The Linglong granitoid complex (LGC) is composed of four major plutonic units that intruded and cooled in the Middle Jurassic (170-155 Ma). Gravity-anomaly modeling indicates that the LGC is a sheet-like laccolith, le...The Linglong granitoid complex (LGC) is composed of four major plutonic units that intruded and cooled in the Middle Jurassic (170-155 Ma). Gravity-anomaly modeling indicates that the LGC is a sheet-like laccolith, less than 10 km thick, that dips shallowly below the surface toward the Tancheng-Lujiang (Tan-Lu) fault, a major lithospheric structure in Eastern China. Measurements of foliation in the field and measurements of planar and linear magnetic fabrics from the study of anisotropy of magnetic susceptibility in the LGC indicate that foliation is dominantly shallowly dipping and magnetic lineation is mainly parallel to the dip direction of the laccolith toward the Tan-Lu fault zone. The trend of lineations is consistent with flow of magma up the thrust to reach shallower levels. The magma of the LGC probably originated by crustal melting within the Tan-Lu fault zone and the emplacement of magma occurred along a shallowly-dipping thrust that drained the Tan-Lu fault zone, the mechanism of which is mainly dike-fed model.展开更多
Researchers have achieved great success in dealing with 2 D images using deep learning.In recent years,3 D computer vision and geometry deep learning have gained ever more attention.Many advanced techniques for 3 D sh...Researchers have achieved great success in dealing with 2 D images using deep learning.In recent years,3 D computer vision and geometry deep learning have gained ever more attention.Many advanced techniques for 3 D shapes have been proposed for different applications.Unlike 2 D images,which can be uniformly represented by a regular grid of pixels,3 D shapes have various representations,such as depth images,multi-view images,voxels,point clouds,meshes,implicit surfaces,etc.The performance achieved in different applications largely depends on the representation used,and there is no unique representation that works well for all applications.Therefore,in this survey,we review recent developments in deep learning for 3 D geometry from a representation perspective,summarizing the advantages and disadvantages of different representations for different applications.We also present existing datasets in these representations and further discuss future research directions.展开更多
基金the National Natural Science Foundation of China (Grant No. 49772149), NFS of the United States (NFS/INT-9507687) and Doctoral Foundation of Ministry of Education of China (1996-1998).
文摘The Linglong granitoid complex (LGC) is composed of four major plutonic units that intruded and cooled in the Middle Jurassic (170-155 Ma). Gravity-anomaly modeling indicates that the LGC is a sheet-like laccolith, less than 10 km thick, that dips shallowly below the surface toward the Tancheng-Lujiang (Tan-Lu) fault, a major lithospheric structure in Eastern China. Measurements of foliation in the field and measurements of planar and linear magnetic fabrics from the study of anisotropy of magnetic susceptibility in the LGC indicate that foliation is dominantly shallowly dipping and magnetic lineation is mainly parallel to the dip direction of the laccolith toward the Tan-Lu fault zone. The trend of lineations is consistent with flow of magma up the thrust to reach shallower levels. The magma of the LGC probably originated by crustal melting within the Tan-Lu fault zone and the emplacement of magma occurred along a shallowly-dipping thrust that drained the Tan-Lu fault zone, the mechanism of which is mainly dike-fed model.
基金supported by the National Natural Science Foundation of China(61828204,61872440)Beijing Municipal Natural Science Foundation(L182016)+2 种基金Youth Innovation Promotion Association CAS,CCF-Tencent Open FundRoyal Society Newton Advanced Fellowship(NAF\R2\192151)the Royal Society(IES\R1\180126)。
文摘Researchers have achieved great success in dealing with 2 D images using deep learning.In recent years,3 D computer vision and geometry deep learning have gained ever more attention.Many advanced techniques for 3 D shapes have been proposed for different applications.Unlike 2 D images,which can be uniformly represented by a regular grid of pixels,3 D shapes have various representations,such as depth images,multi-view images,voxels,point clouds,meshes,implicit surfaces,etc.The performance achieved in different applications largely depends on the representation used,and there is no unique representation that works well for all applications.Therefore,in this survey,we review recent developments in deep learning for 3 D geometry from a representation perspective,summarizing the advantages and disadvantages of different representations for different applications.We also present existing datasets in these representations and further discuss future research directions.
