Articles | Volume 12, issue 1
https://doi.org/10.5194/gmd-12-1-2019
© Author(s) 2019. 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-12-1-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
02 Jan 2019
Development and technical paper |
| 02 Jan 2019
| 02 Jan 2019
GemPy 1.0: open-source stochastic geological modeling and inversion
Miguel de la Varga
CORRESPONDING AUTHOR
Institute for Computational Geoscience and Reservoir Engineering, RWTH
Aachen University, Aachen, Germany
Alexander Schaaf
Institute for Computational Geoscience and Reservoir Engineering, RWTH
Aachen University, Aachen, Germany
Florian Wellmann
Institute for Computational Geoscience and Reservoir Engineering, RWTH
Aachen University, Aachen, Germany
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Alexander Schaaf, Miguel de la Varga, Florian Wellmann, and Clare E. Bond
Geosci. Model Dev., 14, 3899–3913, https://doi.org/10.5194/gmd-14-3899-2021, https://doi.org/10.5194/gmd-14-3899-2021, 2021
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Uncertainty is an inherent property of any model of the subsurface. We show how geological topology information – how different regions of rocks in the subsurface are connected – can be used to train uncertain geological models to reduce uncertainty. More widely, the method demonstrates the use of probabilistic machine learning (Bayesian inference) to train structural geological models on auxiliary geological knowledge that can be encoded in graph structures.
Fabian Antonio Stamm, Miguel de la Varga, and Florian Wellmann
Solid Earth, 10, 2015–2043, https://doi.org/10.5194/se-10-2015-2019, https://doi.org/10.5194/se-10-2015-2019, 2019
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.
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.
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.
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.
Alexander Schaaf, Miguel de la Varga, Florian Wellmann, and Clare E. Bond
Geosci. Model Dev., 14, 3899–3913, https://doi.org/10.5194/gmd-14-3899-2021, https://doi.org/10.5194/gmd-14-3899-2021, 2021
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Uncertainty is an inherent property of any model of the subsurface. We show how geological topology information – how different regions of rocks in the subsurface are connected – can be used to train uncertain geological models to reduce uncertainty. More widely, the method demonstrates the use of probabilistic machine learning (Bayesian inference) to train structural geological models on auxiliary geological knowledge that can be encoded in graph structures.
Stephanie Thiesen, Diego M. Vieira, Mirko Mälicke, Ralf Loritz, J. Florian Wellmann, and Uwe Ehret
Hydrol. Earth Syst. Sci., 24, 4523–4540, https://doi.org/10.5194/hess-24-4523-2020, https://doi.org/10.5194/hess-24-4523-2020, 2020
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A spatial interpolator has been proposed for exploring the information content of the data in the light of geostatistics and information theory. It showed comparable results to traditional interpolators, with the advantage of presenting generalization properties. We discussed three different ways of combining distributions and their implications for the probabilistic results. By its construction, the method provides a suitable and flexible framework for uncertainty analysis and decision-making.
Fabian Antonio Stamm, Miguel de la Varga, and Florian Wellmann
Solid Earth, 10, 2015–2043, https://doi.org/10.5194/se-10-2015-2019, https://doi.org/10.5194/se-10-2015-2019, 2019
Alexander Schaaf and Clare E. Bond
Solid Earth, 10, 1049–1061, https://doi.org/10.5194/se-10-1049-2019, https://doi.org/10.5194/se-10-1049-2019, 2019
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Seismic reflection data allow us to infer subsurface structures such as horizon and fault surfaces. The interpretation of this indirect data source is inherently uncertainty, and our work takes a first look at the scope of uncertainties involved in the interpretation of 3-D seismic data. We show how uncertainties of fault interpretations can be related to data quality and discuss the implications for the 3-D modeling of subsurface structures derived from 3-D seismic data.
Raphael Schneeberger, Miguel de La Varga, Daniel Egli, Alfons Berger, Florian Kober, Florian Wellmann, and Marco Herwegh
Solid Earth, 8, 987–1002, https://doi.org/10.5194/se-8-987-2017, https://doi.org/10.5194/se-8-987-2017, 2017
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Structural 3-D modelling has become a widely used technique within applied projects. We performed a typical modelling workflow for a study site with the occurrence of an underground facility. This exceptional setting enabled us to test the surface-based extrapolation of faults with the mapped faults underground. We estimated the extrapolation-related uncertainty with probabilistic 2-D interpolation. This research was conducted to improve structural 3-D modelling in less-constrained areas.
J. Florian Wellmann, Sam T. Thiele, Mark D. Lindsay, and Mark W. Jessell
Geosci. Model Dev., 9, 1019–1035, https://doi.org/10.5194/gmd-9-1019-2016, https://doi.org/10.5194/gmd-9-1019-2016, 2016
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We often obtain knowledge about the subsurface in the form of structural geological models, as a basis for subsurface usage or resource extraction. Here, we provide a modelling code to construct such models on the basis of significant deformational events in geological history, encapsulated in kinematic equations. Our methods simplify complex dynamic processes, but enable us to evaluate how events interact, and finally how certain we are about predictions of structures in the subsurface.
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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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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.
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.
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.
Matt J. Fischer
EGUsphere, https://doi.org/10.5194/egusphere-2023-643, https://doi.org/10.5194/egusphere-2023-643, 2023
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In paleoclimate research, proxy system models or PSMs model chemical or biological systems which receive environmental inputs, and output e.g. geochemical signals. The environmental inputs are rarely noiseless, which causes problems when calibrating multi-input PSMs. Here a PSM is developed which includes generalized noise in both model inputs and outputs, and prior information. A quasi-Bayesian method enhances the stability of the solution of the Measurement Error Proxy System Model.
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.
John Mern and Jef Caers
Geosci. Model Dev., 16, 289–313, https://doi.org/10.5194/gmd-16-289-2023, https://doi.org/10.5194/gmd-16-289-2023, 2023
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In this work, we formulate the sequential geoscientific data acquisition problem as a problem that is similar to playing chess against nature, except the pieces are not fully observed. Solutions to these problems are given in AI and rarely used in geoscientific data planning. We illustrate our approach to a simple 2D problem of mineral exploration.
Ziqi Gao, Yifeng Wang, Petros Vasilakos, Cesunica E. Ivey, Khanh Do, and Armistead G. Russell
Geosci. Model Dev., 15, 9015–9029, https://doi.org/10.5194/gmd-15-9015-2022, https://doi.org/10.5194/gmd-15-9015-2022, 2022
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While the national ambient air quality standard of ozone is based on the 3-year average of the fourth highest 8 h maximum (MDA8) ozone concentrations, these predicted extreme values using numerical methods are always biased low. We built four computational models (GAM, MARS, random forest and SVR) to predict the fourth highest MDA8 ozone in Southern California using precursor emissions, meteorology and climatological patterns. All models presented acceptable performance, with GAM being the best.
Zhihao Wang, Jason Goetz, and Alexander Brenning
Geosci. Model Dev., 15, 8765–8784, https://doi.org/10.5194/gmd-15-8765-2022, https://doi.org/10.5194/gmd-15-8765-2022, 2022
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A lack of inventory data can be a limiting factor in developing landslide predictive models, which are crucial for supporting hazard policy and decision-making. We show how case-based reasoning and domain adaptation (transfer-learning techniques) can effectively retrieve similar landslide modeling situations for prediction in new data-scarce areas. Using cases in Italy, Austria, and Ecuador, our findings support the application of transfer learning for areas that require rapid model development.
Cited articles
Aug, C.: Modélisation géologique 3D et caractérisation des
incertitudes
par la méthode du champ de potentiel: PhD thesis, PhD thesis, ENSMP,
Paris, 2004.
Ayachit, U.: The ParaView Guide: A Parallel Visualization Application,
Kitware, Inc., USA, 2015.
Bardossy, G. and Fodor, J.: Evaluation of Uncertainties and Risks in
Geology: New Mathematical Approaches for their Handling, Springer, Berlin,
Germany,
2004.
Baydin, A. G., Pearlmutter, B. A., Radul, A. A., and Siskind, J. M.:
Automatic differentiation in machine learning: a survey, arXiv preprint
arXiv:1502.05767, 2015.
Bellman, R.: Dynamic Programming, Courier Corporation, Dover Books on Computer Science,
366 pp., ISBN:9780486317199, 2013.
Betancourt, M., Byrne, S., Livingstone, S., and Girolami, M.,: The geometric
foundations of Hamiltonian Monte Carlo, Bernoulli, 23, 2257–2298, 2017.
Bishop, C. M.: Model-based machine learning, Philos. T. R. Soc. A, 371,
https://doi.org/10.1098/rsta.2012.0222, 2013.
Blender Online Community: Blender – a 3D modelling and rendering package,
Blender Foundation, Blender Institute, Amsterdam,
available at: http://www.blender.org (last access: 17 December 2018), 2017.
Bond, C. E.: Uncertainty in structural interpretation: Lessons to be learnt,
J. Struct. Geol., 74, 185–200, 2015.
Bond, C. E., Gibbs, A. D., Shipton, Z. K., and Jones, S.: What do you think
this is? “Conceptual uncertainty” in
geoscience interpretation, GSA Today, 17, https://doi.org/10.1130/GSAT01711A.1, 2007.
Caers, J.: Introduction, in: Modeling Uncertainty in the Earth Sciences,
John Wiley & Sons, Ltd, Chichester, UK, 1–8, 2011.
Calcagno, P., Chiles, J.-P., Courrioux, G., and Guillen, A.: Geological
modelling from field data and geological knowledge: Part I. Modelling method
coupling 3D potential-field interpolation and geological rules: Recent
Advances in Computational Geodynamics: Theory, Numerics and Applications,
Phys. Earth Planet. In., 171, 147–157, 2008.
Caumon, G.: Towards Stochastic Time-Varying Geological Modeling, Math.
Geosci., 42, 555–569, 2010.
Caumon, G., Collon-Drouaillet, P., Le Carlier de Veslud, C., Viseur, S., and
Sausse, J.: Surface-Based 3D Modeling of Geological Structures,
Math. Geosci., 41, 927–945, 2009.
Caumon, G., Gray, G., Antoine, C., and Titeux, M.-O.:
Three-Dimensional Implicit Stratigraphic Model Building From Remote Sensing
Data on Tetrahedral Meshes: Theory and Application to a Regional Model of La
Popa Basin, NE Mexico, IEEE T. Geosci. Remote,
51, 1613–1621, 2013.
Chatfield, C.: Model Uncertainty, Data Mining and Statistical Inference,
J. Roy. Stat. Soc. A,
158, 419–466, 1995.
Chiles, J.-P. and Delfiner, P.: Geostatistics: modeling spatial uncertainty,
vol. 497, John Wiley & Sons, 2009.
Chiles, J.-P., Aug, C., Guillen, A., and Lees, T.: Modelling of Geometry of
Geological Units and its Uncertainty in 3D From Structural
Data:The Potential-Field Method, Perth, 2004.
Christakos, G.: On the assimilation of uncertain physical knowledge bases:
Bayesian and non-Bayesian techniques, Adv. Water Resour., 25,
1257–1274, 2002.
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, 2015.
Cohen, J. S.: Computer algebra and symbolic computation: Mathematical
methods, Universities Press, 2003.
Dagum, L. and Menon, R.: OpenMP: an industry standard API for shared-memory
programming, IEEE Comput. Sci. Eng., 5, 46–55, 1998.
de la Varga, M. and Wellmann, J. F.: Structural geologic modeling as an
inference problem: A Bayesian perspective, Interpretation, 4, 1–16, 2016.
de la Varga, M., Schaaf, A., Wellmann, F.,
Meeßen, F. C., and Wagner, F.: cgre-aachen/gempy: GemPy 1.0
pre-release (Version 1.0), Zenodo, https://doi.org/10.5281/zenodo.1186118,
2018.
Dentith, M. and Mudge, S. T.: Geophysics for the mineral exploration
geoscientist, Cambridge University Press, 2014.
Duane, S., Kennedy, A. D., Pendleton, B. J., and Roweth, D.: Hybrid monte
carlo, Phys. Lett. B, 195, 216–222, 1987.
Fiorio, C. and Gustedt, J.: Two linear time union-find strategies for image
processing, Theor. Comput. Sci., 154, 165–181, 1996.
Geuzaine, C. and Remacle, J.-F.: Gmsh: A 3-D finite element mesh generator
with
built-in pre-and post-processing facilities, Int. J.
Numer. Meth. Eng., 79, 1309–1331, 2009.
Geweke, J.: Evaluating the accuracy
of sampling-based approaches to the
calculation of posterior moments, vol. 196, Federal Reserve Bank of
Minneapolis, Research Department Minneapolis, MN, USA, 1991.
Gonçalves, Í. G., Kumaira, S., and Guadagnin, F.: A machine
learning approach to the potential-field method for implicit modeling of
geological structures, Comput. Geosci., 103, 173–182, 2017.
Haario, H., Saksman, E., and Tamminen, J.: An adaptive metropolis algorithm,
Bernoulli, 7 223–242, 2001.
Hillier, M. J., Schetselaar, E. M., de Kemp, E. A., and Perron, G.:
Three-Dimensional Modelling of Geological Surfaces Using Generalized
Interpolation with Radial Basis Functions, Math. Geosci., 46,
931–953, 2014.
Hoffman, M. D. and Gelman, A.: The No-U-turn sampler: adaptively setting path
lengths in Hamiltonian Monte Carlo, J. Mach. Learn. Res.,
15, 1593–1623, 2014.
Hunter, J. D.: Matplotlib: A 2D graphics environment, Comput. Sci.
Eng., 9, 90–95, 2007.
Jaccard, P.: The distribution of the flora in the alpine zone, New
Phytol., 11, 37–50, 1912.
Jessell, M. W., Ailleres, L., and Kemp, A. E.: Towards an Integrated
Inversion of Geoscientific data: what price of Geology?, Tectonophysics,
490, 294–306, 2010.
Jordan, M. I.: Learning in graphical models, vol. 89, Springer Science &
Business Media, 1998.
Koller, D. and Friedman, N.: Probabilistic Graphical Models: Principles and Techniques – Adaptive Computation and
Machine Learning, The MIT Press, 2009.
Kucukelbir, A., Ranganath, R., Gelman, A., and Blei, D.: Automatic
variational inference in Stan, Proceedings of the 28th International
Conference on Neural Information Processing Systems, Montreal, Canada, 1, 568–576,
2015.
Kucukelbir, A., Tran, D., Ranganath, R., Gelman, A., and Blei, D. M.:
Automatic Differentiation Variational Inference, arXiv preprint
arXiv:1603.00788, J. Mach. Learn. Res., 18, 430–474, 2017.
Lajaunie, C., Courrioux, G., and Manuel, L.: Foliation fields and 3D
cartography in geology: Principles of a method based on potential
interpolation, Math. Geol., 29, 571–584, 1997.
Lark, R. M., Mathers, S. J., Thorpe, S., Arkley, S. L. B., Morgan, D. J., and
Lawrence, D. J. D.: A statistical assessment of the uncertainty in a 3-D
geological framework model, P. Geologists Assoc., 124,
946–958, 2013.
Lauritzen, S. L., Dawid, A. P., Larsen, B. N., and Leimer, H.-G.:
Independence
properties of directed Markov fields, Networks, 20, 491–505, 1990.
Lawson, C. L., Hanson, R. J., Kincaid, D. R., and Krogh, F. T.: Basic linear
algebra subprograms for Fortran usage, ACM T. Math.
Software, 5, 308–323, 1979.
Lindsay, M., Ailleres, L., Jessell, M. W., de Kemp, E., 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, 2012.
Lindsay, M. D., Jessell, M. W., Ailleres, L., Perrouty, S., de Kemp, E., and
Betts, P. G.: Geodiversity: Exploration of 3D geological model space,
Tectonophysics, 594, 27–37, 2013a.
Lindsay, M. D., Perrouty, S., Jessell, M. W., and Ailleres, L.: Making the
link between geological and geophysical uncertainty: geodiversity in the
Ashanti Greenstone Belt, Geophys. J. Int., 195, 903–922,
2013b.
Lorensen, W. E. and Cline, H. E.: Marching cubes: A high resolution 3D
surface
construction algorithm, in: ACM siggraph computer graphics, 21,
163–169, 1987.
Mallet, J.-L.: Space-time mathematical framework for sedimentary geology,
Math. Geol., 36, 1–32, 2004.
Marechal, A.: Kriging seismic data in presence of faults, in: Geostatistics
for natural resources characterization, Springer, 271–294, 1984.
Matheron, G.: Splines and kriging: their formal equivalence,
Down-to-earth-statistics: Solutions looking for geological problems,
77–95, 1981.
McKinney, W.: pandas: a foundational Python library for data analysis and
statistics, Python for High Performance and Scientific Computing, 1–9,
2011.
McLane, M., Gouveia, J., Citron, G. P., MacKay, J., and Rose, P. R.:
Responsible reporting of uncertain petroleum reserves, AAPG Bull., 92,
1431–1452, 2008.
Mosegaard, K. and Tarantola, A.: Monte Carlo sampling of solutions to
inverse
problems, J. Geophys. Res., 100, 12–431, 1995.
Nabighian, M. N., Ander, M. E., Grauch, V. J. S., Hansen, R. O., LaFehr, T.
R., Li, Y., Pearson, W. C., Peirce, J. W., Phillips, J. D., and
Ruder, M. E.: Historical development of the gravity
method in exploration, Geophysics, 70, 63ND–89ND, https://doi.org/10.1190/1.2133785, 2005.
Nagy, D.: The gravitational attraction of a right rectangular prism,
Geophysics, 31, 362–371, 1966.
Ogilvie, J. F.: A Monte-Carlo approach to error propagation, Comput.
Chem., 8, 205–207, 1984.
Patil, A., Huard, D., and Fonnesbeck, C. J.: PyMC: Bayesian stochastic
modelling in Python, J. Stat. Softw., 35, 1–81, 2010.
Rall, L. B.: Automatic differentiation: Techniques and Applications, Lecture Notes in Computer
Science, Springer-Verlag Berlin Heidelberg, https://doi.org/10.1007/3-540-10861-0, 1981.
Robitaille, T. P., Tollerud, E. J., Greenfield, P.,
Droettboom, M., Bray, E., Aldcroft, T., Davis, M., Ginsburg, A.,
Price-Whelan, A. M., Kerzendorf, W. E., Conley, A., Crighton, N.,
Barbary, K., Muna, D., Ferguson, H., Grollier, F.,
Parikh, M. M., Nair, P. H., Gunther, H. M., Deil, C., Woillez, J.,
Conseil, S., Kramer, R., Turner, J. E. H., Singer, L., Fox, R.,
Weaver, B. A., Zabalza, V., Edwards, Z. I., Azalee Bostroem, K.,
Burke, D. J., Casey, A. R., Crawford, S. M., Dencheva, N., Ely, J.,
Jenness, T., Labrie, K., Lim, P. L., Pierfederici, F.,
Pontzen, A., Ptak, A., Refsdal, B., Servillat, M., and
Streicher, O.: Astropy: A community Python package for astronomy, Astron.
Astrophys., 558, https://doi.org/10.1051/0004-6361/201322068, 2013.
Salvatier, J., Wiecki, T. V., and Fonnesbeck, C.: Probabilistic programming
in Python using PyMC3, PeerJ Computer Science, 2, 55,
https://doi.org/10.7717/peerj-cs.55,
2016.
Sambridge, M. and Mosegaard, K.: Monte Carlo methods in geophysical inverse
problems, Rev. Geophys, 40, https://doi.org/10.1029/2000RG000089, 2002.
Schaaf, A.: Geological Inference based on Kinematic Structural Models,
Master's thesis, RWTH Aachen University, Aachen, Germany, 2017.
Schroeder, W. J., Lorensen, B., and Martin, K.: The visualization toolkit: an
object-oriented approach to 3D graphics, Kitware, 2004.
Shannon, E. C.: A mathematical theory of communication, AT&T Tech. J., 27,
https://doi.org/10.1002/j.1538-7305.1948.tb01338.x, 1948.
Stamm, F. A.: Bayesian Decision Theory in Structural Geological Modeling –
How Reducing Uncertainties Affects Reservoir Value Estimations, Master's
thesis, RWTH Aachen University, Aachen, Germany, 2017.
Tarantola, A.: Inverse problem theory and methods for model parameter
estimation, Society for Industrial Mathematics, 2005.
Tarantola, A. and Valette, B.: Inverse Problems = Quest for Information,
J. Geophys., 50, 159–170, 1982.
Theano Development Team: Theano: A Python framework for fast computation
of mathematical expressions, arXiv e-prints, abs/1605.02688,
http://arxiv.org/abs/1605.02688 (last access: 17 December 2018), 2016.
Thiele, S. T., Jessell, M. W., Lindsay, M., Ogarko, V., Wellmann, J. F., and
Pakyuz-Charrier, E.: The topology of geology 1: Topological analysis,
J. Struct. Geol., 91, 27–38, 2016a.
Thiele, S. T., Jessell, M. W., Lindsay, M., Wellmann, J. F., and
Pakyuz-Charrier, E.: The topology of geology 2: Topological uncertainty,
J. Struct. Geol., 91, 74–87, 2016b.
van der Walt, S., Schönberger, J. L., Nunez-Iglesias, J., Boulogne, F.,
Warner, J. D., Yager, N., Gouillart, E., and Yu, T.: scikit-image: image
processing in Python, PeerJ, 2, 453, https://doi.org/10.7717/peerj.453, 2014.
van der Walt, S., Schönberger, J. L., Nunez-Iglesias, J., Boulogne,
F., Warner, J. D., Yager, N., Gouillart, E., Yu, T., and the scikit-image
contributors: scikit-image: image processing in Python, PeerJ, 2, e453,
https://doi.org/10.7717/peerj.453,
2014.
van Rossum, G., Warsaw, B., and Coghlan, N.: PEP 8: style guide for Python
code, available at: http://www.python.org/dev/peps/pep-0008/ (last access: 17 December 2018),
2001.
Wackernagel, H.: Multivariate geostatistics: an introduction with
applications, Springer Science & Business Media, 2013.
Walt, S. V. D., Colbert, S. C., and Varoquaux, G.: The NumPy array: a
structure for efficient numerical computation, Comput. Sci. Eng., 13,
22–30, 2011.
Wang, H., Wellmann, J. F., Li, Z., Wang, X., and Liang, R. Y.: A
Segmentation
Approach for Stochastic Geological Modeling Using Hidden Markov Random
Fields, Math. Geosci., 49, 145–177, 2017.
Waskom, M., Botvinnik, O., O'Kane, D., Hobson, P., Lukauskas, S., Gemperline,
D. C., Augspurger, T., Halchenko, Y., Cole, J. B., Warmenhoven, J.,
de Ruiter, J., Pye, C., Hoyer, S., Vanderplas, J., Villalba, S., Kunter, G.,
Quintero, E., Bachant, P., Martin, M., Meyer, K., Miles, A., Ram, Y.,
Yarkoni, T., Williams, M. L., Evans, C., Fitzgerald, C. B., Fonnesbeck,
C., Lee, A., and Qalieh, A.: mwaskom/seaborn: v0.8.1 (September 2017),
https://doi.org/10.5281/zenodo.883859, 2017.
Wellmann, F. and Caumon, G.: 3-D Structural geological models: Concepts, methods, and
uncertainties, Cedric Schmelzbach, Adv. Geophys., 59, Elsevier, 1–121,
https://doi.org/10.1016/bs.agph.2018.09.001, 2018.
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, 2012.
Wellmann, J. F., Horowitz, F. G., Schill, E., and Regenauer-Lieb, K.:
Towards
incorporating uncertainty of structural data in 3D geological inversion,
Tectonophysics, 490, 141–151, 2010.
Wellmann, J. F., Lindsay, M., Poh, J., and Jessell, M. W.: Validating 3-D
Structural Models with Geological Knowledge for Improved Uncertainty
Evaluations, Energy Proced., 59, 374–381, 2014.
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.
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. Spec. Publ., 453,
SP453–12, https://doi.org/10.1144/SP453.12, 2017.
Wu, K., Otoo, E., and Shoshani, A.: Optimizing connected component labeling
algorithms, Lawrence Berkeley National Laboratory, 2005.
Short summary
GemPy is an open-source Python-based 3-D structural geological modeling software, which allows the implicit (i.e. automatic) creation of complex geological models from interface and orientation data. GemPy is implemented in the programming language Python, making use of a highly efficient underlying library, Theano, for efficient code generation that performs automatic differentiation. This enables the link to probabilistic machine-learning and Bayesian inference frameworks.
GemPy is an open-source Python-based 3-D structural geological modeling software, which allows...
