Active landslides pose a significant threat globally,endangering lives and property.Effective monitoring and forecasting of displacements are essential for the timely warnings and mitigation of these events.Interferom...Active landslides pose a significant threat globally,endangering lives and property.Effective monitoring and forecasting of displacements are essential for the timely warnings and mitigation of these events.Interferometric synthetic aperture radar(InSAR)stands out as an efficient and prevalent tool for monitoring landslide deformation and offers new prospects for displacement prediction.However,challenges such as inherent limitation of satellite viewing geometry,long revisit cycles,and limited data volume hinder its application in displacement forecasting,notably for landslides with near-north-south deformation less detectable by InSAR.To address these issues,we propose a novel strategy for predicting three-dimensional(3D)landslide displacement,integrating InSAR and global navigation satellite system(GNSS)measurements with machine learning(ML).This framework first synergizes InSAR line-of-sight(LOS)results with GNSS horizontal data to reconstruct 3D displacement time series.It then employs ML models to capture complex nonlinear relationships between external triggers,landslide evolutionary states,and 3D displacements,thus enabling accurate future deformation predictions.Utilizing four advanced ML algorithms,i.e.random forest(RF),support vector machine(SVM),long short-term memory(LSTM),and gated recurrent unit(GRU),with Bayesian optimization(BO)for hyperparameter tuning,we applied this innovative approach to the north-facing,slow-moving Xinpu landslide in the Three Gorges Reservoir Area(TGRA)of China.Leveraging over 6.5 years of Sentinel-1 satellite data and GNSS measurements,our framework demonstrates satisfactory and robust prediction performance,with an average root mean square deviation(RMSD)of 9.62 mm and a correlation coefficient(CC)of 0.996.This study presents a promising strategy for 3D displacement prediction,illustrating the efficacy of integrating InSAR monitoring with ML forecasting in enhancing landslide early warning capabilities.展开更多
Shanghai has experienced the greatest land subsidence in China in the past sixty years and produced undesirable environmental impact. However, horizontal ground deformation has not been understood yet. Therefore groun...Shanghai has experienced the greatest land subsidence in China in the past sixty years and produced undesirable environmental impact. However, horizontal ground deformation has not been understood yet. Therefore ground deformation monitoring together with the analysis of its driving forces are critical for geo-hazards early-warning, city planning and sustainable urbanization in Shanghai. In this paper, two-dimensional ground deformation monitoring was performed in Shanghai with SBAS and MSBAS InSAR methods. Twenty-nine Multi-Look Fine 6 （MF6） Radarsat-2 SLC data acquired during 2011-2013 were used to derive vertical ground deformation. Meanwhile, six descending Multi-Look Fine 6 （MF6） and four ascending Multi-Look Fine 2 （MF2） spanning April to August, 2008, were used to derive vertical and horizontal ground deformation during the observation period. The results indicate that vertical and horizontal deformations in 2008 were not homogeneously distributed in different districts ranging from 0-2 cm/year. Vertical deformation rate during 2011-2013 were decreased to less than 1 cm/year in most district of Shanghai area. Activities from groundwater exploitation and rapid urbanization are responsible for most of the ground deformation in Shanghai. Thus, future ground deformation in vertical and horizontal directions should be warranted.展开更多
Paleobiogeography investigates geographical distributions of fossil organisms and controlling factors that affect their distributions in geological history,to reveal the macro-evolution and coordinated development of ...Paleobiogeography investigates geographical distributions of fossil organisms and controlling factors that affect their distributions in geological history,to reveal the macro-evolution and coordinated development of life and the environment.It is a crucial window for understanding the biosphere and the geographical environment.After two centuries of development,paleobiogeographic studies have led to the accumulation of significant amounts of knowledge and data;however,the voluminous outputs present the characteristics of an“isolated island”with a scattered,limited number of authoritative definitions of terminologies and semantic heterogeneity among them.This makes data queries cumbersome,the rate of data reuse low,and data sharing more difficult.A knowledge graph(KG)has the advantage of expressing concepts and their semantic relations,which is an important tool for achieving data organization and fusion,and data mining;further,it is also a key technology for realizing the unrestricted sharing of paleobiogeographic information.Through our efforts over the past two years,a paleobiogeographic KG was developed based on the established construction procedure of the KG,which contains 273 concepts,172 properties,and 47 rules.Meanwhile,the completion of this KG and the construction of a paleobiogeographic platform for display and analysis are now being carried out.展开更多
A knowledge graph(KG)is a knowledge base that integrates and represents data based on a graph-structured data model or topology.Geoscientists have made efforts to construct geosciencerelated KGs to overcome semantic h...A knowledge graph(KG)is a knowledge base that integrates and represents data based on a graph-structured data model or topology.Geoscientists have made efforts to construct geosciencerelated KGs to overcome semantic heterogeneity and facilitate knowledge representation,data integration,and text analysis.However,there is currently no comprehensive paleontology KG or data-driven discovery based on it.In this study,we constructed a two-layer model to represent the ordinal hierarchical structure of the paleontology KG following a top-down construction process.An ontology containing 19365 concepts has been defined up to 2023.On this basis,we derived the synonymy list based on the paleontology KG and designed corresponding online functions in the OneStratigraphy database to showcase the use of the KG in paleontological research.展开更多
Biological classification is the foundation of biology and paleontology,as it arranges all the organisms in a hierarchy that humans can easily follow and understand.It is further used to reconstruct the evolution of l...Biological classification is the foundation of biology and paleontology,as it arranges all the organisms in a hierarchy that humans can easily follow and understand.It is further used to reconstruct the evolution of life.A biological classification system(BCS)that includes all the established fossil taxa would be both useful and challenging for uncovering the life history.Since fossil taxa were originally recorded in various published books and articles written by natural languages,the primary step is to organize all those taxa information in a manner that can be deciphered by a computer system.A Knowledge Graph(KG)is a formalized description framework of semantic knowledge,which represents and retrieves knowledge in a machine-understandable way,and therefore provides an eligible method to represent the BCS.In this paper,a model of the BCS KG including the ontology and fact layers is presented.To put it into practice,the ontology layer of the invertebrate fossil branches was manually developed,while the fact layer was automatically constructed by extracting information from 46 volumes of the Treatise of Invertebrate Paleontology series with the help of natural language processing technology.As a result,27348 taxa nodes spanning fourteen taxonomic ranks were extracted with high accuracy and high efficiency,and the invertebrate fossil branches of the BCS KG was thus installed.This study demonstrates that a properly designed KG model and its automatic construction with the help of natural language processing are reliable and efficient.展开更多
Stratigraphic knowledge,the cornerstone of geoscience,needs to be represented by the Knowledge Graph based upon ontology,in order to apply the state-of-the-art big-data techniques.This study aims to comprehensively co...Stratigraphic knowledge,the cornerstone of geoscience,needs to be represented by the Knowledge Graph based upon ontology,in order to apply the state-of-the-art big-data techniques.This study aims to comprehensively construct the ontologies for the stratigraphic domain.This has been achieved by a federated,crowd intelligence-based collaboration among domain experts of major stratigraphic subdisciplines.The initial step is to enumerate key terms from authoritative references and incorporate them into the Geoscience Professional Knowledge Graphs(GPKGs)of Deep-time Digital Earth Project.During this process,semantic heterogeneities were meticulously addressed by professional judgement aided by an automatic detection of Homonyms at the GPKGs platform.Afterwards,these terms were further differentiated as either classes or properties and arranged in a hierarchical framework in a top-down process.Consequently,seven ontologies are constructed for major stratigraphic branches,i.e.,Lithostratigraphy,Biostratigraphy,Chronostratigraphy,Chemostratigraphy,Magnetostratigraphy,Cyclostratigraphy and Sequence Stratigraphy.The ontology of Biostratigraphy,among them,is elaborated here,as no biostratigraphic ontology has been attempted before to our knowledge.The constructed biostratigraphic ontology comprises following major root classes:Fossil,Biostratigraphic unit,Biostratigraphic horizon.Altogether,they contribute to the eventual dating and correlating of strata in another root class:Biostratigraphic correlation.In summary,the achievements of this study are probably heretofore the most comprehensive ontologies for the stratigraphic domain.Moreover,a proto model of semantic search engine was conceived to discuss potential application of our work for better querying stratigraphic references,utilizing the semantic liaison of the classes in the constructed ontologies.展开更多
基金jointly supported by the International Research Center of Big Data for Sustainable Development Goals(Grant No.CBAS2022GSP02)the National Natural Science Foundation of China(Grant Nos.42072320 and 42372264).
文摘Active landslides pose a significant threat globally,endangering lives and property.Effective monitoring and forecasting of displacements are essential for the timely warnings and mitigation of these events.Interferometric synthetic aperture radar(InSAR)stands out as an efficient and prevalent tool for monitoring landslide deformation and offers new prospects for displacement prediction.However,challenges such as inherent limitation of satellite viewing geometry,long revisit cycles,and limited data volume hinder its application in displacement forecasting,notably for landslides with near-north-south deformation less detectable by InSAR.To address these issues,we propose a novel strategy for predicting three-dimensional(3D)landslide displacement,integrating InSAR and global navigation satellite system(GNSS)measurements with machine learning(ML).This framework first synergizes InSAR line-of-sight(LOS)results with GNSS horizontal data to reconstruct 3D displacement time series.It then employs ML models to capture complex nonlinear relationships between external triggers,landslide evolutionary states,and 3D displacements,thus enabling accurate future deformation predictions.Utilizing four advanced ML algorithms,i.e.random forest(RF),support vector machine(SVM),long short-term memory(LSTM),and gated recurrent unit(GRU),with Bayesian optimization(BO)for hyperparameter tuning,we applied this innovative approach to the north-facing,slow-moving Xinpu landslide in the Three Gorges Reservoir Area(TGRA)of China.Leveraging over 6.5 years of Sentinel-1 satellite data and GNSS measurements,our framework demonstrates satisfactory and robust prediction performance,with an average root mean square deviation(RMSD)of 9.62 mm and a correlation coefficient(CC)of 0.996.This study presents a promising strategy for 3D displacement prediction,illustrating the efficacy of integrating InSAR monitoring with ML forecasting in enhancing landslide early warning capabilities.
基金supported by the China Science National Foundation (No. 41372353)
文摘Shanghai has experienced the greatest land subsidence in China in the past sixty years and produced undesirable environmental impact. However, horizontal ground deformation has not been understood yet. Therefore ground deformation monitoring together with the analysis of its driving forces are critical for geo-hazards early-warning, city planning and sustainable urbanization in Shanghai. In this paper, two-dimensional ground deformation monitoring was performed in Shanghai with SBAS and MSBAS InSAR methods. Twenty-nine Multi-Look Fine 6 （MF6） Radarsat-2 SLC data acquired during 2011-2013 were used to derive vertical ground deformation. Meanwhile, six descending Multi-Look Fine 6 （MF6） and four ascending Multi-Look Fine 2 （MF2） spanning April to August, 2008, were used to derive vertical and horizontal ground deformation during the observation period. The results indicate that vertical and horizontal deformations in 2008 were not homogeneously distributed in different districts ranging from 0-2 cm/year. Vertical deformation rate during 2011-2013 were decreased to less than 1 cm/year in most district of Shanghai area. Activities from groundwater exploitation and rapid urbanization are responsible for most of the ground deformation in Shanghai. Thus, future ground deformation in vertical and horizontal directions should be warranted.
基金supported by the National Natural Science Foundation of China(Nos.42172174,41802017,42250104)the National Key R&D Program of China(No.2018YFE0204201)the Fundamental Research Funds for the Central Universities(No.0206-14380168)。
文摘Paleobiogeography investigates geographical distributions of fossil organisms and controlling factors that affect their distributions in geological history,to reveal the macro-evolution and coordinated development of life and the environment.It is a crucial window for understanding the biosphere and the geographical environment.After two centuries of development,paleobiogeographic studies have led to the accumulation of significant amounts of knowledge and data;however,the voluminous outputs present the characteristics of an“isolated island”with a scattered,limited number of authoritative definitions of terminologies and semantic heterogeneity among them.This makes data queries cumbersome,the rate of data reuse low,and data sharing more difficult.A knowledge graph(KG)has the advantage of expressing concepts and their semantic relations,which is an important tool for achieving data organization and fusion,and data mining;further,it is also a key technology for realizing the unrestricted sharing of paleobiogeographic information.Through our efforts over the past two years,a paleobiogeographic KG was developed based on the established construction procedure of the KG,which contains 273 concepts,172 properties,and 47 rules.Meanwhile,the completion of this KG and the construction of a paleobiogeographic platform for display and analysis are now being carried out.
基金supported by the National Natural Science Foundation of China(Nos.41725007,42250104,41830323,42002015,and 42302001)the Fundamental Research Funds for the Central Universities(Nos.020614380168,JZ2023HGQA0144 and JZ2023HGTA0175)。
文摘A knowledge graph(KG)is a knowledge base that integrates and represents data based on a graph-structured data model or topology.Geoscientists have made efforts to construct geosciencerelated KGs to overcome semantic heterogeneity and facilitate knowledge representation,data integration,and text analysis.However,there is currently no comprehensive paleontology KG or data-driven discovery based on it.In this study,we constructed a two-layer model to represent the ordinal hierarchical structure of the paleontology KG following a top-down construction process.An ontology containing 19365 concepts has been defined up to 2023.On this basis,we derived the synonymy list based on the paleontology KG and designed corresponding online functions in the OneStratigraphy database to showcase the use of the KG in paleontological research.
基金supported by the National Key R&D Program of China(No.2018YFE0204201)the National Natural Science Foundation of China(Nos.92255301,42302001)Jiangsu Innovation Support Plan for International Science and Technology Cooperation Programm(No.BZ2023068)。
文摘Biological classification is the foundation of biology and paleontology,as it arranges all the organisms in a hierarchy that humans can easily follow and understand.It is further used to reconstruct the evolution of life.A biological classification system(BCS)that includes all the established fossil taxa would be both useful and challenging for uncovering the life history.Since fossil taxa were originally recorded in various published books and articles written by natural languages,the primary step is to organize all those taxa information in a manner that can be deciphered by a computer system.A Knowledge Graph(KG)is a formalized description framework of semantic knowledge,which represents and retrieves knowledge in a machine-understandable way,and therefore provides an eligible method to represent the BCS.In this paper,a model of the BCS KG including the ontology and fact layers is presented.To put it into practice,the ontology layer of the invertebrate fossil branches was manually developed,while the fact layer was automatically constructed by extracting information from 46 volumes of the Treatise of Invertebrate Paleontology series with the help of natural language processing technology.As a result,27348 taxa nodes spanning fourteen taxonomic ranks were extracted with high accuracy and high efficiency,and the invertebrate fossil branches of the BCS KG was thus installed.This study demonstrates that a properly designed KG model and its automatic construction with the help of natural language processing are reliable and efficient.
基金supported by the National Natural Science Foundation of China(Grant No.41725007)National Key R&D Program of China(Grant No.2018YFE0204201)+1 种基金Fundamental Research Funds for the Central Universities(0206-14380121)Frontiers Science Center for Critical Earth Material Cycling Fund(JBGS2101).
文摘Stratigraphic knowledge,the cornerstone of geoscience,needs to be represented by the Knowledge Graph based upon ontology,in order to apply the state-of-the-art big-data techniques.This study aims to comprehensively construct the ontologies for the stratigraphic domain.This has been achieved by a federated,crowd intelligence-based collaboration among domain experts of major stratigraphic subdisciplines.The initial step is to enumerate key terms from authoritative references and incorporate them into the Geoscience Professional Knowledge Graphs(GPKGs)of Deep-time Digital Earth Project.During this process,semantic heterogeneities were meticulously addressed by professional judgement aided by an automatic detection of Homonyms at the GPKGs platform.Afterwards,these terms were further differentiated as either classes or properties and arranged in a hierarchical framework in a top-down process.Consequently,seven ontologies are constructed for major stratigraphic branches,i.e.,Lithostratigraphy,Biostratigraphy,Chronostratigraphy,Chemostratigraphy,Magnetostratigraphy,Cyclostratigraphy and Sequence Stratigraphy.The ontology of Biostratigraphy,among them,is elaborated here,as no biostratigraphic ontology has been attempted before to our knowledge.The constructed biostratigraphic ontology comprises following major root classes:Fossil,Biostratigraphic unit,Biostratigraphic horizon.Altogether,they contribute to the eventual dating and correlating of strata in another root class:Biostratigraphic correlation.In summary,the achievements of this study are probably heretofore the most comprehensive ontologies for the stratigraphic domain.Moreover,a proto model of semantic search engine was conceived to discuss potential application of our work for better querying stratigraphic references,utilizing the semantic liaison of the classes in the constructed ontologies.
