi, R., Piechocka, A., Grose, L., de la Varga, M., Ailleres, L., and Pirot, G.: Automated geological map deconstruction for 3D model construction using map2loop 1.0 and map2model 1.0, Geosci. Model Dev., 14, 5063–5092, https://doi.org/10.5194/gmd-14-5063-2021, 2021.
Jessell, M. W., Aillères, L., and de Kemp, E. A.: Towards an Integrated Inversion of Geoscientific data: what price of Geology?, Tectonophysics, 490, 294–306, 2010.
Jessell, M. W., Aillères, L., de Kemp, E. A., Lindsay, M., Wellman, F., Hillier, M., Laurent, G., Carmichael, T., and Martin, R.: Next Generation Three-Dimensional Geologic Modelling and Inversion. Soc. Econ. Geol., Spec. P., 18, 261–272, 2014.
Johnson, J. F. and Hoe, D. H. K.: Designing an agent-based model for the efficient removal of red imported fire ant colonies, Sim. Ser., 45, 361–367, 2013.
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, 1997.
Levy, S., Martens, K., and Van Der Heijden, R.: Agent-based models and self-organisation: Addressing common criticisms and the role of agent-based modelling in urban planning, Town Plan. Rev., 87, 321–338, 2016.
Lindsay, M. D., Jessell, M. W., Aillères, L., Perrouty, S., de Kemp, E. A., and Betts, P. G.: Geodiversity: Exploration of 3D geological model space, Tectonophysics, 594, 27–37, 2013.
Liscano, R., Baker, K., and Meech, J.: The use of ontologies and meta-knowledge to facilitate the sharing of knowledge in a multi-agent personal communication system, Lecture Notes, in: Computer Science, Springer, Berlin, Heidelberg, (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 1822, 187–200, https://doi.org/10.1007/3-540-45486-1_16, 2000.
MacCormack, K. E. and Eyles, C. H.: Assessing the impact of program selection on the accuracy of 3D geologic models, Geosphere, 8, 534–543, https://doi.org/10.1130/GES00732.1, 2012.
Ménager, L.: Consensus, communication and knowledge: An extension with Bayesian agents, Math. Soc. Sci., 51, 274–279, 2006.
Mnasri, S., Nasri, N., van den Bossche, A., and Val, T.: A new multi-agent particle swarm algorithm based on birds accents for the 3D indoor deployment problem, ISA Transact., 91, 262–280, 2019.
Motieyan, H. and Mesgari, M. S.: An Agent-Based Modelling approach for sustainable urban planning from land use and public transit perspectives, Cities, 81, 91–100, 2018.
Nelson, J., Hugh, O., Angel, N., García, E., Chahine, J., and Socci, N. D.: The energy landscape theory of protein folding: Insights into folding mechanisms and scenarios, Adv. Protein Chem., 53, 87–130, https://doi.org/10.1016/S0065-3233(00)53003-4, 2000.
Parquer, M., Yan, N., Colombera, L., Mountney, N. P., Collon, P., and Caumon, G.: Combined inverse and forward numerical modelling for reconstruction of channel evolution and facies distributions in fluvial meander-belt deposits, Mar. Petrol. Geol., 117, 104409, https://doi.org/10.1016/j.marpetgeo.2020.104409, 2020.
Parunak, H. V., Baker, A. D., and Clark, S. J.: AARIA agent architecture: from manufacturing requirements to agent-based system design, Integr. Comput.-Aid. E., 8, 45–58, 2001.
Pellerin, J., Botella, A., Bonneau, F., Mazuyer, A., Chauvin, B., Lévy, B., and Caumon, G.: RINGMesh: A programming library for developing mesh-based geomodeling applications, Comp. Geos., 104, 93–100, https://doi.org/10.1016/j.cageo.2017.03.005, 2017.
Ramsay, J. G.: The Geometry of Conjugate Fold Systems, Geol. Mag., 99, 516–526, https://doi.org/10.1017/S0016756800059823, 1962.
Ramsay, J. G.: Folding and Fracturing of Rocks, McGraw-Hill, New York, 568 pp., ISBN 13 978-0-0705-1170-5, 1967.
Reynolds, C. W.: Flocks, herds and schools: A distributed behavioral model, SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques, Anaheim, California, USA, 27–31 July 1987, 25–34, https://doi.org/10.1145/37401.37406, 1987.
Rigotti, N. A. and Wallace, R. B.: Using agent-based models to address “wicked problems” like tobacco use: A report from the institute of medicine, Ann. Intern. Med., 163, 469–470, 2015.
Schatten, K.: Large-scale solar magnetic field, mapping: I, SpringerPlus, 2–21, 2013.
Schlögl, M., Richter, G., Avian, M., Thaler, T., Heiss, G., Lenz, G., and Fuchs, S.: On the nexus between landslide susceptibility and transport infrastructure – an agent-based approach, Nat. Hazards Earth Syst. Sci., 19, 201–219, https://doi.org/10.5194/nhess-19-201-2019, 2019.
Semenchenko, A., Oliveira, G., and Atman, A. P. F.: Hybrid agent-based model for quantitative in-silico cell-free protein synthesis, BioSystems, 150, 22–34, https://doi.org/10.1016/j.biosystems.2016.07.008, 2016.
Shoemake, K.: Animating Rotation with Quaternion Curves, SIGGRAPH Computer Graphics, 19, 245–254, 1985.
Siegfried, R.: Modeling and Simulation of Complex Systems: A Framework for Efficient Agent-based Modeling and Simulation, Springer Fachmedien, Wiesbaden, 227 pp., https://doi.org/10.1007/978-3-658-07529-3, 2014.
Skirrow, R. G., Murr, J., Schofield, A., Huston, D. L., van der Wielen, S., Czarnota, K., Coghlan, R., Highet, L. M., Connolly, D., Doublier, M., and Duan, J.: Mapping iron oxide Cu-Au (IOCG) mineral potential in Australia using a knowledge-driven mineral systems-based approach, Ore Geol. Rev., 113, 103011, https://doi.org/10.1016/j.oregeorev.2019.103011, 2019.
Thiele, S. T., Jessell, M. W., Lindsay, M., Ogarko, V., Wellmann, J. F., and Pakyuz-Charrier E.: The topology of geology 1: Topological analysis, J. Struct. Geol., 91, 27–38, 2016.
Tieskens, K. F., Shaw, B. J., Haer, T., Schulp, C. J. E., and Verburg, P. H.: Cultural landscapes of the future: using agent-based modelling to discuss and develop the use and management of the cultural landscape of South West Devon, Landscape Ecol., 32, 2113–2132, https://doi.org/10.1007/s10980-017-0502-2, 2017.
Torrens, P. M.: Agent based models and the spatial sciences, Geography Compass, 4, 428–448, 2010.
Valbuena, D., Verburg, P. H., Veldkamp, A., Bregt, A. K., and Ligtenberg, A.: Effects of farmers' decisions on the landscape structure of a Dutch rural region: An agent-based approach, Landscape and Urban Planning, 97, 98–110, https://doi.org/10.1016/j.landurbplan.2010.05.001, 2010.
Von Neumann, J.: Theory of self-reproducing automata, edited by: Burks, A. W., University of Illinois Press, Urbana and London, 408 pp., 1966.
Wellmann, F. and Caumon, G.: Chapter One – 3-D Structural geological models: Concepts, methods, and uncertainties, edited by: Schmelzbach, C., Adv. Geophys., Elsevier, 59, 1–121, ISSN 0065-2687, ISBN 978-0-1281-5208-9, https://doi.org/10.1016/bs.agph.2018.09.001, 2018.
Wellmann, F., Schaaf, A., de la Varga, M., and von Hagke, C.: Chapter 15 – From Google Earth to 3D Geology Problem 2: Seeing Below the Surface of the Digital Earth, edited by: Billi, A. and Fagereng, Å., Developments in Structural Geology and Tectonics, Elsevier, 5, 189–204, ISSN 2542-9000, ISBN 978-0-1281-4048-2, https://doi.org/10.1016/B978-0-12-814048-2.00015-6, 2019.
White, D.: Seismic characterization and time-lapse imaging during seven years of CO2 flood in the Weyburn field, Saskatchewan, Canada, Int. J. Greenh. Gas Con., 16, S78–S94, https://doi.org/10.1016/j.ijggc.2013.02.006, 2013.
Wilensky, U.: NetLogo Wave Machine 3D model, Center for Connected Learning and Computer-Based Modelling, Northwestern University [code], Evanston, IL, available at: http://ccl.northwestern.edu/netlogo/models/WaveMachine3D (last access: 30 September 2021), 1996.
Wilensky, U.: NetLogo Flocking model, Center for Connected Learning and Computer-Based Modelling, Northwestern University [code], Evanston, IL, available at: http://ccl.northwestern.edu/netlogo/models/Flocking (last access: 30 September 2021), 1998.
Wilensky, U.: NetLogo, Center for Connected Learning and Computer-Based Modelling, Northwestern University, Evanston, IL, available at: http://ccl.northwestern.edu/netlogo/ (last access: 30 September 2021), 1999.
Wilensky, U. and Rand, W.: An Introduction to Agent Based Modelling – Modelling Natural, Social and Engineered Complex Systems with NetLogo, Massachusetts Institute of Technology, Cambridge, MA, USA, ISBN 978-0-2627-3189-8, 2015.
Wolfram, S.: Cellular Automata and Complexity; Collected Papers, 1st edn., Westview Press, Boca Raton, Florida, USA, 596 pp., https://doi.org/10.1201/9780429494093, 1994.
Woodcock, N. H.: Specification of Fabric Shapes using an eigenvalue method, Geol. Soc. Amer. Bull., 88, 1231–1236, 1977.
Zuparic, M., Jauregui, V., Prokopenko, M., and Yue, Y.: Quantifying the impact of communication on performance in multi-agent teams, Artificial Life and Robotics, 22, 357–373, 2017.
This is a proof of concept and review paper of spatial agents, with initial research focusing on geomodelling. The results may be of interest to others working on complex regional geological modelling with sparse data. Structural agent-based swarming behaviour is key to advancing this field. The study provides groundwork for research in structural geology 3D modelling with spatial agents. This work was done with NetLogo, a free agent modelling platform used mostly for teaching complex systems.
This is a proof of concept and review paper of spatial agents, with initial research focusing on...