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Computationally Enabled 4D Visualizations Facilitate the Detection of Rock Fracture Patterns from Acoustic Emissions.

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Title: Computationally Enabled 4D Visualizations Facilitate the Detection of Rock Fracture Patterns from Acoustic Emissions.
Authors: Hohl, Alexander1, D. Griffith, Adam1, Eppes, Martha Cary1, Delmelle, Eric1
Source: Rock Mechanics & Rock Engineering. Sep2018, Vol. 51 Issue 9, p2733-2746. 14p.
Subjects: Rock testing, Rock noise, Fracture mechanics, Crack initiation (Fracture mechanics), Three-dimensional imaging
Abstract: Monitoring and predicting crack propagation in rock using acoustic emission (AE) technology is integral to a variety of sub-disciplines in the geosciences. The utility of existing AE data, however, is severely limited by prevailing visualization techniques, which suffer from problems of occlusion. Here, we introduce a novel approach to visualize 3D data through time (4D data) using unfiltered individual (AE) event data collected from a granite boulder for a period over 3 years. We implement a 3D extension of Ripley’s K function to evaluate the magnitude of clustering, and use the scale at which clustering is strongest to parameterize three-dimensional kernel density estimation (3DKDE) of AE events. We develop a parallel approach that features a load balancing technique to decrease the computational effort for 3DKDE and hence, reduce execution time. The results from the 3DKDE allow for comprehensible visualization of high AE density areas—best reflecting the actual location of subcritical cracking—and their changes through time, which is a substantial improvement over most existing methods. Our framework is scalable and portable to a variety of other disciplines such as epidemiology, ecology, and any point data by extension. Assumptions and limitations are identified as well as possible future research directions. [ABSTRACT FROM AUTHOR]
Copyright of Rock Mechanics & Rock Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="DE" term="%22Rock+testing%22">Rock testing</searchLink><br /><searchLink fieldCode="DE" term="%22Rock+noise%22">Rock noise</searchLink><br /><searchLink fieldCode="DE" term="%22Fracture+mechanics%22">Fracture mechanics</searchLink><br /><searchLink fieldCode="DE" term="%22Crack+initiation+%28Fracture+mechanics%29%22">Crack initiation (Fracture mechanics)</searchLink><br /><searchLink fieldCode="DE" term="%22Three-dimensional+imaging%22">Three-dimensional imaging</searchLink>
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  Data: Monitoring and predicting crack propagation in rock using acoustic emission (AE) technology is integral to a variety of sub-disciplines in the geosciences. The utility of existing AE data, however, is severely limited by prevailing visualization techniques, which suffer from problems of occlusion. Here, we introduce a novel approach to visualize 3D data through time (4D data) using unfiltered individual (AE) event data collected from a granite boulder for a period over 3 years. We implement a 3D extension of Ripley’s K function to evaluate the magnitude of clustering, and use the scale at which clustering is strongest to parameterize three-dimensional kernel density estimation (3DKDE) of AE events. We develop a parallel approach that features a load balancing technique to decrease the computational effort for 3DKDE and hence, reduce execution time. The results from the 3DKDE allow for comprehensible visualization of high AE density areas—best reflecting the actual location of subcritical cracking—and their changes through time, which is a substantial improvement over most existing methods. Our framework is scalable and portable to a variety of other disciplines such as epidemiology, ecology, and any point data by extension. Assumptions and limitations are identified as well as possible future research directions. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Rock Mechanics & Rock Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s00603-018-1488-z
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        Text: English
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      – SubjectFull: Rock noise
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      – SubjectFull: Fracture mechanics
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      – SubjectFull: Crack initiation (Fracture mechanics)
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      – TitleFull: Computationally Enabled 4D Visualizations Facilitate the Detection of Rock Fracture Patterns from Acoustic Emissions.
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              M: 09
              Text: Sep2018
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