The Anninghe fault is a major left-lateral strike-slip fault in southwest China and a seismic gap with a potential earthquake larger than MW 7.0 lies in the Mianning-Xichang segment according to recent observations.Th...The Anninghe fault is a major left-lateral strike-slip fault in southwest China and a seismic gap with a potential earthquake larger than MW 7.0 lies in the Mianning-Xichang segment according to recent observations.The shallow structure of this region can offer a glimpse into the geometry of the fault,which plays an important role in earthquake hazard mitigation.To further investigate the sedimentary structure of the Anninghe fault zone,two dense linear arrays with a station spacing of around 80 m were deployed across the fault.In this study,the H/V spectral ratio(HVSR),together with its peak frequency at each station site,was obtained by applying the Nakamura method.Our findings demonstrate that the peak frequency behaves in high correlation with lithology and is controlled by topography.HVSR in foothills or regions with magmatic intrusion shows a single peak at about 2–3 Hz.In locations with abundant Quaternary sedimentation,such as Anninghe valleys and fracture zones,another low-frequency peak around 0.4 Hz can be noticed in HVSR.By using the empirical relationship,the thickness of the sedimentary layer around the fault fracture zone is estimated to be 300–600 m.Furthermore,the sedimentary interface shows a downward dip to the east,possibly influenced by the east-west extrusion stress.Considering the resonance effect,buildings with 6–9 stories in the valley area of the Anninghe require additional attention in earthquake hazard prevention.展开更多
Finding the right spatially aware web service in a heterogeneous distributed environment using criteria such as service type,version,time,space,and scale has become a challenge in the integration of geospatial informa...Finding the right spatially aware web service in a heterogeneous distributed environment using criteria such as service type,version,time,space,and scale has become a challenge in the integration of geospatial information services.A new method for retrieving Open Geospatial Consortium(OGC)Web Service(OWS)that deals with this challenge using page crawling,link detection,service capability matching,and ontology reasoning,is described in this paper.Its major components are distributed OWS,the OWS search engine,the OWS ontology generator,the ontology-based OWS catalog service,and the ontology-based multi-protocol OWS client.Experimental results show that the execution time of this proposed method equals only 0.26 of that of Nutch’s method.In addition,the precision is much higher.Moreover,this proposed method can carry out complex OWS reasoning-based queries.It is being used successfully for the Antarctica multi-protocol OWS portal of the Geo-Information Web Service Portal of the Polar.展开更多
Long-term and large-scale lake statistics are meaningful for the study of environment change,but many of the existing methods are labourintensive and time-consuming.To overcome this problem,a novel method for long-te...Long-term and large-scale lake statistics are meaningful for the study of environment change,but many of the existing methods are labourintensive and time-consuming.To overcome this problem,a novel method for long-term and large-scale lake extraction by shape-factorsand machine-learning-based water body classification is proposed.An experiment was conducted to extract the lakes in the Yangtze River basin(YRB)from 2008 to 2018 with the Joint Research Centre’s Global Surface Water Dataset(JRC GSW)data and OSM data.The results show:1)The proposed method is automatically and successfully executed.2)The number of river–lake complexes is between 3008 and 4697,representing 3.56%–5.70%of the total water bodies.3)The areas of the lakes and rivers in the YRB were obtained,and the accuracy of water classification in each year was stable between 90.2%and 93.6%.Comparing the back propagation neural network,random forest,and support vector machine models,we found that the three machine learning models have similar classification accuracy for the scenario.4)Fragmented and incomplete small rivers in the JRC GSW data,unchecked training samples,and overlapped shape factors are the three error sources.Future work will focus on addressing these three error sources.展开更多
基金This study was jointly supported by the Key Research and Development Program of China(2021YFC3000704,2018YFC1503400)the National Natural Science Foundation of China(42125401)the special fund of Key Laboratory of Earthquake Prediction,CEA(2021IEF0103).
文摘The Anninghe fault is a major left-lateral strike-slip fault in southwest China and a seismic gap with a potential earthquake larger than MW 7.0 lies in the Mianning-Xichang segment according to recent observations.The shallow structure of this region can offer a glimpse into the geometry of the fault,which plays an important role in earthquake hazard mitigation.To further investigate the sedimentary structure of the Anninghe fault zone,two dense linear arrays with a station spacing of around 80 m were deployed across the fault.In this study,the H/V spectral ratio(HVSR),together with its peak frequency at each station site,was obtained by applying the Nakamura method.Our findings demonstrate that the peak frequency behaves in high correlation with lithology and is controlled by topography.HVSR in foothills or regions with magmatic intrusion shows a single peak at about 2–3 Hz.In locations with abundant Quaternary sedimentation,such as Anninghe valleys and fracture zones,another low-frequency peak around 0.4 Hz can be noticed in HVSR.By using the empirical relationship,the thickness of the sedimentary layer around the fault fracture zone is estimated to be 300–600 m.Furthermore,the sedimentary interface shows a downward dip to the east,possibly influenced by the east-west extrusion stress.Considering the resonance effect,buildings with 6–9 stories in the valley area of the Anninghe require additional attention in earthquake hazard prevention.
基金This work has been supported in part by the National Basic Research Program of China(973 Program)under Grant 2011CB707101the National Natural Science Foundation of China under Grant 41023001,41021061the ShenZhen R&D Foundation under Grant CXB200903090023A.
文摘Finding the right spatially aware web service in a heterogeneous distributed environment using criteria such as service type,version,time,space,and scale has become a challenge in the integration of geospatial information services.A new method for retrieving Open Geospatial Consortium(OGC)Web Service(OWS)that deals with this challenge using page crawling,link detection,service capability matching,and ontology reasoning,is described in this paper.Its major components are distributed OWS,the OWS search engine,the OWS ontology generator,the ontology-based OWS catalog service,and the ontology-based multi-protocol OWS client.Experimental results show that the execution time of this proposed method equals only 0.26 of that of Nutch’s method.In addition,the precision is much higher.Moreover,this proposed method can carry out complex OWS reasoning-based queries.It is being used successfully for the Antarctica multi-protocol OWS portal of the Geo-Information Web Service Portal of the Polar.
基金supported by the National Nature Science Foundation of China(nos.41971351,41771422,41890822).
文摘Long-term and large-scale lake statistics are meaningful for the study of environment change,but many of the existing methods are labourintensive and time-consuming.To overcome this problem,a novel method for long-term and large-scale lake extraction by shape-factorsand machine-learning-based water body classification is proposed.An experiment was conducted to extract the lakes in the Yangtze River basin(YRB)from 2008 to 2018 with the Joint Research Centre’s Global Surface Water Dataset(JRC GSW)data and OSM data.The results show:1)The proposed method is automatically and successfully executed.2)The number of river–lake complexes is between 3008 and 4697,representing 3.56%–5.70%of the total water bodies.3)The areas of the lakes and rivers in the YRB were obtained,and the accuracy of water classification in each year was stable between 90.2%and 93.6%.Comparing the back propagation neural network,random forest,and support vector machine models,we found that the three machine learning models have similar classification accuracy for the scenario.4)Fragmented and incomplete small rivers in the JRC GSW data,unchecked training samples,and overlapped shape factors are the three error sources.Future work will focus on addressing these three error sources.
