In the new era of earth system science in conjunction with the digital revolution,new platforms and programs are required for facilitating the utilization of geoscience data,especially to improve the integration of st...In the new era of earth system science in conjunction with the digital revolution,new platforms and programs are required for facilitating the utilization of geoscience data,especially to improve the integration of structured data with unstructured data for solving complex problems.Big data is not just matter of size but most importantly how easily and effectively it can be used.A major goal is to facilitate a move from traditional scientific approaches to a more modern approach that involves big data analytics.展开更多
In recent years,we have entered the so-called Fourth Paradigm with the regular production of huge amount of observational data.Big data is often characterized by the three‘V's:Volume of data,Variety and Velocity....In recent years,we have entered the so-called Fourth Paradigm with the regular production of huge amount of observational data.Big data is often characterized by the three‘V's:Volume of data,Variety and Velocity.The concept of big data can potentially address some existing issues in areas of geoscience and geoengineering.Large-scale,comprehensive,multidirectional and multifield geotechnical monitoring is becoming a reality in the very near future.展开更多
THE USE OF KNOWLEDGE GRAPH IN NATURAL SCIENCE Knowledge graph is a field of Artificial Intelligence(AI)that aims to represent knowledge in the form of graphs,consisting of nodes and edges which represent entities and ...THE USE OF KNOWLEDGE GRAPH IN NATURAL SCIENCE Knowledge graph is a field of Artificial Intelligence(AI)that aims to represent knowledge in the form of graphs,consisting of nodes and edges which represent entities and relationships between nodes respectively(Aidan et al.,2022).Although the knowledge graph was popularized recently due to use of this idea in Google’s search engine in 2012(Amit,2012),its root can be traced back to the emergence of the Semantic Web as well as earlier works in ontology(Aggarwal,2021).展开更多
Technological progress and the rapid increase in geochemical data often create bottlenecks in many studies,because current methods are designed using limited number of data and cannot handle large datasets.In geoscien...Technological progress and the rapid increase in geochemical data often create bottlenecks in many studies,because current methods are designed using limited number of data and cannot handle large datasets.In geoscience,tectonic discrimination illustrates this issue,using geochemical analyses to define tectonic settings when most of the geological record is missing,which is the case for most of the older portion of the Earth’s crust.Basalts are the primary target for tectonic discrimination because they are volcanic rocks found within all tectonic settings,and their chemical compositions can be an effective way to understand tectonics-related mantle processes.However,the classical geochemical discriminant methods have limitations as they are based on a limited number of 2 or 3-dimensional diagrams and need successive and subjective steps that often offers non-unique solutions.Also,weathering,erosion,and orogenic processes can modify the chemical composition of basalts and eliminate or obscure other complementary geotectonic records.To address those limitations,supervised machine learning techniques(a part of artificial intelligence)are being utilized more often as a tool to analyze multidimensional datasets and statistically process data to tackle big data challenges.This contribution starts by reviewing the current state of tectonic discrimination methods using supervised machine learning.Deep learning,especially Convolutional Neural Network(CNN)is the most accurate approach.However,it requires a large dataset and considerable processing time,and the gain of accuracy can be at the expense of interpretability.Therefore,this study designed guidelines for data pre-processing,tectonic setting classification and objectively evaluating the model performance.We also identify research gaps and propose potential directions for the application of supervised machine learning to tectonic discrimination research,aimed at closing the divide between earth scientists and data scientists.展开更多
The 2030 Agenda for Sustainable Development is a programmatic document for future global development.In the past five years,all countries of the world have made great efforts to achieve the United Nations Sustainable ...The 2030 Agenda for Sustainable Development is a programmatic document for future global development.In the past five years,all countries of the world have made great efforts to achieve the United Nations Sustainable Development Goals(SDGs).展开更多
The information age has been a product of, and has been fueled by, the process of decades-long digitization aided by digital technologies that progressed rapidly, consequently transforming human societies globally.
文摘In the new era of earth system science in conjunction with the digital revolution,new platforms and programs are required for facilitating the utilization of geoscience data,especially to improve the integration of structured data with unstructured data for solving complex problems.Big data is not just matter of size but most importantly how easily and effectively it can be used.A major goal is to facilitate a move from traditional scientific approaches to a more modern approach that involves big data analytics.
文摘In recent years,we have entered the so-called Fourth Paradigm with the regular production of huge amount of observational data.Big data is often characterized by the three‘V's:Volume of data,Variety and Velocity.The concept of big data can potentially address some existing issues in areas of geoscience and geoengineering.Large-scale,comprehensive,multidirectional and multifield geotechnical monitoring is becoming a reality in the very near future.
基金financially supported by the National Natural Science Foundation of China (Nos.42050102,42050101)。
文摘THE USE OF KNOWLEDGE GRAPH IN NATURAL SCIENCE Knowledge graph is a field of Artificial Intelligence(AI)that aims to represent knowledge in the form of graphs,consisting of nodes and edges which represent entities and relationships between nodes respectively(Aidan et al.,2022).Although the knowledge graph was popularized recently due to use of this idea in Google’s search engine in 2012(Amit,2012),its root can be traced back to the emergence of the Semantic Web as well as earlier works in ontology(Aggarwal,2021).
基金supported the open fund of State Key Laboratory of Geological Processes and Mineral Resources,China University of Geosciences,Wuhan (Grant No.GPMR202201).
文摘Technological progress and the rapid increase in geochemical data often create bottlenecks in many studies,because current methods are designed using limited number of data and cannot handle large datasets.In geoscience,tectonic discrimination illustrates this issue,using geochemical analyses to define tectonic settings when most of the geological record is missing,which is the case for most of the older portion of the Earth’s crust.Basalts are the primary target for tectonic discrimination because they are volcanic rocks found within all tectonic settings,and their chemical compositions can be an effective way to understand tectonics-related mantle processes.However,the classical geochemical discriminant methods have limitations as they are based on a limited number of 2 or 3-dimensional diagrams and need successive and subjective steps that often offers non-unique solutions.Also,weathering,erosion,and orogenic processes can modify the chemical composition of basalts and eliminate or obscure other complementary geotectonic records.To address those limitations,supervised machine learning techniques(a part of artificial intelligence)are being utilized more often as a tool to analyze multidimensional datasets and statistically process data to tackle big data challenges.This contribution starts by reviewing the current state of tectonic discrimination methods using supervised machine learning.Deep learning,especially Convolutional Neural Network(CNN)is the most accurate approach.However,it requires a large dataset and considerable processing time,and the gain of accuracy can be at the expense of interpretability.Therefore,this study designed guidelines for data pre-processing,tectonic setting classification and objectively evaluating the model performance.We also identify research gaps and propose potential directions for the application of supervised machine learning to tectonic discrimination research,aimed at closing the divide between earth scientists and data scientists.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA19030000 and XDA19090000)。
文摘The 2030 Agenda for Sustainable Development is a programmatic document for future global development.In the past five years,all countries of the world have made great efforts to achieve the United Nations Sustainable Development Goals(SDGs).
基金supported by the International Research Center of Big Data for Sustainable Development Goals (CBAS) (CBASYX0906)the National Natural Science Foundation of China (42376246)the Chinese Academy of Sciences Strategic Priority Research Program of the Big Earth Data Science Engineering Program (CASEarth) (XDA19090000,XDA19030000)。
文摘The information age has been a product of, and has been fueled by, the process of decades-long digitization aided by digital technologies that progressed rapidly, consequently transforming human societies globally.
