Articles | Volume 15, issue 9
https://doi.org/10.5194/gmd-15-3641-2022
© Author(s) 2022. 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-15-3641-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
09 May 2022
Model description paper |
| 09 May 2022
| 09 May 2022
Blockworlds 0.1.0: a demonstration of anti-aliased geophysics for probabilistic inversions of implicit and kinematic geological models
Richard Scalzo
CORRESPONDING AUTHOR
School of Mathematics and Statistics, The University of Sydney, Darlington, NSW 2008, Australia
ARC Industrial Transformation and Training Centre in Data Analytics for Resources and the Environment (DARE), Australia
Mark Lindsay
Centre for Exploration Targeting, School of Earth Sciences, The University of Western Australia, Crawley, WA 6009, Australia
CSIRO Mineral Resources, Kensington, WA 6151, Australia
ARC Industrial Transformation and Training Centre in Data Analytics for Resources and the Environment (DARE), Australia
Mark Jessell
Centre for Exploration Targeting, School of Earth Sciences, The University of Western Australia, Crawley, WA 6009, Australia
ARC Industrial Transformation and Training Centre in Data Analytics for Resources and the Environment (DARE), Australia
Guillaume Pirot
Centre for Exploration Targeting, School of Earth Sciences, The University of Western Australia, Crawley, WA 6009, Australia
ARC Industrial Transformation and Training Centre in Data Analytics for Resources and the Environment (DARE), Australia
Jeremie Giraud
Centre for Exploration Targeting, School of Earth Sciences, The University of Western Australia, Crawley, WA 6009, Australia
RING Team, GeoRessources, Université de Lorraine, 54000, Nancy, France
ARC Industrial Transformation and Training Centre in Data Analytics for Resources and the Environment (DARE), Australia
Edward Cripps
Department of Mathematics and Statistics, The University of Western Australia, Crawley, WA 6009, Australia
ARC Industrial Transformation and Training Centre in Data Analytics for Resources and the Environment (DARE), Australia
Sally Cripps
School of Mathematics and Statistics, The University of Sydney, Darlington, NSW 2008, Australia
CSIRO Data61, Eveleigh, NSW 2015, Australia
ARC Industrial Transformation and Training Centre in Data Analytics for Resources and the Environment (DARE), Australia
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Ship weather routing has the potential to reduce CO2 emissions, but it currently lacks open and verifiable research. The Python-refactored VISIR-2 model considers currents, waves, and wind to optimise routes. The model was validated, and its computational performance is quasi-linear. For a ferry sailing in the Mediterranean Sea, VISIR-2 yields the largest percentage emission savings for upwind navigation. Given the vessel performance curve, the model is generalisable across various vessel types.
Soyoung Ha, Jonathan J. Guerrette, Ivette Hernández Baños, William C. Skamarock, and Michael G. Duda
Geosci. Model Dev., 17, 4199–4211, https://doi.org/10.5194/gmd-17-4199-2024, https://doi.org/10.5194/gmd-17-4199-2024, 2024
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To mitigate the imbalances in the initial conditions, this study introduces our recent implementation of the incremental analysis update (IAU) in the Model for Prediction Across Scales – Atmospheric (MPAS-A) component coupled with the Joint Effort for Data assimilation Integration (JEDI) through the cycling system. A month-long cycling run demonstrates the successful implementation of the IAU capability in the MPAS–JEDI cycling system.
Federica Castino, Feijia Yin, Volker Grewe, Hiroshi Yamashita, Sigrun Matthes, Simone Dietmüller, Sabine Baumann, Manuel Soler, Abolfazl Simorgh, Maximilian Mendiguchia Meuser, Florian Linke, and Benjamin Lührs
Geosci. Model Dev., 17, 4031–4052, https://doi.org/10.5194/gmd-17-4031-2024, https://doi.org/10.5194/gmd-17-4031-2024, 2024
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We introduce SolFinder 1.0, a decision-making tool to select trade-offs between different objective functions for optimal aircraft trajectories, including fuel use, flight time, NOx emissions, contrail distance, and climate impact. The module is included in the AirTraf 3.0 submodel and uses weather conditions simulated by the EMAC atmospheric model. This paper focuses on the ability of SolFinder to identify eco-efficient trajectories, reducing a flight's climate impact at limited cost penalties.
Ali Dashti, Jens C. Grimmer, Christophe Geuzaine, Florian Bauer, and Thomas Kohl
Geosci. Model Dev., 17, 3467–3485, https://doi.org/10.5194/gmd-17-3467-2024, https://doi.org/10.5194/gmd-17-3467-2024, 2024
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This study developed new meshing workflows to enable the automatic generation of meshes that follow geological models. The workflow allows for importing several geological models as input for Gmsh and later exporting the same number of high-quality meshes. This way, geological uncertainty is directly included in the numerical simulations. This study evaluates the impact of the geological uncertainty on thermohydraulic performance of two reservoirs for high-temperature heat storage applications.
Wangbin Shen, Zhaohui Lin, Zhengkun Qin, and Juan Li
Geosci. Model Dev., 17, 3447–3465, https://doi.org/10.5194/gmd-17-3447-2024, https://doi.org/10.5194/gmd-17-3447-2024, 2024
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In this study, a land surface image assimilation system capable of optimizing the spatial structure of the background field is constructed by introducing the curvelet analysis method and taking the similarity of image structure as a weak constraint. The findings demonstrate that the assimilation of surface soil moisture observation images effectively and reasonably enhances the spatial structure of soil moisture analysis field.
Luan Carlos de Sena Monteiro Ozelim, Michéle Dal Toé Casagrande, and André Luís Brasil Cavalcante
Geosci. Model Dev., 17, 3175–3197, https://doi.org/10.5194/gmd-17-3175-2024, https://doi.org/10.5194/gmd-17-3175-2024, 2024
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The paper addresses synthetic dataset challenges in nonlinear constitutive modeling of soils, providing two datasets: one with 2000 soil types, 40 test conditions each (160 000 triaxial tests), and a second with 2048 soil types, 42 test conditions each (172 032 triaxial tests). Each dataset is a 4000×10 matrix applicable for multivariate forecasting and geotechnical simulations. In addition, a new Python code is introduced, empowering researchers to leverage Python packages for NorSand analyses.
Jeffrey Henry Curtis, Nicole Riemer, and Matthew West
EGUsphere, https://doi.org/10.5194/egusphere-2024-825, https://doi.org/10.5194/egusphere-2024-825, 2024
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This paper introduces a numerical method for simulating particle-based aerosol transport in atmospheric models. We detail the various advection order method’s numerical properties and demonstrate its implementation in a 3D weather prediction model (WRF) for the first time. Particle-based techniques improve the accuracy of aerosol size and composition predictions, which are key for aerosol-cloud and aerosol-radiation interactions.
Daniel Giles, Matthew M. Graham, Mosè Giordano, Tuomas Koskela, Alexandros Beskos, and Serge Guillas
Geosci. Model Dev., 17, 2427–2445, https://doi.org/10.5194/gmd-17-2427-2024, https://doi.org/10.5194/gmd-17-2427-2024, 2024
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Digital twins of physical and human systems informed by real-time data are becoming ubiquitous across a wide range of settings. Progress for researchers is currently limited by a lack of tools to run these models effectively and efficiently. A key challenge is the optimal use of high-performance computing environments. The work presented here focuses on a developed open-source software platform which aims to improve this usage, with an emphasis placed on flexibility, efficiency, and scalability.
Stefan J. Miller, Paul A. Makar, and Colin J. Lee
Geosci. Model Dev., 17, 2197–2219, https://doi.org/10.5194/gmd-17-2197-2024, https://doi.org/10.5194/gmd-17-2197-2024, 2024
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This work outlines a new solver written in Fortran to calculate the partitioning of metastable aerosols at thermodynamic equilibrium based on the forward algorithms of ISORROPIA II. The new code includes numerical improvements that decrease the computational speed (compared to ISORROPIA II) while improving the accuracy of the partitioning solution.
Fernanda Alvarado-Neves, Laurent Ailleres, Lachlan Grose, Alexander R. Cruden, and Robin Armit
Geosci. Model Dev., 17, 1975–1993, https://doi.org/10.5194/gmd-17-1975-2024, https://doi.org/10.5194/gmd-17-1975-2024, 2024
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Previous work has demonstrated that adding geological knowledge to modelling methods creates more accurate and reliable models. Following this reasoning, we added constraints from magma emplacement mechanisms into existing modelling frameworks to improve the 3D characterisation of igneous intrusions. We tested the method on synthetic and real-world case studies, and the results show that our method can reproduce intrusion morphologies with no manual processing and using realistic datasets.
André R. Brodtkorb, Anna Benedictow, Heiko Klein, Arve Kylling, Agnes Nyiri, Alvaro Valdebenito, Espen Sollum, and Nina Kristiansen
Geosci. Model Dev., 17, 1957–1974, https://doi.org/10.5194/gmd-17-1957-2024, https://doi.org/10.5194/gmd-17-1957-2024, 2024
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It is vital to know the extent and concentration of volcanic ash in the atmosphere during a volcanic eruption. Whilst satellite imagery may give an estimate of the ash right now (assuming no cloud coverage), we also need to know where it will be in the coming hours. This paper presents a method for estimating parameters for a volcanic eruption based on satellite observations of ash in the atmosphere. The software package is open source and applicable to similar inversion scenarios.
Kees Nederhoff, Maarten van Ormondt, Jay Veeramony, Ap van Dongeren, José Antonio Álvarez Antolínez, Tim Leijnse, and Dano Roelvink
Geosci. Model Dev., 17, 1789–1811, https://doi.org/10.5194/gmd-17-1789-2024, https://doi.org/10.5194/gmd-17-1789-2024, 2024
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Forecasting tropical cyclones and their flooding impact is challenging. Our research introduces the Tropical Cyclone Forecasting Framework (TC-FF), enhancing cyclone predictions despite uncertainties. TC-FF generates global wind and flood scenarios, valuable even in data-limited regions. Applied to cases like Cyclone Idai, it showcases potential in bettering disaster preparation, marking progress in handling cyclone threats.
Younghun Kang and Ethan J. Kubatko
Geosci. Model Dev., 17, 1603–1625, https://doi.org/10.5194/gmd-17-1603-2024, https://doi.org/10.5194/gmd-17-1603-2024, 2024
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Models used to simulate the flow of coastal and riverine waters, including flooding, require a geometric representation (or mesh) of geographic features that exhibit a range of disparate spatial scales from large, open waters to small, narrow channels. Representing these features in an accurate way without excessive computational overhead presents a challenge. Here, we develop an automatic mesh generation tool to help address this challenge. Our results demonstrate the efficacy of our approach.
Hui Wan, Kai Zhang, Christopher J. Vogl, Carol S. Woodward, Richard C. Easter, Philip J. Rasch, Yan Feng, and Hailong Wang
Geosci. Model Dev., 17, 1387–1407, https://doi.org/10.5194/gmd-17-1387-2024, https://doi.org/10.5194/gmd-17-1387-2024, 2024
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Sophisticated numerical models of the Earth's atmosphere include representations of many physical and chemical processes. In numerical simulations, these processes need to be calculated in a certain sequence. This study reveals the weaknesses of the sequence of calculations used for aerosol processes in a global atmosphere model. A revision of the sequence is proposed and its impacts on the simulated global aerosol climatology are evaluated.
Christopher J. Vogl, Hui Wan, Carol S. Woodward, and Quan M. Bui
Geosci. Model Dev., 17, 1409–1428, https://doi.org/10.5194/gmd-17-1409-2024, https://doi.org/10.5194/gmd-17-1409-2024, 2024
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Generally speaking, accurate climate simulation requires an accurate evolution of the underlying mathematical equations on large computers. The equations are typically formulated and evolved in process groups. Process coupling refers to how the evolution of each group is combined with that of other groups to evolve the full set of equations for the whole atmosphere. This work presents a mathematical framework to evaluate methods without the need to first implement the methods.
Tom Keel, Chris Brierley, and Tamsin Edwards
Geosci. Model Dev., 17, 1229–1247, https://doi.org/10.5194/gmd-17-1229-2024, https://doi.org/10.5194/gmd-17-1229-2024, 2024
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Jet streams are an important control on surface weather as their speed and shape can modify the properties of weather systems. Establishing trends in the operation of jet streams may provide some indication of the future of weather in a warming world. Despite this, it has not been easy to establish trends, as many methods have been used to characterise them in data. We introduce a tool containing various implementations of jet stream statistics and algorithms that works in a standardised manner.
Frédéric Parrenin, Marie Bouchet, Christo Buizert, Emilie Capron, Ellen Corrick, Russell Drysdale, Kenji Kawamura, Amaëlle Landais, Robert Mulvaney, Ikumi Oyabu, and Sune Rasmussen
EGUsphere, https://doi.org/10.5194/egusphere-2023-2911, https://doi.org/10.5194/egusphere-2023-2911, 2024
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The Paleochrono1 probablistic dating model allows to derive a common and optimized chronology for several paleoclimatic sites from various archives (ice cores, speleothems, marine cores, lake cores, etc.). It combines prior sedimentation scenarios with chronological information such as dated horizons, dated intervals, stratigraphic links and (for ice cores) Delta-depth observations. Paleochrono1 is available under the MIT open-source license.
Amir Golparvar, Matthias Kästner, and Martin Thullner
Geosci. Model Dev., 17, 881–898, https://doi.org/10.5194/gmd-17-881-2024, https://doi.org/10.5194/gmd-17-881-2024, 2024
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Coupled reaction transport modelling is an established and beneficial method for studying natural and synthetic porous material, with applications ranging from industrial processes to natural decompositions in terrestrial environments. Up to now, a framework that explicitly considers the porous structure (e.g. from µ-CT images) for modelling the transport of reactive species is missing. We presented a model that overcomes this limitation and represents a novel numerical simulation toolbox.
Stefan Hergarten
Geosci. Model Dev., 17, 781–794, https://doi.org/10.5194/gmd-17-781-2024, https://doi.org/10.5194/gmd-17-781-2024, 2024
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The Voellmy rheology has been widely used for simulating snow and rock avalanches. Recently, a modified version of this rheology was proposed, which turned out to be able to predict the observed long runout of large rock avalanches theoretically. The software MinVoellmy presented here is the first numerical implementation of the modified rheology. It consists of MATLAB and Python classes, where simplicity and parsimony were the design goals.
Arjun Babu Nellikkattil, Danielle Lemmon, Travis Allen O'Brien, June-Yi Lee, and Jung-Eun Chu
Geosci. Model Dev., 17, 301–320, https://doi.org/10.5194/gmd-17-301-2024, https://doi.org/10.5194/gmd-17-301-2024, 2024
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This study introduces a new computational framework called Scalable Feature Extraction and Tracking (SCAFET), designed to extract and track features in climate data. SCAFET stands out by using innovative shape-based metrics to identify features without relying on preconceived assumptions about the climate model or mean state. This approach allows more accurate comparisons between different models and scenarios.
Mohammad Mortezazadeh, Jean-François Cossette, Ashu Dastoor, Jean de Grandpré, Irena Ivanova, and Abdessamad Qaddouri
Geosci. Model Dev., 17, 335–346, https://doi.org/10.5194/gmd-17-335-2024, https://doi.org/10.5194/gmd-17-335-2024, 2024
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The interpolation process is the most computationally expensive step of the semi-Lagrangian (SL) approach. In this paper we implement a new interpolation scheme into the semi-Lagrangian approach which has the same computational cost as a third-order polynomial scheme but with the accuracy of a fourth-order interpolation scheme. This improvement is achieved by using two third-order backward and forward polynomial interpolation schemes in two consecutive time steps.
Boris Gailleton, Luca C. Malatesta, Guillaume Cordonnier, and Jean Braun
Geosci. Model Dev., 17, 71–90, https://doi.org/10.5194/gmd-17-71-2024, https://doi.org/10.5194/gmd-17-71-2024, 2024
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This contribution presents a new method to numerically explore the evolution of mountain ranges and surrounding areas. The method helps in monitoring with details on the timing and travel path of material eroded from the mountain ranges. It is particularly well suited to studies juxtaposing different domains – lakes or multiple rock types, for example – and enables the combination of different processes.
Denise Degen, Daniel Caviedes Voullième, Susanne Buiter, Harrie-Jan Hendricks Franssen, Harry Vereecken, Ana González-Nicolás, and Florian Wellmann
Geosci. Model Dev., 16, 7375–7409, https://doi.org/10.5194/gmd-16-7375-2023, https://doi.org/10.5194/gmd-16-7375-2023, 2023
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In geosciences, we often use simulations based on physical laws. These simulations can be computationally expensive, which is a problem if simulations must be performed many times (e.g., to add error bounds). We show how a novel machine learning method helps to reduce simulation time. In comparison to other approaches, which typically only look at the output of a simulation, the method considers physical laws in the simulation itself. The method provides reliable results faster than standard.
Carlos Spa, Otilio Rojas, and Josep de la Puente
Geosci. Model Dev., 16, 7237–7252, https://doi.org/10.5194/gmd-16-7237-2023, https://doi.org/10.5194/gmd-16-7237-2023, 2023
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This paper develops a calibration methodology of all absorbing techniques typically used by Fourier pseudo-spectral time-domain (PSTD) methods for geoacoustic wave simulations. The main contributions of the paper are (a) an implementation and quantitative comparison of all absorbing techniques available for PSTD methods through a simple and robust numerical experiment, and (b) a validation of these absorbing techniques in several 3-D seismic scenarios with gradual heterogeneity complexities.
Joonlee Lee, Myong-In Lee, Sunlae Tak, Eunkyo Seo, and Yong-Keun Lee
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-221, https://doi.org/10.5194/gmd-2023-221, 2023
Revised manuscript accepted for GMD
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We developed a snow assimilation system using satellite data based on a land surface model. The snow states produced by the assimilation system demonstrate high performance in all regions, including transition regions, compared to the satellite data and land model. As snow significantly influences energy and water balance at the atmosphere-land boundary, this approach allows for a more accurate prediction of atmospheric conditions by realistically representing atmosphere-land interactions.
Michael Hillier, Florian Wellmann, Eric A. de Kemp, Boyan Brodaric, Ernst Schetselaar, and Karine Bédard
Geosci. Model Dev., 16, 6987–7012, https://doi.org/10.5194/gmd-16-6987-2023, https://doi.org/10.5194/gmd-16-6987-2023, 2023
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Neural networks can be used effectively to model three-dimensional geological structures from point data, sampling geological interfaces, units, and structural orientations. Existing neural network approaches for this type of modelling are advanced by the efficient incorporation of unconformities, new knowledge inputs, and improved data fitting techniques. These advances permit the modelling of more complex geology in diverse geological settings, different-sized areas, and various data regimes.
Tor Nordam, Ruben Kristiansen, Raymond Nepstad, Erik van Sebille, and Andy M. Booth
Geosci. Model Dev., 16, 5339–5363, https://doi.org/10.5194/gmd-16-5339-2023, https://doi.org/10.5194/gmd-16-5339-2023, 2023
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We describe and compare two common methods, Eulerian and Lagrangian models, used to simulate the vertical transport of material in the ocean. They both solve the same transport problems but use different approaches for representing the underlying equations on the computer. The main focus of our study is on the numerical accuracy of the two approaches. Our results should be useful for other researchers creating or using these types of transport models.
Mathieu Gravey and Grégoire Mariethoz
Geosci. Model Dev., 16, 5265–5279, https://doi.org/10.5194/gmd-16-5265-2023, https://doi.org/10.5194/gmd-16-5265-2023, 2023
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Multiple‐point geostatistics are widely used to simulate complex spatial structures based on a training image. The use of these methods relies on the possibility of finding optimal training images and parametrization of the simulation algorithms. Here, we propose finding an optimal set of parameters using only the training image as input. The main advantage of our approach is to remove the risk of overfitting an objective function.
Siting Li, Ping Wang, Hong Wang, Yue Peng, Zhaodong Liu, Wenjie Zhang, Hongli Liu, Yaqiang Wang, Huizheng Che, and Xiaoye Zhang
Geosci. Model Dev., 16, 4171–4191, https://doi.org/10.5194/gmd-16-4171-2023, https://doi.org/10.5194/gmd-16-4171-2023, 2023
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Optimizing the initial state of atmospheric chemistry model input is one of the most essential methods to improve forecast accuracy. Considering the large computational load of the model, we introduce an ensemble optimal interpolation scheme (EnOI) for operational use and efficient updating of the initial fields of chemical components. The results suggest that EnOI provides a practical and cost-effective technique for improving the accuracy of chemical weather numerical forecasts.
Thomas Richter, Véronique Dansereau, Christian Lessig, and Piotr Minakowski
Geosci. Model Dev., 16, 3907–3926, https://doi.org/10.5194/gmd-16-3907-2023, https://doi.org/10.5194/gmd-16-3907-2023, 2023
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Sea ice covers not only the pole regions but affects the weather and climate globally. For example, its white surface reflects more sunlight than land. The oceans around the poles are therefore kept cool, which affects the circulation in the oceans worldwide. Simulating the behavior and changes in sea ice on a computer is, however, very difficult. We propose a new computer simulation that better models how cracks in the ice change over time and show this by comparing to other simulations.
Emma J. MacKie, Michael Field, Lijing Wang, Zhen Yin, Nathan Schoedl, Matthew Hibbs, and Allan Zhang
Geosci. Model Dev., 16, 3765–3783, https://doi.org/10.5194/gmd-16-3765-2023, https://doi.org/10.5194/gmd-16-3765-2023, 2023
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Earth scientists often have to fill in spatial gaps in measurements. This gap-filling or interpolation can be accomplished with geostatistical methods, where the statistical relationships between measurements are used to inform how these gaps should be filled. Despite the broad utility of these methods, there are few freely available geostatistical software applications. We present GStatSim, a Python package for performing different geostatistical interpolation methods.
Ian Madden, Simone Marras, and Jenny Suckale
Geosci. Model Dev., 16, 3479–3500, https://doi.org/10.5194/gmd-16-3479-2023, https://doi.org/10.5194/gmd-16-3479-2023, 2023
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To aid risk managers who may wish to rapidly assess tsunami risk but may lack high-performance computing infrastructure, we provide an accessible software package able to rapidly model tsunami inundation over real topography by leveraging Google's Tensor Processing Unit, a high-performance hardware. Minimally trained users can take advantage of the rapid modeling abilities provided by this package via a web browser thanks to the ease of use of Google Cloud Platform.
Youtong Rong, Paul Bates, and Jeffrey Neal
Geosci. Model Dev., 16, 3291–3311, https://doi.org/10.5194/gmd-16-3291-2023, https://doi.org/10.5194/gmd-16-3291-2023, 2023
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A novel subgrid channel (SGC) model is developed for river–floodplain modelling, allowing utilization of subgrid-scale bathymetric information while performing computations on relatively coarse grids. By including adaptive artificial diffusion, potential numerical instability, which the original SGC solver had, in low-friction regions such as urban areas is addressed. Evaluation of the new SGC model through structured tests confirmed that the accuracy and stability have improved.
Xiaqiong Zhou and Hann-Ming Henry Juang
Geosci. Model Dev., 16, 3263–3274, https://doi.org/10.5194/gmd-16-3263-2023, https://doi.org/10.5194/gmd-16-3263-2023, 2023
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The National Centers for Environmental Prediction Global Forecast System version 16 experienced model instability failures in real-time runs resolved by increasing the minimum thickness depth parameter. Further investigation revealed that the issue was caused by the advection of geopotential heights at the model's layer interfaces. By replacing high-order boundary conditions with zero-gradient boundary conditions for interface-wind reconstruction, the instability was effectively addressed.
Grant T. Euen, Shangxin Liu, Rene Gassmöller, Timo Heister, and Scott D. King
Geosci. Model Dev., 16, 3221–3239, https://doi.org/10.5194/gmd-16-3221-2023, https://doi.org/10.5194/gmd-16-3221-2023, 2023
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Due to the increasing availability of high-performance computing over the past few decades, numerical models have become an important tool for research. Here we test two geodynamic codes that produce such models: ASPECT, a newer code, and CitcomS, an older one. We show that they produce solutions that are extremely close. As methods and codes become more complex over time, showing reproducibility allows us to seamlessly link previously known information to modern methodologies.
Mohammad Kazem Sharifian, Georges Kesserwani, Alovya Ahmed Chowdhury, Jeffrey Neal, and Paul Bates
Geosci. Model Dev., 16, 2391–2413, https://doi.org/10.5194/gmd-16-2391-2023, https://doi.org/10.5194/gmd-16-2391-2023, 2023
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This paper describes a new release of the LISFLOOD-FP model for fast and efficient flood simulations. It features a new non-uniform grid generator that uses multiwavelet analyses to sensibly coarsens the resolutions where the local topographic variations are smooth. Moreover, the model is parallelised on the graphical processing units (GPUs) to further boost computational efficiency. The performance of the model is assessed for five real-world case studies, noting its potential applications.
Bruno K. Zürcher
Geosci. Model Dev., 16, 1697–1711, https://doi.org/10.5194/gmd-16-1697-2023, https://doi.org/10.5194/gmd-16-1697-2023, 2023
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We present a novel algorithm to efficiently compute Barnes interpolation, which is a method for transforming data values recorded at irregularly spaced points into a corresponding regular grid. In contrast to naive implementations with an algorithmic complexity that depends on the product of the number of sample points and the number of grid points, our approach reduces this dependency to their sum.
David H. Marsico and Paul A. Ullrich
Geosci. Model Dev., 16, 1537–1551, https://doi.org/10.5194/gmd-16-1537-2023, https://doi.org/10.5194/gmd-16-1537-2023, 2023
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Climate models involve several different components, such as the atmosphere, ocean, and land models. Information needs to be exchanged, or remapped, between these models, and devising algorithms for performing this exchange is important for ensuring the accuracy of climate simulations. In this paper, we examine the efficacy of several traditional and novel approaches to remapping on the sphere and demonstrate where our approaches offer improvement.
Moritz Liebl, Jörg Robl, Stefan Hergarten, David Lundbek Egholm, and Kurt Stüwe
Geosci. Model Dev., 16, 1315–1343, https://doi.org/10.5194/gmd-16-1315-2023, https://doi.org/10.5194/gmd-16-1315-2023, 2023
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In this study, we benchmark a topography-based model for glacier erosion (OpenLEM) with a well-established process-based model (iSOSIA). Our experiments show that large-scale erosion patterns and particularly the transformation of valley length geometry from fluvial to glacial conditions are very similar in both models. This finding enables the application of OpenLEM to study the influence of climate and tectonics on glaciated mountains with reasonable computational effort on standard PCs.
James Kent, Thomas Melvin, and Golo Albert Wimmer
Geosci. Model Dev., 16, 1265–1276, https://doi.org/10.5194/gmd-16-1265-2023, https://doi.org/10.5194/gmd-16-1265-2023, 2023
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This paper introduces the Met Office's new shallow water model. The shallow water model is a building block towards the Met Office's new atmospheric dynamical core. The shallow water model is tested on a number of standard spherical shallow water test cases, including flow over mountains and unstable jets. Results show that the model produces similar results to other shallow water models in the literature.
Anthony Gruber, Max Gunzburger, Lili Ju, Rihui Lan, and Zhu Wang
Geosci. Model Dev., 16, 1213–1229, https://doi.org/10.5194/gmd-16-1213-2023, https://doi.org/10.5194/gmd-16-1213-2023, 2023
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This work applies a novel technical tool, multifidelity Monte Carlo (MFMC) estimation, to three climate-related benchmark experiments involving oceanic, atmospheric, and glacial modeling. By considering useful quantities such as maximum sea height and total (kinetic) energy, we show that MFMC leads to predictions which are more accurate and less costly than those obtained by standard methods. This suggests MFMC as a potential drop-in replacement for estimation in realistic climate models.
Piyoosh Jaysaval, Glenn E. Hammond, and Timothy C. Johnson
Geosci. Model Dev., 16, 961–976, https://doi.org/10.5194/gmd-16-961-2023, https://doi.org/10.5194/gmd-16-961-2023, 2023
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We present a robust and highly scalable implementation of numerical forward modeling and inversion algorithms for geophysical electrical resistivity tomography data. The implementation is publicly available and developed within the framework of PFLOTRAN (http://www.pflotran.org), an open-source, state-of-the-art massively parallel subsurface flow and transport simulation code. The paper details all the theoretical and implementation aspects of the new capabilities along with test examples.
Lucas Schauer, Michael J. Schmidt, Nicholas B. Engdahl, Stephen D. Pankavich, David A. Benson, and Diogo Bolster
Geosci. Model Dev., 16, 833–849, https://doi.org/10.5194/gmd-16-833-2023, https://doi.org/10.5194/gmd-16-833-2023, 2023
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We develop a multi-dimensional, parallelized domain decomposition strategy for mass-transfer particle tracking methods in two and three dimensions, investigate different procedures for decomposing the domain, and prescribe an optimal tiling based on physical problem parameters and the number of available CPU cores. For an optimally subdivided diffusion problem, the parallelized algorithm achieves nearly perfect linear speedup in comparison with the serial run-up to thousands of cores.
Cited articles
Backus, G. and Gilbert, F.: The Resolving Power of Gross Earth Data, Geophys. J. Royal Astron. Soc., 16, 169–205, https://doi.org/10.1111/j.1365-246X.1968.tb00216.x, 1968. a
Backus, G. and Gilbert, F.: Uniqueness in the inversion of inaccurate gross
Earth data, Philos. T. Roy. Soc. Lond A, 266, 123–192,
https://doi.org/10.1111/j.1365-246X.1968.tb00216.x, 1970. a
Backus, G. E.: Long-wave elastic anisotropy produced by horizontal layering, J. Geophys. Res., 67, 4427–4440, 1962. a
Backus, G. E. and Gilbert, J. F.: Numerical Applications of a Formalism for
Geophysical Inverse Problems, Geophys. J. Roy. Astron. Soc., 13, 247–276, https://doi.org/10.1111/j.1365-246X.1967.tb02159.x, 1967. a
Beardsmore, G., Durrant-Whyte, H., and Callaghan, S. O.: A Bayesian inference tool for geophysical joint inversions, ASEG Extended Abstracts 2016.1 (2016), 1–10, https://doi.org/10.1071/ASEG2016ab131, 2016. a, b, c
Bond, C. E.: Uncertainty in structural interpretation: Lessons to be learnt,
J. Struct. Geol., 74, 185–200, https://doi.org/10.1016/j.jsg.2015.03.003, 2015. a
Bosch, M.: Lithologic tomography: From plural geophysical data to lithology
estimation, J. Geophys. Res.-Solid, 104, 749–766, https://doi.org/10.1029/1998JB900014, 1999. a
Bosch, M.: Inference Networks in Earth Models with Multiple Components and Data, in: Geophysical Monograph Series, edited by: Moorkamp, M., Lelièvre, P. G., Linde, N., and Khan, A., John Wiley & Sons, Inc, Hoboken, NJ, 29–47, https://doi.org/10.1002/9781118929063.ch3, 2016. a
Bosch, M., Guillen, A., and Ledru, P.: Lithologic tomography: an application to geophysical data from the Cadomian belt of northern Brittany, France,
Tectonophysics, 331, 197–227, https://doi.org/10.1016/S0040-1951(00)00243-2, 2001. a
Brunetti, C., Bianchi, M., Pirot, G., and Linde, N.: Hydrogeological Model
Selection Among Complex Spatial Priors, Water Resour. Res., 55, 6729–6753, https://doi.org/10.1029/2019WR024840, 2019. a
Cai, H. and Zhdanov, M.: Application of Cauchy-type integrals in developing
effective methods for depth-to-basement inversion of gravity and gravity
gradiometry data, Geophysics, 80, G81–G94, https://doi.org/10.1190/geo2014-0332.1, 2015. a
Calcagno, P., Chilès, J., Courrioux, G., and Guillen, A.: Geological modelling from field data and geological knowledge, Phys. Earth Planet. Inter., 171, 147–157, https://doi.org/10.1016/j.pepi.2008.06.013, 2008. a, b
Capdeville, Y., Guillot, L., and Marigo, J. J.: 1-D non-periodic homogenization for the seismic wave equation. Geophysical Journal International, Geophys. J. Int., 181, 897–910, 2010. a
Catmull, E.: A hidden-surface algorithm with anti-aliasing, in: Proceedings of the 5th Annual Conference on Computer Graphics and Interactive Techniques,
Atlanta, Georgia, USA, 6–11, https://doi.org/10.1145/800248.807360, 1978. a
Cockett, R., Kang, S., Heagy, L. J., Pidlisecky, A., and Oldenburg, D. W.:
SimPEG: An open source framework for simulation and gradient based parameter estimation in geophysical applications, Comput. Geosci., 85, 142–154, https://doi.org/10.1016/j.cageo.2015.09.015, 2015. a, b
Cook, R. L.: Stochastic sampling in computer graphics, ACM T. Graph., 5, 51–72, 1986. a
Cordua, K. S., Hansen, T. M., and Mosegaard, K.: Monte Carlo full-waveform
inversion of crosshole GPR data using multiple-point geostatistical a priori
information, Gwophysics, 77, H19–H31, https://doi.org/10.1190/geo2011-0170.1, 2012. a
Crow, F. C.: The aliasing porblem in computer-generated shaded images, Commun. ACM, 20, 799–805, 1977. a
de la Varga, M., Schaaf, A., and Wellmann, F.: GemPy 1.0: open-source stochastic geological modeling and inversion, Geosci. Model Dev., 12, 1–32, https://doi.org/10.5194/gmd-12-1-2019, 2019. a
de Pasquale, G., Linde, N., Doetsch, J., and Holbrook, W. S.: Probabilistic
inference of subsurface heterogeneity and interface geometry using geophysical data, Geophys. J. Int., 217, 816–831, https://doi.org/10.1093/gji/ggz055, 2019. a
Frodeman, R.: Geological reasoning: Geology as an interpretive and historical
science, Geol. Soc. Am. Bull., 107, 960–0968, https://doi.org/10.1130/0016-7606(1995)107<0960:GRGAAI>2.3.CO;2, 1995. a
Gallardo, L. A. and Meju, M. A.: Structure-coupled multiphysics imaging in
geophysical sciences, Rev.f Geophys., 49, RG1003, https://doi.org/10.1029/2010RG000330, 2011. a
Giraud, J., Pakyuz-Charrier, E., Jessell, M., Lindsay, M., Martin, R., and
Ogarko, V.: Uncertainty reduction through geologically conditioned
petrophysical constraints in joint inversion, Geophysics, 82, ID19–ID34,
https://doi.org/10.1190/geo2016-0615.1, 2017. a
Giraud, J., Pakyuz-Charrier, E., Ogarko, V., Jessell, M., Lindsay, M., and
Martin, R.: Impact of uncertain geology in constrained geophysical inversion,
ASEG Extend. Abstr., 2018, 1, https://doi.org/10.1071/ASEG2018abM1_2F, 2018. a
Giraud, J., Lindsay, M., Ogarko, V., Jessell, M., Martin, R., and
Pakyuz-Charrier, E.: Integration of geoscientific uncertainty into geophysical inversion by means of local gradient regularization, Solid Earth,
10, 193–210, https://doi.org/10.5194/se-10-193-2019, 2019a. a
Giraud, J., Ogarko, V., Lindsay, M., Pakyuz-Charrier, E., Jessell, M., and
Martin, R.: Sensitivity of constrained joint inversions to geological and
petrophysical input data uncertainties with posterior geological analysis, Geophys. J. Int., 218, 666–688, https://doi.org/10.1093/gji/ggz152, 2019b. a
Giraud, J., Lindsay, M., and Jessell, M.: Generalization of level-set inversion to an arbitrary number of geologic units in a regularized least-squares framework, Geophysics, 86, R623–R637, https://doi.org/10.1190/geo2020-0263.1, 2021a. a
Giraud, J., Ogarko, V., Martin, R., Jessell, M., and Lindsay, M.: Structural, petrophysical, and geological constraints in potential field inversion using the Tomofast-x v1.0 open-source code, Geosci. Model Dev., 14, 6681–6709, https://doi.org/10.5194/gmd-14-6681-2021, 2021b. a, b
Götze, H. and Lahmeyer, B.: Application of three‐dimensional interactive
modeling in gravity and magnetics, Geophysics, 53, 1096–1108, https://doi.org/10.1190/1.1442546, 1988. a
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. a
Haario, H., Saksman, E., and Tamminen, J.: An Adaptive Metropolis Algorithm, Bernoulli, 223, ISBN 1350-7265, https://doi.org/10.2307/3318737, 2001. a
Haber, E. and Heldmann, S.: An octree multigrid method for quasi-static
Maxwell's equations with highly discontinuous coefficients, J. Comput. Phys., 223, 783–796, https://doi.org/10.1016/j.jcp.2006.10.012, 2007. a
Hastings, W. K.: Monte Carlo sampling methods using Markov chains and their
applications, Biometrika, 57, 97–109, https://doi.org/10.1093/biomet/57.1.97, 1970. a
Heagy, L., Kang, S., Fournier, D., Rosenkjaer, G. K., Capriotti, J., Astic, T., Cowan, D. C., Marchant, D., Mitchell, M., Kuttai, J., Werthmüller, D., Caudillo Mata, L. A., Ye, Z.-K., Koch, F., Smithyman, B., Martens, K., Miller, C., Gohlke, C., … and Perez, F.: simpeg/simpeg: Simulation (v0.14.0), Zenodo [code], https://doi.org/10.5281/zenodo.3860973, 2020. a, b
Houlding, S. W.: 3D Geoscience modeling: computer techniques for geological
characterization, Springer Verlag, 85–90, ISBN 3-540-58015-8, 1994. a
Jessell, M., Pakyuz-Charrier, E., Lindsay, M., Giraud, J., and de Kemp, E.:
Assessing and Mitigating Uncertainty in Three-Dimensional Geologic Models in Contrasting Geologic Scenarios, in: Metals, Minerals, and Society, SEG – Society of Economic Geologists, https://doi.org/10.5382/SP.21.04, 2018. a
Jessell, M. W. and Valenta, R. K.: Structural geophysics: Integrated structural and geophysical modelling, in: Structural Geology and Personal Computers, edited by: De Paor, D. G., Elsevier, Oxford, UK, 303–324, https://doi.org/10.1016/S1874-561X(96)80027-7, 1993. a, b
Jessell, M. W., Ailleres, L., and de Kemp, E. A.: Towards an integrated
inversion of geoscientific data: What price of geology?, Tectonophysics, 490, 294–306, https://doi.org/10.1016/j.tecto.2010.05.020, 2010. a
Koene, E. F. M., Wittsten, J., and Robertsson, J. O. A.: Finite-difference
modeling of 2-D wave propagation in the vicinity of dipping interfaces: a
comparison of anti-aliasing and equivalent medium approaches, https://doi.org/10.1093/gji/ggab444, 2021. a
Lajaunie, C., Courrioux, G., and Manuel, L.: Foliation fields and 3D cartography in geology: Principles of a method based on potential interpolation, Math. Geol., 29, 571–584, https://doi.org/10.1007/BF02775087, 1997. a, b
Li, W., Lu, W., Qian, J., and Li, Y.: A multiple level-set method for 3D inversion of magnetic data, Geophysics, 82, J61–J81, https://doi.org/10.1190/GEO2016-0530.1, 2017. a
Li, Y. and Oldenburg, D. W.: 3-D inversion of gravity data, Geophysics, 63,
109–119, https://doi.org/10.1190/1.1444302, 1998. a
Linde, N., Ginsbourger, D., Irving, J., Nobile, F., and Doucet, A.: On
uncertainty quantification in hydrogeology and hydrogeophysics, Adv. Water Resour., 110, 166–181, https://doi.org/10.1016/j.advwatres.2017.10.014, 2017. a
Lindsay, M., Jessell, M., Ailleres, L., Perrouty, S., de Kemp, E., and Betts,
P.: Geodiversity: Exploration of 3D geological model space, Tectonophysics,
594, 27–37, https://doi.org/10.1016/j.tecto.2013.03.013, 2013. a
Lindsay, M. D., Aillères, L., Jessell, M. W., de Kemp, E. A., and Betts, P. G.: Locating and quantifying geological uncertainty in three-dimensional
models: Analysis of the Gippsland Basin, southeastern Australia, Tectonophysics, 546–547, 10–27, https://doi.org/10.1016/j.tecto.2012.04.007, 2012. a
McCalman, L., O'Callaghan, S. T., Reid, A., Shen, D., Carter, S., Krieger, L., Beardsmore, G. R., Bonilla, E. V., and Ramos, F. T.: Distributed Bayesian
geophysical inversions, in: Proceedings of the Thirty-Ninth Workshop on
Geothermal Reservoir Engineering, Stanford University, Stanford, California, USA, 1–11, 2014. a, b, c
Metropolis, N. and Ulam, S.: The Monte Carlo Method, J. Am. Stat. Assoc., 44, 335–341, 1949. a
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., and
Teller, E.: Equation of State Calculations by Fast Computing Machines, J. Chem. Phys., 21, 1087–1092, https://doi.org/10.1063/1.1699114, 1953. a
Moczo, P., Kristek, J., Vavrycuk, V., Archuleta, R. J., and Halada, L.: 3d heterogeneous staggered-grid finite-difference modeling of seismic motion
with volume harmonic and arithmetic averaging of elastic moduli and densities, Bull. Seismol. Soc. Am., 92, 3042–3066, 2002. a
Mosegaard, K. and Sambridge, M.: Monte Carlo analysis of inverse problems,
Inverse Problems, 18, R29–R54, https://doi.org/10.1088/0266-5611/18/3/201, 2002. a, b
Mosegaard, K. and Tarantola, A.: Monte Carlo sampling of solutions to inverse
problems, J. Geophys. Res.-Solid, 100, 12431–12447, https://doi.org/10.1029/94JB03097, 1995. a, b
Muir, F., Dellinger, J., Etgen, J., and Nichols, D.: Modeling elastic fields
across irregular boundaries, Geophysics, 57, 1189–1193, 1992. a
Nishimura, A., Dunson, D., and Lu, J.: Discontinuous Hamiltonian Monte Carlo for discrete parameters and discontinuous likelihoods, Biometrika, 107, 365–380, https://doi.org/10.1093/biomet/asz083, 2020. a
Ogarko, V., Giraud, J., Martin, R., and Jessell, M.: Disjoint interval bound
constraints using the alternating direction method of multipliers for
geologically constrained inversion: Application to gravity data, Geophysics,
86, G1–G11, https://doi.org/10.1190/geo2019-0633.1, 2021. a
Okabe, M.: Analytical expressions for gravity anomalies due to homogeneous
polyhedral bodies and translations into magnetic anomalies, Geophysics, 44,
730, https://doi.org/10.1190/1.1440973, 1979. a
Olierook, H. K., Scalzo, R., Kohn, D., Chandra, R., Farahbakhsh, E., Clark, C., Reddy, S. M., and Müller, R. D.: Bayesian geological and geophysical data
fusion for the construction and uncertainty quantification of 3D geological
models, Geosci. Front., 12, 479–493, https://doi.org/10.1016/j.gsf.2020.04.015, 2020. a
Osher, S. and Sethian, J. A.: Fronts propagating with curvature-dependent
speed: Algorithms based on Hamilton-Jacobi formulations, J. Comput. Phys., 79, 12–49, https://doi.org/10.1016/0021-9991(88)90002-2, 1988. a
Öztireli, A. C.: A Comprehensive Theory and Variational Framework for
Anti-aliasing Sampling Patterns, Comput. Graph. Forum, 39, 133–148, 2020. a
Pakyuz-Charrier, E., Giraud, J., Ogarko, V., Lindsay, M., and Jessell, M.:
Drillhole uncertainty propagation for three-dimensional geological modeling
using Monte Carlo, Tectonophysics, 747–748, 16–39, https://doi.org/10.1016/j.tecto.2018.09.005, 2018a. a
Pakyuz-Charrier, E., Lindsay, M., Ogarko, V., Giraud, J., and Jessell, M.:
Monte Carlo simulation for uncertainty estimation on structural data in implicit 3-D geological modeling, a guide for disturbance distribution selection and parameterization, Solid Earth, 9, 385–402,
https://doi.org/10.5194/se-9-385-2018, 2018b. a, b, c
Pakyuz-Charrier, E., Jessell, M., Giraud, J., Lindsay, M., and Ogarko, V.:
Topological analysis in Monte Carlo simulation for uncertainty propagation,
Solid Earth, 10, 1663–1684, https://doi.org/10.5194/se-10-1663-2019, 2019. a
Patil, A., Huard, D., and Fonnesbeck, C. J.: PyMC: Bayesian stochastic
modelling in Python, J. Stat. Softw., 35, 1–81, https://doi.org/10.18637/jss.v035.i04, 2010. a
Perrouty, S., Lindsay, M., Jessell, M., Aillères, L., Martin, R., and
Bourassa, Y.: 3D modeling of the Ashanti Belt, southwest Ghana: Evidence for
a litho-stratigraphic control on gold occurrences within the Birimian Sefwi
Group, Ore Geol. Rev., 63, 252–264, https://doi.org/10.1016/j.oregeorev.2014.05.011, 2014. a
Pirot, G., Renard, P., Huber, E., Straubhaar, J., and Huggenberger, P.:
Influence of conceptual model uncertainty on contaminant transport forecasting in braided river aquifers, J. Hydrol., 531, 124–141,
https://doi.org/10.1016/j.jhydrol.2015.07.036, 2015. a
Pirot, G., Linde, N., Mariethoz, G., and Bradford, J. H.: Probabilistic
inversion with graph cuts: Application to the Boise Hydrogeophysical Research
Site, Water Resour. Res., 53, 1231–1250, https://doi.org/10.1002/2016WR019347, 2017. a
Pirot, G., Huber, E., Irving, J., and Linde, N.: Reduction of conceptual model uncertainty using ground-penetrating radar profiles: Field-demonstration for a braided-river aquifer, J. Hydrol., 571, 254–264,
https://doi.org/10.1016/j.jhydrol.2019.01.047, 2019a. a
Pirot, G., Krityakierne, T., Ginsbourger, D., and Renard, P.: Contaminant
source localization via Bayesian global optimization, Hydrol. Earth Syst. Sci., 23, 351–369, https://doi.org/10.5194/hess-23-351-2019, 2019b. a
Quigley, M. C., Bennetts, L. G., Durance, P., Kuhnert, P. M., Lindsay, M. D.,
Pembleton, K. G., Roberts, M. E., and White, C. J.: The provision and utility of science and uncertainty to decision-makers: earth science case studies, Environ. Syst. Decis., 39, 307–348, https://doi.org/10.1007/s10669-019-09728-0, 2019. a
Rawlinson, N., Fichtner, A., Sambridge, M., and Young, M. K.: Seismic
Tomography and the Assessment of Uncertainty, in: Advances in Geophysics, vol. 55, Elsevier, 1–76, https://doi.org/10.1016/bs.agph.2014.08.001, 2014. a, b, c
Scalzo, R. A.: rscalzo/blockworlds: (v0.1.0-beta.3), Zenodo [code], https://doi.org/10.5281/zenodo.5759225, 2021. a
Sambridge, M., Bodin, T., Gallagher, K., and Tkalcic, H.: Transdimensional
inference in the geosciences, Philos. T. Roy. Soc. A, 371, 20110547, https://doi.org/10.1098/rsta.2011.0547, 2012.
a, b
Santosa, F.: A level-set approach for inverse problems involving obstacles,
ESAIM: Control, Optimisation and Calculus of Variations, 1, 17–33,
https://doi.org/10.1051/cocv:1996101, 1996. a
Scalzo, R., Kohn, D., Olierook, H., Houseman, G., Chandra, R., Girolami, M.,
and Cripps, S.: Efficiency and robustness in Monte Carlo sampling for 3-D geophysical inversions with Obsidian v0.1.2: setting up for success,
Geosci. Model Dev., 12, 2941–2960, https://doi.org/10.5194/gmd-12-2941-2019, 2019. a, b, c, d
Schmidt, S., Anikiev, D., Götze, H.-J., Gomez Garcia, A., Gomez Dacal, M. L., Meessen, C., Plonka, C., Rodriguez Piceda, C., Spooner, C., and
Scheck-Wenderoth, M.: IGMAS+ – a tool for interdisciplinary 3D potential
field modelling of complex geological structures, in: EGU General Assembly 2020, EGU2020-8383, https://doi.org/10.5194/egusphere-egu2020-8383, 2020. a
Tarantola, A. and Valette, B.: Generalized nonlinear inverse problems solved
using the least squares criterion, Rev. Geophys., 20, 219–232,
https://doi.org/10.1029/RG020i002p00219, 1982. a
Varouchakis, E. A., Yetilmezsoy, K., and Karatzas, G. P.: A decision-making
framework for sustainable management of groundwater resources under
uncertainty: combination of Bayesian risk approach and statistical tools,
Water Policy, 21, 602–622, https://doi.org/10.2166/wp.2019.128, 2019. a
Wang, Z., Yin, Z., Caers, J., and Zuo, R.: A Monte Carlo-based framework for
risk-return analysis in mineral prospectivity mapping, Geosci. Front., 11, 2297–2308, https://doi.org/10.1016/j.gsf.2020.02.010, 2020. a
Wellmann, F. and Caumon, G.: 3-D Structural geological models: Concepts,
methods, and uncertainties, in: Advances in Geophysics, vol. 59, Elsevier, 1–121, https://doi.org/10.1016/bs.agph.2018.09.001, 2018. a, b, c, d
Wellmann, J. F. and Regenauer-Lieb, K.: Uncertainties have a meaning: Information entropy as a quality measure for 3-D geological models,
Tectonophysics, 526–529, 207–216, https://doi.org/10.1016/j.tecto.2011.05.001, 2012. a
Wellmann, J. F., Thiele, S. T., Lindsay, M. D., and Jessell, M. W.: pynoddy 1.0: an experimental platform for automated 3-D kinematic and potential field modelling, Geosci. Model Dev., 9, 1019–1035,
https://doi.org/10.5194/gmd-9-1019-2016, 2016. a
Wellmann, J. F., de la Varga, M., Murdie, R. E., Gessner, K., and Jessell, M.: Uncertainty estimation for a geological model of the Sandstone greenstone
belt, Western Australia – insights from integrated geological and geophysical inversion in a Bayesian inference framework, Geol. Soc. Lond. Spec. Publ., 453, 41–56, https://doi.org/10.1144/SP453.12, 2017. a, b, c, d
Witter, J. B., Trainor-Guitton, W. J., and Siler, D. L.: Uncertainty and risk
evaluation during the exploration stage of geothermal development: A review,
Geothermics, 78, 233–242, https://doi.org/10.1016/j.geothermics.2018.12.011, 2019. a
Zhdanov, M. S. and Liu, X.: 3-D Cauchy-type integrals for terrain correction of gravity and gravity gradiometry data, Geophys. J. Int., 194, 249–268, https://doi.org/10.1093/gji/ggt120, 2013. a
Zheglova, P., Lelievre, P. G., and Farquharson, C. G.: Multiple level-set joint inversion of traveltime and gravity data with application to ore delineation: A synthetic study, Geophysics, 83, R13–R30, https://doi.org/10.1190/GEO2016-0675.1, 2018. a
Short summary
This paper addresses numerical challenges in reasoning about geological models constrained by sensor data, especially models that describe the history of an area in terms of a sequence of events. Our method ensures that small changes in simulated geological features, such as the position of a boundary between two rock layers, do not result in unrealistically large changes to resulting sensor measurements, as occur presently using several popular modeling packages.
This paper addresses numerical challenges in reasoning about geological models constrained by...
