Experience of borehole seismic sounding for the assessment of physical state of rock mass using 2D and 3D representations

AbstractThe southern part of the Apulia region (the Salento peninsula) has been the site of at least fifteen collapse events due to sinkholes in the last twenty years. The majority of these occurred in "soft" carbonate rocks (calcarenites). Man–made and/or natural cavities are sometimes assets of historical and archaeological significance. This paper provides a methodology for the evaluation of sinkhole hazard in "soft" carbonate rocks, combining seismic and mine engineering methods.Acase study of a natural cavity which is called Grotta delle Veneri is illustrated. For this example the approach was: i) 2D and 3D seismic methods to study the physical-mechanical characteristics of the rock mass that constitutes the roof of the cave; and ii) scaled span empirical analysis in order to evaluate the instability of the crown pillar’s caves.

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Mathematically provable correct implementation of integrated 2D and 3D representations

Lecture Notes in Geoinformation and Cartography - Advances in 3D Geoinformation Systems ◽

10.1007/978-3-540-72135-2_15 ◽

2008 ◽

pp. 247-278

Author(s):

Rodney Thompson ◽

Peter van Oosterom

Keyword(s):

2D And 3D ◽

Correct Implementation ◽

3D Representations

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View-Volume Network for Semantic Scene Completion from a Single Depth Image

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2018/101 ◽

2018 ◽

Cited By ~ 8

Author(s):

Yuxiao Guo ◽

Xin Tong

Keyword(s):

Computational Cost ◽

Depth Map ◽

Depth Image ◽

Context Information ◽

Efficiency And Effectiveness ◽

Semantic Scene ◽

3D Scene ◽

3D Volume ◽

2D And 3D ◽

3D Representations

We introduce a View-Volume convolutional neural network (VVNet) for inferring the occupancy and semantic labels of a volumetric 3D scene from a single depth image. Our method extracts the detailed geometric features from the input depth image with a 2D view CNN and then projects the features into a 3D volume according to the input depth map via a projection layer. After that, we learn the 3D context information of the scene with a 3D volume CNN for computing the result volumetric occupancy and semantic labels. With combined 2D and 3D representations, the VVNet efficiently reduces the computational cost, enables feature extraction from multi-channel high resolution inputs, and thus significantly improve the result accuracy. We validate our method and demonstrate its efficiency and effectiveness on both synthetic SUNCG and real NYU dataset.

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