Articles | Volume 14, issue 11
https://doi.org/10.5194/gmd-14-6661-2021
© 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-14-6661-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Spatial agents for geological surface modelling
Geological Survey of Canada,
3D Earth Imaging and Modelling Lab,
601 Booth Street, Ottawa K0E 1E9, Canada
Related authors
Michael Hillier, Florian Wellmann, Eric de Kemp, Ernst Schetselaar, Boyan Brodaric, and Karine Bédard
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-290, https://doi.org/10.5194/gmd-2022-290, 2023
Preprint under review for GMD
Short summary
Short summary
Neural networks can be used effectively to model three-dimensional geological structures from point data, sampling geological interfaces, units, and orientations of structural features. Existing neural network approaches for this type of modelling are advanced by the efficient incorporation of unconformities, new knowledge inputs, and new techniques to improve data fitting. These advances permit the modelling of large scale geological structures with low fitting error using noisy datasets.
Michael Hillier, Florian Wellmann, Eric de Kemp, Ernst Schetselaar, Boyan Brodaric, and Karine Bédard
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-290, https://doi.org/10.5194/gmd-2022-290, 2023
Preprint under review for GMD
Short summary
Short summary
Neural networks can be used effectively to model three-dimensional geological structures from point data, sampling geological interfaces, units, and orientations of structural features. Existing neural network approaches for this type of modelling are advanced by the efficient incorporation of unconformities, new knowledge inputs, and new techniques to improve data fitting. These advances permit the modelling of large scale geological structures with low fitting error using noisy datasets.
Related subject area
Solid Earth
ClinoformNet-1.0: stratigraphic forward modeling and deep learning for seismic clinoform delineation
Addressing challenges in uncertainty quantification: the case of geohazard assessments
Reconstructing tephra fall deposits via ensemble-based data assimilation techniques
DeepISMNet: three-dimensional implicit structural modeling with convolutional neural network
PySubdiv: open-source geological modelling and reconstruction by non-manifold subdivision surfaces
Towards automatic finite-element methods for geodynamics via Firedrake
MagmaFOAM-1.0: a modular framework for the simulation of magmatic systems
A global, spherical finite-element model for post-seismic deformation using Abaqus
SMAUG v1.0 – a user-friendly muon simulator for the imaging of geological objects in 3-D
CliffDelineaTool v1.2.0: an algorithm for identifying coastal cliff base and top positions
Capturing the interactions between ice sheets, sea level and the solid Earth on a range of timescales: a new “time window” algorithm
Structural, petrophysical, and geological constraints in potential field inversion using the Tomofast-x v1.0 open-source code
RHEA v1.0: Enabling fully coupled simulations with hydro-geomechanical heterogeneity
Modelling of faults in LoopStructural 1.0
PALEOSTRIPv1.0 – a user-friendly 3D backtracking software to reconstruct paleo-bathymetries
LoopStructural 1.0: time-aware geological modelling
Sub3DNet1.0: a deep-learning model for regional-scale 3D subsurface structure mapping
Analytical solutions for mantle flow in cylindrical and spherical shells
Towards a model for structured mass movements: the OpenLISEM hazard model 2.0a
GO_3D_OBS: the multi-parameter benchmark geomodel for seismic imaging method assessment and next-generation 3D survey design (version 1.0)
PLUME-MoM-TSM 1.0.0: a volcanic column and umbrella cloud spreading model
HydrothermalFoam v1.0: a 3-D hydrothermal transport model for natural submarine hydrothermal systems
Synthetic seismicity distribution in Guerrero–Oaxaca subduction zone, Mexico, and its implications on the role of asperities in Gutenberg–Richter law
A new open-source viscoelastic solid earth deformation module implemented in Elmer (v8.4)
CobWeb 1.0: machine learning toolbox for tomographic imaging
pygeodyn 1.1.0: a Python package for geomagnetic data assimilation
IMEX_SfloW2D 1.0: a depth-averaged numerical flow model for pyroclastic avalanches
A multilayer approach and its application to model a local gravimetric quasi-geoid model over the North Sea: QGNSea V1.0
Development 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 expansion
Geodynamic diagnostics, scientific visualisation and StagLab 3.0
SaLEM (v1.0) – the Soil and Landscape Evolution Model (SaLEM) for simulation of regolith depth in periglacial environments
SILLi 1.0: a 1-D numerical tool quantifying the thermal effects of sill intrusions
The SPACE 1.0 model: a Landlab component for 2-D calculation of sediment transport, bedrock erosion, and landscape evolution
Ellipsoids (v1.0): 3-D magnetic modelling of ellipsoidal bodies
Global-scale modelling of melting and isotopic evolution of Earth's mantle: melting modules for TERRA
pynoddy 1.0: an experimental platform for automated 3-D kinematic and potential field modelling
Open-source modular solutions for flexural isostasy: gFlex v1.0
FPLUME-1.0: An integral volcanic plume model accounting for ash aggregation
PyXRD v0.6.7: a free and open-source program to quantify disordered phyllosilicates using multi-specimen X-ray diffraction profile fitting
r.randomwalk v1, a multi-functional conceptual tool for mass movement routing
Improving the global applicability of the RUSLE model – adjustment of the topographical and rainfall erosivity factors
PLUME-MoM 1.0: A new integral model of volcanic plumes based on the method of moments
Thermo-hydro-mechanical processes in fractured rock formations during a glacial advance
On the sensitivity of 3-D thermal convection codes to numerical discretization: a model intercomparison
Verification of an ADER-DG method for complex dynamic rupture problems
A semi-implicit, second-order-accurate numerical model for multiphase underexpanded volcanic jets
Numerical model of crustal accretion and cooling rates of fast-spreading mid-ocean ridges
A hierarchical mesh refinement technique for global 3-D spherical mantle convection modelling
Hui Gao, Xinming Wu, Jinyu Zhang, Xiaoming Sun, and Zhengfa Bi
Geosci. Model Dev., 16, 2495–2513, https://doi.org/10.5194/gmd-16-2495-2023, https://doi.org/10.5194/gmd-16-2495-2023, 2023
Short summary
Short summary
We propose a workflow to automatically generate synthetic seismic data and corresponding stratigraphic labels (e.g., clinoform facies, relative geologic time, and synchronous horizons) by geological and geophysical forward modeling. Trained with only synthetic datasets, our network works well to accurately and efficiently predict clinoform facies in 2D and 3D field seismic data. Such a workflow can be easily extended for other geological and geophysical scenarios in the future.
Ibsen Chivata Cardenas, Terje Aven, and Roger Flage
Geosci. Model Dev., 16, 1601–1615, https://doi.org/10.5194/gmd-16-1601-2023, https://doi.org/10.5194/gmd-16-1601-2023, 2023
Short summary
Short summary
We discuss challenges in uncertainty quantification for geohazard assessments. The challenges arise from limited data and the one-off nature of geohazard features. The challenges include the credibility of predictions, input uncertainty, and assumptions’ impact. Considerations to increase credibility of the quantification are provided. Crucial tasks in the quantification are the exhaustive scrutiny of the background knowledge coupled with the assessment of deviations of assumptions made.
Leonardo Mingari, Antonio Costa, Giovanni Macedonio, and Arnau Folch
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-246, https://doi.org/10.5194/gmd-2022-246, 2022
Revised manuscript accepted for GMD
Short summary
Short summary
Two novel techniques for ensemble-based data assimilation, suitable for semi-positive-definite variables with highly skewed uncertainty distributions such as tephra deposit mass loading, are applied to reconstruct the tephra fallout deposit resulting from the 2015 Calbuco eruption in Chile. The deposit spatial distribution and the ashfall volume according to the analyses are in good agreement with estimations based on field measurements and isopach maps reported in previous studies.
Zhengfa Bi, Xinming Wu, Zhaoliang Li, Dekuan Chang, and Xueshan Yong
Geosci. Model Dev., 15, 6841–6861, https://doi.org/10.5194/gmd-15-6841-2022, https://doi.org/10.5194/gmd-15-6841-2022, 2022
Short summary
Short summary
We present an implicit modeling method based on deep learning to produce a geologically valid and structurally compatible model from unevenly sampled structural data. Trained with automatically generated synthetic data with realistic features, our network can efficiently model geological structures without the need to solve large systems of mathematical equations, opening new opportunities for further leveraging deep learning to improve modeling capacity in many Earth science applications.
Mohammad Moulaeifard, Simon Bernard, and Florian Wellmann
EGUsphere, https://doi.org/10.5194/egusphere-2022-685, https://doi.org/10.5194/egusphere-2022-685, 2022
Short summary
Short summary
In this work, we propose a flexible framework to generate and interact with geological models using explicit surface representations. The essence of the work lies in the determination of the flexible control mesh, topologically similar to the main geological structure, watertight and controllable with few number of control points to manage the geological structures. We exploited subdivision surface method in our work, which is commonly used in animation and gaming industry.
D. Rhodri Davies, Stephan C. Kramer, Sia Ghelichkhan, and Angus Gibson
Geosci. Model Dev., 15, 5127–5166, https://doi.org/10.5194/gmd-15-5127-2022, https://doi.org/10.5194/gmd-15-5127-2022, 2022
Short summary
Short summary
Firedrake is a state-of-the-art system that automatically generates highly optimised code for simulating finite-element (FE) problems in geophysical fluid dynamics. It creates a separation of concerns between employing the FE method and implementing it. Here, we demonstrate the applicability and benefits of Firedrake for simulating geodynamical flows, with a focus on the slow creeping motion of Earth's mantle over geological timescales, which is ultimately the engine driving our dynamic Earth.
Federico Brogi, Simone Colucci, Jacopo Matrone, Chiara Paola Montagna, Mattia De' Michieli Vitturi, and Paolo Papale
Geosci. Model Dev., 15, 3773–3796, https://doi.org/10.5194/gmd-15-3773-2022, https://doi.org/10.5194/gmd-15-3773-2022, 2022
Short summary
Short summary
Computer simulations play a fundamental role in understanding volcanic phenomena. The growing complexity of these simulations requires the development of flexible computational tools that can easily switch between sub-models and solution techniques as well as optimizations. MagmaFOAM is a newly developed library that allows for maximum flexibility for solving multiphase volcanic flows and promotes collaborative work for in-house and community model development, testing, and comparison.
Grace A. Nield, Matt A. King, Rebekka Steffen, and Bas Blank
Geosci. Model Dev., 15, 2489–2503, https://doi.org/10.5194/gmd-15-2489-2022, https://doi.org/10.5194/gmd-15-2489-2022, 2022
Short summary
Short summary
We present a finite-element model of post-seismic solid Earth deformation built in the software package Abaqus for the purpose of calculating post-seismic deformation in the far field of major earthquakes. The model is benchmarked against an existing open-source post-seismic model demonstrating good agreement. The advantage over existing models is the potential for simple modification to include 3-D Earth structure, non-linear rheologies and alternative or multiple sources of stress change.
Alessandro Lechmann, David Mair, Akitaka Ariga, Tomoko Ariga, Antonio Ereditato, Ryuichi Nishiyama, Ciro Pistillo, Paola Scampoli, Mykhailo Vladymyrov, and Fritz Schlunegger
Geosci. Model Dev., 15, 2441–2473, https://doi.org/10.5194/gmd-15-2441-2022, https://doi.org/10.5194/gmd-15-2441-2022, 2022
Short summary
Short summary
Muon tomography is a technology that is used often in geoscientific research. The know-how of data analysis is, however, still possessed by physicists who developed this technology. This article aims at providing geoscientists with the necessary tools to perform their own analyses. We hope that a lower threshold to enter the field of muon tomography will allow more geoscientists to engage with muon tomography. SMAUG is set up in a modular way to allow for its own modules to work in between.
Zuzanna M. Swirad and Adam P. Young
Geosci. Model Dev., 15, 1499–1512, https://doi.org/10.5194/gmd-15-1499-2022, https://doi.org/10.5194/gmd-15-1499-2022, 2022
Short summary
Short summary
Cliff base and top lines that delimit coastal cliff faces are usually manually digitized based on maps, aerial photographs, terrain models, etc. However, manual mapping is time consuming and depends on the mapper's decisions and skills. To increase the objectivity and efficiency of cliff mapping, we developed CliffDelineaTool, an algorithm that identifies cliff base and top positions along cross-shore transects using elevation and slope characteristics.
Holly Kyeore Han, Natalya Gomez, and Jeannette Xiu Wen Wan
Geosci. Model Dev., 15, 1355–1373, https://doi.org/10.5194/gmd-15-1355-2022, https://doi.org/10.5194/gmd-15-1355-2022, 2022
Short summary
Short summary
Interactions between ice sheets, sea level and the solid Earth occur over a range of timescales from years to tens of thousands of years. This requires coupled ice-sheet–sea-level models to exchange information frequently, leading to a quadratic increase in computation time with the number of model timesteps. We present a new sea-level model algorithm that allows coupled models to improve the computational feasibility and precisely capture short-term interactions within longer simulations.
Jérémie Giraud, Vitaliy Ogarko, Roland Martin, Mark Jessell, and Mark Lindsay
Geosci. Model Dev., 14, 6681–6709, https://doi.org/10.5194/gmd-14-6681-2021, https://doi.org/10.5194/gmd-14-6681-2021, 2021
Short summary
Short 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 and provide theoretical details of the open-source algorithm we use.
José M. Bastías Espejo, Andy Wilkins, Gabriel C. Rau, and Philipp Blum
Geosci. Model Dev., 14, 6257–6272, https://doi.org/10.5194/gmd-14-6257-2021, https://doi.org/10.5194/gmd-14-6257-2021, 2021
Short summary
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, https://doi.org/10.5194/gmd-14-6197-2021, https://doi.org/10.5194/gmd-14-6197-2021, 2021
Short summary
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, https://doi.org/10.5194/gmd-14-5285-2021, https://doi.org/10.5194/gmd-14-5285-2021, 2021
Short summary
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, https://doi.org/10.5194/gmd-14-3915-2021, https://doi.org/10.5194/gmd-14-3915-2021, 2021
Short summary
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, https://doi.org/10.5194/gmd-14-3421-2021, https://doi.org/10.5194/gmd-14-3421-2021, 2021
Short summary
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, https://doi.org/10.5194/gmd-14-1899-2021, https://doi.org/10.5194/gmd-14-1899-2021, 2021
Short summary
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, https://doi.org/10.5194/gmd-14-1841-2021, https://doi.org/10.5194/gmd-14-1841-2021, 2021
Short summary
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, https://doi.org/10.5194/gmd-14-1773-2021, https://doi.org/10.5194/gmd-14-1773-2021, 2021
Short summary
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.
Mattia de' Michieli Vitturi and Federica Pardini
Geosci. Model Dev., 14, 1345–1377, https://doi.org/10.5194/gmd-14-1345-2021, https://doi.org/10.5194/gmd-14-1345-2021, 2021
Short summary
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, https://doi.org/10.5194/gmd-13-6547-2020, https://doi.org/10.5194/gmd-13-6547-2020, 2020
Short summary
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, https://doi.org/10.5194/gmd-13-6361-2020, https://doi.org/10.5194/gmd-13-6361-2020, 2020
Short summary
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, https://doi.org/10.5194/gmd-13-1155-2020, https://doi.org/10.5194/gmd-13-1155-2020, 2020
Short summary
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, https://doi.org/10.5194/gmd-13-315-2020, https://doi.org/10.5194/gmd-13-315-2020, 2020
Short summary
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, https://doi.org/10.5194/gmd-12-3795-2019, https://doi.org/10.5194/gmd-12-3795-2019, 2019
Short summary
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, https://doi.org/10.5194/gmd-12-581-2019, https://doi.org/10.5194/gmd-12-581-2019, 2019
Short summary
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, https://doi.org/10.5194/gmd-11-4797-2018, https://doi.org/10.5194/gmd-11-4797-2018, 2018
Short summary
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, https://doi.org/10.5194/gmd-11-4317-2018, https://doi.org/10.5194/gmd-11-4317-2018, 2018
Short summary
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, https://doi.org/10.5194/gmd-11-3071-2018, https://doi.org/10.5194/gmd-11-3071-2018, 2018
Short summary
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.
Fabio Crameri
Geosci. Model Dev., 11, 2541–2562, https://doi.org/10.5194/gmd-11-2541-2018, https://doi.org/10.5194/gmd-11-2541-2018, 2018
Short summary
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, https://doi.org/10.5194/gmd-11-1641-2018, https://doi.org/10.5194/gmd-11-1641-2018, 2018
Short summary
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, https://doi.org/10.5194/gmd-11-43-2018, https://doi.org/10.5194/gmd-11-43-2018, 2018
Short summary
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, https://doi.org/10.5194/gmd-10-4577-2017, https://doi.org/10.5194/gmd-10-4577-2017, 2017
Short summary
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, https://doi.org/10.5194/gmd-10-3591-2017, https://doi.org/10.5194/gmd-10-3591-2017, 2017
Short summary
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, https://doi.org/10.5194/gmd-9-1399-2016, https://doi.org/10.5194/gmd-9-1399-2016, 2016
Short summary
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, https://doi.org/10.5194/gmd-9-1019-2016, https://doi.org/10.5194/gmd-9-1019-2016, 2016
Short summary
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, https://doi.org/10.5194/gmd-9-997-2016, https://doi.org/10.5194/gmd-9-997-2016, 2016
Short summary
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, https://doi.org/10.5194/gmd-9-431-2016, https://doi.org/10.5194/gmd-9-431-2016, 2016
Short summary
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, https://doi.org/10.5194/gmd-9-41-2016, https://doi.org/10.5194/gmd-9-41-2016, 2016
Short summary
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, https://doi.org/10.5194/gmd-8-4027-2015, https://doi.org/10.5194/gmd-8-4027-2015, 2015
Short summary
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, https://doi.org/10.5194/gmd-8-2893-2015, https://doi.org/10.5194/gmd-8-2893-2015, 2015
Short summary
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, https://doi.org/10.5194/gmd-8-2447-2015, https://doi.org/10.5194/gmd-8-2447-2015, 2015
Short summary
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, https://doi.org/10.5194/gmd-8-2167-2015, https://doi.org/10.5194/gmd-8-2167-2015, 2015
Short summary
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, https://doi.org/10.5194/gmd-7-2065-2014, https://doi.org/10.5194/gmd-7-2065-2014, 2014
C. Pelties, A.-A. Gabriel, and J.-P. Ampuero
Geosci. Model Dev., 7, 847–866, https://doi.org/10.5194/gmd-7-847-2014, https://doi.org/10.5194/gmd-7-847-2014, 2014
S. Carcano, L. Bonaventura, T. Esposti Ongaro, and A. Neri
Geosci. Model Dev., 6, 1905–1924, https://doi.org/10.5194/gmd-6-1905-2013, https://doi.org/10.5194/gmd-6-1905-2013, 2013
P. Machetel and C. J. Garrido
Geosci. Model Dev., 6, 1659–1672, https://doi.org/10.5194/gmd-6-1659-2013, https://doi.org/10.5194/gmd-6-1659-2013, 2013
D. R. Davies, J. H. Davies, P. C. Bollada, O. Hassan, K. Morgan, and P. Nithiarasu
Geosci. Model Dev., 6, 1095–1107, https://doi.org/10.5194/gmd-6-1095-2013, https://doi.org/10.5194/gmd-6-1095-2013, 2013
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.
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, J., Li, Y., Jessell, M. W., Giraud, J., Li, C., Wu, L., Li, F., and Liu, S.:
3D geological structure inversion from Noddy-generated magnetic data using
deep learning methods, Comp. Geos., 149, 104701,
https://doi.org/10.1016/j.cageo.2021.104701, 2021.
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.
Heppenstall, A. J., Crooks, A. T., See, L. M., and Batty, M. (Eds.):
Agent-Based Models of Geographical Systems, Springer, Dordrecht, ISBN-10: 9048189268, 2012.
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.
Hillier, M., Wellmann, F., Brodaric, B., de Kemp, E., and Schetselaar, E.:
Three-Dimensional Structural Geological Modeling Using Graph Neural
Networks, Math. Geosci., https://doi.org/10.1007/s11004-021-09945-x, 2021.
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/10.3997/1365-2397.26.10.28558, 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.
Laurent, G., Ailleres, L., Grose, L., Caumon, G., Jessell, M.,
and Armit, R.:Implicit Modeling of Folds and Overprinting Deformation, Earth Planet. Sc. Lett., 456, 26–38,
2016.
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.
Sprague, K., de Kemp, E. A., Wong, W., McGaughey, J., Perron, G., and Barrie, T.:
Spatial targeting using queries in a 3-D GIS environment with application to
mineral exploration, Comp. Geos., 32, 396–418,
https://doi.org/10.1016/j.cageo.2005.07.008, 2006.
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.
Zhang, T.-F., Tilke, P., Dupont, E., Zhu, L.-C., Liang, L., Bailey, W.:
Generating geologically realistic 3D reservoir facies models using deep
learning of sedimentary architecture with generative adversarial networks,
Petroleum
Science, 16, 541–549, https://doi.org/10.1007/s12182-019-0328-4, 2019.
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.
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.
This is a proof of concept and review paper of spatial agents, with initial research focusing on...