Articles | Volume 14, issue 11
https://doi.org/10.5194/gmd-14-6711-2021
© Author(s) 2021. 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-14-6711-2021
© 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
| 
04 Nov 2021
Development and technical paper |
| 04 Nov 2021
| 04 Nov 2021
dh2loop 1.0: an open-source Python library for automated processing and classification of geological logs
Ranee Joshi
CORRESPONDING AUTHOR
Mineral Exploration Cooperative Research Centre, Centre for
Exploration Targeting, School of Earth Sciences, The University of Western
Australia, Perth, Australia
Kavitha Madaiah
Mineral Exploration Cooperative Research Centre, Centre for
Exploration Targeting, School of Earth Sciences, The University of Western
Australia, Perth, Australia
Mark Jessell
Mineral Exploration Cooperative Research Centre, Centre for
Exploration Targeting, School of Earth Sciences, The University of Western
Australia, Perth, Australia
Mark Lindsay
Mineral Exploration Cooperative Research Centre, Centre for
Exploration Targeting, School of Earth Sciences, The University of Western
Australia, Perth, Australia
CSIRO Mineral Resources, CSIRO - Kensington, Australian Resources
Research Centre (ARRC), Kensington, Australia
Guillaume Pirot
Mineral Exploration Cooperative Research Centre, Centre for
Exploration Targeting, School of Earth Sciences, The University of Western
Australia, Perth, Australia
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.
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
Short summary
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.
Vitaliy Ogarko and Mark Jessell
EGUsphere, https://doi.org/10.5194/egusphere-2025-1294, https://doi.org/10.5194/egusphere-2025-1294, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
We developed a new method to reconstruct underground rock layers from drillhole data, using an advanced algorithm to ensure geologically realistic results. By combining data from multiple drillholes, our approach reduces uncertainty and improves accuracy. Tested on South Australian data, it successfully predicted stratigraphy and highlighted ways to enhance data quality. This innovation makes geological analysis more reliable, aiding exploration and resource management.
Mark Douglas Lindsay, Vitaliy Ogarko, Jeremie Giraud, and Mosayeb Khademi
EGUsphere, https://doi.org/10.5194/egusphere-2024-3754, https://doi.org/10.5194/egusphere-2024-3754, 2025
This preprint is open for discussion and under review for Solid Earth (SE).
Short summary
Short summary
Geophysical data is used to understand the Earth's structure. Geophysical inversion is an optimisation technique that constructs a model of the Earth using geophysical data. Inversion is complex and requires constraints to produce a plausible model. Overfitting is a disadvantageous effect that can affect techniques that rely on optimisation. A new feature in an inversion platform reduces overfitting by informing inversion of spatial uncertainty contained within the geophysical data.
Léonard Moracchini, Guillaume Pirot, Kerry Bardot, Mark W. Jessell, and James L. McCallum
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-154, https://doi.org/10.5194/gmd-2024-154, 2024
Revised manuscript under review for GMD
Short summary
Short summary
To facilitate the exploration of alternative hydrogeological scenarios, we propose to approximate costly physical simulations of contaminant transport by more affordable shortest distances computations. It enables to accept or reject scenarios within a predefined confidence interval. In particular, it can allow to estimate the probability of a fault acting as a preferential path or a barrier.
Alan Robert Alexander Aitken, Ian Delaney, Guillaume Pirot, and Mauro A. Werder
The Cryosphere, 18, 4111–4136, https://doi.org/10.5194/tc-18-4111-2024, https://doi.org/10.5194/tc-18-4111-2024, 2024
Short summary
Short summary
Understanding how glaciers generate sediment and transport it to the ocean is important for understanding ocean ecosystems and developing knowledge of the past cryosphere from marine sediments. This paper presents a new way to simulate sediment transport in rivers below ice sheets and glaciers and quantify volumes and characteristics of sediment that can be used to reveal the hidden record of the subglacial environment for both past and present glacial conditions.
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
Short summary
Short summary
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.
Jiateng Guo, Xuechuang Xu, Luyuan Wang, Xulei Wang, Lixin Wu, Mark Jessell, Vitaliy Ogarko, Zhibin Liu, and Yufei Zheng
Geosci. Model Dev., 17, 957–973, https://doi.org/10.5194/gmd-17-957-2024, https://doi.org/10.5194/gmd-17-957-2024, 2024
Short summary
Short summary
This study proposes a semi-supervised learning algorithm using pseudo-labels for 3D geological modelling. We establish a 3D geological model using borehole data from a complex real urban local survey area in Shenyang and make an uncertainty analysis of this model. The method effectively expands the sample space, which is suitable for geomodelling and uncertainty analysis from boreholes. The modelling results perform well in terms of spatial morphology and geological semantics.
Jérémie Giraud, Hoël Seillé, Mark D. Lindsay, Gerhard Visser, Vitaliy Ogarko, and Mark W. Jessell
Solid Earth, 14, 43–68, https://doi.org/10.5194/se-14-43-2023, https://doi.org/10.5194/se-14-43-2023, 2023
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 a new interpretation of the composition and structure towards to east of the profile that we modelled.
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.
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.
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
Short summary
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.
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
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
A GPU parallelization of the neXtSIM-DG dynamical core (v0.3.1)
The Earth System Grid Federation (ESGF) Virtual Aggregation (CMIP6 v20240125)
Can AI be enabled to perform dynamical downscaling? A latent diffusion model to mimic kilometer-scale COSMO5.0_CLM9 simulations
Moving beyond post hoc explainable artificial intelligence: a perspective paper on lessons learned from dynamical climate modeling
Remote-sensing-based forest canopy height mapping: some models are useful, but might they provide us with even more insights when combined?
Checking the consistency of 3D geological models
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
A Time-Dependent Three-Dimensional Magnetopause Model Based on Quasi-elastodynamic Theory
RiverBedDynamics v1.0: A Landlab component for computing two-dimensional sediment transport and river bed evolution
Skillful neural network predictions of Saharan dust
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
OpenMindat v1.0.0 R package: A machine interface to Mindat open data to facilitate data-intensive geoscience discoveries
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
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
A close look at using national ground stations for the statistical modeling of NO2
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
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
Robert Jendersie, Christian Lessig, and Thomas Richter
Geosci. Model Dev., 18, 3017–3040, https://doi.org/10.5194/gmd-18-3017-2025, https://doi.org/10.5194/gmd-18-3017-2025, 2025
Short summary
Short summary
Accurate computer simulations are critical to understanding how climate change will affect local communities. An important part of such simulations is sea ice, which affects even distant areas in the long term. In our work, we explore how GPUs (graphics processing units), computer chips originally designed for gaming allow for faster simulation of sea ice with a new software, the neXtSIM-DG dynamical core. We discuss multiple options and demonstrate that using GPUs makes more accurate simulations feasible.
Ezequiel Cimadevilla, Bryan N. Lawrence, and Antonio S. Cofiño
Geosci. Model Dev., 18, 2461–2478, https://doi.org/10.5194/gmd-18-2461-2025, https://doi.org/10.5194/gmd-18-2461-2025, 2025
Short summary
Short summary
The Earth System Grid Federation (ESGF) stores an enormous amount of climate data spread across millions of files in data centres all over the world. Accessing and working with this scientific information is quite complex. This work presents ESGF Virtual Aggregation, an approach that combines data from different sources into a format that is ready for analysis straightaway.
Elena Tomasi, Gabriele Franch, and Marco Cristoforetti
Geosci. Model Dev., 18, 2051–2078, https://doi.org/10.5194/gmd-18-2051-2025, https://doi.org/10.5194/gmd-18-2051-2025, 2025
Short summary
Short summary
High-resolution weather data are crucial for many applications, typically generated via resource-intensive numerical models through dynamical downscaling. We developed an AI model using latent diffusion models (LDMs) to mimic this process, increasing weather data resolution over Italy from 25 to 2 km. LDM outperforms other methods, accurately capturing local patterns and extreme events. This approach offers a cost-effective alternative, with potential disruptive application in climate sciences.
Ryan J. O'Loughlin, Dan Li, Richard Neale, and Travis A. O'Brien
Geosci. Model Dev., 18, 787–802, https://doi.org/10.5194/gmd-18-787-2025, https://doi.org/10.5194/gmd-18-787-2025, 2025
Short summary
Short summary
We draw from traditional climate modeling practices to make recommendations for machine-learning (ML)-driven climate science. Our intended audience is climate modelers who are relatively new to ML. We show how component-level understanding – obtained when scientists can link model behavior to parts within the overall model – should guide the development and evaluation of ML models. Better understanding yields a stronger basis for trust in the models. We highlight several examples to demonstrate.
Nikola Besic, Nicolas Picard, Cédric Vega, Jean-Daniel Bontemps, Lionel Hertzog, Jean-Pierre Renaud, Fajwel Fogel, Martin Schwartz, Agnès Pellissier-Tanon, Gabriel Destouet, Frédéric Mortier, Milena Planells-Rodriguez, and Philippe Ciais
Geosci. Model Dev., 18, 337–359, https://doi.org/10.5194/gmd-18-337-2025, https://doi.org/10.5194/gmd-18-337-2025, 2025
Short summary
Short summary
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 analysis of these remote-sensing-based products.
Marion N. Parquer, Eric A. de Kemp, Boyan Brodaric, and Michael J. Hillier
Geosci. Model Dev., 18, 71–100, https://doi.org/10.5194/gmd-18-71-2025, https://doi.org/10.5194/gmd-18-71-2025, 2025
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Yaxin Gu, Yi Wang, Fengsi Wei, Xueshang Feng, Andrey Samsonov, Xiaojian Song, Boyi Wang, Pingbing Zuo, Chaowei Jiang, Yalan Chen, Xiaojun Xu, and Zhilu Zhou
EGUsphere, https://doi.org/10.5194/egusphere-2024-3012, https://doi.org/10.5194/egusphere-2024-3012, 2024
Short summary
Short summary
This study presents the POS model, the first time-dependent three-dimensional magnetopause model. The POS model captures the real-time movement and shape of the magnetopause with superior accuracy. Its concise formulation and fast computational speed make it suitable for future onboard satellite deployment, enhancing space weather forecasting capabilities and offering new methodologies for magnetopause modeling on other planets.
Angel D. Monsalve, Samuel R. Anderson, Nicole M. Gasparini, and Elowyn M. Yager
EGUsphere, https://doi.org/10.5194/egusphere-2024-3390, https://doi.org/10.5194/egusphere-2024-3390, 2024
Short summary
Short summary
Rivers shape landscapes by moving sediments and changing their beds, but existing computer models oversimplify these processes by only considering erosion. We developed new software that simulates how rivers transport sediments and change over time through both erosion and deposition. This helps scientists and engineers better predict river behavior for water management, flood prevention, and ecosystem protection.
Trish Ewa Nowak, Andy T. Augousti, Benno I. Simmons, and Stefan Siegert
EGUsphere, https://doi.org/https://doi.org/10.48550/arXiv.2406.11754, https://doi.org/https://doi.org/10.48550/arXiv.2406.11754, 2024
Short summary
Short summary
Described here DustNet model uses advanced neural networks to accurately predict the Saharan dust transport in the atmosphere. It offers fast and precise forecasts with predictions achieved in just 2.1 seconds on a standard computer. This innovative approach outperforms traditional models, which take hours to produce a forecast and use high energy super-computers. By making high-quality dust monitoring accessible and efficient, DustNet can improve weather, climate and air quality forecasts.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Xiang Que, Jiyin Zhang, Weilin Chen, Jolyon Ralph, and Xiaogang Ma
EGUsphere, https://doi.org/10.5194/egusphere-2024-1141, https://doi.org/10.5194/egusphere-2024-1141, 2024
Short summary
Short summary
This paper describes an R package as the machine interface to the open data of Mindat.org, one of the world's most widely used databases of mineral species and their distribution. In the past decades many geoscientists have been using the Mindat data, but an open data service has never been fully established. The machine interface described in this paper will be an efficient way to meet the overwhelming data needs.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Foeke Boersma and Meng Lu
EGUsphere, https://doi.org/10.5194/egusphere-2023-1260, https://doi.org/10.5194/egusphere-2023-1260, 2023
Short summary
Short summary
Air pollution harms health and society. Understanding and predicting it is crucial. Various models are developed to model air pollution. However, the consistency exhibited by a model in different areas is commonly neglected. Our study accounts for this and shows lower accuracy near busy roads, but higher in less populated areas. Considering location characteristics in air pollution predictions is important in comparing statistical models and understanding the health-society-space relationship.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
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.
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.
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
Short summary
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.
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.
Cited articles
Ailleres, L., Jessell, M., de Kemp, E., Caumon, G., Wellmann, F., Grose,
L., Armit, R., Lindsay, M., Giraud, J., Brodaric, B., Harrison, M., and
Courrioux, G.: Loop – Enabling 3D stochastic geological modelling, ASEG
Extended Abstracts, 2019, 1–3, https://doi.org/10.1080/22020586.2019.12072955, 2019.
Anand, R. R. and Butt, C. R. M.: A guide for mineral exploration through
the regolith in the Yilgarn Craton, Western Australia, Aust. J.
Earth Sci., 57, 1015–1114,
https://doi.org/10.1080/08120099.2010.522823, 2010.
Arabjamaloei, R., Edalatkha, S., Jamshidi, E., Nabaei, M., Beidokhti, M.,
and Azad, M.: Exact Lithologic Boundary Detection Based on Wavelet Transform
Analysis and Real-Time Investigation of Facies Discontinuities Using
Drilling Data, Pet. Sci. Technol., 29, 569–578, https://doi.org/10.1080/10916460903419206, 2011.
Barley, M. E., Brown, S. J. A., Krapez, B., and Kositcin, N.: Physical
volcanology and geochemistry of a Late Archaean volcanic arc: Kurnalpi and
Gindalbie Terranes, Eastern Goldfields Superterrane, Western Australia,
Precambrian Res., 161, 53–76, https://doi.org/10.1016/j.precamres.2007.06.019, 2008.
Chace, F. M.: Abbreviations in field and mine geological mapping, Econ. Geol., 51, 712–723, 1956.
Cockbain, A. E.: Regolith geology of the Yilgarn Craton – Introduction,
Aust. J. Earth Sci., 49, 1–1, https://doi.org/10.1046/j.1440-0952.2002.00913.x, 2002.
Cohen, A.: FuzzyWuzzy: Fuzzy string matching in python, ChairNerd Blog, available at: http://chairnerd.seatgeek.com/fuzzywuzzy-fuzzy-string-matching-in-python/ (last accessed: 29 September 2021), 2011.
Culshaw, M. G.: From concept towards reality: developing the attributed 3D
geological model of the shallow subsurface, Q. J. Eng.
Geol. Hydroge., 38, 231–284, https://doi.org/10.1144/1470-9236/04-072, 2005.
Eggleton, R. A.: The regolith glossary, Cooperative Centre for Landscape Evolution and Mineral Exploration, 8, 144 pp., National Capital Printing, Canberra, Australia, 2001.
Emelyanova, I., Pervukhina, M., Clennell, M., and Dyt, C.: Unsupervised identification of electrofacies employing machine learning, 79th EAGE Conference and Exhibition 2017, Paris, France, 12–16 June 2017, EAGE, 2017.
Erkan, G. and Radev, D. R.: LexRank: Graph-based lexical centrality as
salience in text summarization, J. Artif. Intell. Res.,
22, 457–479, https://doi.org/10.1613/jair.1523, 2004.
Fuentes, I., Padarian, J., Iwanaga, T., and Vervoort, R. W.: 3D lithological
mapping of borehole descriptions using word embeddings, Comput.
Geosci., 141, 104516, https://doi.org/10.1016/j.cageo.2020.104516, 2020.
Gillespie, M. and Styles, M.: BGS rock classification scheme, Volume 1, Classification of igneous rocks, Keyworth, Nottingham, British Geological Survey, 31 pp., RR/99/006, 1999.
Gonzalez, J., Rodrigues, P., and Cohen, A.: Fuzzywuzzy: Fuzzy string matching in python, available at: https://github.com/seatgeek/fuzzywuzzy (last access: 30 September 2021), 2017.
Gregorio, F. D. and Varrazzo, D.: psycopg2, available at: http://initd.org/psycopg/docs/ (Stand: 17 June 2021) (last access: 30 September 2021), 2018.
Griffin, R. E.: When are Old Data New Data?, GeoResJ, 6, 92–97,
https://doi.org/10.1016/j.grj.2015.02.004, 2015.
Groves, D. I., Goldfarb, R. J., Knox-Robinson, C. M., Ojala, J., Gardoll,
S., Yun, G. Y., and Holyland, P.: Late-kinematic timing of orogenic gold
deposits and significance for computer-based exploration techniques with
emphasis on the Yilgarn Block, Western Australia, Ore Geol. Rev., 17,
1–38, https://doi.org/10.1016/S0169-1368(00)00002-0, 2000.
Hall, M. and Keppie, F.: Striplog: new open source software
for handling and analysing discontinuous and qualitative data, oral paper at
the Atlantic Geoscience Society Colloquium, Truro, Nova Scotia, Canada, 5–6 February 2016.
Hallsworth, C. R. and Knox, R.: BGS rock classification scheme, Volume 3, classification of sediments and sedimentary rocks, British Geological Survey, Keyworth, Nottingham, 24 pp., RR/99/003, 1999.
Higgins, R. F. and Mehta, S.: SeatGeek, Harvard Business School Case 819-013, October 2018.
Hill, E. J., Robertson, J., and Uvarova, Y.: Multiscale hierarchical
domaining and compression of drill hole data, Comput. Geosci.,
79, 47–57, https://doi.org/10.1016/j.cageo.2015.03.005, 2015.
Hill, E. J., Pearce, M. A., and Stromberg, J. M.: Improving Automated
Geological Logging of Drill Holes by Incorporating Multiscale Spatial
Methods, Math. Geosci., 53, 21–53, https://doi.org/10.1007/s11004-020-09859-0, 2020.
Jallan, Y., Brogan, E., Ashuri, B., and Clevenger, C. M.: Application of
Natural Language Processing and Text Mining to Identify Patterns in
Construction-Defect Litigation Cases, Journal of Legal Affairs and Dispute
Resolution in Engineering and Construction, 11, 04519024, https://doi.org/10.1061/(Asce)La.1943-4170.0000308, 2019.
Joshi, R., Madaiah, K., and Jessell, M.: Loop3D/dh2loop: dh2loop first release (v1.0.0), Zenodo [data set], https://doi.org/10.5281/zenodo.4043568, 2020.
Kumari, S., Mohan, A., and Saberwal, G.: Hidden duplicates: 10s or 100s of
Indian trials, registered with ClinicalTrials.gov, have not been registered
in India, as required by law, PLoS One, 15, e0234925,
https://doi.org/10.1371/journal.pone.0234925, 2020.
Lark, R. M., Thorpe, S., Kessler, H., and Mathers, S. J.: Interpretative modelling of a geological cross section from boreholes: sources of uncertainty and their quantification, Solid Earth, 5, 1189–1203, https://doi.org/10.5194/se-5-1189-2014, 2014.
Le Vaillant, M., Hill, J., and Barnes, S. J.: Simplifying drill-hole domains
for 3D geochemical modelling: An example from the Kevitsa Ni-Cu-(PGE)
deposit, Ore Geol. Rev., 90, 388–398, https://doi.org/10.1016/j.oregeorev.2017.05.020,
2017.
Levenshtein, V.: Leveinshtein distance, available at: http://en.wikipedia.org/wiki/Levenshtein_distance (last access: 30 September 2021), 1965.
Lin, C.-Y. and Hovy, E.: Automatic evaluation of summaries using N-gram co-occurrence statistics, in: Proceedings of the 2003 Human
Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, Edmonton, Canada, May–June 2003, 71–78, https://doi.org/10.3115/1073445.1073465, 2003.
Lindsay, M.: Geological Interpretation of Geophysics Support from
Machine Learning of Drillcore, in: Geological Survey of Western Australia Open Day 2019, Perth, Australia, available at: https://geodocs.dmirs.wa.gov.au/Web/documentlist/16/DirectLink/GeologicalInterpretationGS (last access: 30 September 2021), 2019.
Liu, K., Hogan, W. R., and Crowley, R. S.: Natural Language Processing
methods and systems for biomedical ontology learning, J. Biomed. Inform., 44,
163–179, https://doi.org/10.1016/j.jbi.2010.07.006, 2011.
Liu, T. and Guo, J.: Text similarity computing based on standard deviation, in: Advances in Intelligent Computing, edited by: Huang, D. S., Zhang, X. P., and Huang, G. B.,
International Conference on Intelligent Computing 2005, Lecture Notes in Computer Science, vol. 3644, 456–464, https://doi.org/10.1007/11538059_48, 2005.
Loper, E. and Bird, S.: NLTK: the natural language toolkit, arXiv [preprint], arXiv:cs/0205028, 17 May 2002.
Martínez-Vargas, A.: PyGSLIB [online], Python 2.7 package,
Opengeostat Consulting, available at:
https://github.com/opengeostat/pygslib (last access: 30 September 2021), 2016.
McKinney, W.: pandas: a foundational Python library for data analysis
and statistics, Python for High Performance and Scientific Computing, 14, 1–9, 2011.
McMillan, A. and Powell, J.: British Geological Survey Rock Classification Scheme,
Volume 4: The Classification of Artificial (man made) Ground and Natural Superficial Deposits: Applications to Geological Maps and Datasets in the UK, British Geolgoical Survey Research Report, RR 99-04, 1999.
Miles, A. and Bechhofer, S.: SKOS simple knowledge organization
system reference, W3C recommendation, 18, World Wide Web Consortium, available at: http://www.w3.org/TR/skos-reference/ (last access: 30 September 2021), 2009.
Müller, T., Cotterell, R., Fraser, A., and Schütze, H.: Joint lemmatization
and morphological tagging with lemming, in: Proceedings of the 2015 Conference on Empirical Methods in Natural Language
Processing, Association for Computational Linguistics, Lisbon, Portugal, 2268–2274, https://doi.org/10.18653/v1/D15-1272, 2015.
Myers, J.: Precambrian Tectonic History of the West Australian Craton and
Adjacent Orogens, Annu. Rev. Earth Pl. Sc., 21, 453–485,
https://doi.org/10.1146/annurev.ea.21.050193.002321, 1993.
Okuda, T., Tanaka, E., and Kasai, T.: A method for the correction of garbled
words based on the Levenshtein metric, IEEE T. Comput., 100,
172–178, 1976.
Otter, D. W., Medina, J. R., and Kalita, J. K.: A Survey of the Usages of
Deep Learning for Natural Language Processing, IEEE T. Neural Net. Lear., 32, 604–624, https://doi.org/10.1109/TNNLS.2020.2979670, 2020.
Padarian, J. and Fuentes, I.: Word embeddings for application in geosciences: development, evaluation, and examples of soil-related concepts, SOIL, 5, 177–187, https://doi.org/10.5194/soil-5-177-2019, 2019.
Park, S. H., Ryu, K. H., and Gilbert, D.: Fast similarity search for protein
3D structures using topological pattern matching based on spatial relations,
Int. J. Neural. Syst., 15, 287–296, https://doi.org/10.1142/S0129065705000244, 2005.
Qiu, Q., Xie, Z., Wu, L., and Tao, L.: Dictionary-Based Automated
Information Extraction From Geological Documents Using a Deep Learning
Algorithm, Earth and Space Science, 7, e2019EA000993, https://doi.org/10.1029/2019ea000993, 2020.
Ralph, J..: Mindat.org, available at: https://www.mindat.org/min-50468.html, last access:
30 September 2021.
Raymond, O., Duclaux, G., Boisvert, E., Cipolloni, C., Cox, S., Laxton, J., Letourneau, F., Richard, S., Ritchie, A., and Sen, M.:
GeoSciML v3.0 – a significant upgrade of the CGI-IUGS geoscience data model, EGU General Assembly 2012, Vienna, Austria, 22–27 April, 2012, 2711, available at: https://ui.adsabs.harvard.edu/abs/2012EGUGA..14.2711R (last access: 30 September 2021), 2012.
Recasens, M., Danescu-Niculescu-Mizil, C., and Jurafsky, D.: Linguistic
models for analyzing and detecting biased language, in: Proceedings
of the 51st Annual Meeting of the Association for Computational
Linguistics, Sofia, Bulgaria, August 2013, Volume 1: Long Papers, 1650–1659, available at: https://aclanthology.org/P13-1162.pdf (last access: 30 September 2021),
2013.
Richard, S. M. and CGI Interoperability Working Group: GeoSciML – a GML application for geoscience information interchange, Digital Mapping Techniques 2007, Workshop Proceedings: U.S. Geological Survey, 1285, 47–59, 2007.
Riganti, A., Farrell, T. R., Ellis, M. J., Irimies, F., Strickland, C. D.,
Martin, S. K., and Wallace, D. J.: 125 years of legacy data at the
Geological Survey of Western Australia: Capture and delivery, GeoResJ, 6,
175–194, https://doi.org/10.1016/j.grj.2015.02.015, 2015.
Rivera-Quiroz, F. A. and Miller, J.: Extracting Data from Legacy
Taxonomic Literature: Applications for planning field work, Biodiversity Information Science and Standards, 3, e37082, https://doi.org/10.3897/biss.3.37082, 2019.
Robertson, S.: BGS rock classification scheme, Volume 2, Classification
of metamorphic rocks, British Geological Survey Research Report, RR 99–02, 24 pp., 1999.
Rosenbaum, M. S., McMillan, A. A., Powell, J. H., Cooper, A. H., Culshaw, M.
G., and Northmore, K. J.: Classification of artificial (man-made) ground,
Eng. Geol., 69, 399–409, https://doi.org/10.1016/S0013-7952(02)00282-X, 2003.
Ross, P. S., Bourke, A., and Fresia, B.: A multi-sensor logger for rock
cores: Methodology and preliminary results from the Matagami mining camp,
Canada, Ore Geol. Rev., 53, 93–111, https://doi.org/10.1016/j.oregeorev.2013.01.002,
2013.
Rothwell, R. G. and Rack, F. R.: New techniques in sediment core analysis:
an introduction, New Techniques in Sediment Core Analysis, The Geological Society of London, 267, 1–29, https://doi.org/10.1144/Gsl.Sp.2006.267.01.01, 2006.
Schetselaar, E. M. and Lemieux, D.: A drill hole query algorithm for
extracting lithostratigraphic contacts in support of 3D geologic modelling
in crystalline basement, Comput. Geosci., 44, 146–155,
https://doi.org/10.1016/j.cageo.2011.10.015, 2012.
Simons, B., Boisvert, E., Brodaric, B., Cox, S., Duffy, T. R., Johnson,B. R., Laxton, J. L., and Richard, S.: GeoSciML: enabling the exchange of geological map data, ASEG Extended Abstracts,
2006, 1–4, https://doi.org/10.1071/ASEG2006ab162, 2006.
Smith, M. J., Keesstra, S., and Rose, J.: Use of legacy data in
geomorphological research, GeoResJ, 6, 74–80, https://doi.org/10.1016/j.grj.2015.02.008,
2015.
Vearncombe, J., Conner, G., and Bright, S.: Value from legacy data, T. I. Min.
Metall. B, 125, 231–246, https://doi.org/10.1080/03717453.2016.1190442, 2016.
Vearncombe, J., Riganti, A., Isles, D., and Bright, S.: Data upcycling, Ore
Geol. Rev., 89, 887–893, https://doi.org/10.1016/j.oregeorev.2017.07.009, 2017.
Wang, C. and Ma, X.: Text Mining to Facilitate Domain Knowledge
Discovery, in: Text Mining-Analysis, Programming and Application,
IntechOpen, https://doi.org/10.5772/intechopen.85362, 2019.
Wilbur, W. J. and Sirotkin, K.: The Automatic Identification of Stop Words,
J. Inf. Sci., 18, 45–55, https://doi.org/10.1177/016555159201800106,
1992.
Zhou, C. Y., Ouyang, J. W., Ming, W. H., Zhang, G. H., Du, Z. C., and Liu,
Z.: A Stratigraphic Prediction Method Based on Machine Learning, Appl.
Sci.-Basel, 9, 3553, https://doi.org/10.3390/app9173553, 2019.
Zhou, Q., Liu, H. H., Bodvarsson, G. S., and Oldenburg, C. M.: Flow and
transport in unsaturated fractured rock: effects of multiscale heterogeneity
of hydrogeologic properties, J. Contam. Hydrol., 60, 1–30, https://doi.org/10.1016/s0169-7722(02)00080-3, 2003.
Zhu, G., Gao, M., Kong, F., and Li, K.: Application of Logging While
Drilling Tool in Formation Boundary Detection and Geo-steering, Sensors
(Basel), 19, 2754, https://doi.org/10.3390/s19122754, 2019.
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.
We have developed a software that allows the user to extract and standardize drill hole...
