Articles | Volume 14, issue 8
Geosci. Model Dev., 14, 5063–5092, 2021
https://doi.org/10.5194/gmd-14-5063-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue: The Loop 3D stochastic geological modelling platform – development...
Geosci. Model Dev., 14, 5063–5092, 2021
https://doi.org/10.5194/gmd-14-5063-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
16 Aug 2021
Development and technical paper
| 16 Aug 2021
Automated geological map deconstruction for 3D model construction using map2loop 1.0 and map2model 1.0
Mark Jessell et al.
Related authors
Guillaume Pirot, Ranee Joshi, Jérémie Giraud, Mark Douglas Lindsay, and Mark Walter Jessell
Geosci. Model Dev., 15, 4689–4708, https://doi.org/10.5194/gmd-15-4689-2022, https://doi.org/10.5194/gmd-15-4689-2022, 2022
Short summary
Short summary
Results of a survey launched among practitioners in the mineral industry show that despite recognising the importance of uncertainty quantification it is not very well performed due to lack of data, time requirements, poor tracking of interpretations and relative complexity of uncertainty quantification. To alleviate the latter, we provide an open-source set of local and global indicators to measure geological uncertainty among an ensemble of geological models.
Richard Scalzo, Mark Lindsay, Mark Jessell, Guillaume Pirot, Jeremie Giraud, Edward Cripps, and Sally Cripps
Geosci. Model Dev., 15, 3641–3662, https://doi.org/10.5194/gmd-15-3641-2022, https://doi.org/10.5194/gmd-15-3641-2022, 2022
Short summary
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.
Mark Jessell, Jiateng Guo, Yunqiang Li, Mark Lindsay, Richard Scalzo, Jérémie Giraud, Guillaume Pirot, Ed Cripps, and Vitaliy Ogarko
Earth Syst. Sci. Data, 14, 381–392, https://doi.org/10.5194/essd-14-381-2022, https://doi.org/10.5194/essd-14-381-2022, 2022
Short summary
Short summary
To robustly train and test automated methods in the geosciences, we need to have access to large numbers of examples where we know
the answer. We present a suite of synthetic 3D geological models with their gravity and magnetic responses that allow researchers to test their methods on a whole range of geologically plausible models, thus overcoming one of the fundamental limitations of automation studies.
Ranee Joshi, Kavitha Madaiah, Mark Jessell, Mark Lindsay, and Guillaume Pirot
Geosci. Model Dev., 14, 6711–6740, https://doi.org/10.5194/gmd-14-6711-2021, https://doi.org/10.5194/gmd-14-6711-2021, 2021
Short summary
Short summary
We have developed a software that allows the user to extract and standardize drill hole information from legacy datasets and/or different drilling campaigns. It also provides functionality to upscale the lithological information. These functionalities were possible by developing thesauri to identify and group geological terminologies together.
Jeremie Giraud, Hoël Seillé, Mark D. Lindsay, Gerhard Visser, Vitaliy Ogarko, and Mark W. Jessell
Solid Earth Discuss., https://doi.org/10.5194/se-2021-124, https://doi.org/10.5194/se-2021-124, 2021
Revised manuscript under review for SE
Short summary
Short summary
We propose and apply a workflow to combine the modelling and interpretation of magnetic anomalies and resistivity anomalies to better image the basement. We test the method on a synthetic case study and apply it to real world data from the Cloncurry area (Queensland, Australia), which is prospective for economic minerals. Results suggest new interpretation about the composition and structure towards to east of the profile that we modelled.
Jérémie Giraud, Vitaliy Ogarko, Roland Martin, Mark Jessell, and Mark Lindsay
Geosci. Model Dev., 14, 6681–6709, https://doi.org/10.5194/gmd-14-6681-2021, https://doi.org/10.5194/gmd-14-6681-2021, 2021
Short summary
Short summary
We review different techniques to model the Earth's subsurface from geophysical data (gravity field anomaly, magnetic field anomaly) using geological models and measurements of the rocks' properties. We show examples of application using idealised examples reproducing realistic features and provide theoretical details of the open-source algorithm we use.
Mahtab Rashidifard, Jérémie Giraud, Mark Lindsay, Mark Jessell, and Vitaliy Ogarko
Solid Earth, 12, 2387–2406, https://doi.org/10.5194/se-12-2387-2021, https://doi.org/10.5194/se-12-2387-2021, 2021
Short summary
Short summary
One motivation for this study is to develop a workflow that enables the integration of geophysical datasets with different coverages that are quite common in exploration geophysics. We have utilized a level set approach to achieve this goal. The utilized technique parameterizes the subsurface in the same fashion as geological models. Our results indicate that the approach is capable of integrating information from seismic data in 2D to guide the 3D inversion results of the gravity data.
Lachlan Grose, Laurent Ailleres, Gautier Laurent, Guillaume Caumon, Mark Jessell, and Robin Armit
Geosci. Model Dev., 14, 6197–6213, https://doi.org/10.5194/gmd-14-6197-2021, https://doi.org/10.5194/gmd-14-6197-2021, 2021
Short summary
Short summary
Fault discontinuities in rock packages represent the plane where two blocks of rock have moved. They are challenging to incorporate into geological models because the geometry of the faulted rock units are defined by not only the location of the discontinuity but also the kinematics of the fault. In this paper, we outline a structural geology framework for incorporating faults into geological models by directly incorporating kinematics into the mathematical framework of the model.
Lachlan Grose, Laurent Ailleres, Gautier Laurent, and Mark Jessell
Geosci. Model Dev., 14, 3915–3937, https://doi.org/10.5194/gmd-14-3915-2021, https://doi.org/10.5194/gmd-14-3915-2021, 2021
Short summary
Short summary
LoopStructural is an open-source 3D geological modelling library with a model design allowing for multiple different algorithms to be used for comparison for the same geology. Geological structures are modelled using structural geology concepts and techniques, allowing for complex structures such as overprinted folds and faults to be modelled. In the paper, we demonstrate automatically generating a 3-D model from map2loop-processed geological survey data of the Flinders Ranges, South Australia.
Jérémie Giraud, Mark Lindsay, Mark Jessell, and Vitaliy Ogarko
Solid Earth, 11, 419–436, https://doi.org/10.5194/se-11-419-2020, https://doi.org/10.5194/se-11-419-2020, 2020
Short summary
Short summary
We propose a methodology for the identification of rock types using geophysical and geological information. It relies on an algorithm used in machine learning called
self-organizing maps, to which we add plausibility filters to ensure that the results respect base geological rules and geophysical measurements. Application in the Yerrida Basin (Western Australia) reveals that the thinning of prospective greenstone belts at depth could be due to deep structures not seen from surface.
Evren Pakyuz-Charrier, Mark Jessell, Jérémie Giraud, Mark Lindsay, and Vitaliy Ogarko
Solid Earth, 10, 1663–1684, https://doi.org/10.5194/se-10-1663-2019, https://doi.org/10.5194/se-10-1663-2019, 2019
Short summary
Short summary
This paper improves the Monte Carlo simulation for uncertainty propagation (MCUP) method for 3-D geological modeling. Topological heterogeneity is observed in the model suite. The study demonstrates that such heterogeneity arises from piecewise nonlinearity inherent to 3-D geological models and contraindicates use of global uncertainty estimation methods. Topological-clustering-driven uncertainty estimation is proposed as a demonstrated alternative to address plausible model heterogeneity.
Jeremie Giraud, Mark Lindsay, Vitaliy Ogarko, Mark Jessell, Roland Martin, and Evren Pakyuz-Charrier
Solid Earth, 10, 193–210, https://doi.org/10.5194/se-10-193-2019, https://doi.org/10.5194/se-10-193-2019, 2019
Short summary
Short summary
We propose the quantitative integration of geology and geophysics in an algorithm integrating the probability of observation of rocks with gravity data to improve subsurface imaging. This allows geophysical modelling to adjust models preferentially in the least certain areas while honouring geological information and geophysical data. We validate our algorithm using an idealized case and apply it to the Yerrida Basin (Australia), where we can recover the geometry of buried greenstone belts.
Evren Pakyuz-Charrier, Mark Lindsay, Vitaliy Ogarko, Jeremie Giraud, and Mark Jessell
Solid Earth, 9, 385–402, https://doi.org/10.5194/se-9-385-2018, https://doi.org/10.5194/se-9-385-2018, 2018
Short summary
Short summary
MCUE is a method that produces probabilistic 3-D geological models by sampling from distributions that represent the uncertainty of the initial input dataset. This process generates numerous plausible datasets used to produce a range of statistically plausible 3-D models which are combined into a single probabilistic model. In this paper, improvements to distribution selection and parameterization for input uncertainty are proposed.
Xiaojun Feng, Enyuan Wang, Jérôme Ganne, Roland Martin, and Mark W. Jessell
Solid Earth Discuss., https://doi.org/10.5194/se-2017-142, https://doi.org/10.5194/se-2017-142, 2018
Preprint withdrawn
J. Florian Wellmann, Sam T. Thiele, Mark D. Lindsay, and Mark W. Jessell
Geosci. Model Dev., 9, 1019–1035, https://doi.org/10.5194/gmd-9-1019-2016, https://doi.org/10.5194/gmd-9-1019-2016, 2016
Short summary
Short summary
We often obtain knowledge about the subsurface in the form of structural geological models, as a basis for subsurface usage or resource extraction. Here, we provide a modelling code to construct such models on the basis of significant deformational events in geological history, encapsulated in kinematic equations. Our methods simplify complex dynamic processes, but enable us to evaluate how events interact, and finally how certain we are about predictions of structures in the subsurface.
Guillaume Pirot, Ranee Joshi, Jérémie Giraud, Mark Douglas Lindsay, and Mark Walter Jessell
Geosci. Model Dev., 15, 4689–4708, https://doi.org/10.5194/gmd-15-4689-2022, https://doi.org/10.5194/gmd-15-4689-2022, 2022
Short summary
Short summary
Results of a survey launched among practitioners in the mineral industry show that despite recognising the importance of uncertainty quantification it is not very well performed due to lack of data, time requirements, poor tracking of interpretations and relative complexity of uncertainty quantification. To alleviate the latter, we provide an open-source set of local and global indicators to measure geological uncertainty among an ensemble of geological models.
Richard Scalzo, Mark Lindsay, Mark Jessell, Guillaume Pirot, Jeremie Giraud, Edward Cripps, and Sally Cripps
Geosci. Model Dev., 15, 3641–3662, https://doi.org/10.5194/gmd-15-3641-2022, https://doi.org/10.5194/gmd-15-3641-2022, 2022
Short summary
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.
Fernanda Alvarado-Neves, Laurent Ailleres, Lachlan Grose, Alexander R. Cruden, and Robin Armit
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-88, https://doi.org/10.5194/gmd-2022-88, 2022
Preprint under review for GMD
Short summary
Short summary
We introduce a method to model igneous intrusions for 3D geological modelling. We use a parameterization of the intrusion body geometry that could be constrained using field observations. Using this parametrization, we simulate distance thresholds that represent the lateral and vertical extent of the intrusion body. We demonstrate the method with two case studies, and we present a comparison with Radial Basis Function interpolation using a case study of a sill complex located in NW Australia.
Mark Jessell, Jiateng Guo, Yunqiang Li, Mark Lindsay, Richard Scalzo, Jérémie Giraud, Guillaume Pirot, Ed Cripps, and Vitaliy Ogarko
Earth Syst. Sci. Data, 14, 381–392, https://doi.org/10.5194/essd-14-381-2022, https://doi.org/10.5194/essd-14-381-2022, 2022
Short summary
Short summary
To robustly train and test automated methods in the geosciences, we need to have access to large numbers of examples where we know
the answer. We present a suite of synthetic 3D geological models with their gravity and magnetic responses that allow researchers to test their methods on a whole range of geologically plausible models, thus overcoming one of the fundamental limitations of automation studies.
Ranee Joshi, Kavitha Madaiah, Mark Jessell, Mark Lindsay, and Guillaume Pirot
Geosci. Model Dev., 14, 6711–6740, https://doi.org/10.5194/gmd-14-6711-2021, https://doi.org/10.5194/gmd-14-6711-2021, 2021
Short summary
Short summary
We have developed a software that allows the user to extract and standardize drill hole information from legacy datasets and/or different drilling campaigns. It also provides functionality to upscale the lithological information. These functionalities were possible by developing thesauri to identify and group geological terminologies together.
Jeremie Giraud, Hoël Seillé, Mark D. Lindsay, Gerhard Visser, Vitaliy Ogarko, and Mark W. Jessell
Solid Earth Discuss., https://doi.org/10.5194/se-2021-124, https://doi.org/10.5194/se-2021-124, 2021
Revised manuscript under review for SE
Short summary
Short summary
We propose and apply a workflow to combine the modelling and interpretation of magnetic anomalies and resistivity anomalies to better image the basement. We test the method on a synthetic case study and apply it to real world data from the Cloncurry area (Queensland, Australia), which is prospective for economic minerals. Results suggest new interpretation about the composition and structure towards to east of the profile that we modelled.
Jérémie Giraud, Vitaliy Ogarko, Roland Martin, Mark Jessell, and Mark Lindsay
Geosci. Model Dev., 14, 6681–6709, https://doi.org/10.5194/gmd-14-6681-2021, https://doi.org/10.5194/gmd-14-6681-2021, 2021
Short summary
Short summary
We review different techniques to model the Earth's subsurface from geophysical data (gravity field anomaly, magnetic field anomaly) using geological models and measurements of the rocks' properties. We show examples of application using idealised examples reproducing realistic features and provide theoretical details of the open-source algorithm we use.
Mahtab Rashidifard, Jérémie Giraud, Mark Lindsay, Mark Jessell, and Vitaliy Ogarko
Solid Earth, 12, 2387–2406, https://doi.org/10.5194/se-12-2387-2021, https://doi.org/10.5194/se-12-2387-2021, 2021
Short summary
Short summary
One motivation for this study is to develop a workflow that enables the integration of geophysical datasets with different coverages that are quite common in exploration geophysics. We have utilized a level set approach to achieve this goal. The utilized technique parameterizes the subsurface in the same fashion as geological models. Our results indicate that the approach is capable of integrating information from seismic data in 2D to guide the 3D inversion results of the gravity data.
Lachlan Grose, Laurent Ailleres, Gautier Laurent, Guillaume Caumon, Mark Jessell, and Robin Armit
Geosci. Model Dev., 14, 6197–6213, https://doi.org/10.5194/gmd-14-6197-2021, https://doi.org/10.5194/gmd-14-6197-2021, 2021
Short summary
Short summary
Fault discontinuities in rock packages represent the plane where two blocks of rock have moved. They are challenging to incorporate into geological models because the geometry of the faulted rock units are defined by not only the location of the discontinuity but also the kinematics of the fault. In this paper, we outline a structural geology framework for incorporating faults into geological models by directly incorporating kinematics into the mathematical framework of the model.
Lachlan Grose, Laurent Ailleres, Gautier Laurent, and Mark Jessell
Geosci. Model Dev., 14, 3915–3937, https://doi.org/10.5194/gmd-14-3915-2021, https://doi.org/10.5194/gmd-14-3915-2021, 2021
Short summary
Short summary
LoopStructural is an open-source 3D geological modelling library with a model design allowing for multiple different algorithms to be used for comparison for the same geology. Geological structures are modelled using structural geology concepts and techniques, allowing for complex structures such as overprinted folds and faults to be modelled. In the paper, we demonstrate automatically generating a 3-D model from map2loop-processed geological survey data of the Flinders Ranges, South Australia.
Mark D. Lindsay, Sandra Occhipinti, Crystal Laflamme, Alan Aitken, and Lara Ramos
Solid Earth, 11, 1053–1077, https://doi.org/10.5194/se-11-1053-2020, https://doi.org/10.5194/se-11-1053-2020, 2020
Short summary
Short summary
Integrated interpretation of multiple datasets is a key skill required for better understanding the composition and configuration of the Earth's crust. Geophysical and 3D geological modelling are used here to aid the interpretation process in investigating anomalous and cryptic geophysical signatures which suggest a more complex structure and history of a Palaeoproterozoic basin in Western Australia.
Jérémie Giraud, Mark Lindsay, Mark Jessell, and Vitaliy Ogarko
Solid Earth, 11, 419–436, https://doi.org/10.5194/se-11-419-2020, https://doi.org/10.5194/se-11-419-2020, 2020
Short summary
Short summary
We propose a methodology for the identification of rock types using geophysical and geological information. It relies on an algorithm used in machine learning called
self-organizing maps, to which we add plausibility filters to ensure that the results respect base geological rules and geophysical measurements. Application in the Yerrida Basin (Western Australia) reveals that the thinning of prospective greenstone belts at depth could be due to deep structures not seen from surface.
Evren Pakyuz-Charrier, Mark Jessell, Jérémie Giraud, Mark Lindsay, and Vitaliy Ogarko
Solid Earth, 10, 1663–1684, https://doi.org/10.5194/se-10-1663-2019, https://doi.org/10.5194/se-10-1663-2019, 2019
Short summary
Short summary
This paper improves the Monte Carlo simulation for uncertainty propagation (MCUP) method for 3-D geological modeling. Topological heterogeneity is observed in the model suite. The study demonstrates that such heterogeneity arises from piecewise nonlinearity inherent to 3-D geological models and contraindicates use of global uncertainty estimation methods. Topological-clustering-driven uncertainty estimation is proposed as a demonstrated alternative to address plausible model heterogeneity.
Jeremie Giraud, Mark Lindsay, Vitaliy Ogarko, Mark Jessell, Roland Martin, and Evren Pakyuz-Charrier
Solid Earth, 10, 193–210, https://doi.org/10.5194/se-10-193-2019, https://doi.org/10.5194/se-10-193-2019, 2019
Short summary
Short summary
We propose the quantitative integration of geology and geophysics in an algorithm integrating the probability of observation of rocks with gravity data to improve subsurface imaging. This allows geophysical modelling to adjust models preferentially in the least certain areas while honouring geological information and geophysical data. We validate our algorithm using an idealized case and apply it to the Yerrida Basin (Australia), where we can recover the geometry of buried greenstone belts.
Guillaume Pirot, Tipaluck Krityakierne, David Ginsbourger, and Philippe Renard
Hydrol. Earth Syst. Sci., 23, 351–369, https://doi.org/10.5194/hess-23-351-2019, https://doi.org/10.5194/hess-23-351-2019, 2019
Short summary
Short summary
To localize the source of a contaminant in the subsurface, based on concentration observations at some wells, we propose to test different possible locations and minimize the misfit between observed and simulated concentrations. We use a global optimization technique that relies on an expected improvement criterion, which allows a good exploration of the parameter space, avoids the trapping of local minima and quickly localizes the source of the contaminant on the presented synthetic cases.
Evren Pakyuz-Charrier, Mark Lindsay, Vitaliy Ogarko, Jeremie Giraud, and Mark Jessell
Solid Earth, 9, 385–402, https://doi.org/10.5194/se-9-385-2018, https://doi.org/10.5194/se-9-385-2018, 2018
Short summary
Short summary
MCUE is a method that produces probabilistic 3-D geological models by sampling from distributions that represent the uncertainty of the initial input dataset. This process generates numerous plausible datasets used to produce a range of statistically plausible 3-D models which are combined into a single probabilistic model. In this paper, improvements to distribution selection and parameterization for input uncertainty are proposed.
Xiaojun Feng, Enyuan Wang, Jérôme Ganne, Roland Martin, and Mark W. Jessell
Solid Earth Discuss., https://doi.org/10.5194/se-2017-142, https://doi.org/10.5194/se-2017-142, 2018
Preprint withdrawn
Samuel T. Thiele, Lachlan Grose, Anindita Samsu, Steven Micklethwaite, Stefan A. Vollgger, and Alexander R. Cruden
Solid Earth, 8, 1241–1253, https://doi.org/10.5194/se-8-1241-2017, https://doi.org/10.5194/se-8-1241-2017, 2017
Short summary
Short summary
We demonstrate a new method that enhances our ability to interpret large datasets commonly used in the earth sciences, including point clouds and rasters. Implemented as plugins for CloudCompare and QGIS, we use a least-cost-path solver to track structures and contacts through data, allowing for expert-guided interpretation in a way that seamlessly utilises computing power to optimise the interpretation process and improve objectivity and consistency.
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
Short summary
Short summary
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
Short summary
Short summary
We often obtain knowledge about the subsurface in the form of structural geological models, as a basis for subsurface usage or resource extraction. Here, we provide a modelling code to construct such models on the basis of significant deformational events in geological history, encapsulated in kinematic equations. Our methods simplify complex dynamic processes, but enable us to evaluate how events interact, and finally how certain we are about predictions of structures in the subsurface.
Related subject area
Earth and space science informatics
Towards physics-inspired data-driven weather forecasting: integrating data assimilation with a deep spatial-transformer-based U-NET in a case study with ERA5
Fast infrared radiative transfer calculations using graphics processing units: JURASSIC-GPU v2.0
CSDMS: a community platform for numerical modeling of Earth surface processes
A new methodological framework for geophysical sensor combinations associated with machine learning algorithms to understand soil attributes
Model calibration using ESEm v1.1.0 – an open, scalable Earth system emulator
Turbidity maximum zone index: a novel model for remote extraction of the turbidity maximum zone in different estuaries
LAND-SUITE V1.0: a suite of tools for statistically-based landslide susceptibility zonation
dh2loop 1.0: an open-source Python library for automated processing and classification of geological logs
Copula-based synthetic data augmentation for machine-learning emulators
A spatially explicit approach to simulate urban heat mitigation with InVEST (v3.8.0)
S-SOM v1.0: a structural self-organizing map algorithm for weather typing
Using Shapley additive explanations to interpret extreme gradient boosting predictions of grassland degradation in Xilingol, China
Current status on the need for improved accessibility to climate models code
ClimateNet: an expert-labeled open dataset and deep learning architecture for enabling high-precision analyses of extreme weather
A spatiotemporal weighted regression model (STWR v1.0) for analyzing local nonstationarity in space and time
A new end-to-end workflow for the Community Earth System Model (version 2.0) for the Coupled Model Intercomparison Project Phase 6 (CMIP6)
HyLands 1.0: a hybrid landscape evolution model to simulate the impact of landslides and landslide-derived sediment on landscape evolution
Comparative analysis of atmospheric radiative transfer models using the Atmospheric Look-up table Generator (ALG) toolbox (version 2.0)
Fast domain-aware neural network emulation of a planetary boundary layer parameterization in a numerical weather forecast model
VISIR-1.b: ocean surface gravity waves and currents for energy-efficient navigation
Topological data analysis and machine learning for recognizing atmospheric river patterns in large climate datasets
Global hydro-climatic biomes identified via multitask learning
A run control framework to streamline profiling, porting, and tuning simulation runs and provenance tracking of geoscientific applications
An improved logistic regression model based on a spatially weighted technique (ILRBSWT v1.0) and its application to mineral prospectivity mapping
High-performance software framework for the calculation of satellite-to-satellite data matchups (MMS version 1.2)
A data model of the Climate and Forecast metadata conventions (CF-1.6) with a software implementation (cf-python v2.1)
Reverse engineering model structures for soil and ecosystem respiration: the potential of gene expression programming
A high-fidelity multiresolution digital elevation model for Earth systems
CPMIP: measurements of real computational performance of Earth system models in CMIP6
Automatic delineation of geomorphological slope units with r.slopeunits v1.0 and their optimization for landslide susceptibility modeling
Community Intercomparison Suite (CIS) v1.4.0: a tool for intercomparing models and observations
Asynchronous communication in spectral-element and discontinuous Galerkin methods for atmospheric dynamics – a case study using the High-Order Methods Modeling Environment (HOMME-homme_dg_branch)
GO2OGS 1.0: a versatile workflow to integrate complex geological information with fault data into numerical simulation models
An open and extensible framework for spatially explicit land use change modelling: the lulcc R package
Plant functional type classification for earth system models: results from the European Space Agency's Land Cover Climate Change Initiative
Non-singular spherical harmonic expressions of geomagnetic vector and gradient tensor fields in the local north-oriented reference frame
An approach to enhance pnetCDF performance in environmental modeling applications
A strategy for GIS-based 3-D slope stability modelling over large areas
An approach to computing direction relations between separated object groups
Improving computational efficiency in large linear inverse problems: an example from carbon dioxide flux estimation
Coupling technologies for Earth System Modelling
Quality assessment concept of the World Data Center for Climate and its application to CMIP5 data
A web service based tool to plan atmospheric research flights
Automated continuous verification for numerical simulation
Ashesh Chattopadhyay, Mustafa Mustafa, Pedram Hassanzadeh, Eviatar Bach, and Karthik Kashinath
Geosci. Model Dev., 15, 2221–2237, https://doi.org/10.5194/gmd-15-2221-2022, https://doi.org/10.5194/gmd-15-2221-2022, 2022
Short summary
Short summary
There is growing interest in data-driven weather forecasting, i.e., to predict the weather by using a deep neural network that learns from the evolution of past atmospheric patterns. Here, we propose three components to add to the current data-driven weather forecast models to improve their performance. These components involve a feature that incorporates physics into the neural network, a method to add data assimilation, and an algorithm to use several different time intervals in the forecast.
Paul F. Baumeister and Lars Hoffmann
Geosci. Model Dev., 15, 1855–1874, https://doi.org/10.5194/gmd-15-1855-2022, https://doi.org/10.5194/gmd-15-1855-2022, 2022
Short summary
Short summary
The efficiency of the numerical simulation of radiative transport is shown on modern server-class graphics cards (GPUs). The low-cost prefactor on GPUs compared to general-purpose processors (CPUs) enables future large retrieval campaigns for multi-channel data from infrared sounders aboard low-orbit satellites. The validated research software JURASSIC is available in the public domain.
Gregory E. Tucker, Eric W. H. Hutton, Mark D. Piper, Benjamin Campforts, Tian Gan, Katherine R. Barnhart, Albert J. Kettner, Irina Overeem, Scott D. Peckham, Lynn McCready, and Jaia Syvitski
Geosci. Model Dev., 15, 1413–1439, https://doi.org/10.5194/gmd-15-1413-2022, https://doi.org/10.5194/gmd-15-1413-2022, 2022
Short summary
Short summary
Scientists use computer simulation models to understand how Earth surface processes work, including floods, landslides, soil erosion, river channel migration, ocean sedimentation, and coastal change. Research benefits when the software for simulation modeling is open, shared, and coordinated. The Community Surface Dynamics Modeling System (CSDMS) is a US-based facility that supports research by providing community support, computing tools and guidelines, and educational resources.
Danilo César de Mello, Gustavo Vieira Veloso, Marcos Guedes de Lana, Fellipe Alcantara de Oliveira Mello, Raul Roberto Poppiel, Diego Ribeiro Oquendo Cabrero, Luis Augusto Di Loreto Di Raimo, Carlos Ernesto Gonçalves Reynaud Schaefer, Elpídio Inácio Fernandes Filho, Emilson Pereira Leite, and José Alexandre Melo Demattê
Geosci. Model Dev., 15, 1219–1246, https://doi.org/10.5194/gmd-15-1219-2022, https://doi.org/10.5194/gmd-15-1219-2022, 2022
Short summary
Short summary
We used soil parent material, terrain attributes, and geophysical data from the soil surface to test and compare different and unprecedented geophysical sensor combination, as well as different machine learning algorithms to model and predict several soil attributes. Also, we analyzed the importance of pedoenvironmental variables. The soil attributes were modeled throughout different machine learning algorithms and related to different geophysical sensor combinations.
Duncan Watson-Parris, Andrew Williams, Lucia Deaconu, and Philip Stier
Geosci. Model Dev., 14, 7659–7672, https://doi.org/10.5194/gmd-14-7659-2021, https://doi.org/10.5194/gmd-14-7659-2021, 2021
Short summary
Short summary
The Earth System Emulator (ESEm) provides a fast and flexible framework for emulating a wide variety of Earth science datasets and tools for constraining (or tuning) models of any complexity. Three distinct use cases are presented that demonstrate the utility of ESEm and provide some insight into the use of machine learning for emulation in these different settings. The open-source Python package is freely available so that it might become a valuable tool for the community.
Chongyang Wang, Li Wang, Danni Wang, Dan Li, Chenghu Zhou, Hao Jiang, Qiong Zheng, Shuisen Chen, Kai Jia, Yangxiaoyue Liu, Ji Yang, Xia Zhou, and Yong Li
Geosci. Model Dev., 14, 6833–6846, https://doi.org/10.5194/gmd-14-6833-2021, https://doi.org/10.5194/gmd-14-6833-2021, 2021
Short summary
Short summary
The turbidity maximum zone (TMZ) is a special phenomenon in estuaries worldwide. However, the extraction methods and criteria used to describe the TMZ vary significantly both spatially and temporally. This study proposes an new index, the turbidity maximum zone index, based on the corresponding relationship of total suspended solid concentration and Chl a concentration, which could better extract TMZs in different estuaries and on different dates.
Mauro Rossi, Txomin Bornaetxea, and Paola Reichenbach
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-343, https://doi.org/10.5194/gmd-2021-343, 2021
Revised manuscript accepted for GMD
Short summary
Short summary
LAND-SUITE is a software designed to support landslide susceptibility zonation. The software integrate, extend and complete LAND-SE (Rossi et al., 2010; Rossi & Reichenbach, 2016). The software is implemented in R, a free software environment for statistical computing and graphics, and gives expert users the possibility to perform more easily, more flexible and more informed statistically-based landslide susceptibility applications and zonations.
Ranee Joshi, Kavitha Madaiah, Mark Jessell, Mark Lindsay, and Guillaume Pirot
Geosci. Model Dev., 14, 6711–6740, https://doi.org/10.5194/gmd-14-6711-2021, https://doi.org/10.5194/gmd-14-6711-2021, 2021
Short summary
Short summary
We have developed a software that allows the user to extract and standardize drill hole information from legacy datasets and/or different drilling campaigns. It also provides functionality to upscale the lithological information. These functionalities were possible by developing thesauri to identify and group geological terminologies together.
David Meyer, Thomas Nagler, and Robin J. Hogan
Geosci. Model Dev., 14, 5205–5215, https://doi.org/10.5194/gmd-14-5205-2021, https://doi.org/10.5194/gmd-14-5205-2021, 2021
Short summary
Short summary
A major limitation in training machine-learning emulators is often caused by the lack of data. This paper presents a cheap way to increase the size of training datasets using statistical techniques and thereby improve the performance of machine-learning emulators.
Martí Bosch, Maxence Locatelli, Perrine Hamel, Roy P. Remme, Jérôme Chenal, and Stéphane Joost
Geosci. Model Dev., 14, 3521–3537, https://doi.org/10.5194/gmd-14-3521-2021, https://doi.org/10.5194/gmd-14-3521-2021, 2021
Short summary
Short summary
The article presents a novel approach to simulate urban heat mitigation from land use/land cover data based on three biophysical mechanisms: tree shade, evapotranspiration and albedo. An automated procedure is proposed to calibrate the model parameters to best fit temperature observations from monitoring stations. A case study in Lausanne, Switzerland, shows that the approach outperforms regressions based on satellite data and provides valuable insights into design heat mitigation policies.
Quang-Van Doan, Hiroyuki Kusaka, Takuto Sato, and Fei Chen
Geosci. Model Dev., 14, 2097–2111, https://doi.org/10.5194/gmd-14-2097-2021, https://doi.org/10.5194/gmd-14-2097-2021, 2021
Short summary
Short summary
This study proposes a novel structural self-organizing map (S-SOM) algorithm. The superiority of S-SOM is that it can better recognize the difference (or similarity) among spatial (or temporal) data used for training and thus improve the clustering quality compared to traditional SOM algorithms.
Batunacun, Ralf Wieland, Tobia Lakes, and Claas Nendel
Geosci. Model Dev., 14, 1493–1510, https://doi.org/10.5194/gmd-14-1493-2021, https://doi.org/10.5194/gmd-14-1493-2021, 2021
Short summary
Short summary
Extreme gradient boosting (XGBoost) can provide alternative insights that conventional land-use models are unable to generate. Shapley additive explanations (SHAP) can interpret the results of the purely data-driven approach. XGBoost achieved similar and robust simulation results. SHAP values were useful for analysing the complex relationship between the different drivers of grassland degradation.
Juan A. Añel, Michael García-Rodríguez, and Javier Rodeiro
Geosci. Model Dev., 14, 923–934, https://doi.org/10.5194/gmd-14-923-2021, https://doi.org/10.5194/gmd-14-923-2021, 2021
Short summary
Short summary
This work shows that it continues to be hard, if not impossible, to obtain some of the most used climate models worldwide. We reach this conclusion through a systematic study and encourage all development teams and research centres to make public the models they use to produce scientific results.
Prabhat, Karthik Kashinath, Mayur Mudigonda, Sol Kim, Lukas Kapp-Schwoerer, Andre Graubner, Ege Karaismailoglu, Leo von Kleist, Thorsten Kurth, Annette Greiner, Ankur Mahesh, Kevin Yang, Colby Lewis, Jiayi Chen, Andrew Lou, Sathyavat Chandran, Ben Toms, Will Chapman, Katherine Dagon, Christine A. Shields, Travis O'Brien, Michael Wehner, and William Collins
Geosci. Model Dev., 14, 107–124, https://doi.org/10.5194/gmd-14-107-2021, https://doi.org/10.5194/gmd-14-107-2021, 2021
Short summary
Short summary
Detecting extreme weather events is a crucial step in understanding how they change due to climate change. Deep learning (DL) is remarkable at pattern recognition; however, it works best only when labeled datasets are available. We create
ClimateNet– an expert-labeled curated dataset – to train a DL model for detecting weather events and predicting changes in extreme precipitation. This work paves the way for DL-based automated, high-fidelity, and highly precise analytics of climate data.
Xiang Que, Xiaogang Ma, Chao Ma, and Qiyu Chen
Geosci. Model Dev., 13, 6149–6164, https://doi.org/10.5194/gmd-13-6149-2020, https://doi.org/10.5194/gmd-13-6149-2020, 2020
Short summary
Short summary
This paper presents a spatiotemporal weighted regression (STWR) model for exploring nonstationary spatiotemporal processes in nature and socioeconomics. A value change rate is introduced in the temporal kernel, which presents significant model fitting and accuracy in both simulated and real-world data. STWR fully incorporates observed data in the past and outperforms geographic temporal weighted regression (GTWR) and geographic weighted regression (GWR) models in several experiments.
Sheri Mickelson, Alice Bertini, Gary Strand, Kevin Paul, Eric Nienhouse, John Dennis, and Mariana Vertenstein
Geosci. Model Dev., 13, 5567–5581, https://doi.org/10.5194/gmd-13-5567-2020, https://doi.org/10.5194/gmd-13-5567-2020, 2020
Short summary
Short summary
Every generation of MIP exercises introduces new layers of complexity and an exponential growth in the amount of data requested. CMIP6 required us to develop a new tool chain and forced us to change our methodologies. The new methods discussed in this paper provided us with an 18 times faster speedup over our existing methods. This allowed us to meet our deadlines and we were able to publish more than half a million data sets on the Earth System Grid Federation (ESGF) for the CMIP6 project.
Benjamin Campforts, Charles M. Shobe, Philippe Steer, Matthias Vanmaercke, Dimitri Lague, and Jean Braun
Geosci. Model Dev., 13, 3863–3886, https://doi.org/10.5194/gmd-13-3863-2020, https://doi.org/10.5194/gmd-13-3863-2020, 2020
Short summary
Short summary
Landslides shape the Earth’s surface and are a dominant source of terrestrial sediment. Rivers, then, act as conveyor belts evacuating landslide-produced sediment. Understanding the interaction among rivers and landslides is important to predict the Earth’s surface response to past and future environmental changes and for mitigating natural hazards. We develop HyLands, a new numerical model that provides a toolbox to explore how landslides and rivers interact over several timescales.
Jorge Vicent, Jochem Verrelst, Neus Sabater, Luis Alonso, Juan Pablo Rivera-Caicedo, Luca Martino, Jordi Muñoz-Marí, and José Moreno
Geosci. Model Dev., 13, 1945–1957, https://doi.org/10.5194/gmd-13-1945-2020, https://doi.org/10.5194/gmd-13-1945-2020, 2020
Short summary
Short summary
The modeling of light propagation through the atmosphere is key to process satellite images and to understand atmospheric processes. However, existing atmospheric models can be complex to use in practical applications. Here we aim at providing a new software tool to facilitate using advanced models and to generate large databases of simulated data. As a test case, we use this tool to analyze differences between several atmospheric models, showing the capabilities of this open-source tool.
Jiali Wang, Prasanna Balaprakash, and Rao Kotamarthi
Geosci. Model Dev., 12, 4261–4274, https://doi.org/10.5194/gmd-12-4261-2019, https://doi.org/10.5194/gmd-12-4261-2019, 2019
Short summary
Short summary
Parameterizations are frequently used in models representing physical phenomena and are often the computationally expensive portions of the code. Using model output from simulations performed using a weather model, we train deep neural networks to provide an accurate alternative to a physics-based parameterization. We demonstrate that a domain-aware deep neural network can successfully simulate the entire diurnal cycle of the boundary layer physics and the results are transferable.
Gianandrea Mannarini and Lorenzo Carelli
Geosci. Model Dev., 12, 3449–3480, https://doi.org/10.5194/gmd-12-3449-2019, https://doi.org/10.5194/gmd-12-3449-2019, 2019
Short summary
Short summary
The VISIR ship-routing model is updated in order to deal with ocean currents.
The optimal tracks we computed through VISIR in the Atlantic ocean show great seasonal and regional variability, following a variable influence of surface gravity waves and currents. We assess how these tracks contribute to voyage energy-efficiency gains through a standard indicator (EEOI) of the International Maritime Organization. Also, the new model features are validated against an exact analytical benchmark.
Grzegorz Muszynski, Karthik Kashinath, Vitaliy Kurlin, Michael Wehner, and Prabhat
Geosci. Model Dev., 12, 613–628, https://doi.org/10.5194/gmd-12-613-2019, https://doi.org/10.5194/gmd-12-613-2019, 2019
Short summary
Short summary
We present the automated method for recognizing atmospheric rivers in climate data, i.e., climate model output and reanalysis product. The method is based on topological data analysis and machine learning, both of which are powerful tools that the climate science community often does not use. An advantage of the proposed method is that it is free of selection of subjective threshold conditions on a physical variable. This method is also suitable for rapidly analyzing large amounts of data.
Christina Papagiannopoulou, Diego G. Miralles, Matthias Demuzere, Niko E. C. Verhoest, and Willem Waegeman
Geosci. Model Dev., 11, 4139–4153, https://doi.org/10.5194/gmd-11-4139-2018, https://doi.org/10.5194/gmd-11-4139-2018, 2018
Short summary
Short summary
Common global land cover and climate classifications are based on vegetation–climatic characteristics derived from observational data, ignoring the interaction between the local climate and biome. Here, we model the interplay between vegetation and local climate by discovering spatial relationships among different locations. The resulting global
hydro-climatic biomescorrespond to regions of coherent climate–vegetation interactions that agree well with traditional global land cover maps.
Wendy Sharples, Ilya Zhukov, Markus Geimer, Klaus Goergen, Sebastian Luehrs, Thomas Breuer, Bibi Naz, Ketan Kulkarni, Slavko Brdar, and Stefan Kollet
Geosci. Model Dev., 11, 2875–2895, https://doi.org/10.5194/gmd-11-2875-2018, https://doi.org/10.5194/gmd-11-2875-2018, 2018
Short summary
Short summary
Next-generation geoscientific models are based on complex model implementations and workflows. Next-generation HPC systems require new programming paradigms and code optimization. In order to meet the challenge of running complex simulations on new massively parallel HPC systems, we developed a run control framework that facilitates code portability, code profiling, and provenance tracking to reduce both the duration and the cost of code migration and development, while ensuring reproducibility.
Daojun Zhang, Na Ren, and Xianhui Hou
Geosci. Model Dev., 11, 2525–2539, https://doi.org/10.5194/gmd-11-2525-2018, https://doi.org/10.5194/gmd-11-2525-2018, 2018
Short summary
Short summary
Geographically weighted regression is a widely used method to deal with spatial heterogeneity, which is common in geostatistics. However, most existing software does not support logistic regression and cannot deal with missing data, which exist extensively in mineral prospectivity mapping. This work generalized logistic regression to spatial statistics based on a spatially weighted technique. The new model also supports an anisotropic local window, which is another innovative point.
Thomas Block, Sabine Embacher, Christopher J. Merchant, and Craig Donlon
Geosci. Model Dev., 11, 2419–2427, https://doi.org/10.5194/gmd-11-2419-2018, https://doi.org/10.5194/gmd-11-2419-2018, 2018
Short summary
Short summary
For calibration and validation purposes it is necessary to detect simultaneous data acquisitions from different spaceborne platforms. We present an algorithm and a software system which implements a general approach to resolve this problem. The multisensor matchup system (MMS) can detect simultaneous acquisitions in a large dataset (> 100 TB) and extract data for matching locations for further analysis. The MMS implements a flexible software infrastructure and allows for high parallelization.
David Hassell, Jonathan Gregory, Jon Blower, Bryan N. Lawrence, and Karl E. Taylor
Geosci. Model Dev., 10, 4619–4646, https://doi.org/10.5194/gmd-10-4619-2017, https://doi.org/10.5194/gmd-10-4619-2017, 2017
Short summary
Short summary
We present a formal data model for version 1.6 of the CF (Climate and Forecast) metadata conventions that provide a description of the physical meaning of geoscientific data and their spatial and temporal properties. We describe the CF conventions and how they lead to our CF data model, and compare it other data models for storing data and metadata. We present cf-python version 2.1: a software implementation of the CF data model capable of manipulating any CF-compliant dataset.
Iulia Ilie, Peter Dittrich, Nuno Carvalhais, Martin Jung, Andreas Heinemeyer, Mirco Migliavacca, James I. L. Morison, Sebastian Sippel, Jens-Arne Subke, Matthew Wilkinson, and Miguel D. Mahecha
Geosci. Model Dev., 10, 3519–3545, https://doi.org/10.5194/gmd-10-3519-2017, https://doi.org/10.5194/gmd-10-3519-2017, 2017
Short summary
Short summary
Accurate representation of land-atmosphere carbon fluxes is essential for future climate projections, although some of the responses of CO2 fluxes to climate often remain uncertain. The increase in available data allows for new approaches in their modelling. We automatically developed models for ecosystem and soil carbon respiration using a machine learning approach. When compared with established respiration models, we found that they are better in prediction as well as offering new insights.
Xinqiao Duan, Lin Li, Haihong Zhu, and Shen Ying
Geosci. Model Dev., 10, 239–253, https://doi.org/10.5194/gmd-10-239-2017, https://doi.org/10.5194/gmd-10-239-2017, 2017
Short summary
Short summary
This article proposes an optimized transformation for topographic datasets. The resulting topographic grid exhibits good surface approximation and quasi-uniform high-quality. Both features of the processed topography build a concrete base from which improved endogenous or exogenous parameters can be derived, and makes it suitable for Earth and environmental simulations.
Venkatramani Balaji, Eric Maisonnave, Niki Zadeh, Bryan N. Lawrence, Joachim Biercamp, Uwe Fladrich, Giovanni Aloisio, Rusty Benson, Arnaud Caubel, Jeffrey Durachta, Marie-Alice Foujols, Grenville Lister, Silvia Mocavero, Seth Underwood, and Garrett Wright
Geosci. Model Dev., 10, 19–34, https://doi.org/10.5194/gmd-10-19-2017, https://doi.org/10.5194/gmd-10-19-2017, 2017
Short summary
Short summary
Climate models are among the most computationally expensive scientific applications in the world. We present a set of measures of computational performance that can be used to compare models that are independent of underlying hardware and the model formulation. They are easy to collect and reflect performance actually achieved in practice. We are preparing a systematic effort to collect these metrics for the world's climate models during CMIP6, the next Climate Model Intercomparison Project.
Massimiliano Alvioli, Ivan Marchesini, Paola Reichenbach, Mauro Rossi, Francesca Ardizzone, Federica Fiorucci, and Fausto Guzzetti
Geosci. Model Dev., 9, 3975–3991, https://doi.org/10.5194/gmd-9-3975-2016, https://doi.org/10.5194/gmd-9-3975-2016, 2016
Short summary
Short summary
Slope units are morphological mapping units bounded by drainage and divide lines that maximize within-unit homogeneity and between-unit heterogeneity. We use r.slopeunits, a software for the automatic delination of slope units. We outline an objective procedure to optimize the software input parameters for landslide susceptibility (LS) zonation. Optimization is achieved by maximizing an objective function that simultaneously evaluates terrain aspect segmentation quality and LS model performance.
Duncan Watson-Parris, Nick Schutgens, Nicholas Cook, Zak Kipling, Philip Kershaw, Edward Gryspeerdt, Bryan Lawrence, and Philip Stier
Geosci. Model Dev., 9, 3093–3110, https://doi.org/10.5194/gmd-9-3093-2016, https://doi.org/10.5194/gmd-9-3093-2016, 2016
Short summary
Short summary
In this paper we describe CIS, a new command line tool for the easy visualization, analysis and comparison of a wide variety of gridded and ungridded data sets used in Earth sciences. Users can now use a single tool to not only view plots of satellite, aircraft, station or model data, but also bring them onto the same spatio-temporal sampling. This allows robust, quantitative comparisons to be made easily. CIS is an open-source project and welcomes input from the community.
Benjamin F. Jamroz and Robert Klöfkorn
Geosci. Model Dev., 9, 2881–2892, https://doi.org/10.5194/gmd-9-2881-2016, https://doi.org/10.5194/gmd-9-2881-2016, 2016
Short summary
Short summary
The scalability of computational applications on current and next-generation supercomputers is increasingly limited by the cost of inter-process communication. We implement communication hiding data exchange in the High-Order Methods Modeling Environment (HOMME) for the time integration of the hydrostatic fluid equations using both the spectral-element and discontinuous Galerkin methods. The presented approach produces significant performance and scalability gains in large-scale simulations.
T. Fischer, D. Naumov, S. Sattler, O. Kolditz, and M. Walther
Geosci. Model Dev., 8, 3681–3694, https://doi.org/10.5194/gmd-8-3681-2015, https://doi.org/10.5194/gmd-8-3681-2015, 2015
Short summary
Short summary
We present a workflow to convert geological models into the open-source VTU format for usage in numerical simulation models. Tackling relevant scientific questions or engineering tasks often involves multidisciplinary approaches. Conversion workflows are needed between the diverse tools of the various disciplines. Our approach offers an open-source, platform-independent, robust, and comprehensible method that is potentially useful for a multitude of similar environmental studies.
S. Moulds, W. Buytaert, and A. Mijic
Geosci. Model Dev., 8, 3215–3229, https://doi.org/10.5194/gmd-8-3215-2015, https://doi.org/10.5194/gmd-8-3215-2015, 2015
Short summary
Short summary
The contribution of lulcc is to provide a free and open-source framework for land use change modelling. The software, which is provided as an R package, addresses problems associated with the current paradigm of closed-source, specialised land use change modelling software which disrupt the scientific process. It is an attempt to move the discipline towards open and transparent science and to ensure land use change models are accessible to scientists working across the geosciences.
B. Poulter, N. MacBean, A. Hartley, I. Khlystova, O. Arino, R. Betts, S. Bontemps, M. Boettcher, C. Brockmann, P. Defourny, S. Hagemann, M. Herold, G. Kirches, C. Lamarche, D. Lederer, C. Ottlé, M. Peters, and P. Peylin
Geosci. Model Dev., 8, 2315–2328, https://doi.org/10.5194/gmd-8-2315-2015, https://doi.org/10.5194/gmd-8-2315-2015, 2015
Short summary
Short summary
Land cover is an essential variable in earth system models and determines conditions driving biogeochemical, energy and water exchange between ecosystems and the atmosphere. A methodology is presented for mapping plant functional types used in global vegetation models from a updated land cover classification system and open-source conversion tool, resulting from a consultative process among map producers and modelers engaged in the European Space Agency’s Land Cover Climate Change Initiative.
J. Du, C. Chen, V. Lesur, and L. Wang
Geosci. Model Dev., 8, 1979–1990, https://doi.org/10.5194/gmd-8-1979-2015, https://doi.org/10.5194/gmd-8-1979-2015, 2015
D. C. Wong, C. E. Yang, J. S. Fu, K. Wong, and Y. Gao
Geosci. Model Dev., 8, 1033–1046, https://doi.org/10.5194/gmd-8-1033-2015, https://doi.org/10.5194/gmd-8-1033-2015, 2015
M. Mergili, I. Marchesini, M. Alvioli, M. Metz, B. Schneider-Muntau, M. Rossi, and F. Guzzetti
Geosci. Model Dev., 7, 2969–2982, https://doi.org/10.5194/gmd-7-2969-2014, https://doi.org/10.5194/gmd-7-2969-2014, 2014
Short summary
Short summary
The article deals with strategies to (i) reduce computation time and to (ii) appropriately account for uncertain input parameters when applying an open source GIS sliding surface model to estimate landslide susceptibility for a 90km² study area in central Italy. For (i), the area is split into a large number of tiles, enabling the exploitation of multi-processor computing environments. For (ii), the model is run with various parameter combinations to compute the slope failure probability.
H. Yan, Z. Wang, and J. Li
Geosci. Model Dev., 6, 1591–1599, https://doi.org/10.5194/gmd-6-1591-2013, https://doi.org/10.5194/gmd-6-1591-2013, 2013
V. Yadav and A. M. Michalak
Geosci. Model Dev., 6, 583–590, https://doi.org/10.5194/gmd-6-583-2013, https://doi.org/10.5194/gmd-6-583-2013, 2013
S. Valcke, V. Balaji, A. Craig, C. DeLuca, R. Dunlap, R. W. Ford, R. Jacob, J. Larson, R. O'Kuinghttons, G. D. Riley, and M. Vertenstein
Geosci. Model Dev., 5, 1589–1596, https://doi.org/10.5194/gmd-5-1589-2012, https://doi.org/10.5194/gmd-5-1589-2012, 2012
M. Stockhause, H. Höck, F. Toussaint, and M. Lautenschlager
Geosci. Model Dev., 5, 1023–1032, https://doi.org/10.5194/gmd-5-1023-2012, https://doi.org/10.5194/gmd-5-1023-2012, 2012
M. Rautenhaus, G. Bauer, and A. Dörnbrack
Geosci. Model Dev., 5, 55–71, https://doi.org/10.5194/gmd-5-55-2012, https://doi.org/10.5194/gmd-5-55-2012, 2012
P. E. Farrell, M. D. Piggott, G. J. Gorman, D. A. Ham, C. R. Wilson, and T. M. Bond
Geosci. Model Dev., 4, 435–449, https://doi.org/10.5194/gmd-4-435-2011, https://doi.org/10.5194/gmd-4-435-2011, 2011
Cited articles
Ailleres, L. and Betts, P.: Geometrical and geophysical modelling of an
inverted Middle Proterozoic fault system, Mount Isa Terrain, Australia,
Conference Abstracts: 3D Modelling of Natural Objects: a Challenge for the
2000, vol. 2, Nancy, France, 4–5 June 1998.
Ailleres, L., Grose, L., Laurent, G., Armit, R., Jessell, M. W., Caumon, G.,
de Kemp, E., and Wellmann, J. F.: Loop – a new open source platform for 3D
geo-structural simulations. Three-dimensional geological mapping workshop,
Resources for Future Generations meeting, Vancouver, Canada, 16–21 June 2018, 14–18, 2018.
Aitken, A. R. A., Occhipinti, S. A., Lindsay, M. D., and Trench, A.: A role for
data richness mapping in exploration decision making, Ore Geol. Rev.,
99, 398–410, 2018.
Allmendinger, R. W.: GMDE: Extracting quantitative information from geologic
maps, Geosphere, 16, 1495–1507, 2020.
Alvarado, F., Ailleres, L., Grose, L., Cruden, A., and Armit, R.: Modelling
of Igneous Intrusions Based on Emplacement Mechanisms, American Geophysical
Union, Fall Meeting 2020, abstract #IN048-07, 2020.
Argand, E.: Les nappes de recouvrement des Alpes Pennines et leur
prolongement structuraux, Mat. Carte géol. Suisse, N.S., XXXI livr.,
1911.
Aug, C., Chilès, J. P., Courrioux, G., and Lajaunie, C.: 3-D geological
modelling and uncertainty: the potential field method, in: Geostatistics Banff, edited by: Leuangthong, O.
and Deutsch, C. V., Proceedings Seventh
International Geostatistics Congress, Dordrecht, Kluwer, 145–154, 2005.
Bigi, S., Conti, A., Casero, P., Ruggiero, L., Recanati, R., and Lipparini,
L.: Geological model of the central Periadriatic basin (Apennines, Italy),
Mar. Petrol. Geol., 42, 107–121, 2021.
Bond, C. E.: Uncertainty in structural interpretation: Lessons to be learnt,
J. Struct. Geol., 74, 185–200, 2015.
Bond, C. E., Gibbs, A., Shipton, Z., and Jones, S.: What do you think this
is? “Conceptual uncertainty” in geoscience interpretation, GSA Today, 17,
4–10, 2007.
Bonham-Carter, G. F.: Geographic Information Systems for Geoscientists,
Pergamon, 398 pp., 1994.
Bonham-Carter, G. F. and Broome, J.: Tools for Effective Use of Geological
Map Data: A Topic for GeoComputation Research? Proceedings of the 3rd
International Conference on GeoComputation, University of Bristol, United
Kingdom, 17–19 September 1998, available at: http://www.geocomputation.org/1998/97/gc_97.htm (last access: 9 August 2021), 1998.
Brodaric, B., Fox, P., and McGuinness, D. L.: Geoscience knowledge
representation in cyberinfrastructure, Comput. Geosci., 35,
697–699, https://doi.org/10.1016/j.cageo.2009.01.001, 2009.
Bugge, A. J., Lie, J. E., Evensen, A. K., Faleide, J. I., and Clark, S.:
Automatic extraction of dislocated horizons from 3D seismic data using nonlocal trace matching, Geophysics, 84, IM77–IM86, 2019.
Burns, K. L.: Lithologic Topology and Structural Vector Fields Applied to
Subsurface Prediction in Geology, GIS/LIS'88, Proceedings 3rd Annual
International Conference, Exhibits and Workshops, Volume 1, San Antonio,
Texas, 30 November–2 December 1988, 26–34, 1988.
Calcagno, P., Chilès, 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, Phys. Earth
Planet. Int., 171, 147–157, 2008.
Carmichael, T. L. and Ailleres, L.: Method and analysis for the upscaling
of structural data, J. Struct. Geol., 83, 121–133, https://doi.org/10.1016/j.jsg.2015.09.002, 2016.
Caumon, G., Lepage, F., Sword, C. H., and Mallet, J.-L.: Building and editing
a sealed geological model, Math. Geol., 36, 405–424, 2004.
Caumon, G., Collon-Drouaillet, P., Le Carlier de Veslud, C., Sausse, J., and
Viseur, S.: Surface-based 3-D modelling 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.
Colman-Sadd, S. P., Ash, J. S., and Nolan, L. W.: GEOLEGEND: A database system
for managing geological map units in a Geographic Information System,
Comput. Geosci., 23, 715–724, 1997.
Cowan, E. J., Beatson, R. K., Ross, H. J., Fright, W. R., McLennan, T. J., Evans,
T. R., Carr, J. C., Lane, R. G., Bright, D. V., Gillman, A. J., Oshust, P. A., and
Titley, M.: Practical implicit geological modelingodelling, in: Proc.
5th Int. Mining Conf., Australian Inst. Mining Metallurgy, edited by: Dominy, S., Australasian Institute of Mining and Metallurgy, Melbourne, 89–99, 2003.
de Kemp, E. A.: Three-dimensional projection of curvi-linear geological
features through direction cosine interpolation of structural field
observations, Comput. Geosci., 24, 269–284, 1998.
de Kemp, E. A., Jessell, M. W., Aillères, L., Schetselaar, E. M., Hillier,
M., Lindsay, M. D., and Brodaric, B.: Earth model construction in challenging
geologic terrain: Designing workflows and algorithms that makes sense,
Exploration 2017 Conference Paper, Decennial Mineral Exploration Conferences, Toronto, 2017.
de la Varga, M. and Wellmann, J. F.: Structural geologic modelling as an
inference problem: A Bayesian perspective, Interpretation, 4,
SM1–SM16, 2016.
de la Varga, M., Schaaf, A., and Wellmann, F.: GemPy 1.0: open-source stochastic geological modeling and inversion, Geosci. Model Dev., 12, 1–32, https://doi.org/10.5194/gmd-12-1-2019, 2019.
de Rose, Y., Grose, L., Jessell, M., and Thomson, R.: Loop3D/map2loop-2: First Release (Version 1), Zenodo [data set], https://doi.org/10.5281/zenodo.4288476, 2020.
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S.,
Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S.,
Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., and Alsdorf,
D.: The Shuttle Radar Topography Mission, Rev. Geophys., 45,
RG2004, https://doi.org/10.1029/2005RG000183, 2007.
Fernández, O.: Obtaining a best fitting plane through 3D georeferenced
data, J. Struct. Geol., 27, 855–858,
https://doi.org/10.1016/j.jsg.2004.12.004, 2005.
Frank, T., Tertois, A. L., and Mallet, J. L.: 3-D-reconstruction of complex
geological interfaces from irregularly distributed and noisy point data,
Comput. Geosci., 33, 932–943, 2007.
Freeman, L. C.: A set of measures of centrality based on betweenness,
Sociometry, 40, 35–41, 1977.
Geological Survey of Western Australia: 1:500 000 State interpreted bedrock
geology of Western Australia, 2016: Geological Survey of Western Australia,
digital data layer, available at: http://www.dmp.wa.gov.au/geoview (last access: 9 August 2021), 2016.
Geoscience Australia: Digital Elevation Model (DEM) Shuttle Radar Topography
Mission (SRTM) 1 Second over Australian Bathymetry Topography: Geoscience
Australia, digital dataset, available at: http://gaservices.ga.gov.au (last access: 9 August 2021), 2016.
Geoscience Australia and Australian Stratigraphy Commission: Australian
Stratigraphic Units Database, available at: https://asud.ga.gov.au/ (last access: 9 August 2021), 2017.
Giraud, J., Lindsay, M., and Jessell, M.: Generalization of level-set
inversion to an arbitrary number of geological units in a regularized
least-squares framework, Geophysics, 86, R612–R637,
https://doi.org/10.1190/geo2020-0263.1, 2020.
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. Discuss. [preprint], https://doi.org/10.5194/gmd-2021-14, in review, 2021.
Grose, L., Ailleres, L., Laurent, G., Armit, R., and Jessell, M.: Inversion
of geological knowledge for fold geometry, J. Struct. Geol., 119,
1–14, 2019.
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.
Guo, J., Li, Y., Jessell, M.W., Giraud, J., Li, C., Wu, L., Li, F., and Liu,
S.: 3D geological structure inversion from Noddy-generated magnetic data
using deep learning methods, Comput. Geosci., 149, 104701, https://doi.org/10.1016/j.cageo.2021.104701, 2021.
Harrap, R.: A Legend Language for Geologic Maps, Precambrian Times, 1, 1–9,
2001.
Hillier, M., Schetselaar, E. M., de Kemp, E. A., and Perron, G.: 3-D modelling
of geological surfaces using generalized interpolation with radial basis
functions, Math. Geosci., 46, 931–953, https://doi.org/10.1007/s11004-014-9540-3, 2014.
Houlding, S. W.: 3D geoscience modelling computer techniques for geological
characterization, Springer-Verlag. John Wiley & Sons Inc, New York,
London, Sydney, Toronto, 1994.
Jessell, M. W.: “NODDY- An interactive map creation package”, unpublished
MSc thesis, University of London, 52 pp., 1981.
Jessell, M.: Current and future limits to automated 3D geological model construction, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-632, https://doi.org/10.5194/egusphere-egu21-632, 2021.
Jessell, M. W. and Valenta, R. K.: Structural Geophysics: Integrated
structural and geophysical mapping, in: Structural Geology and Personal
Computers, edited by: DePaor, D. G., Elsevier Science Ltd, Oxford, 303–324, 542 pp.,
1996.
Jessell, M. W., Aillères, L., de Kemp, E., Lindsay, M., Wellmann, F.,
Hillier, M., and Martin, R.: Next Generation Three-Dimensional Geologic
Modeling and Inversion, Society of Economic Geologists Special Publication
18, 261–272, 2014.
Jessell, M., de Rose, Y., and Joshi, R.: Loop3D/map2loop2-notebooks: map2loop Notebooks v 1.0 (GMD version) (Version v1.0), Zenodo [code], https://doi.org/10.5281/zenodo.5084548, 2021a.
Jessell, M., Ogarko, V., de Rose, Y., Joshi, R., Grose, L., and de la Varga, M.: Loop3D/map2loop-2: GMD 2021 release (Version v1.0.1), Zenodo [code], https://doi.org/10.5281/zenodo.5084585, 2021b.
Joshi, R., Madaiah, K., Jessell, M., Lindsay, M., and Pirot, G.: dh2loop 1.0: an open-source python library for automated processing and classification of geological logs, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2020-391, in review, 2021.
Kelka, U., Westwerlund, S., and Peeters, L.: GIS based fault and fracture
network analysis. Abstract, Sub 20 Conference, Perth, Australia, 12–13
February 2020, available at: https://wp.csiro.au/sub20/program/ (last access: 9 August 2021), 2020.
Lajaunie, C., Courrioux, G., and Manuel, L.: Foliation fields and 3-D
cartography in geology: Principles of a method based on potential
interpolation, Math. Geol., 29, 571–84, 1997.
Laurent, G., Caumon, G., Bouziat, A., and Jessell, M. W.: A parametric method
to model 3-D displacements around faults with volumetric vector fields,
Tectonophysics, 590, 83–93, 2013.
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, 2012.
Lindsay, M. D., Occhipinti, S., Laflamme, C., Aitken, A., and Ramos, L.: Mapping undercover: integrated geoscientific interpretation and 3D modelling of a Proterozoic basin, Solid Earth, 11, 1053–1077, https://doi.org/10.5194/se-11-1053-2020, 2020.
Ma, X. and Fox, P.: Recent progress on geologic time ontologies and
considerations for future works, Earth Sci. Informatics, 6, 31–46,
https://doi.org/10.1007/s12145-013-0110-x, 2013.
Mallet, J. L.: Discrete smooth interpolation, Comput.-Aided Des., 24,
263–270, 1992.
Mallet, J. L.: Geomodelling, New York, NY, Oxford University Press, 599 pp.,
2002.
Mallet, J. L.: Space-Time Mathematical Framework for Sedimentary Geology,
Math. Geol., 36, 1–32, 2004.
Martin, R., Monteiller, V., Komatitsch, D., Perrouty, S., Jessell, M. W.,
Bonvalot, S., and Lindsay, M.: Gravity inversion using wavelet-based
compression on parallel hybrid CPU/GPU systems: application to South-West
Ghana, Geophys. J. Int., 195, 1594–1619, https://doi.org/10.1093/gji/ggt334, 2013.
Mayoraz, R., Mann, C. E., and Parriaux, A.: Three-dimensional modelling of
complex geological structures: new development tools for creating 3-D
volumes, in: Computer Modelling of
Geologic Surfaces and Volumes, edited by: Hamilton, D. E. and Jones, T. A., AAPG Computer Applications in Geology, 1,
261–272, 1992.
Moretti, I.: Working in complex areas: New restoration workflow based
control, 2-D and 3-D restorations, Mar. Petrol. Geol., 25,
205–218, 2008.
NASA/JPL: ASTER, available at: https://asterweb.jpl.nasa.gov/gdem.asp (last access: 9 August 2021), 2009.
Ogarko, V.: Loop3D/map2model_cpp: GMD release (Version V1.0), Zenodo [code], https://doi.org/10.5281/zenodo.5084582, 2021.
Pakyuz-Charrier, E., Giraud, J., Ogarko, V., Lindsay, M., and Jessell, M.:
Drillhole uncertainty propagation for three-dimensional geological modelling
using Monte Carlo, Tectonophysics, 747–748, 16–39, https://doi.org/10.1016/j.tecto.2018.09.005, 2018a.
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.
Perrin, M. and Rainaud, J.-F.: Shared Earth Modelling: Knowledge Driven
Solutions for Building and Managing Subsurface 3D Geological Models, IFP Energies Nouvelles, 2013.
Ragan, D. M.: Structural Geology: Introduction to Geometrical Techniques,
4th edn., Cambridge University Press, 632 pp., 2009.
Ramsay, J. G.: Folding and Fracturing of Rocks, McGraw-Hill, New York,
1967.
Rauch, A., Sartori, M., Rossi, E., Baland, P., and Castelltort, S.: Trace
Information Extraction (TIE): A new approach to extract structural
information from traces in geological maps, J. Struct. Geol.,
126, 286–300, 2019.
Sopwith, T.: A Treatise on Isometrical Drawing as Applicable to Geological
and Mining Plans, Picturesque Delineations of Ornamental Grounds,
Perspective Views and Working Plans of Buildings and Machinery, and to
General Purposes of Civil Engineering, John Weald, London, 1834.
Spampinato, G. P. T., Ailleres, L., Betts, P. G., and Armit, R. J.: Crustal
architecture of the Thomson Orogen in Queensland inferred from potential
field forward modelling, Austr. J. Earth, 62, 581–601, 2015.
Thiele, S. T., Jessell, M. W., Lindsay, M., Ogarko, V., Wellmann, F., and
Pakyuz-Charrier, E.: The topology of geology 1: Topological analysis,
J. Struct. Geol., 91, 27–38, 2016.
Thorne, A. M. and Trendall, A. F.: Geology of the Fortescue Group, Pilbara Craton, Western Australia: Western Australia
Geological Survey, Bulletin 144, 249 pp., 2001.
Varnes, D. J.: The Logic of Geological Maps, With Reference to Their
Interpretation and Use for Engineering Purposes, U.S. Geological Survey
Professional Paper 837, 54 pp., 1974.
Vasuki, Y., Holden, E. J., Kovesi, P., and Micklethwaite, S.: An interactive
image segmentation method for lithological boundary detection: A rapid
mapping tool for geologists, Comput. Geosci., 100, 27–40, 2017.
Wellmann, F., de la Varga, M., Murdie, R. E., Gessner, K., and Jessell, M.
W.: 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, Geological Society,
London, Special Publications, 453, 41–52, 2017.
Wellmann, J. F. and Caumon, G.: 3-D Structural geological models: Concepts,
methods, and uncertainties, Adv. Geophys., 59, 1–121, 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., Schaaf, A., de la Varga, M., and von Hagke, C.: From Google
Earth to 3D Geology Problem 2: Seeing Below the Surface of the Digital
Earth, Developments in Structural Geology and Tectonics, 5,
189–204, 2019.
Wu, X. and Hale, D.: 3D seismic image processing for faults, Geophysics, 81,
IM1–IM11, 2015.
Wu, Q., Xu, H., and Zou, X.: An effective method for 3-D
geological modelling with multisource data integration, Comput.
Geosci., 31, 35–43, 2005.
Short summary
We have developed software that allows the user to extract sufficient information from unmodified digital maps and associated datasets that we are able to use to automatically build 3D geological models. By automating the process we are able to remove human bias from the procedure, which makes the workflow reproducible.
We have developed software that allows the user to extract sufficient information from...
