Articles | Volume 18, issue 14
https://doi.org/10.5194/gmd-18-4469-2025
© Author(s) 2025. 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-18-4469-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
23 Jul 2025
Model description paper |
| 23 Jul 2025
| 23 Jul 2025
SubsurfaceBreaks v. 1.0: a supervised detection of fault-related structures on triangulated models of subsurface homoclinal interfaces
Faculty of Geology, Geophysics and Environmental Protection, AGH University of Science and Technology, Mickiewicza 30, 30-059 Kraków, Poland
Christian Gerhards
Institute of Geophysics and Geoinformatics, TU Bergakademie Freiberg, Gustav-Zeuner-Straße 12, 09599 Freiberg, Germany
Peter Menzel
Institute of Geophysics and Geoinformatics, TU Bergakademie Freiberg, Gustav-Zeuner-Straße 12, 09599 Freiberg, Germany
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Michał P. Michalak, Janusz Morawiec, and Peter Menzel
EGUsphere, https://doi.org/10.5194/egusphere-2024-3327, https://doi.org/10.5194/egusphere-2024-3327, 2025
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This study analyzes geological faults using triangular surface data to model displaced horizons, considering scenarios with and without elevation uncertainties. Formal proofs and computational experiments show that without elevation errors, identical dip directions occur. Even with uncertainties, the expected dip direction remains consistent. The findings offer insights for predicting fault geometry in data-sparse environments, improving fault modeling with imprecise elevation data.
Michał P. Michalak, Lesław Teper, Florian Wellmann, Jerzy Żaba, Krzysztof Gaidzik, Marcin Kostur, Yuriy P. Maystrenko, and Paulina Leonowicz
Solid Earth, 13, 1697–1720, https://doi.org/10.5194/se-13-1697-2022, https://doi.org/10.5194/se-13-1697-2022, 2022
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When characterizing geological/geophysical surfaces, various geometric attributes are calculated, such as dip angle (1D) or dip direction (2D). However, the boundaries between specific values may be subjective and without optimization significance, resulting from using default color palletes. This study proposes minimizing cosine distance among within-cluster observations to detect 3D anomalies. Our results suggest that the method holds promise for identification of megacylinders or megacones.
Michał P. Michalak, Janusz Morawiec, and Peter Menzel
EGUsphere, https://doi.org/10.5194/egusphere-2024-3327, https://doi.org/10.5194/egusphere-2024-3327, 2025
Short summary
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This study analyzes geological faults using triangular surface data to model displaced horizons, considering scenarios with and without elevation uncertainties. Formal proofs and computational experiments show that without elevation errors, identical dip directions occur. Even with uncertainties, the expected dip direction remains consistent. The findings offer insights for predicting fault geometry in data-sparse environments, improving fault modeling with imprecise elevation data.
Michał P. Michalak, Lesław Teper, Florian Wellmann, Jerzy Żaba, Krzysztof Gaidzik, Marcin Kostur, Yuriy P. Maystrenko, and Paulina Leonowicz
Solid Earth, 13, 1697–1720, https://doi.org/10.5194/se-13-1697-2022, https://doi.org/10.5194/se-13-1697-2022, 2022
Short summary
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When characterizing geological/geophysical surfaces, various geometric attributes are calculated, such as dip angle (1D) or dip direction (2D). However, the boundaries between specific values may be subjective and without optimization significance, resulting from using default color palletes. This study proposes minimizing cosine distance among within-cluster observations to detect 3D anomalies. Our results suggest that the method holds promise for identification of megacylinders or megacones.
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Geosci. Model Dev., 18, 1487–1504, https://doi.org/10.5194/gmd-18-1487-2025, https://doi.org/10.5194/gmd-18-1487-2025, 2025
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Tao Yuan, Shijie Zhong, and Geruo A
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Sia Ghelichkhan, Angus Gibson, D. Rhodri Davies, Stephan C. Kramer, and David A. Ham
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Zahra Azarm, Hamid Mehrabi, and Saeed Nadi
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-15, https://doi.org/10.5194/gmd-2024-15, 2024
Revised manuscript accepted for GMD
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The article introduces a new method using deep CNN and PSInSAR to estimate land subsidence, addressing the limitations of traditional methods. It focuses on Isfahan province, demonstrating substantial improvement over traditional techniques. The deep CNN method showed a 70 % enhancement in subsidence prediction, with the study area experiencing over 38 cm of subsidence between 2014 and 2020.
Rene Gassmöller, Juliane Dannberg, Wolfgang Bangerth, Elbridge Gerry Puckett, and Cedric Thieulot
Geosci. Model Dev., 17, 4115–4134, https://doi.org/10.5194/gmd-17-4115-2024, https://doi.org/10.5194/gmd-17-4115-2024, 2024
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Numerical models that use simulated particles are a powerful tool for investigating flow in the interior of the Earth, but the accuracy of these models is not fully understood. Here we present two new benchmarks that allow measurement of model accuracy. We then document that better accuracy matters for applications like convection beneath an oceanic plate. Our benchmarks and methods are freely available to help the community develop better models.
Malte Jörn Ziebarth and Sebastian von Specht
Geosci. Model Dev., 17, 2783–2828, https://doi.org/10.5194/gmd-17-2783-2024, https://doi.org/10.5194/gmd-17-2783-2024, 2024
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Thermal energy from Earth’s active interior constantly dissipates through Earth’s surface. This heat flow is not spatially uniform, and its exact pattern is hard to predict since it depends on crustal and mantle properties, both varying across scales. Our new model REHEATFUNQ addresses this difficulty by treating the fluctuations of heat flow within a region statistically. REHEATFUNQ estimates the regional distribution of heat flow and quantifies known structural signals therein.
Shouzhi Chen, Yongshuo H. Fu, Mingwei Li, Zitong Jia, Yishuo Cui, and Jing Tang
Geosci. Model Dev., 17, 2509–2523, https://doi.org/10.5194/gmd-17-2509-2024, https://doi.org/10.5194/gmd-17-2509-2024, 2024
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It is still a challenge to achieve an accurate simulation of vegetation phenology in the dynamic global vegetation models (DGVMs). We implemented and coupled the spring and autumn phenology models into one of the DGVMs, LPJ-GUESS, and substantially improved the accuracy in capturing the start and end dates of growing seasons. Our study highlights the importance of getting accurate phenology estimations to reduce the uncertainties in plant distribution and terrestrial carbon and water cycling.
Enjiang Yue, Mengjiao Qin, Linshu Hu, Sensen Wu, and Zhenhong Du
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-46, https://doi.org/10.5194/gmd-2024-46, 2024
Revised manuscript accepted for GMD
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Laboratory earthquakes is important means to understand natural earthquakes while previous work focused on transient prediction, lacking future prediction capability. We propose a method and evaluate on data from lab experiments with different slip behaviors. It outperforms state-of-the-art methods in modeling slip moments, intervals and predictions beyond trained horizons especially for challenging slip scenarios, which is crucial for quasi-periodic geophysical process like seismicity.
Dechao An, Jinyun Guo, Xiaotao Chang, Zhenming Wang, Yongjun Jia, Xin Liu, Valery Bondur, and Heping Sun
Geosci. Model Dev., 17, 2039–2052, https://doi.org/10.5194/gmd-17-2039-2024, https://doi.org/10.5194/gmd-17-2039-2024, 2024
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Seafloor topography, as fundamental geoinformation in marine surveying and mapping, plays a crucial role in numerous scientific studies. In this paper, we focus on constructing a high-precision seafloor topography and bathymetry model for the Philippine Sea (5° N–35° N, 120° E–150° E), based on shipborne bathymetric data and marine gravity anomalies, and evaluate the reliability of the model's accuracy.
Octavi Gómez-Novell, Bruno Pace, Francesco Visini, Joanna Faure Walker, and Oona Scotti
Geosci. Model Dev., 16, 7339–7355, https://doi.org/10.5194/gmd-16-7339-2023, https://doi.org/10.5194/gmd-16-7339-2023, 2023
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Knowing the rate at which earthquakes happen along active faults is crucial to characterize the hazard that they pose. We present an approach (Paleoseismic EArthquake CHronologies, PEACH) to correlate and compute seismic histories using paleoseismic data, a type of data that characterizes past seismic activity from the geological record. Our approach reduces the uncertainties of the seismic histories and overall can improve the knowledge on fault rupture behavior for the seismic hazard.
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
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We present the development of a code to simulate simultaneously the dynamics of landscapes over geological time and the evolution of the concentration of cosmogenic isotopes in grains throughout their transport from the slopes to the river outlets. This new model makes it possible to study the relationship between the detrital signal of cosmogenic isotope concentration measured in sediment and the erosion--deposition processes in watersheds.
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
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We present version 2 of the numerical code IMEX-SfloW2D. With this version it is possible to simulate a wide range of volcanic mass flows (pyroclastic avalanches, lahars, pyroclastic surges), and here we present its application to transient dilute pyroclastic density currents (PDCs). A simulation of the 1883 Krakatau eruption demonstrates the capability of the numerical model to face a complex natural case involving the propagation of PDCs over the sea surface and across topographic obstacles.
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
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The waxing and waning of the Antarctic ice sheet caused the Earth’s surface to deform, which is stabilizing the ice sheet and mainly determined by the spatially variable viscosity of the mantle. Including this feedback in model simulations led to significant differences in ice sheet extent and ice thickness over the last glacial cycle. The results underline and quantify the importance of including this local feedback effect in ice sheet models when simulating the Antarctic ice sheet evolution.
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
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We propose a Hermite–Birkhoff radial basis function (HRBF) formulation, AdaHRBF, with an adaptive gradient magnitude for continuous 3D stratigraphic potential field (SPF) modeling of multiple stratigraphic interfaces. In the linear system of HRBF interpolants constrained by the scattered on-contact attribute points and off-contact attitude points of a set of strata in 3D space, we add a novel optimization term to iteratively obtain the true gradient magnitude.
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
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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 control points, to manage the geological structures. We exploited the subdivision surface method in our work, which is commonly used in the animation and gaming industry.
Leonardo Mingari, Antonio Costa, Giovanni Macedonio, and Arnau Folch
Geosci. Model Dev., 16, 3459–3478, https://doi.org/10.5194/gmd-16-3459-2023, https://doi.org/10.5194/gmd-16-3459-2023, 2023
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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.
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
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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
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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.
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
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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.
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
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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
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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
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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
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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
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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
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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
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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.
Eric A. de Kemp
Geosci. Model Dev., 14, 6661–6680, https://doi.org/10.5194/gmd-14-6661-2021, https://doi.org/10.5194/gmd-14-6661-2021, 2021
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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.
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
Cited articles
An, Y., Guo, J., Ye, Q., Childs, C., Walsh, J., and Dong, R.: Deep convolutional neural network for automatic fault recognition from 3D seismic datasets, Comput. Geosci., 153, 104776, https://doi.org/10.1016/j.cageo.2021.104776, 2021.
Bardziński, W., Lewandowski, J., Więckowski, R., and Zieliński, T.: Objaśnienia do Szczegółowej Mapy Geologicznej Polski w skali 1:50000, ark, Częstochowa (845), Wydawnictwa Geologiczne, Warszawa, 72 pp., 1986.
Bi, Z., Wu, X., Li, Z., Chang, D., and Yong, X.: DeepISMNet: three-dimensional implicit structural modeling with convolutional neural network, Geosci. Model Dev., 15, 6841–6861, https://doi.org/10.5194/gmd-15-6841-2022, 2022.
Bishop, C. M.: Pattern Recognition and Machine Learning (Information Science and Statistics), Springer-Verlag, Berlin, Heidelberg, ISBN-10 0-387-31073-8, ISBN-13 978-0387-31073-2 2006.
CGAL.org: CGAL, Computational Geometry Algorithms Library, https://www.cgal.org (last access: 17 July 2025), 2023.
Childs, C., Manzocchi, T., Walsh, J. J., Bonson, C. G., Nicol, A., and Schöpfer, M. P. J.: A geometric model of fault zone and fault rock thickness variations, J. Struct. Geol., 31, 117–127, https://doi.org/10.1016/j.jsg.2008.08.009, 2009.
Choi, J., Cho, H., Kwac, J., and Davis, L. S.: Toward sparse coding on cosine distance, in: 2014 22nd International Conference on Pattern Recognition, https://doi.org/10.1109/ICPR.2014.757, 2014.
Cracknell, M. J. and Reading, A. M.: Geological mapping using remote sensing data: A comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information, Comput. Geosci., 63, 22–33, https://doi.org/10.1016/j.cageo.2013.10.008, 2014.
Dadlez, R., Narkiewicz, M., Stephenson, R. A., Visser, M. T. M., and van Wees, J. D.: Tectonic evolution of the Mid-Polish Trough: modelling implications and significance for central European geology, Tectonophysics, 252, 179–195, https://doi.org/10.1016/0040-1951(95)00104-2, 1995.
Dayczak-Calikowska, K. and Moryc, W.: Rozwój basenu sedymentacyjnego i paleotektonika jury środkowej na obszarze Polski, Geol. Q., 32, 117–136, 1988.
De Berg, M., Cheong, O., Van Kreveld, M., and Overmars, M.: Computational Geometry: Algorithms and Applications, 3rd Edn., Springer, 364 pp., https://doi.org/10.2307/3620533, 2008.
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 Oliveira Neto, E. R., Fatah, T. Y. A., Dias, R. M., Freire, A. F. M., and Lupinacci, W. M.: Curvature analysis and its correlation with faults and fractures in presalt carbonates, Santos Basin, Brazil, Mar. Petr. Geol., 158, 106572, https://doi.org/10.1016/j.marpetgeo.2023.106572, 2023.
Fisher, N. I.: Statistical analysis of circular data, Cambridge University Press, 277 pp., https://doi.org/10.1017/cbo9780511564345, 1993.
Fisher, N. I., Huntington, J. F., Jacket, D. R., Willcox, M. E., and Creasey, J. W.: Spatial analysis of two-dimensional orientation data., J. Int. Assoc. Math. Geol., 17, 177–194, https://doi.org/10.1007/BF01033153, 1985.
Garbowska, J.: Interrelation between microfauna and nature of dogger deposits of the Czestochowa Jura (Poland), Acta Palaeontol. Pol., 23, 89–105, 1978.
Guo, J., Xu, X., Wang, L., Wang, X., Wu, L., Jessell, M., Ogarko, V., Liu, Z., and Zheng, Y.: GeoPDNN 1.0: a semi-supervised deep learning neural network using pseudo-labels for three-dimensional shallow strata modelling and uncertainty analysis in urban areas from borehole data, Geosci. Model Dev., 17, 957–973, https://doi.org/10.5194/gmd-17-957-2024, 2024.
Hammah, R. E. and Curran, J. H.: On distance measures for the fuzzy K-means algorithm for joint data, Rock Mech. Rock Eng., 32, 1–27, https://doi.org/10.1007/s006030050041, 1999.
Hermański, S.: Wpływ prac odwadniających kopalnictwa rud żelaza na kształtowanie warunków hydrogeologicznych w rejonie częstochowsko-kłobuckim, Rudy Żelaza, 9–10, 13–16, 1971.
Hermański, S.: Mapa stropu i miąższości warstw kościeliskich, Rejon Żarki-Wieluń. Skala 1:100000, in: Razowska, L., Pacholewski, A., and Zembal, M.: Badania procesów hydrogeochemicznych w obszarach wypełniania się kopalnianych lejów depresyjnych, Centralne Archiwum Geologiczne, 1997.
Hu, X., Bürgmann, R., Xu, X., Fielding, E., and Liu, Z.: Machine-Learning Characterization of Tectonic, Hydrological and Anthropogenic Sources of Active Ground Deformation in California, J. Geophys. Res.-Sol. Ea., 126, e2021JB022373, https://doi.org/10.1029/2021JB022373, 2021.
Jiang, Z., Mallants, D., Gao, L., Munday, T., Mariethoz, G., and Peeters, L.: Sub3DNet1.0: a deep-learning model for regional-scale 3D subsurface structure mapping, Geosci. Model Dev., 14, 3421–3435, https://doi.org/10.5194/gmd-14-3421-2021, 2021.
Jüstel, A., Correira, A. E., Pischke, M., de la Varga, M., and Wellmann, F.: GemGIS – Spatial Data Processing for Geomodeling, J. Open Source Softw., 7, 3709, https://doi.org/10.21105/joss.03709, 2022.
Jüstel, A., de la Varga, M., Chudalla, N., Wagner, J. D., Back, S., and Wellmann, F.: From Maps to Models – Tutorials for structural geological modeling using GemPy and GemGIS, J. Open Source Educ., 6, 185, https://doi.org/10.21105/jose.00185, 2023.
Kaur, H., Zhang, Q., Witte, P., Liang, L., Wu, L., and Fomel, S.: Deep-learning-based 3D fault detection for carbon capture and storage, Geophysics, 88, IM101–IM112, https://doi.org/10.1190/geo2022-0755.1, 2023.
Kopik, J.: Lower and Middle Jurassic of the north-eastern margin of the Upper Silesian Coal Basin, Biul. Państwowego Inst. Geol., 378, 67–129, 1998 (in Polish with English summary).
Kuhn, S., Cracknell, M. J., and Reading, A. M.: Lithologic mapping using Random Forests applied to geophysical and remote-sensing data: A demonstration study from the Eastern Goldfields of Australia, Geophysics, 83, B183–B193, https://doi.org/10.1190/geo2017-0590.1, 2018.
Marynowski, L., Zatoń, M., Simoneit, B., and Otto, A.: Compositions, sources and depositional environments of organic matter from the Middle Jurassic clays of Poland, Appl. Geochem., 22, 2456–2485, https://doi.org/10.1016/j.apgeochem.2007.06.015, 2007.
Mattéo, L., Manighetti, I., Tarabalka, Y., Gaucel, J. M., van den Ende, M., Mercier, A., Tasar, O., Girard, N., Leclerc, F., Giampetro, T., Dominguez, S., and Malavieille, J.: Automatic Fault Mapping in Remote Optical Images and Topographic Data With Deep Learning, J. Geophys. Res.-Sol. Ea., 126, e2020JB021269, https://doi.org/10.1029/2020JB021269, 2021.
Matyja, B. A. and Wierzbowski, A.: Ammonites and stratigraphy of the uppermost Bajocian and Lower Bathonian between Częstochowa and Wieluń Central Poland, Acta Geol. Pol., 50, 191–209, 2000.
Matyszkiewicz, J., Kochman, A., Rzepa, G., Gołębiowska, B., Krajewski, M., Gaidzik, K., and Żaba, J.: Epigenetic silicification of the Upper Oxfordian limestones in the Sokole Hills (Kraków-Częstochowa Upland): Relationship to facies development and tectonics, Acta Geol. Pol., 65, 181–203, https://doi.org/10.1515/agp-2015-0007, 2015.
Michalak, M. P.: SubsurfaceBreaks v. 1.0: A supervised detection of fault-related structures on triangulated models of subsurface homoclinal interfaces: Input and Processed Data, Zenodo [code and data set], https://doi.org/10.5281/zenodo.14589469, 2024.
Michalak, M.: Numerical limitations of the attainment of the orientation of geological planes, Open Geosci., 10, 395–402, https://doi.org/10.1515/geo-2018-0031, 2018.
Michalak, M. P., Bardziński, W., Teper, L., and Małolepszy, Z.: Using Delaunay triangulation and cluster analysis to determine the orientation of a sub-horizontal and noise including contact in Kraków-Silesian Homocline, Poland, Comput. Geosci., 133, 104322, https://doi.org/10.1016/j.cageo.2019.104322, 2019.
Michalak, M. P., Kuzak, R., Gładki, P., Kulawik, A., and Ge, Y.: Constraining uncertainty of fault orientation using a combinatorial algorithm, Comput. Geosci., 154, 104777, https://doi.org/10.1016/j.cageo.2021.104777, 2021.
Michalak, M. P., Teper, L., Wellmann, F., Żaba, J., Gaidzik, K., Kostur, M., Maystrenko, Y. P., and Leonowicz, P.: Clustering has a meaning: optimization of angular similarity to detect 3D geometric anomalies in geological terrains, Solid Earth, 13, 1697–1720, https://doi.org/10.5194/se-13-1697-2022, 2022.
Pedregosa, F., Varoquaux, G., Gramfort, A., Vincent, M., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, É.: Scikit-learn: Machine Learning in Python, J. Mach. Learn. Res., 12, 2825–2830, 2011.
Razowska, L.: Changes of groundwater chemistry caused by the flooding of iron mines (Czestochowa region, southern Poland), J. Hydrol., 244, 17–32, https://doi.org/10.1016/S0022-1694(00)00420-0, 2001.
Razowska, L., Pacholewski, A., and Zembal, M.: Badania procesów hydrogeochemicznych w obszarach wypełniania się kopalnianych lejów depresyjnych, Centralne Archiwum Geologiczne, 1997.
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., and Prabhat, F.: Deep learning and process understanding for data-driven Earth system science, Nature, 566, 195–204, https://doi.org/10.1038/s41586-019-0912-1, 2019.
Shalev-Shwartz, S. and Ben-David, S.: Understanding machine learning: From theory to algorithms, Cambridge University Press, 397 pp., https://doi.org/10.1017/CBO9781107298019, 2013.
Singleton, J. S. and Gans, P. B.: Structural and stratigraphic evolution of the Calico Mountains: Implications for early Miocene extension and Neogene transpression in the central Mojave Desert, California, Geosphere, 4, 459–479, https://doi.org/10.1130/GES00143.1, 2008.
Słonka, Ł. and Krzywiec, P.: Upper Jurassic carbonate buildups in the Miechów Trough, southern Poland – insights from seismic data interpretations, Solid Earth, 11, 1097–1119, https://doi.org/10.5194/se-11-1097-2020, 2020.
Vapnik, V. N.: The nature of statistical learning theory, Statistics for Engineering and Information Science, Springer-Verlag, New York, ISBN 978-1-4419-3160-3, 2000.
Vega-Ramirez, L. A., Spelz, R. M., Negrete-Aranda, R., Neumann, F., Caress, D. W., Clague, D. A., Paduan, J. B., Contreras, J., and Peña-Dominguez, J. G.: A new method for fault-scarp detection using linear discriminant analysis in high-resolution bathymetry data from the alarcón rise and pescadero basin, Tectonics, 40, e2021TC006925, https://doi.org/10.1029/2021TC006925, 2021.
Wang, H., Zhang, L., Yin, K., Luo, H., and Li, J.: Landslide identification using machine learning, Geosci. Front., 12, 351–364, https://doi.org/10.1016/j.gsf.2020.02.012, 2021.
Wang, Y., Ksienzyk, A. K., Liu, M., and Brönner, M.: Multi-geophysical data integration using cluster analysis: Assisting geological mapping in Trøndelag, Mid-Norway, Geophys. J. Int., 225, 1142–1157, https://doi.org/10.1093/gji/ggaa571, 2020.
Więckowski, R., Zieliński, T., Bardziński, W., and Lewandowski, J.: Szczegółowa Mapa Geologiczna Polski w skali 1:50 000 arkusz: Częstochowa, arkusz Częstochowa (845), Państwowy Instytut Geologiczny – Państwowy Instytut Badawczy, 1985.
Xiong, Y. and Zuo, R.: A positive and unlabeled learning algorithm for mineral prospectivity mapping, Comput. Geosci., 147, 104667, https://doi.org/10.1016/j.cageo.2020.104667, 2021.
Yang, J., Xu, J., Lv, Y., Zhou, C., Zhu, Y., and Cheng, W.: Deep learning-based automated terrain classification using high-resolution DEM data, Int. J. Appl. Earth Obs. Geoinf., 118, 103249, https://doi.org/10.1016/j.jag.2023.103249, 2023.
Zhan, J., Xu, P., Chen, J., Wang, Q., Zhang, W., and Han, X.: Comprehensive characterization and clustering of orientation data: A case study from the Songta dam site, China, Eng. Geol., 225, 3–18, https://doi.org/10.1016/j.enggeo.2017.01.010, 2017.
Znosko, J.: Tektonika obszaru częstochowskiego, Przegląd Geol., 8, 418–424, 1960.
Short summary
Using geometric features of synthetic triangulated models of subsurface homoclinal interfaces, we applied machine learning to detect faults. Testing on real borehole data validated its effectiveness across various fault orientations. The supervised approach represents a significant improvement over older methods that relied on simpler clustering techniques which were capable of identifying fewer orientations of potential faults.
Using geometric features of synthetic triangulated models of subsurface homoclinal interfaces,...
