Strain Analysis

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Kieran F. Mulchrone - One of the best experts on this subject based on the ideXlab platform.

  • Finite Strain Analysis Using Shape and Point Data
    Developments in Structural Geology and Tectonics, 2019
    Co-Authors: Kieran F. Mulchrone
    Abstract:

    Abstract Understanding the finite Strain state of a rock or its spatial variation is an important component of structural geology. In this chapter the problem of calculating finite Strain using shape data and point data is considered in detail. The first step in Strain Analysis is data acquisition, and the first section is concerned with semiautomatic extraction of data for Strain Analysis from digital images. Two methods for Strain Analysis are considered: the first method (MRL, mean radial length) uses the shapes of Strain markers, and the second method (Delaunay triangulation nearest neighbor method) uses the relative positions of Strain marker centroids. In each case, relevant image files, data, and C++ code are provided. Background theory is discussed, and an example application is considered emphasizing that finite Strain Analysis is best used in conjunction with a range of structural observations.

  • Mathematica code for image Analysis, semi-automatic parameter extraction and Strain Analysis
    Computers & Geosciences, 2013
    Co-Authors: Kieran F. Mulchrone, Dave J. Mccarthy, Patrick A. Meere
    Abstract:

    Geological Strain Analysis is a common task for structural geologists. This contribution presents software written on top of the Mathematica platform which allows for rapid semi-automatic Strain Analysis. After an initial step of manual identification of Strain markers, the software performs image Analysis, parameter extraction and Strain Analysis using the shape and relative spatial positioning of markers. Bootstrap estimates of sampling errors are calculated and suitable graphical output is generated. Three representative samples of lithologies typically used in Strain Analysis are analysed to test the software. We present an automated method for geological Strain Analysis techniques using Mathematica.Grain boundary parameters are automatically extracted using image Analysis.Strain estimates are made using object separation and object shape properties.Both methods compare well to previously published manual estimates.A statistical technique allows error estimates to be calculated using the bootstrap method.

  • An analytical error for the mean radial length method of Strain Analysis
    Journal of Structural Geology, 2005
    Co-Authors: Kieran F. Mulchrone
    Abstract:

    Abstract An analytical expression for the error associated with estimating the axial ratio ( R s ) and orientation ( ϕ s ) of the finite Strain ellipse with the mean radial length method of Strain Analysis is presented. Analytical errors are computationally efficient and compare excellently with errors calculated by the computationally intensive bootstrap approach.

  • SAPE: a program for semi-automatic parameter extraction for Strain Analysis
    Journal of Structural Geology, 2005
    Co-Authors: Kieran F. Mulchrone, Patrick A. Meere, Kingshuk Roy Choudhury
    Abstract:

    Abstract SAPE (Semi-Automatic Parameter Extraction) is a program for extracting information relevant to Strain Analysis from input digital images. Input images are manually produced by tracing the outlines of objects of interest. This is a less onerous, more objective and much faster task than manual measurement or digitisation of object data (i.e. aspect ratio R i and orientation ϕ i ). SAPE rapidly extracts the required data by using a simple region-growing algorithm to identify regions of interest. Subsequently, the second moments are calculated for each region enabling the common Strain Analysis parameters to be readily computed. The performance of SAPE was tested on three samples (microphotographs of deformed sandstone and oolite as well as a meter scale photograph of a deformed conglomerate) using a non-overlap statistic. This statistic is a normalised measure of overlap between two regions and can be used to compare different fits applied to the same region. In each case SAPE out-performed the manual method. SAPE offers significant advantages in terms of speed, objectivity and robustness and should facilitate the collection of large datasets for Strain Analysis.

  • Application of Delaunay triangulation to the nearest neighbour method of Strain Analysis
    Journal of Structural Geology, 2003
    Co-Authors: Kieran F. Mulchrone
    Abstract:

    Abstract The nearest neighbour method of Strain Analysis is re-evaluated and a method for objectively determining nearest neighbours, namely the Delaunay triangulation, is applied. A simulation study and application to a real set of data demonstrates that this approach makes the NNM of Strain Analysis a practical (and computationally more efficient) alternative to the Fry and associated methods. Once nearest neighbours are selected centre–centre distances can be processed by normalisation and enhancement and the best fit ellipse is determined using a steepest gradient non-linear least squares algorithm applied to the polar equation of a centred ellipse. A simulation study indicates that the technique is a valid one and estimates the Strain ellipse well at the 95% confidence interval. Application to a set of natural oolite data shows that there is a systematic variation of error with selection factor and it is suggested that the best estimate of the Strain ellipse is obtained by choosing the selection factor which minimises the error.

Patrick A. Meere - One of the best experts on this subject based on the ideXlab platform.

  • Mathematica code for image Analysis, semi-automatic parameter extraction and Strain Analysis
    Computers & Geosciences, 2013
    Co-Authors: Kieran F. Mulchrone, Dave J. Mccarthy, Patrick A. Meere
    Abstract:

    Geological Strain Analysis is a common task for structural geologists. This contribution presents software written on top of the Mathematica platform which allows for rapid semi-automatic Strain Analysis. After an initial step of manual identification of Strain markers, the software performs image Analysis, parameter extraction and Strain Analysis using the shape and relative spatial positioning of markers. Bootstrap estimates of sampling errors are calculated and suitable graphical output is generated. Three representative samples of lithologies typically used in Strain Analysis are analysed to test the software. We present an automated method for geological Strain Analysis techniques using Mathematica.Grain boundary parameters are automatically extracted using image Analysis.Strain estimates are made using object separation and object shape properties.Both methods compare well to previously published manual estimates.A statistical technique allows error estimates to be calculated using the bootstrap method.

  • SAPE: a program for semi-automatic parameter extraction for Strain Analysis
    Journal of Structural Geology, 2005
    Co-Authors: Kieran F. Mulchrone, Patrick A. Meere, Kingshuk Roy Choudhury
    Abstract:

    Abstract SAPE (Semi-Automatic Parameter Extraction) is a program for extracting information relevant to Strain Analysis from input digital images. Input images are manually produced by tracing the outlines of objects of interest. This is a less onerous, more objective and much faster task than manual measurement or digitisation of object data (i.e. aspect ratio R i and orientation ϕ i ). SAPE rapidly extracts the required data by using a simple region-growing algorithm to identify regions of interest. Subsequently, the second moments are calculated for each region enabling the common Strain Analysis parameters to be readily computed. The performance of SAPE was tested on three samples (microphotographs of deformed sandstone and oolite as well as a meter scale photograph of a deformed conglomerate) using a non-overlap statistic. This statistic is a normalised measure of overlap between two regions and can be used to compare different fits applied to the same region. In each case SAPE out-performed the manual method. SAPE offers significant advantages in terms of speed, objectivity and robustness and should facilitate the collection of large datasets for Strain Analysis.

H. H. Cheng - One of the best experts on this subject based on the ideXlab platform.

  • Strain Analysis of a wrinkled SiGe bilayer thin film
    Journal of Applied Physics, 2012
    Co-Authors: Guo-en Chang, C. R. Chang, H. H. Cheng
    Abstract:

    We report a Strain Analysis on a wrinkled semiconductor pattern formed by a p-doped bilayer thin film that is compressively Strained. The Strain distribution is studied with a theoretical Analysis using a non-linear plate theory in conjunction with a detailed morphology measurement. The results show that the normal Strain reduces continuously as the wrinkle amplitude increases, due to the stretching effect, and that the variation in the Strain along the wrinkle edge is dominated by the bending effect, which agrees reasonably with the Raman measurement.

Jonathan Chan - One of the best experts on this subject based on the ideXlab platform.

  • the learning curve for competency in right ventricular longitudinal Strain Analysis
    Journal of The American Society of Echocardiography, 2020
    Co-Authors: R Chamberlain, G Scalia, Yong Wee, Su Hnin Hlaing, Abbie Lee, Ian Hotham, Estelle Pagetaylor, Surendran Sabapathy, Jonathan Chan
    Abstract:

    The application of myocardial Strain by two-dimensional speckle-tracking to quantify right ventricular (RV) function has recently been endorsed by the American Society of Echocardiography/European Association of Cardiovascular Imaging Joint Industry Task Force, with emphasis on the need for standardization and quality control.1 However, there are no specific recommendations to date for the level of training required for accurate RV Strain Analysis for independent reporting. Our group has previously demonstrated the existence of a learning curve for left ventricular global longitudinal Strain Analysis,2 while others have demonstrated similar learning curves for wall motion Analysis during stress echocardiography3 and visual estimation of left ventricular ejection fraction.4 The aim of this study was to determine whether there is a learning curve for RV longitudinal Strain Analysis for novice readers to achieve independent competency in consistency and reproducibility.

  • left ventricular global Strain Analysis by two dimensional speckle tracking echocardiography the learning curve
    Journal of The American Society of Echocardiography, 2017
    Co-Authors: Jonathan Chan, Kenji Shiino, Nchafatso G Obonyo, J Hanna, Robert Chamberlain, Andrew Small, Isabel G Scalia, W Scalia, Akira Yamada
    Abstract:

    Background The application of left ventricular (LV) global Strain by speckle-tracking is becoming more widespread, with the potential for incorporation into routine clinical echocardiography in selected patients. There are no guidelines or recommendations for the training requirements to achieve competency. The aim of this study was to determine the learning curve for global Strain Analysis and determine the number of studies that are required for independent reporting. Methods Three groups of novice observers (cardiology fellows, cardiac sonographers, medical students) received the same standardized training module prior to undertaking retrospective global Strain Analysis on 100 patients over a period of 3 months. To assess the effect of learning, quartiles of 25 patients were read successively by each blinded observer, and the results were compared to expert for correlation. Results Global longitudinal Strain (GLS) had uniform learning curves and was the easiest to learn, requiring a minimum of 50 patients to achieve expert competency (intraclass correlation coefficient > 0.9) in all three groups over a period of 3 months. Prior background knowledge in echocardiography is an influential factor affecting the learning for interobserver reproducibility and time efficiency. Short-axis Strain Analysis using global circumferential stain and global radial Strain did not yield a comprehensive learning curve, and expert level was not achieved by the end of the study. Conclusions There is a significant learning curve associated with LV Strain Analysis. We recommend a minimum of 50 studies for training to achieve competency in GLS Analysis.

V. A. Zarutskii - One of the best experts on this subject based on the ideXlab platform.

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