scholarly journals Visual analytics of aftershock point cloud data in complex fault systems

Solid Earth ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 1397-1407 ◽  
Author(s):  
Chisheng Wang ◽  
Junzhuo Ke ◽  
Jincheng Jiang ◽  
Min Lu ◽  
Wenqun Xiu ◽  
...  
Keyword(s):  
Visual Analytics ◽  
Point Cloud ◽  
Visual Analysis ◽  
Central Italy ◽  
Prototype System ◽  
Point Cloud Data ◽  
Flexible Display ◽  
Cloud Data ◽  
Visual Computing ◽  
Central Italy Earthquake

Abstract. Aftershock point cloud data provide direct evidence for the characteristics of underground faults. However, there has been a dearth of studies using state-of-the-art visual analytics methods to explore the data. In this paper, we present a novel interactive visual analysis approach for visualizing the aftershock point cloud. Our method employs a variety of interactive operations, rapid visual computing functions, flexible display modes, and various filtering approaches to present and explore the desired information for the fault geometry and aftershock dynamics. The case study conducted for the 2016 Central Italy earthquake sequence shows that the proposed approach can facilitate the discovery of the geometry of the four main fault segments and three secondary fault segments. It can also clearly reveal the spatiotemporal evolution of the aftershocks, helping to find the fluid-driven mechanism of this sequence. An open-source prototype system based on the approach is also developed and is freely available.

scholarly journals Visual analytics of the aftershock point cloud data in complex fault systems

2019 ◽  
Author(s):  
Chisheng Wang ◽  
Junzhuo Ke ◽  
Jincheng Jiang ◽  
Min Lu ◽  
Wenqun Xiu ◽  
...  
Keyword(s):  
Visual Analytics ◽  
Point Cloud ◽  
Visual Analysis ◽  
Central Italy ◽  
Prototype System ◽  
Point Cloud Data ◽  
Flexible Display ◽  
Cloud Data ◽  
Central Italy Earthquake ◽  
Spatio Temporal

Abstract. Aftershock point cloud data provide direct evidence for the characteristics of underground faults. However, there has been a dearth of studies using the state-of-art visual analytics methods to explore the data. In this paper, we present a novel interactive visual analysis approach for visualizing the aftershock point cloud. Our method employs a variety of interactive operations, rapid visual computing functions, flexible display modes and various filtering approaches to present and explore the desired information for the fault geometry and aftershock dynamics. The case study conducted for the 2016 central Italy earthquake sequence shows that the proposed approach can facilitate the discovery of the geometry of the four main fault segments and three secondary fault segments. It can also clearly reveal the spatio-temporal evolution of the aftershocks, helping to find the fluid-driven mechanism of this sequence. An open-source prototype system based on the approach is also developed and is freely available.

scholarly journals An Intelligent and Confident System for Automatic Surface Defect Quantification in 3D

2015 ◽  
Vol 649 ◽  
pp. 46-53 ◽  
Author(s):  
Mitul Tailor ◽  
Jon Petzing ◽  
Michael Jackson
Keyword(s):  
Point Cloud ◽  
Surface Defect ◽  
Visual Analysis ◽  
Surface Defects ◽  
Metal Plate ◽  
Measuring Instruments ◽  
Point Cloud Data ◽  
Defect Analysis ◽  
Cloud Data ◽  
Defect Quantification

Automatic surface defect inspection within mass production of high-precision components is growing in demand and requires better measurement and automated analysis systems. Many manufacturing industries may reject manufactured parts that exhibit even minor defects, because a defect might result in an operational failure at a later stage. Defect quantification (depth, area and volume) is a key element in quality assurance in order to determine the pass or failure criterion of manufactured parts. Existing human visual analysis of surface defects is qualitative and subjective to varying interpretation. Non-contact and three dimensional (3D) analyses should provide a robust and systematic quantitative approach for defect analysis. Various 3D measuring instruments generate point cloud data as an output, although they work on different physical principles. Instrument’s native software processing of point cloud data is often subject to issues of repeatability and may be non-traceable causing significant concern with data confidence.This work reports the development of novel traceable surface defect artefacts produced using the Rockwell hardness test equipment on flat metal plate, and the development of a novel, traceable, repeatable, mathematical solution for automatic defect detection and quantification in 3D. Moreover, in order to build-up the confidence in automatic defect analysis system and generated data, mathematical simulated defect artefacts (soft-artefact) have been created. This is then extended to a surface defect on a piston crown that is measured and quantified using a parallel optical coherence tomography instrument integrated with 6 axis robot. The results show that surface defect quantification using implemented solution is efficient, robust and more repeatable than current alternative approaches.

Realization of a Prototype System for Point Cloud Data Processing in Reverse Engineering

2011 ◽  
Vol 421 ◽  
pp. 419-422 ◽  
Author(s):  
Yong Zhuo ◽  
Juan Peng ◽  
Yan Jun Wu
Keyword(s):  
Reverse Engineering ◽  
Point Cloud ◽  
Processing System ◽  
Prototype System ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Data Registration ◽  
Function Modules ◽  
Data Topology ◽  
And Function

In Reverse Engineering (RE), Point Cloud Data (PCD) processing is of great importance. But at present, a number of key issues about its algorithms are unresolved. This article mainly introduces the author who has done the research and put forward some specific algorithms on the aspects of data topology reconstruction, multi-view data registration and data reduction, and then developed a PCD processing system --3DPointshop, based on OpenGL and MFC. Through a series of instances of tests show that the prototype system which contains the algorithms and function modules will be able to implement on PCD processing well and could achieve the practical application level.

Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds

2020 ◽  
Author(s):  
Jiayong Yu ◽  
Longchen Ma ◽  
Maoyi Tian, ◽  
Xiushan Lu
Keyword(s):  
Point Cloud ◽  
Image Data ◽  
Point Clouds ◽  
Optical Image ◽  
Point Cloud Data ◽  
Feature Points ◽  
Lidar System ◽  
Registration Accuracy ◽  
Cloud Data ◽  
Intensity Image

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

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