Articles | Volume 18, issue 1
https://doi.org/10.5194/gmd-18-71-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-71-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
13 Jan 2025
Methods for assessment of models |
| 13 Jan 2025
| 13 Jan 2025
Checking the consistency of 3D geological models
Marion N. Parquer
Three-dimensional Earth Imaging and Modelling, Geological Survey of Canada, Natural Resources Canada, 601 Booth Street, Ottawa, ON K1A 0E8, Canada
Three-dimensional Earth Imaging and Modelling, Geological Survey of Canada, Natural Resources Canada, 601 Booth Street, Ottawa, ON K1A 0E8, Canada
Boyan Brodaric
Three-dimensional Earth Imaging and Modelling, Geological Survey of Canada, Natural Resources Canada, 601 Booth Street, Ottawa, ON K1A 0E8, Canada
Michael J. Hillier
Three-dimensional Earth Imaging and Modelling, Geological Survey of Canada, Natural Resources Canada, 601 Booth Street, Ottawa, ON K1A 0E8, Canada
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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.
Eric A. de Kemp
Geosci. Model Dev., 14, 6661–6680, https://doi.org/10.5194/gmd-14-6661-2021, https://doi.org/10.5194/gmd-14-6661-2021, 2021
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This is a proof of concept and review paper of spatial agents, with initial research focusing on geomodelling. The results may be of interest to others working on complex regional geological modelling with sparse data. Structural agent-based swarming behaviour is key to advancing this field. The study provides groundwork for research in structural geology 3D modelling with spatial agents. This work was done with NetLogo, a free agent modelling platform used mostly for teaching complex systems.
Related subject area
Earth and space science informatics
The effect of lossy compression of numerical weather prediction data on data analysis: a case study using enstools-compression 2023.11
GNNWR: an open-source package of spatiotemporal intelligent regression methods for modeling spatial and temporal nonstationarity
Random forests with spatial proxies for environmental modelling: opportunities and pitfalls
An improved global pressure and zenith wet delay model with optimized vertical correction considering the spatiotemporal variability in multiple height-scale factors
kNNDM CV: k-fold nearest-neighbour distance matching cross-validation for map accuracy estimation
Remote sensing-based high-resolution mapping of the forest canopy height: some models are useful, but might they be even more if combined?
Accelerating Lagrangian transport simulations on graphics processing units: performance optimizations of Massive-Parallel Trajectory Calculations (MPTRAC) v2.6
Focal-TSMP: deep learning for vegetation health prediction and agricultural drought assessment from a regional climate simulation
Tomofast-x 2.0: an open-source parallel code for inversion of potential field data with topography using wavelet compression
Functional analysis of variance (ANOVA) for carbon flux estimates from remote sensing data
The 4D reconstruction of dynamic geological evolution processes for renowned geological features
Moving beyond post-hoc XAI: Lessons learned from dynamical climate modeling
Machine learning for numerical weather and climate modelling: a review
Overcoming barriers to enable convergence research by integrating ecological and climate sciences: the NCAR–NEON system Version 1
Ensemble of optimised machine learning algorithms for predicting surface soil moisture content at a global scale
Hazard assessment modeling and software development of earthquake-triggered landslides in the Sichuan–Yunnan area, China
A generalized spatial autoregressive neural network method for three-dimensional spatial interpolation
The Common Community Physics Package (CCPP) Framework v6
Causal deep learning models for studying the Earth system
A methodological framework for improving the performance of data-driven models: a case study for daily runoff prediction in the Maumee domain, USA
SHAFTS (v2022.3): a deep-learning-based Python package for simultaneous extraction of building height and footprint from sentinel imagery
Bayesian atmospheric correction over land: Sentinel-2/MSI and Landsat 8/OLI
Twenty-five years of the IPCC Data Distribution Centre at the DKRZ and the Reference Data Archive for CMIP data
Effectiveness and computational efficiency of absorbing boundary conditions for full-waveform inversion
LAND-SUITE V1.0: a suite of tools for statistically based landslide susceptibility zonation
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
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
Automated geological map deconstruction for 3D model construction using map2loop 1.0 and map2model 1.0
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)
Oriol Tintó Prims, Robert Redl, Marc Rautenhaus, Tobias Selz, Takumi Matsunobu, Kameswar Rao Modali, and George Craig
Geosci. Model Dev., 17, 8909–8925, https://doi.org/10.5194/gmd-17-8909-2024, https://doi.org/10.5194/gmd-17-8909-2024, 2024
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Advanced compression techniques can drastically reduce the size of meteorological datasets (by 5 to 150 times) without compromising the data's scientific value. We developed a user-friendly tool called
enstools-compressionthat makes this compression simple for Earth scientists. This tool works seamlessly with common weather and climate data formats. Our work shows that lossy compression can significantly improve how researchers store and analyze large meteorological datasets.
Ziyu Yin, Jiale Ding, Yi Liu, Ruoxu Wang, Yige Wang, Yijun Chen, Jin Qi, Sensen Wu, and Zhenhong Du
Geosci. Model Dev., 17, 8455–8468, https://doi.org/10.5194/gmd-17-8455-2024, https://doi.org/10.5194/gmd-17-8455-2024, 2024
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In geography, understanding how relationships between different factors change over time and space is crucial. This study implements two neural-network-based spatiotemporal regression models and an open-source Python package named Geographically Neural Network Weighted Regression to capture relationships between factors. This makes it a valuable tool for researchers in fields such as environmental science, urban planning, and public health.
Carles Milà, Marvin Ludwig, Edzer Pebesma, Cathryn Tonne, and Hanna Meyer
Geosci. Model Dev., 17, 6007–6033, https://doi.org/10.5194/gmd-17-6007-2024, https://doi.org/10.5194/gmd-17-6007-2024, 2024
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Spatial proxies, such as coordinates and distances, are often used as predictors in random forest models for predictive mapping. In a simulation and two case studies, we investigated the conditions under which their use is appropriate. We found that spatial proxies are not always beneficial and should not be used as a default approach without careful consideration. We also provide insights into the reasons behind their suitability, how to detect them, and potential alternatives.
Chunhua Jiang, Xiang Gao, Huizhong Zhu, Shuaimin Wang, Sixuan Liu, Shaoni Chen, and Guangsheng Liu
Geosci. Model Dev., 17, 5939–5959, https://doi.org/10.5194/gmd-17-5939-2024, https://doi.org/10.5194/gmd-17-5939-2024, 2024
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With ERA5 hourly data, we show spatiotemporal characteristics of pressure and zenith wet delay (ZWD) and propose an empirical global pressure and ZWD grid model with a broader operating space which can provide accurate pressure, ZWD, zenith hydrostatic delay, and zenith tropospheric delay estimates for any selected time and location over globe. IGPZWD will be of great significance for the tropospheric augmentation in real-time GNSS positioning and atmospheric water vapor remote sensing.
Jan Linnenbrink, Carles Milà, Marvin Ludwig, and Hanna Meyer
Geosci. Model Dev., 17, 5897–5912, https://doi.org/10.5194/gmd-17-5897-2024, https://doi.org/10.5194/gmd-17-5897-2024, 2024
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Estimation of map accuracy based on cross-validation (CV) in spatial modelling is pervasive but controversial. Here, we build upon our previous work and propose a novel, prediction-oriented k-fold CV strategy for map accuracy estimation in which the distribution of geographical distances between prediction and training points is taken into account when constructing the CV folds. Our method produces more reliable estimates than other CV methods and can be used for large datasets.
Nikola Besic, Nicolas Picard, Cédric Vega, Lionel Hertzog, Jean-Pierre Renaud, Fajwel Fogel, Agnès Pellissier-Tanon, Gabriel Destouet, Milena Planells-Rodriguez, and Philippe Ciais
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-95, https://doi.org/10.5194/gmd-2024-95, 2024
Revised manuscript accepted for GMD
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The creation of advanced mapping models for forest attributes, utilizing remote sensing data and incorporating machine or deep learning methods, has become a key area of interest in the domain of forest observation and monitoring. This paper introduces a method where we blend and collectively interpret five models dedicated to estimating forest canopy height. We achieve this through Bayesian model averaging, offering a comprehensive approach to height estimation in forest ecosystems.
Lars Hoffmann, Kaveh Haghighi Mood, Andreas Herten, Markus Hrywniak, Jiri Kraus, Jan Clemens, and Mingzhao Liu
Geosci. Model Dev., 17, 4077–4094, https://doi.org/10.5194/gmd-17-4077-2024, https://doi.org/10.5194/gmd-17-4077-2024, 2024
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Lagrangian particle dispersion models are key for studying atmospheric transport but can be computationally intensive. To speed up simulations, the MPTRAC model was ported to graphics processing units (GPUs). Performance optimization of data structures and memory alignment resulted in runtime improvements of up to 75 % on NVIDIA A100 GPUs for ERA5-based simulations with 100 million particles. These optimizations make the MPTRAC model well suited for future high-performance computing systems.
Mohamad Hakam Shams Eddin and Juergen Gall
Geosci. Model Dev., 17, 2987–3023, https://doi.org/10.5194/gmd-17-2987-2024, https://doi.org/10.5194/gmd-17-2987-2024, 2024
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In this study, we use deep learning and a climate simulation to predict the vegetation health as it would be observed from satellites. We found that the developed model can help to identify regions with a high risk of agricultural drought. The main applications of this study are to estimate vegetation products for periods where no satellite data are available and to forecast the future vegetation response to climate change based on climate scenarios.
Vitaliy Ogarko, Kim Frankcombe, Taige Liu, Jeremie Giraud, Roland Martin, and Mark Jessell
Geosci. Model Dev., 17, 2325–2345, https://doi.org/10.5194/gmd-17-2325-2024, https://doi.org/10.5194/gmd-17-2325-2024, 2024
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We present a major release of the Tomofast-x open-source gravity and magnetic inversion code that is enhancing its performance and applicability for both industrial and academic studies. We focus on real-world mineral exploration scenarios, while offering flexibility for applications at regional scale or for crustal studies. The optimisation work described in this paper is fundamental to allowing more complete descriptions of the controls on magnetisation, including remanence.
Jonathan Hobbs, Matthias Katzfuss, Hai Nguyen, Vineet Yadav, and Junjie Liu
Geosci. Model Dev., 17, 1133–1151, https://doi.org/10.5194/gmd-17-1133-2024, https://doi.org/10.5194/gmd-17-1133-2024, 2024
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The cycling of carbon among the land, oceans, and atmosphere is a closely monitored process in the global climate system. These exchanges between the atmosphere and the surface can be quantified using a combination of atmospheric carbon dioxide observations and computer models. This study presents a statistical method for investigating the similarities and differences in the estimated surface–atmosphere carbon exchange when different computer model assumptions are invoked.
Jiateng Guo, Zhibin Liu, Xulei Wang, Lixin Wu, Shanjun Liu, and Yunqiang Li
Geosci. Model Dev., 17, 847–864, https://doi.org/10.5194/gmd-17-847-2024, https://doi.org/10.5194/gmd-17-847-2024, 2024
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This study proposes a 3D and temporally dynamic (4D) geological modeling method. Several simulation and actual cases show that the 4D spatial and temporal evolution of regional geological formations can be modeled easily using this method with smooth boundaries. The 4D modeling system can dynamically present the regional geological evolution process under the timeline, which will be helpful to the research and teaching on the formation of typical and complex geological features.
Ryan O'Loughlin, Dan Li, and Travis O'Brien
EGUsphere, https://doi.org/10.5194/egusphere-2023-2969, https://doi.org/10.5194/egusphere-2023-2969, 2024
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We draw from traditional climate modeling practices to make recommendations for AI-driven climate science. In particular, we show how component-level understanding–which is obtained when scientists can link model behavior to parts within the overall model–should guide the development and evaluation of AI models. Better understanding can lead to a stronger basis for trust in these models. We highlight several examples to demonstrate.
Catherine O. de Burgh-Day and Tennessee Leeuwenburg
Geosci. Model Dev., 16, 6433–6477, https://doi.org/10.5194/gmd-16-6433-2023, https://doi.org/10.5194/gmd-16-6433-2023, 2023
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Machine learning (ML) is an increasingly popular tool in the field of weather and climate modelling. While ML has been used in this space for a long time, it is only recently that ML approaches have become competitive with more traditional methods. In this review, we have summarized the use of ML in weather and climate modelling over time; provided an overview of key ML concepts, methodologies, and terms; and suggested promising avenues for further research.
Danica L. Lombardozzi, William R. Wieder, Negin Sobhani, Gordon B. Bonan, David Durden, Dawn Lenz, Michael SanClements, Samantha Weintraub-Leff, Edward Ayres, Christopher R. Florian, Kyla Dahlin, Sanjiv Kumar, Abigail L. S. Swann, Claire M. Zarakas, Charles Vardeman, and Valerio Pascucci
Geosci. Model Dev., 16, 5979–6000, https://doi.org/10.5194/gmd-16-5979-2023, https://doi.org/10.5194/gmd-16-5979-2023, 2023
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We present a novel cyberinfrastructure system that uses National Ecological Observatory Network measurements to run Community Terrestrial System Model point simulations in a containerized system. The simple interface and tutorials expand access to data and models used in Earth system research by removing technical barriers and facilitating research, educational opportunities, and community engagement. The NCAR–NEON system enables convergence of climate and ecological sciences.
Qianqian Han, Yijian Zeng, Lijie Zhang, Calimanut-Ionut Cira, Egor Prikaziuk, Ting Duan, Chao Wang, Brigitta Szabó, Salvatore Manfreda, Ruodan Zhuang, and Bob Su
Geosci. Model Dev., 16, 5825–5845, https://doi.org/10.5194/gmd-16-5825-2023, https://doi.org/10.5194/gmd-16-5825-2023, 2023
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Using machine learning, we estimated global surface soil moisture (SSM) to aid in understanding water, energy, and carbon exchange. Ensemble models outperformed individual algorithms in predicting SSM under different climates. The best-performing ensemble included K-neighbours Regressor, Random Forest Regressor, and Extreme Gradient Boosting. This is important for hydrological and climatological applications such as water cycle monitoring, irrigation management, and crop yield prediction.
Xiaoyi Shao, Siyuan Ma, and Chong Xu
Geosci. Model Dev., 16, 5113–5129, https://doi.org/10.5194/gmd-16-5113-2023, https://doi.org/10.5194/gmd-16-5113-2023, 2023
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Scientific understandings of the distribution of coseismic landslides, followed by emergency and medium- and long-term risk assessment, can reduce landslide risk. The aim of this study is to propose an improved three-stage spatial prediction strategy and develop corresponding hazard assessment software called Mat.LShazard V1.0, which provides a new application tool for coseismic landslide disaster prevention and mitigation in different stages.
Junda Zhan, Sensen Wu, Jin Qi, Jindi Zeng, Mengjiao Qin, Yuanyuan Wang, and Zhenhong Du
Geosci. Model Dev., 16, 2777–2794, https://doi.org/10.5194/gmd-16-2777-2023, https://doi.org/10.5194/gmd-16-2777-2023, 2023
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We develop a generalized spatial autoregressive neural network model used for three-dimensional spatial interpolation. Taking the different changing trend of geographic elements along various directions into consideration, the model defines spatial distance in a generalized way and integrates it into the process of spatial interpolation with the theories of spatial autoregression and neural network. Compared with traditional methods, the model achieves better performance and is more adaptable.
Dominikus Heinzeller, Ligia Bernardet, Grant Firl, Man Zhang, Xia Sun, and Michael Ek
Geosci. Model Dev., 16, 2235–2259, https://doi.org/10.5194/gmd-16-2235-2023, https://doi.org/10.5194/gmd-16-2235-2023, 2023
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The Common Community Physics Package is a collection of physical atmospheric parameterizations for use in Earth system models and a framework that couples the physics to a host model’s dynamical core. A primary goal for this effort is to facilitate research and development of physical parameterizations and physics–dynamics coupling methods while offering capabilities for numerical weather prediction operations, for example in the upcoming implementation of the Global Forecast System (GFS) v17.
Tobias Tesch, Stefan Kollet, and Jochen Garcke
Geosci. Model Dev., 16, 2149–2166, https://doi.org/10.5194/gmd-16-2149-2023, https://doi.org/10.5194/gmd-16-2149-2023, 2023
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A recent statistical approach for studying relations in the Earth system is to train deep learning (DL) models to predict Earth system variables given one or several others and use interpretable DL to analyze the relations learned by the models. Here, we propose to combine the approach with a theorem from causality research to ensure that the deep learning model learns causal rather than spurious relations. As an example, we apply the method to study soil-moisture–precipitation coupling.
Yao Hu, Chirantan Ghosh, and Siamak Malakpour-Estalaki
Geosci. Model Dev., 16, 1925–1936, https://doi.org/10.5194/gmd-16-1925-2023, https://doi.org/10.5194/gmd-16-1925-2023, 2023
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Data-driven models (DDMs) gain popularity in earth and environmental systems, thanks in large part to advancements in data collection techniques and artificial intelligence (AI). The performance of these models is determined by the underlying machine learning (ML) algorithms. In this study, we develop a framework to improve the model performance by optimizing ML algorithms and demonstrate the effectiveness of the framework using a DDM to predict edge-of-field runoff in the Maumee domain, USA.
Ruidong Li, Ting Sun, Fuqiang Tian, and Guang-Heng Ni
Geosci. Model Dev., 16, 751–778, https://doi.org/10.5194/gmd-16-751-2023, https://doi.org/10.5194/gmd-16-751-2023, 2023
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We developed SHAFTS (Simultaneous building Height And FootprinT extraction from Sentinel imagery), a multi-task deep-learning-based Python package, to estimate average building height and footprint from Sentinel imagery. Evaluation in 46 cities worldwide shows that SHAFTS achieves significant improvement over existing machine-learning-based methods.
Feng Yin, Philip E. Lewis, and Jose L. Gómez-Dans
Geosci. Model Dev., 15, 7933–7976, https://doi.org/10.5194/gmd-15-7933-2022, https://doi.org/10.5194/gmd-15-7933-2022, 2022
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The proposed SIAC atmospheric correction method provides consistent surface reflectance estimations from medium spatial-resolution satellites (Sentinel 2 and Landsat 8) with per-pixel uncertainty information. The outputs from SIAC have been validated against a wide range of ground measurements, and it shows that SIAC can provide accurate estimations of both surface reflectance and atmospheric parameters, with meaningful uncertainty information.
Martina Stockhause and Michael Lautenschlager
Geosci. Model Dev., 15, 6047–6058, https://doi.org/10.5194/gmd-15-6047-2022, https://doi.org/10.5194/gmd-15-6047-2022, 2022
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The Data Distribution Centre (DDC) of the Intergovernmental Panel on Climate Change (IPCC) celebrates its 25th anniversary in 2022. DDC Partner DKRZ has supported the IPCC Assessments and preserved the quality-assured, citable climate model data underpinning the Assessment Reports over these years over the long term. With the introduction of the IPCC FAIR Guidelines into the current AR6, the value of DDC services has been recognized. However, DDC sustainability remains unresolved.
Daiane Iglesia Dolci, Felipe A. G. Silva, Pedro S. Peixoto, and Ernani V. Volpe
Geosci. Model Dev., 15, 5857–5881, https://doi.org/10.5194/gmd-15-5857-2022, https://doi.org/10.5194/gmd-15-5857-2022, 2022
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We investigate and compare the theoretical and computational characteristics of several absorbing boundary conditions (ABCs) for the full-waveform inversion (FWI) problem. The different ABCs are implemented in an optimized computational framework called Devito. The computational efficiency and memory requirements of the ABC methods are evaluated in the forward and adjoint wave propagators, from simple to realistic velocity models.
Mauro Rossi, Txomin Bornaetxea, and Paola Reichenbach
Geosci. Model Dev., 15, 5651–5666, https://doi.org/10.5194/gmd-15-5651-2022, https://doi.org/10.5194/gmd-15-5651-2022, 2022
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LAND-SUITE is a software package designed to support landslide susceptibility zonation. The software integrates, extends, and completes LAND-SE (Rossi et al., 2010; Rossi and 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 easier, more flexible, and more informed statistically based landslide susceptibility applications and zonations.
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
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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
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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
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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
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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
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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
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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.
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
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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
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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.
Mark Jessell, Vitaliy Ogarko, Yohan de Rose, Mark Lindsay, Ranee Joshi, Agnieszka Piechocka, Lachlan Grose, Miguel de la Varga, Laurent Ailleres, and Guillaume Pirot
Geosci. Model Dev., 14, 5063–5092, https://doi.org/10.5194/gmd-14-5063-2021, https://doi.org/10.5194/gmd-14-5063-2021, 2021
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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.
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
Cited articles
Allen, J. F.: Maintaining knowledge about temporal intervals, Commun. ACM, 26, 832–843, 1983.
Alsop, G. I. and Holdsworth, R. E.: Vergence and facing patterns in large scale sheath folds, J. Struct. Geol. 21, 1335–1349, 1999.
Annen, C.: Implications of incremental emplacement of magma bodies for magma differentiation, thermal aureole dimensions and plutonism–volcanism relationships, Tectonophysics, 500, 3–10, https://doi.org/10.1016/j.tecto.2009.04.010, 2011.
Arora, H., Langenhan, C., Petzold, F., Eisenstadt, V., and Althoff, K.-D.: METIS-GAN: An approach to generate spatial configurations using deep learning and semantic building models, in: ECPPM 2021 – eWork and eBusiness in Architecture, Engineering and Construction, CRC Press, 596 pp., https://doi.org/10.1201/9781003191476, 2021.
Aubry, M. P., Berggren, W. A., Van Couvering, J. A., and Steininger, F.: Problems in chronostratigraphy: stages, series, unit and boundary stratotypes, global stratotype section and point and tarnished golden spikes, Earth-Sci. Rev., 46, 99–148, https://doi.org/10.1016/S0012-8252(99)00008-2, 1999.
Bai, H., Montési, L. G. J., and Behn, M. D.: MeltMigrator: A MATLAB-based software for modeling three-dimensional melt migration and crustal thickness variations at mid-ocean ridges following a rules-based approach, Geochem. Geophy. Geosy., 18, 445–456, https://doi.org/10.1002/2016GC006686, 2017.
Banerjee, P. K. and Butterfield, R.: Boundary element methods in engineering science, McGraw-Hill (UK), 1981.
Bertoncello, A., Sun, T., Li, H. Mariethoz, G., and Caers, J.: Conditioning Surface-Based Geological Models to Well and Thickness Data, Math. Geosci., 45, 873–893, https://doi.org/10.1007/s11004-013-9455-4, 2013.
Bezhanishvili, N., Ciancia, V., Gabelaia, D., Grilletti, G., Latella, D., and Massink, M.: Geometric Model Checking of Continuous Space, Log. Meth. Comput. Sci., 18, 7:1–7:38, https://doi.org/10.46298/lmcs-18(4:7)2022, 2022.
Bond, C. E.: Uncertainty in structural interpretation: Lessons to be learnt, J. Struct. Geol., 74, 185–200, https://doi.org/10.1016/j.jsg.2015.03.003, 2015.
Botella, A., Lévy, B., and Caumon, G.: Indirect unstructured hex-dominant mesh generation using tetrahedra recombination, Comput. Geosci., 20, 437–451, https://doi.org/10.1007/s10596-015-9484-9, 2016.
Braid, I. C.: The Synthesis of Solids Bounded by Many Faces, Comm. ACM, 18, 209–216, 1975.
Brodaric, B.: Characterizing and representing inference histories in geologic mapping, Int. J. Geogr. Inf. Sci., 26, 265–281, https://doi.org/10.1080/13658816.2011.585992, 2012.
Brodaric, B. and Gahegan, M.: Representing Geoscientific Knowledge in Cyberinfrastructure: challenges, approaches and implementations, in: Geoinformatics, Data to Knowledge, edited by: Sinha, A. K., Geol. Soc. Am. Spec. Pap., 397, 1–20, https://doi.org/10.1130/2006.2397(01), 2006.
Brodaric, B. and Richard, S. M.: The GeoScience Ontology reference. Geological Survey of Canada, Open File, 8796, 34, https://doi.org/10.4095/328296, 2021.
Brodaric, B., Gahegan, M., and Harrap, R.: The art and science of mapping: Computing geological categories from field data, Comput. Geosci., 30, 719–740, https://doi.org/10.1016/j.cageo.2004.05.001, 2004.
Burns, K. L.: Analysis of geological events, Math. Geol., 7, 295–321, 1975.
Burch, T. K.: Data, Models, Theory and Reality: The Structure of Demographic Knowledge, in: Agent-Based Computational Demography, Contributions to Economics, edited by: Billari, F. C. and Prskawetz, A., Physica, Heidelberg, https://doi.org/10.1007/978-3-7908-2715-6_2, 2003.
Burns, K. L.: Lithologic topology and structural vector fields applied to subsurface prediction in geology, Proceedings of International GIS/LIS'88 accessing the World, Third Annual International Conference, San Antonio, Texas, USA, 26–34, ISBN-13 978-0944426210, 1988.
Burns, K. L. and Remfry, J. G.: A Computer Method of Constructing Geological Histories from Field Surveys and Maps, Comput. Geosci., 2, 141–162, 1976.
Burns, K. L., Marshall, B., and Gee, R. D.: Computer-Assisted Geological Mapping, Proceedings of the Australian Institute of Mining and Metallurgy, 323, 41–47, 1969.
Burns, K. L., Shepherd, J., and Marshall, B.: Analysis of Relational Data from Metamorphic Tectonites: Derivation of Deformation Sequences from Overprinting Relations, Proceedings of the International Association for Mathematical Geology (IAMG) 25th International Congress in Sydney, Australia, August 1976, in: Recent Advances in Geomathematics, An International Symposium, edited by: Merriam, D. F., Syracuse University, Computers and Geology Volume 2, 171–199, 1978.
Cherpeau, N., Caumon, G., and Lévy, B.: Stochastic simulations of fault networks in 3-D structural modelling, C. R. Geosci., 342, 687–694, 2010.
Claramunt, C. and Jiang, B.: An integrated representation of spatial and temporal relationships between evolving regions, J. Geogr. Syst., 3, 411–428, 2001.
Crossley, M. D.: Essential topology, Springer-Verlag, London, 226 pp., ISBN 1-85233-782-6, 226, 2005.
de Kemp, E. A.: Truth Tables for consistency-checking 3D geological models, Zenodo [data set], https://doi.org/10.5281/zenodo.13948382, 2024.
de Kemp, E. A., Sprague, K., and Wong, W.: Interpretive Geology with Structural Constraints: An introduction to the SPARSE © plug-in, Americas GOCAD User Meeting, Houston Texas, 1–16, Zenodo https://doi.org/10.5281/zenodo.4646210, 2004.
de Kemp, E. A., Schetselaar, E. M., Hillier, M. J., Lydon, J. W., and Ransom, P. W.: Assessing the Workflow for Regional Scale 3D Geological Modelling: An Example from the Sullivan Time Horizon, Purcell Anticlinorium East Kootenay Region, Southeastern British Columbia, Interpretation, Special section: Building complex and realistic geologic models from sparse data, 4, SM33–SM50, https://doi.org/10.1190/INT-2015-0191.1, 2016.
de Kemp, E. A., Jessell, M. W., Aillères, L., Schetselaar, E. M., Hillier, M., Lindsay, M. D., and Brodaric, B.: Earth model construction in challenging geologic terrain: Designing workflows and algorithms that makes sense, in: Proceedings of Exploration'17: Sixth DMEC – Decennial International Conference on Mineral Exploration, edited by: Tschirhart, V. and Thomas, M. D., Integrating the Geosciences: The Challenge of Discovery, Toronto, Canada, 21–25 October 2017, 419–439, https://www.dmec.ca/decennial-conference-proceedings (last access: 7 January 2025), 2017.
de la Varga, M. and Wellmann, J. F.: Structural geologic modeling as an inference problem: A Bayesian perspective, Interpretation, 4, SM1-SM16, https://doi.org/10.1190/INT-2015-0188.1, 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.
Deutsch, C. V.: All Realizations All the Time, in: Handbook of Mathematical Geosciences, edited by: Daya Sagar, B., Cheng, Q., and Agterberg, F., Springer, Cham, https://doi.org/10.1007/978-3-319-78999-6_7, 2018.
Dutranois, A., Wan-Chiu, L., Dulac, J.-C., Lecomte, J.-F., Callot, J.-P., and Rudkiewicz, J.-L.: Breakthrough in basin modeling using time/space frame, Offshore, 70, https://www.offshore-mag.com/geosciences/article/16763653/breakthrough-in-basin-modeling-using-time-space-frame (last access: 7 January 2025), 2010.
Egenhofer, M. and Franzosa, R.: Point-set topological relations, Int. J. Geogr. Inf. Syst., 5, 161–174, 1991.
Egenhofer, M., Sharma J., and Mark D.: A critical comparison of the 4-intersection and 9- intersection models for spatial relations: formal analysis, in: Autocarto 11, edited by: McMaster, R. and Armstrong, M., Minneapolis, MN, 1–11, 1993.
Egenhofer, M. J.: A formal definition of binary topological relationships, in: Foundations of Data Organization and Algorithms, FODO 1989, Lecture Notes in Computer Science, edited by: Litwin, W. and Schek, H. J., Springer, Berlin, Heidelberg, 367, https://doi.org/10.1007/3-540-51295-0_148, 1989.
Fattah, M.: Physical Geology, On line course, UDEMY, https://www.udemy.com/course/geology-fundamentalz/, last access: 6 April 2024), 2018.
Finkl, C. W. (Ed.): Field Geology, in: Applied Geology, Encyclopedia of Earth Sciences Series, vol 3. Springer, Boston, MA, https://doi.org/10.1007/0-387-30842-3_21, 1984.
Fossen, H.: Structural Geology, Cambridge University Press, New York, 524 pp., ISBN-13 978-1107057647, 2016.
Frank, T.: Advanced visualization and modeling of tetrahedral meshes., Thèse de doctorat dirigée par Mallet, Jean-Laurent Géosciences Vandoeuvre-les-Nancy, INPL 2006, https://hal.univ-lorraine.fr/tel-01752480v1 (last access: 31 December 2024), 2006.
Frodeman, R.: Geological reasoning: geology as an interpretive and historical science, Geol. Soc. Am. Bull., 107, 960–968, 1995.
Galton, A.: Spatial and temporal knowledge representation, Earth Sci. Inform., 2, 169–187, https://doi.org/10.1007/s12145-009-0027-6, 2009.
Garcia, L. F., Abel, M., Perrin, M., and dos Santos Alvarenga, R.: The GeoCore ontology: A core ontology for general use in Geology, Comput. Geosci., 135, 104387, https://doi.org/10.1016/j.cageo.2019.104387, 2020.
Geodes-Solutions: https://geode-solutions.com/opengeode/, last access: 10 September 2024.
Giraud, J., Lindsay, M., Jessell, M., and Ogarko, V.: Towards plausible lithological classification from geophysical inversion: honouring geological principles in subsurface imaging, Solid Earth, 11, 419–436, https://doi.org/10.5194/se-11-419-2020, 2020.
Giraud, J., Caumon, G., Grose, L., Ogarko, V., and Cupillard, P.: Integration of automatic implicit geological modelling in deterministic geophysical inversion, Solid Earth, 15, 63–89, https://doi.org/10.5194/se-15-63-2024, 2024.
Gleeson, T., Wagener, T., Döll, P., Zipper, S. C., West, C., Wada, Y., Taylor, R., Scanlon, B., Rosolem, R., Rahman, S., Oshinlaja, N., Maxwell, R., Lo, M.-H., Kim, H., Hill, M., Hartmann, A., Fogg, G., Famiglietti, J. S., Ducharne, A., de Graaf, I., Cuthbert, M., Condon, L., Bresciani, E., and Bierkens, M. F. P.: GMD perspective: The quest to improve the evaluation of groundwater representation in continental- to global-scale models, Geosci. Model Dev., 14, 7545–7571, https://doi.org/10.5194/gmd-14-7545-2021, 2021.
Gong, P. and Mu, L.: Error detection through consistency checking, Geogr. Inf. Sci., 6, 188–193, 2000.
Grose, L., Ailleres, L., Laurent, G., Armit, R., and Jessell, M.: Inversion of geological knowledge for fold geometry, J. Struct. Geol., 119, 1–14, https://doi.org/10.1016/j.jsg.2018.11.010, 2019.
Groshong Jr., R. H.: 3-D Structural Geology a Practical Guide to Quantitative Surface and Subsurface Map Interpretation, 2nd Edn., Springer-Verlag Berlin Heidelberg, ISBN 13 978-3540310549, 2006.
Harrap, R.: A Legend Language for Geologic Maps, Precambrian Times, Geological Association of Canada, Precambrian Division Newsletter, 1, 3–9, 2001.
Hillier, M., de Kemp, E. A., and Schetselaar, E. M.: Implicitly modelled stratigraphic surfaces using generalized interpolation, in: AIP conference proceedings, 1738, 050004, International Conference of Numerical Analysis and Applied Mathematics, 22–28 September 2015, Rhodes, Greece, https://doi.org/10.1063/1.4951819, 2016.
Hillier, M. 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.
Hillier, M. J., Wellmann, F., Brodaric, B., de Kemp, E. A., and Schetselaar, E.: Three-Dimensional Structural Geological Modeling Using Graph Neural Networks, Math. Geosci., 53, 1725–1749, https://doi.org/10.1007/s11004-021-09945-x, 2021a.
Hillier, M. J., Schetselaar, E. M., and de Kemp, E. A.: SURFE implicit code library repository, (Open Source), GitHub [code], https://github.com/MichaelHillier/surfe (last access: 6 April 2024), 2021b.
Hinojosa, J. H. and Mickus, K. L.: Foreland basin-a FORTRAN program to model the formation of foreland basins resulting from the flexural deflection of the lithosphere caused by a time-varying distributed load, Comput. Geosci., 19, 1321–1332, https://doi.org/10.1016/0098-3004(93)90032-Z, 1993.
Hobbs, B., Regenauer-Lieb, K., and Ord, A.: Thermodynamics of Folding in the Middle to Lower Crust, Geology, 35, 175–178, https://doi.org/10.1130/G23188A, 2007.
Jayr, S., Gringarten, E., Tertois, A.-L., Mallet, J.-L., and Dulac, J.-C.: The need for a correct geological modelling support: The advent of the UVT-transform, First Break, 26, 73–79, https://doi.org/10.3997/1365-2397.26.10.28558, 2008.
Jessell, M., Aillères, L., de Kemp, E. A., Lindsay, M., Wellmann, J., Hillier, M., Laurent, G., Carmichael, T., and Martin, R.: Next generation three-dimensional geologic modeling and inversion, Soc. Eco. Geo. Spc. Pub., 18, 261–272, 2014.
Jessell, M., Ogarko, V., de Rose, Y., Lindsay, M., Joshi, R., Piechocka, A., Grose, L., de la Varga, M., Ailleres, L., and Pirot, G.: Automated geological map deconstruction for 3D model construction using map2loop 1.0 and map2model 1.0, Geosci. Model Dev., 14, 5063–5092, https://doi.org/10.5194/gmd-14-5063-2021, 2021.
Jessell, M. W.: Noddy: an interactive map creation package, Unpublished MSc Thesis, University of London, https://tectonique.net/noddy/ (last access: 31 December 2024), 1981.
Jessell, M. W., Aillères, L., and de Kemp, E. A.: Towards an Integrated Inversion of Geoscientific data: what price of Geology?, Tectonophysics, 490, 294–306, 2010.
Kardel, T. and Paul Maquet, P.: Nicolaus Steno: Biography and Original Papers of a 17th Century Scientist, 2013'th (Kindle) Edition, Springer, 2013th Edn., 739, ISBN-13 978-3642250781, 2012.
Lajaunie, C., Courrioux, G., and Manuel, L.: Foliation fields and 3D cartography in geology: principles of a method based on potential interpolation. Math. Geol., 29, 571–584, https://doi.org/10.1007/BF02775087, 1997.
Lajevardi, S. and Deutsch, C. V.: Stochastic regridding of geological models for flow simulation, B. Can. Petrol. Geol., 63, 374–392, https://doi.org/10.2113/gscpgbull.63.4.374, 2015.
Le, H. H., Gabriel, P., Gietzel, J., and Schaeben, H.: An object-relational spatio-temporal geoscience data model, Comput. Geosci., 57, 104–115, https://doi.org/10.1016/j.cageo.2013.04.014, 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, https://doi.org/10.1016/j.tecto.2012.04.007, 2012.
Lyell, C.: Principles of Geology: Or, the Modern Changes of the Earth and Its Inhabitants, Considered As Illustrative of Geology, Legare Street Press, ISBN-13 978-1015539976, (reprinted 2022), 496, 1833.
Mallet, J.-L.: Space–Time Mathematical Framework for Sedimentary Geology, Math. Geol., 36, 1–32, https://doi.org/10.1023/B:MATG.0000016228.75495.7c, 2004.
Mallet, J.-L., Jacquemin, P., and Cheimanoff, N.: GOCAD project: Geometric modeling of complex geological surfaces, in: SEG Technical Program Expanded Abstracts, 126–128, https://doi.org/10.1190/1.1889515, 1989.
Maxelon, M., Renard, P., Courrioux, G., Brändli, M., and Mancktelow, N.: A workflow to facilitate three-dimensional geometrical modelling of complex poly-deformed geological units, Comput. Geosci., 35, 644–658, https://doi.org/10.1016/j.cageo.2008.06.005, 2009.
McKay, G. and Harris, J. R.: Comparison of the Data-Driven Random Forests Model and a Knowledge-Driven Method for Mineral Prospectivity Mapping: A Case Study for Gold Deposits Around the Huritz Group and Nueltin Suite, Nunavut, Canada. Nat. Resour. Res., 25, 125–143, https://doi.org/10.1007/s11053-015-9274-z, 2016.
McMechan, M. E., Root, K. G., Simony, P. S., and Pattison, D. R. M.: Nailed to the craton: Stratigraphic continuity across the southeastern Canadian Cordillera with tectonic implications for ribbon continent models, Geology, 49, 101–105, https://doi.org/10.1130/G23188A, 2021.
Melnikova, Y., Cordua, K. S., and Mosegaard, K.: History Matching: Towards Geologically Reasonable Models. Abstract from EAGE Integrated Reservoir Modelling: Are we doing it right?, Dubai, United Arab Emirates, https://doi.org/10.3997/2214-4609.20142866, 2012.
Michalak, J.: Topological conceptual model of geological relative time scale for geoinformation systems, Comput. Geosci., 31-7, 865–876, https://doi.org/10.1016/j.cageo.2005.03.001, 2005.
Morley, C. K.: Out-of-sequence thrusts, Tectonics, 7, 539–561, 1988.
Nikoohemat, S., Diakité, A. A., Lehtola, V., Zlatanova, S., and Vosselman, G.: Consistency grammar for 3D indoor model checking, Trans. GIS, 25, 189–212, https://doi.org/10.1111/tgis.12686, 2021.
Pellerin, J., Caumon, G., Julio, C., Mejia-Herrera, P., and Botella, A.: Elements for measuring the complexity of 3D structural models: Connectivity and geometry, Comput. Geosci., 76, 130–140, https://doi.org/10.1016/j.cageo.2015.01.002, 2015.
Pellerin, J., Botella, A., Bonneau, F., Mazuyer, A., Chauvin, B., Lévy, B., and Caumon, G.: RINGMesh: A programming library for developing mesh-based geomodeling applications, Comput. Geosci., 104, 93–100, https://doi.org/10.1016/j.cageo.2017.03.005, 2017.
Perrin, M., Morel, O., Mastella, L., and Alexandre, L.: Geological Time Formalization: an improved formal model for describing time successions and their correlation, Earth Sci. Inform., 4, 81–96, https://doi.org/10.1007/s12145-011-0080-9, 2011.
Perrin, M., Poudret, M., Guiard, N., and Schneider, S.: Chapter 6: Geological Surface Assemblage, in: Shared Earth Modeling, Knowledge driven solutions for building and managing subsurface 3D geological models, Energies Nouvelles Publications 0- TECHNIP, Paris, France, 115–139, https://www.researchgate.net/publication/236901352_Geological_Surface_Assemblage (last access: 7 January 2025), 2013.
Pizzella, L., Alais, R., Lopez, S., Freulon X., and Rivoirard, J.: Taking Better Advantage of Fold Axis Data to Characterize Anisotropy of Complex Folded Structures in the Implicit Modeling Framework, Math. Geosci., 54, 95–130, https://doi.org/10.1007/s11004-021-09950-0, 2022.
Pyrcz, M. J., Sech, R. P., Covault, J. A., Willis, B. J., Sylvester, Z., Sun, T., and Garner, D.: Stratigraphic rule-based reservoir modeling, B. Can. Petrol. Geol., 63, 287–303, 2015.
Qu, Y., Perrin, M., Torabi, A., Abel, M., and Giese, M.: GeoFault: A well-founded fault ontology for interoperability in geological modeling, Comput. Geosci., 182, 105478, https://doi.org/10.1016/j.cageo.2023.105478, 2024.
Ranalli, G.: A stochastic model for strike-slip faulting, Math. Geol., 12, 399–412, https://doi.org/10.1007/BF01029423, 1980.
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, https://doi.org/10.1016/j.jsg.2019.06.007, 2019.
Rothery, D.: Geology: A Complete Introduction, Quercus, 1st Edn., 16 Feb. 2016, ISBN 13 978-1473601550, 2016.
Schaaf, A., de la Varga, M., Wellmann, F., and Bond, C. E.: Constraining stochastic 3-D structural geological models with topology information using approximate Bayesian computation in GemPy 2.1, Geosci. Model Dev., 14, 3899–3913, https://doi.org/10.5194/gmd-14-3899-2021, 2021.
Schetselaar, E. M. and de Kemp, E. A.: Topological encoding of spatial relationships to support geological modelling in a 3-D GIS environment, Int. Assoc. for Mathematical Geology XIth International Congress, Université de Liège – Belgium, https://iamgmembers.org/catalog/index.php?main_page=product_info&cPath=65&products_id=192 (last access: 7 January 2025), 2006.
Shokouhi, P., Kumar, V., Prathipati, S., Hosseini, S. A., Giles, C. L., and Kifer, D.: Physics-informed deep learning for prediction of CO2 storage site response, J. Contam. Hydrol., 241, 103835, https://doi.org/10.1016/j.jconhyd.2021.103835, 2021.
Thapa, P. and McMechan, M. E.: Methodology for portraying 3D structure using ArcGIS: a test case from the southern Canadian Rocky Mountains, British Columbia and Alberta, Geological Survey of Canada, Open File 8576, https://doi.org/10.4095/314941, 2019.
Thiele, S. T., Jessel, 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, https://doi.org/10.1016/j.jsg.2016.08.009, 2016a.
Thiele, S. T., Jessel, M. W., Lindsay, M., Wellmann, J. F., and Pakyuz-Charrier, E.: The topology of geology 2: Topological uncertainty, J. Struct. Geol., 91, 74–87, https://doi.org/10.1016/j.jsg.2016.08.010, 2016b.
van Giffen, B. Herhausen, D., and Fahse, T.: Overcoming the pitfalls and perils of algorithms: A classification of machine learning biases and mitigation methods, J. Bus. Res., 144, 93–106, https://doi.org/10.1016/j.jbusres.2022.01.076, 2022.
Van Oosterom, P.: Maintaining consistent topology including historical data in a large spatial database, Auto-Carto XIII, Proceedings of the Annual Convention and Exposition Technical Papers 7–10 April 1997, Seattle, Washington, 13, 327–336, https://cartogis.org/docs/proceedings/archive/auto-carto-13/pdf/maintaining-consistent-topology-including-historical-data-in-a-large.pdf (last access: 31 December 2024), 1997.
von Harten, J., de la Varga, M., Hillier, M., and Wellmann, F.: Informed Local Smoothing in 3D Implicit Geological Modeling, Minerals, 11, 1281, https://doi.org/10.3390/min11111281, 2021.
Wellmann, F. and Caumon, G.: 3-D Structural geological models: Concepts, methods, and uncertainties, Adv. Geophys., 59, 1–121, https://doi.org/10.1016/bs.agph.2018.09.001, 2018.
Zhan, X., Liang, J., Lu, C., and Hu, G.: Semantic Description and Complete Computer Characterization of Structural Geological Models, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2018-305, 2019.
Zhan, X., Lu, C., and Hu, G.: A Formal Representation of the Semantics of Structural Geological Models, Sci. Program., 5553774, 18 pp., https://doi.org/10.1155/2022/5553774, 2022.
Ziggelaar, A.: The age of Earth in Niels Stensen's geology, Geol. Soc. Am. Mem., 203, 135–142, 2009.
Zlatanova, S., Rahman, A. A., and Shi, W.: Topological models and frameworks for 3D spatial objects, Comput. Geosci., 30, 419–428, 2004.
Short summary
This is a proof-of-concept paper outlining a general approach to how 3D geological models would be checked to be geologically
reasonable. We do this with a consistency-checking tool that looks at geological feature pairs and their spatial, temporal, and internal polarity characteristics. The idea is to assess if geological relationships from a specific 3D geological model match what is allowed in the real world from the perspective of geological principles.
This is a proof-of-concept paper outlining a general approach to how 3D geological models would...
