The Experts below are selected from a list of 324 Experts worldwide ranked by ideXlab platform
Kazuaki Ohta - One of the best experts on this subject based on the ideXlab platform.
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Precise hypocenter distribution of deep low‐frequency earthquakes and its relationship to the Local Geometry of the subducting plate in the Nankai subduction zone, Japan
Journal of Geophysical Research, 2011Co-Authors: Kazuaki Ohta, Satoshi IdeAbstract:[1] We determine the precise hypocenter distribution of deep low-frequency earthquakes (LFEs) in the Nankai subduction zone and compare it with the Local Geometry of the subducting Philippine Sea plate. We apply a new hypocenter determination method utilizing the summed cross correlation coefficient over many stations, termed a network correlation coefficient (NCC), to 112 LFEs in the western Shikoku and 1566 LFEs in the whole Nankai subduction zone. While the catalog depths are widely distributed in some regions, the relocated hypocenters in every region construct a plane surface several km above the oceanic Moho interface and quite consistent with the Geometry of the oceanic Moho. This result strongly supports the hypothesis that LFEs in the Nankai subduction zone occur on the subducting plate boundary and are directly generated by shear slips. If LFEs are indeed direct indicators of the locations of the plate interface, they might be useful to investigate the minute structure of the plate interface. The thin distributions of LFEs indicate that the interface between the subducting and the overriding plates is a distinct very thin boundary, and not a distributed shear zone.
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precise hypocenter distribution of deep low frequency earthquakes and its relationship to the Local Geometry of the subducting plate in the nankai subduction zone japan
Journal of Geophysical Research, 2011Co-Authors: Kazuaki OhtaAbstract:[1] We determine the precise hypocenter distribution of deep low-frequency earthquakes (LFEs) in the Nankai subduction zone and compare it with the Local Geometry of the subducting Philippine Sea plate. We apply a new hypocenter determination method utilizing the summed cross correlation coefficient over many stations, termed a network correlation coefficient (NCC), to 112 LFEs in the western Shikoku and 1566 LFEs in the whole Nankai subduction zone. While the catalog depths are widely distributed in some regions, the relocated hypocenters in every region construct a plane surface several km above the oceanic Moho interface and quite consistent with the Geometry of the oceanic Moho. This result strongly supports the hypothesis that LFEs in the Nankai subduction zone occur on the subducting plate boundary and are directly generated by shear slips. If LFEs are indeed direct indicators of the locations of the plate interface, they might be useful to investigate the minute structure of the plate interface. The thin distributions of LFEs indicate that the interface between the subducting and the overriding plates is a distinct very thin boundary, and not a distributed shear zone.
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precise hypocenter distribution of deep low frequency earthquakes and its relationship to the Local Geometry of the subducting plate in the nankai subduction zone japan
Journal of Geophysical Research, 2011Co-Authors: Kazuaki Ohta, Satoshi IdeAbstract:[1] We determine the precise hypocenter distribution of deep low-frequency earthquakes (LFEs) in the Nankai subduction zone and compare it with the Local Geometry of the subducting Philippine Sea plate. We apply a new hypocenter determination method utilizing the summed cross correlation coefficient over many stations, termed a network correlation coefficient (NCC), to 112 LFEs in the western Shikoku and 1566 LFEs in the whole Nankai subduction zone. While the catalog depths are widely distributed in some regions, the relocated hypocenters in every region construct a plane surface several km above the oceanic Moho interface and quite consistent with the Geometry of the oceanic Moho. This result strongly supports the hypothesis that LFEs in the Nankai subduction zone occur on the subducting plate boundary and are directly generated by shear slips. If LFEs are indeed direct indicators of the locations of the plate interface, they might be useful to investigate the minute structure of the plate interface. The thin distributions of LFEs indicate that the interface between the subducting and the overriding plates is a distinct very thin boundary, and not a distributed shear zone.
Satoshi Ide - One of the best experts on this subject based on the ideXlab platform.
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Precise hypocenter distribution of deep low‐frequency earthquakes and its relationship to the Local Geometry of the subducting plate in the Nankai subduction zone, Japan
Journal of Geophysical Research, 2011Co-Authors: Kazuaki Ohta, Satoshi IdeAbstract:[1] We determine the precise hypocenter distribution of deep low-frequency earthquakes (LFEs) in the Nankai subduction zone and compare it with the Local Geometry of the subducting Philippine Sea plate. We apply a new hypocenter determination method utilizing the summed cross correlation coefficient over many stations, termed a network correlation coefficient (NCC), to 112 LFEs in the western Shikoku and 1566 LFEs in the whole Nankai subduction zone. While the catalog depths are widely distributed in some regions, the relocated hypocenters in every region construct a plane surface several km above the oceanic Moho interface and quite consistent with the Geometry of the oceanic Moho. This result strongly supports the hypothesis that LFEs in the Nankai subduction zone occur on the subducting plate boundary and are directly generated by shear slips. If LFEs are indeed direct indicators of the locations of the plate interface, they might be useful to investigate the minute structure of the plate interface. The thin distributions of LFEs indicate that the interface between the subducting and the overriding plates is a distinct very thin boundary, and not a distributed shear zone.
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precise hypocenter distribution of deep low frequency earthquakes and its relationship to the Local Geometry of the subducting plate in the nankai subduction zone japan
Journal of Geophysical Research, 2011Co-Authors: Kazuaki Ohta, Satoshi IdeAbstract:[1] We determine the precise hypocenter distribution of deep low-frequency earthquakes (LFEs) in the Nankai subduction zone and compare it with the Local Geometry of the subducting Philippine Sea plate. We apply a new hypocenter determination method utilizing the summed cross correlation coefficient over many stations, termed a network correlation coefficient (NCC), to 112 LFEs in the western Shikoku and 1566 LFEs in the whole Nankai subduction zone. While the catalog depths are widely distributed in some regions, the relocated hypocenters in every region construct a plane surface several km above the oceanic Moho interface and quite consistent with the Geometry of the oceanic Moho. This result strongly supports the hypothesis that LFEs in the Nankai subduction zone occur on the subducting plate boundary and are directly generated by shear slips. If LFEs are indeed direct indicators of the locations of the plate interface, they might be useful to investigate the minute structure of the plate interface. The thin distributions of LFEs indicate that the interface between the subducting and the overriding plates is a distinct very thin boundary, and not a distributed shear zone.
Evangelos Kalogerakis - One of the best experts on this subject based on the ideXlab platform.
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high resolution shape completion using deep neural networks for global structure and Local Geometry inference
arXiv: Computer Vision and Pattern Recognition, 2017Co-Authors: Xiaoguang Han, Haibin Huang, Evangelos KalogerakisAbstract:We propose a data-driven method for recovering miss-ing parts of 3D shapes. Our method is based on a new deep learning architecture consisting of two sub-networks: a global structure inference network and a Local Geometry refinement network. The global structure inference network incorporates a long short-term memorized context fusion module (LSTM-CF) that infers the global structure of the shape based on multi-view depth information provided as part of the input. It also includes a 3D fully convolutional (3DFCN) module that further enriches the global structure representation according to volumetric information in the input. Under the guidance of the global structure network, the Local Geometry refinement network takes as input lo-cal 3D patches around missing regions, and progressively produces a high-resolution, complete surface through a volumetric encoder-decoder architecture. Our method jointly trains the global structure inference and Local Geometry refinement networks in an end-to-end manner. We perform qualitative and quantitative evaluations on six object categories, demonstrating that our method outperforms existing state-of-the-art work on shape completion.
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ICCV - High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inference
2017 IEEE International Conference on Computer Vision (ICCV), 2017Co-Authors: Xiaoguang Han, Haibin Huang, Evangelos KalogerakisAbstract:We propose a data-driven method for recovering missing parts of 3D shapes. Our method is based on a new deep learning architecture consisting of two sub-networks: a global structure inference network and a Local Geometry refinement network. The global structure inference network incorporates a long short-term memorized context fusion module (LSTM-CF) that infers the global structure of the shape based on multi-view depth information provided as part of the input. It also includes a 3D fully convolutional (3DFCN) module that further enriches the global structure representation according to volumetric information in the input. Under the guidance of the global structure network, the Local Geometry refinement network takes as input Local 3D patches around missing regions, and progressively produces a high-resolution, complete surface through a volumetric encoder-decoder architecture. Our method jointly trains the global structure inference and Local Geometry refinement networks in an end-to-end manner. We perform qualitative and quantitative evaluations on six object categories, demonstrating that our method outperforms existing state-of-the-art work on shape completion.
Takashi Tatsumi - One of the best experts on this subject based on the ideXlab platform.
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effect of al si substitutions and silanol nests on the Local Geometry of si and al framework sites in silicone rich zeolites a combined high resolution 27al and 29si nmr and density functional theory molecular mechanics study
Journal of Physical Chemistry C, 2009Co-Authors: Jiří Dědeček, Stepan Sklenak, Fei Gao, Jiři Brus, Qingjun Zhu, Takashi TatsumiAbstract:We employed 29Si and 27Al (3Q) magic-angle spinning (MAS) NMR spectroscopy and density functional theory/molecular mechanics (DFT/MM) calculations to investigate the effect of Al/Si substitutions and the presence of silanol nests on the 29Si and 27Al NMR parameters as well as the Local Geometry of SiO4 and AlO4− tetrahedra of the nearest and next-nearest neighboring Si and Al atoms. The silicon-rich zeolite of the chabazite structure (Si/Al 38) was chosen for this study as a representative model of silicon-rich zeolites since it exhibits a low number of distinguishable T sites. Our computational results show the following: (I) Al atoms can occupy three different crystallographic T sites in the framework of chabazite (Si/Al 38). This result is in agreement with two observed 27Al NMR resonances. (II) An Al/Si substitution causes a downshift of the 29Si chemical shift of the nearest neighboring Si atoms (Al−O−Si) by 4−11 ppm. (III) The effect of a more distant Al/Si substitution (Al−O−Si−O−Si) is significant...
Xiaoguang Han - One of the best experts on this subject based on the ideXlab platform.
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high resolution shape completion using deep neural networks for global structure and Local Geometry inference
arXiv: Computer Vision and Pattern Recognition, 2017Co-Authors: Xiaoguang Han, Haibin Huang, Evangelos KalogerakisAbstract:We propose a data-driven method for recovering miss-ing parts of 3D shapes. Our method is based on a new deep learning architecture consisting of two sub-networks: a global structure inference network and a Local Geometry refinement network. The global structure inference network incorporates a long short-term memorized context fusion module (LSTM-CF) that infers the global structure of the shape based on multi-view depth information provided as part of the input. It also includes a 3D fully convolutional (3DFCN) module that further enriches the global structure representation according to volumetric information in the input. Under the guidance of the global structure network, the Local Geometry refinement network takes as input lo-cal 3D patches around missing regions, and progressively produces a high-resolution, complete surface through a volumetric encoder-decoder architecture. Our method jointly trains the global structure inference and Local Geometry refinement networks in an end-to-end manner. We perform qualitative and quantitative evaluations on six object categories, demonstrating that our method outperforms existing state-of-the-art work on shape completion.
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ICCV - High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inference
2017 IEEE International Conference on Computer Vision (ICCV), 2017Co-Authors: Xiaoguang Han, Haibin Huang, Evangelos KalogerakisAbstract:We propose a data-driven method for recovering missing parts of 3D shapes. Our method is based on a new deep learning architecture consisting of two sub-networks: a global structure inference network and a Local Geometry refinement network. The global structure inference network incorporates a long short-term memorized context fusion module (LSTM-CF) that infers the global structure of the shape based on multi-view depth information provided as part of the input. It also includes a 3D fully convolutional (3DFCN) module that further enriches the global structure representation according to volumetric information in the input. Under the guidance of the global structure network, the Local Geometry refinement network takes as input Local 3D patches around missing regions, and progressively produces a high-resolution, complete surface through a volumetric encoder-decoder architecture. Our method jointly trains the global structure inference and Local Geometry refinement networks in an end-to-end manner. We perform qualitative and quantitative evaluations on six object categories, demonstrating that our method outperforms existing state-of-the-art work on shape completion.
