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Geosciences
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Advances and Applications in Computational Geosciences
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Advances and Applications in Computational Geosciences

A special issue of Geosciences (ISSN 2076-3263).

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 20958

Special Issue Editors

Dr. Eun Young Lee

E-Mail Website
Guest Editor
Faculty of Earth Systems and Environmental Sciences, Chonnam National University, Gwangju, Korea
Interests: basin analysis; basin modeling; petrophysics; quantitative geologic data analysis; geologic visualization
Special Issues, Collections and Topics in MDPI journals
Dr. Annarita D’Addabbo

E-Mail Website
Guest Editor
IREA, National Research Council, Bari, Italy
Interests: artificial intelligence; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Dimitrios Piretzidis

E-Mail Website
Guest Editor
Department of Geomatics Engineering, University of Calgary, Calgary, AB, Canada
Interests: geomatics; earth observation; least-squares analysis; geodesy; satellite geodesy; signal processing

Special Issue Information

Dear Colleagues,

In geoscientific fields, computational techniques such as numerical computing, visualization, and simulation have become necessary to improve our understanding of phenomena and evolution of the Earth system. These days, both academy and industry rely heavily on computational tools for their geoscientific work. Machine learning, virtual reality, augmented reality, and artificial intelligence are active development areas for novel geoscientific technologies and applications. We are organizing this Special Issue to provide a multidisciplinary overview of geoscience research and applied case studies involving computational techniques. By collecting these computational geoscientific works in one issue, we aim to enhance our understanding, define the challenges, and enable future collaborations using these modern techniques.

This Special Issue highlights advances and applications in computational geosciences, which include theory, numerical methods, software development, scientific design, and field-based practices. Both theoretical and applied geoscience works are invited for submission to this Special Issue. The theme and application cover all quantitative aspects of models describing and interpreting the Earth system. We welcome contributions from all Earth science disciplines, such as geology, geophysics, petrophysics, geography, geochemistry, environment, hydrology, ecology, and atmospheric and space sciences. We encourage scientists, engineers, and students to introduce recent technological developments, applications, and case studies and to present state-of-the-art capabilities for visualizing geo-referenced data.

Dr. Eun Young Lee
Dr. Annarita D'Addabbo
Dr. Dimitrios Piretzidis
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Geosciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Computational geosciences
  • Computing
  • Visualization
  • Numerical modeling
  • Simulation
  • Software
  • Algorithms
  • Imaging
  • Quantitative mapping
  • Quantitative data analysis
  • Geoscientific model

Published Papers (6 papers)

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Editorial

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2 pages, 155 KiB  
Editorial
Editorial of Special Issue “Advances and Applications in Computational Geosciences”
by Eun Young Lee, Annarita D’Addabbo and Dimitrios Piretzidis
Geosciences 2022, 12(12), 457; https://doi.org/10.3390/geosciences12120457 - 18 Dec 2022
Viewed by 1248
Abstract
In geoscientific fields, mathematical modelling, numerical analysis, visualization, simulation, and other computational techniques have become necessary to improve our understanding of phenomena and evolution of the Earth [...] Full article
(This article belongs to the Special Issue Advances and Applications in Computational Geosciences)

Research

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9 pages, 2203 KiB  
Article
Pilot Study Using ArcGIS Online to Enhance Students’ Learning Experience in Fieldwork
by Sumet Phantuwongraj, Piyaphong Chenrai and Thitiphan Assawincharoenkij
Geosciences 2021, 11(9), 357; https://doi.org/10.3390/geosciences11090357 - 24 Aug 2021
Cited by 14 | Viewed by 5189
Abstract
Applying ArcGIS Online application to geological fieldwork provides an alternative way to teach students. This brief report describes an educational innovation for geological fieldwork with the ArcGIS Online application to examine students’ learning experiences. In comparison to traditional classrooms, this teaching method enables [...] Read more.
Applying ArcGIS Online application to geological fieldwork provides an alternative way to teach students. This brief report describes an educational innovation for geological fieldwork with the ArcGIS Online application to examine students’ learning experiences. In comparison to traditional classrooms, this teaching method enables students to more easily comprehend how geological structures and features connect through the mapping area. This observation indicates that students can think about the structure and deformation events as spatial continuity during acquisition data gathering in the field. Results from independent t-tests between treated and untreated student groups show that the average post-test scores of the treated students were significantly higher than pre-test scores, at a p = 0.05 level, after using ArcGIS Online in fieldwork designed in this study. Therefore, the ArcGIS Online application plays an important role in changing and developing the geological fieldwork in Thailand at the university scale for students. This teaching method could potentially benefit any science teaching and have applications in other disciplines requiring similar skills as well. Full article
(This article belongs to the Special Issue Advances and Applications in Computational Geosciences)
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26 pages, 10149 KiB  
Article
Pretraining Convolutional Neural Networks for Mudstone Petrographic Thin-Section Image Classification
by Rafael Pires de Lima and David Duarte
Geosciences 2021, 11(8), 336; https://doi.org/10.3390/geosciences11080336 - 11 Aug 2021
Cited by 13 | Viewed by 2947
Abstract
Convolutional neural networks (CNN) are currently the most widely used tool for the classification of images, especially if such images have large within- and small between- group variance. Thus, one of the main factors driving the development of CNN models is the creation [...] Read more.
Convolutional neural networks (CNN) are currently the most widely used tool for the classification of images, especially if such images have large within- and small between- group variance. Thus, one of the main factors driving the development of CNN models is the creation of large, labelled computer vision datasets, some containing millions of images. Thanks to transfer learning, a technique that modifies a model trained on a primary task to execute a secondary task, the adaptation of CNN models trained on such large datasets has rapidly gained popularity in many fields of science, geosciences included. However, the trade-off between two main components of the transfer learning methodology for geoscience images is still unclear: the difference between the datasets used in the primary and secondary tasks; and the amount of available data for the primary task itself. We evaluate the performance of CNN models pretrained with different types of image datasets—specifically, dermatology, histology, and raw food—that are fine-tuned to the task of petrographic thin-section image classification. Results show that CNN models pretrained on ImageNet achieve higher accuracy due to the larger number of samples, as well as a larger variability in the samples in ImageNet compared to the other datasets evaluated. Full article
(This article belongs to the Special Issue Advances and Applications in Computational Geosciences)
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15 pages, 5370 KiB  
Article
Geologist in the Loop: A Hybrid Intelligence Model for Identifying Geological Boundaries from Augmented Ground Penetrating Radar
by Adrian Ball and Louisa O’Connor
Geosciences 2021, 11(7), 284; https://doi.org/10.3390/geosciences11070284 - 8 Jul 2021
Cited by 6 | Viewed by 3030
Abstract
Common industry practice means that geological or stratigraphic boundaries are estimated from exploration drill holes. While exploration holes provide opportunities for accurate data at a high resolution down the hole, their acquisition is cost-intensive, which can result in the number of holes drilled [...] Read more.
Common industry practice means that geological or stratigraphic boundaries are estimated from exploration drill holes. While exploration holes provide opportunities for accurate data at a high resolution down the hole, their acquisition is cost-intensive, which can result in the number of holes drilled being reduced. In contrast, sampling with ground-penetrating radar (GPR) is cost-effective, non-destructive, and compact, allowing for denser, continuous data acquisition. One challenge with GPR data is the subjectivity and challenges associated with interpretation. This research presents a hybrid model of geologist and machine learning for the identification of geological boundaries in a lateritic deposit. This model allows for an auditable, probabilistic representation of geologists’ interpretations and can feed into exploration planning and optimising drill campaigns in terms of the density and location of holes. Full article
(This article belongs to the Special Issue Advances and Applications in Computational Geosciences)
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14 pages, 3004 KiB  
Article
Numerical Modelling to Evaluate Sedimentation Effects on Heat Flow and Subsidence during Continental Rifting
by Yeseul Kim, Min Huh and Eun Young Lee
Geosciences 2020, 10(11), 451; https://doi.org/10.3390/geosciences10110451 - 11 Nov 2020
Cited by 9 | Viewed by 3192
Abstract
Sedimentation impacts thermal and subsidence evolution in continental rifting. Estimating the blanketing effect of sediments is crucial to reconstructing the heat flow during rifting. The sedimentary load affects the basin subsidence rate. Numerical investigation of these effects requires active and complex simulations of [...] Read more.
Sedimentation impacts thermal and subsidence evolution in continental rifting. Estimating the blanketing effect of sediments is crucial to reconstructing the heat flow during rifting. The sedimentary load affects the basin subsidence rate. Numerical investigation of these effects requires active and complex simulations of the thermal structure, lithospheric stretching, and sedimentation. In this paper, we introduce a numerical model to quantify these effects, which was developed using the COMSOL Multiphysics® simulation software. Our numerical setting for the analytical and numerical solutions of thermal structure and subsidence is based on previous continental rifting studies. In our model, we accumulate a column of 5 m thick sediment layers with varied stretching factors and sedimentation rates, spanning the syn-rift to early post-rift phases over a period of 12 myr. Our results provide intuitive models to understand these sedimentation effects. The models show that an increase in sedimentation thickness significantly decreases surface heat flow, leading to lower geothermal temperature, and amplifies the subsidence magnitude. The findings also demonstrate that increases in the stretching factor and sedimentation rate enhance the blanketing effect and subsidence rate. Based on these results, we discuss key outcomes for geological applications and the possible limitations of our approach. Full article
(This article belongs to the Special Issue Advances and Applications in Computational Geosciences)
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Review

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16 pages, 1739 KiB  
Review
Modelling of Nutrient Pollution Dynamics in River Basins: A Review with a Perspective of a Distributed Modelling Approach
by Md Jahangir Alam and Dushmanta Dutta
Geosciences 2021, 11(9), 369; https://doi.org/10.3390/geosciences11090369 - 1 Sep 2021
Cited by 8 | Viewed by 3550
Abstract
Nutrient pollution is one of the major issues in water resources management, which has drawn significant investments into the development of many modelling tools to solve pollution problems worldwide. However, the situation remains unchanged, even likely to be exacerbated due to population growth [...] Read more.
Nutrient pollution is one of the major issues in water resources management, which has drawn significant investments into the development of many modelling tools to solve pollution problems worldwide. However, the situation remains unchanged, even likely to be exacerbated due to population growth and climate change. Effective measures to alleviate the issues are essential, dependent upon existing modelling tools’ capacities. More complex models have been developed with technological advancement, though applications are mainly limited to academic reach. Hence, there is a need for a paradigm shift in policymaking that looks for a reliable modelling approach. This paper aims to assess the capacity of existing modelling tools in the context of process-based modelling and provide a future direction in research. The article has categorically divided models into plot scale to basin-wide applications for evaluation and discussed the pros and cons of conceptual and process-based modelling. The potential benefits of distributed modelling approach have been elaborated with highlights of a newly developed distributed model and its application in catchments in Japan and Australia. The distributed model is more adequate for predicting the realistic details of pollution problems in a changing environment. Future research needs to focus on more process-based modelling. Full article
(This article belongs to the Special Issue Advances and Applications in Computational Geosciences)
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