Comput. Geosci.最新文献

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Knowledge graph construction and application in geosciences: A review 知识图谱的构建及其在地球科学中的应用综述
Comput. Geosci. Pub Date : 2021-04-30 DOI: 10.31223/x5z898
Xiaogang Ma
{"title":"Knowledge graph construction and application in geosciences: A review","authors":"Xiaogang Ma","doi":"10.31223/x5z898","DOIUrl":"https://doi.org/10.31223/x5z898","url":null,"abstract":"Knowledge graph (KG) is a topic of great interests to geoscientists as it can be deployed throughout the data life cycle in data-intensive geoscience studies. Nevertheless, comparing with the large amounts of publications on machine learning applications in geosciences, summaries and reviews of geoscience KGs are still limited. The aim of this paper is to present a comprehensive review of KG construction and implementation in geosciences. It consists of four major parts: 1) concepts relevant to KG and approaches for KG construction, 2) KG application in data collection, curation, and service, 3) KG application in data analysis, and 4) challenges and trends of geo-science KG creation and application in the near future. For each of the first three parts, a list of concepts, exemplar studies, and best practices are summarized. Those summaries are synthesized together in the challenge and trend analyses. As artificial intelligence and data science are thriving in geosciences, we hope this review of geoscience KGs can be of value to practitioners in data-intensive geoscience studies.","PeriodicalId":10649,"journal":{"name":"Comput. Geosci.","volume":"32 1","pages":"105082"},"PeriodicalIF":0.0,"publicationDate":"2021-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87383732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 37
Improving the robustness of the Comparison Model Method for the identification of hydraulic transmissivities 提高液压传动系数辨识比较模型方法的鲁棒性
Comput. Geosci. Pub Date : 2021-02-10 DOI: 10.1016/j.cageo.2021.104705
A. Comunian, M. Giudici
{"title":"Improving the robustness of the Comparison Model Method for the identification of hydraulic transmissivities","authors":"A. Comunian, M. Giudici","doi":"10.1016/j.cageo.2021.104705","DOIUrl":"https://doi.org/10.1016/j.cageo.2021.104705","url":null,"abstract":"","PeriodicalId":10649,"journal":{"name":"Comput. Geosci.","volume":"6 1","pages":"104705"},"PeriodicalIF":0.0,"publicationDate":"2021-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81357415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Classification, segmentation and correlation of zoned minerals 分带矿物的分类、分段和对比
Comput. Geosci. Pub Date : 2021-02-01 DOI: 10.31223/x5h897
T. Sheldrake, O. Higgins
{"title":"Classification, segmentation and correlation of zoned minerals","authors":"T. Sheldrake, O. Higgins","doi":"10.31223/x5h897","DOIUrl":"https://doi.org/10.31223/x5h897","url":null,"abstract":"48 Minerals exhibit zoning patterns that can be related to changes in the environment in which they 49 grew. Using statistical methods that have been designed to segment optical images, we have 50 developed a procedure to segment zonation within minerals and correlate these zones between 51 multiple crystals using elemental maps. This allows us to quantify the complexity and variability of 52 chemical zoning between different geological samples. Specifically, we employ a simple linear 53 iterative clustering algorithm, which splits the chemical maps into spatially constrained regions of 54 similar chemistry. The result is a texturally segmented crystal, akin to what would be identified by 55 the human eye. To aid the segmentation and correlation of zones, we also introduce a new method 56 to classify multiple mineral phases within a single thin section. This is based on a finite mixture 57 model approach, which proves very effective in removing mixed pixels that will only introduce 58 noise into the segmentation. We provide an example using the mineral phase plagioclase. Using two 59 contemporaneous samples from an eruptive unit on the island of St. Kitts we show that a volcanic 60 bomb (~10cm) and scoria (~2cm) have similar rim compositions but distinctly different core 61 compositions. Our methodology will enable a statistical characterization of 2D complexity of 62 crystals in a variety of different geo-scientific disciplines. This will allow the genesis of different 63 mineral phases to be characterised and directly compared. 64 65","PeriodicalId":10649,"journal":{"name":"Comput. Geosci.","volume":"75 1","pages":"104876"},"PeriodicalIF":0.0,"publicationDate":"2021-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80827715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 10
Image-based rock typing using grain geometry features 利用颗粒几何特征的基于图像的岩石分型
Comput. Geosci. Pub Date : 2021-01-01 DOI: 10.1016/j.cageo.2021.104703
Yuzhu Wang, Shuyu Sun
{"title":"Image-based rock typing using grain geometry features","authors":"Yuzhu Wang, Shuyu Sun","doi":"10.1016/j.cageo.2021.104703","DOIUrl":"https://doi.org/10.1016/j.cageo.2021.104703","url":null,"abstract":"","PeriodicalId":10649,"journal":{"name":"Comput. Geosci.","volume":"14 3 1","pages":"104703"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78407960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 11
Multi-layer perceptron-based tectonic discrimination of basaltic rocks and an application on the Paleoproterozoic Xiong'er volcanic province in the North China Craton 基于多层感知器的玄武岩构造判别及其在华北克拉通古元古代熊耳火山省的应用
Comput. Geosci. Pub Date : 2021-01-01 DOI: 10.1016/j.cageo.2021.104717
Richen Zhong, Yi Deng, Chang Yu
{"title":"Multi-layer perceptron-based tectonic discrimination of basaltic rocks and an application on the Paleoproterozoic Xiong'er volcanic province in the North China Craton","authors":"Richen Zhong, Yi Deng, Chang Yu","doi":"10.1016/j.cageo.2021.104717","DOIUrl":"https://doi.org/10.1016/j.cageo.2021.104717","url":null,"abstract":"","PeriodicalId":10649,"journal":{"name":"Comput. Geosci.","volume":"51 1","pages":"104717"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84645779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 8
Study on offshore seabed sediment classification based on particle size parameters using XGBoost algorithm 基于XGBoost算法的近海沉积物粒度参数分类研究
Comput. Geosci. Pub Date : 2021-01-01 DOI: 10.1016/j.cageo.2021.104713
Fengfan Wang, Jia Yu, Zhijie Liu, Min Kong, Yunfan Wu
{"title":"Study on offshore seabed sediment classification based on particle size parameters using XGBoost algorithm","authors":"Fengfan Wang, Jia Yu, Zhijie Liu, Min Kong, Yunfan Wu","doi":"10.1016/j.cageo.2021.104713","DOIUrl":"https://doi.org/10.1016/j.cageo.2021.104713","url":null,"abstract":"","PeriodicalId":10649,"journal":{"name":"Comput. Geosci.","volume":"1 1","pages":"104713"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88832499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 16
DeepQuake - An application of CNN for seismo-acoustic event classification in The Netherlands DeepQuake - CNN在荷兰地震声事件分类中的应用
Comput. Geosci. Pub Date : 2020-12-08 DOI: 10.1002/essoar.10505253.2
L. Trani, G. Pagani, J. P. P. Zanetti, C. Chapeland, L. Evers
{"title":"DeepQuake - An application of CNN for seismo-acoustic event classification in The Netherlands","authors":"L. Trani, G. Pagani, J. P. P. Zanetti, C. Chapeland, L. Evers","doi":"10.1002/essoar.10505253.2","DOIUrl":"https://doi.org/10.1002/essoar.10505253.2","url":null,"abstract":"Recent developments of infrastructures and methods are major driving forces in the advances of solid Earth sciences. The deployment of large and dense sensor networks enables data centres to acquir...","PeriodicalId":10649,"journal":{"name":"Comput. Geosci.","volume":"98 1","pages":"104980"},"PeriodicalIF":0.0,"publicationDate":"2020-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76045875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 13
GROOPS: A software toolkit for gravity field recovery and GNSS processing GROOPS:用于重力场恢复和GNSS处理的软件工具包
Comput. Geosci. Pub Date : 2020-11-30 DOI: 10.1002/essoar.10505041.1
T. Mayer-Gürr, S. Behzadpour, A. Eicker, M. Ellmer, Beate Koch, S. Krauss, C. Pock, D. Rieser, S. Strasser, Barbara Süsser-Rechberger, N. Zehentner, A. Kvas
{"title":"GROOPS: A software toolkit for gravity field recovery and GNSS processing","authors":"T. Mayer-Gürr, S. Behzadpour, A. Eicker, M. Ellmer, Beate Koch, S. Krauss, C. Pock, D. Rieser, S. Strasser, Barbara Süsser-Rechberger, N. Zehentner, A. Kvas","doi":"10.1002/essoar.10505041.1","DOIUrl":"https://doi.org/10.1002/essoar.10505041.1","url":null,"abstract":"","PeriodicalId":10649,"journal":{"name":"Comput. Geosci.","volume":"18 1","pages":"104864"},"PeriodicalIF":0.0,"publicationDate":"2020-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80786476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 13
DeepVarveNet: Automatic detection of glacial varves with deep neural networks DeepVarveNet:利用深度神经网络自动检测冰川阀门
Comput. Geosci. Pub Date : 2020-11-01 DOI: 10.1016/j.cageo.2020.104584
A. Fabijańska, Andrew Feder, J. Ridge
{"title":"DeepVarveNet: Automatic detection of glacial varves with deep neural networks","authors":"A. Fabijańska, Andrew Feder, J. Ridge","doi":"10.1016/j.cageo.2020.104584","DOIUrl":"https://doi.org/10.1016/j.cageo.2020.104584","url":null,"abstract":"","PeriodicalId":10649,"journal":{"name":"Comput. Geosci.","volume":"8 1","pages":"104584"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74008694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 7
GeoDenStream: An improved DenStream clustering method for managing entity data within geographical data streams GeoDenStream:一种改进的DenStream聚类方法,用于管理地理数据流中的实体数据
Comput. Geosci. Pub Date : 2020-11-01 DOI: 10.1016/j.cageo.2020.104563
Manqi Li, A. Croitoru, S. Yue
{"title":"GeoDenStream: An improved DenStream clustering method for managing entity data within geographical data streams","authors":"Manqi Li, A. Croitoru, S. Yue","doi":"10.1016/j.cageo.2020.104563","DOIUrl":"https://doi.org/10.1016/j.cageo.2020.104563","url":null,"abstract":"","PeriodicalId":10649,"journal":{"name":"Comput. Geosci.","volume":"278 1","pages":"104563"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80080065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
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