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
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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.
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.
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...
