Articles | Volume 14, issue 11
https://doi.org/10.5194/gmd-14-6661-2021
https://doi.org/10.5194/gmd-14-6661-2021
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 authors

GeoINR 1.0: an implicit neural network approach to three-dimensional geological modelling
Michael Hillier, Florian Wellmann, Eric A. de Kemp, Boyan Brodaric, Ernst Schetselaar, and Karine Bédard
Geosci. Model Dev., 16, 6987–7012, https://doi.org/10.5194/gmd-16-6987-2023,https://doi.org/10.5194/gmd-16-6987-2023, 2023
Short summary

Related subject area

Solid Earth
Modelling detrital cosmogenic nuclide concentrations during landscape evolution in Cidre v2.0
Sébastien Carretier, Vincent Regard, Youssouf Abdelhafiz, and Bastien Plazolles
Geosci. Model Dev., 16, 6741–6755, https://doi.org/10.5194/gmd-16-6741-2023,https://doi.org/10.5194/gmd-16-6741-2023, 2023
Short summary
IMEX_SfloW2D v2: a depth-averaged numerical flow model for volcanic gas–particle flows over complex topographies and water
Mattia de' Michieli Vitturi, Tomaso Esposti Ongaro, and Samantha Engwell
Geosci. Model Dev., 16, 6309–6336, https://doi.org/10.5194/gmd-16-6309-2023,https://doi.org/10.5194/gmd-16-6309-2023, 2023
Short summary
Simulation of a fully coupled 3D glacial isostatic adjustment – ice sheet model for the Antarctic ice sheet over a glacial cycle
Caroline J. van Calcar, Roderik S. W. van de Wal, Bas Blank, Bas de Boer, and Wouter van der Wal
Geosci. Model Dev., 16, 5473–5492, https://doi.org/10.5194/gmd-16-5473-2023,https://doi.org/10.5194/gmd-16-5473-2023, 2023
Short summary
AdaHRBF v1.0: gradient-adaptive Hermite–Birkhoff radial basis function interpolants for three-dimensional stratigraphic implicit modeling
Baoyi Zhang, Linze Du, Umair Khan, Yongqiang Tong, Lifang Wang, and Hao Deng
Geosci. Model Dev., 16, 3651–3674, https://doi.org/10.5194/gmd-16-3651-2023,https://doi.org/10.5194/gmd-16-3651-2023, 2023
Short summary
PySubdiv 1.0: open-source geological modeling and reconstruction by non-manifold subdivision surfaces
Mohammad Moulaeifard, Simon Bernard, and Florian Wellmann
Geosci. Model Dev., 16, 3565–3579, https://doi.org/10.5194/gmd-16-3565-2023,https://doi.org/10.5194/gmd-16-3565-2023, 2023
Short summary

Cited articles

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. 
Download
Short summary
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.