Image processing for quantifying fracture orientation and length scale transitions during brittle deformation

R. E. Rizzo, David Healy, Nathalie J.C. Farrell

Research output: Contribution to conferenceAbstractpeer-review

Abstract

We have implemented a novel image processing tool, namely two-dimensional (2D) Morlet wavelet analysis, capable of detecting changes occurring in fracture patterns at different scales of observation, and able of recognising the dominant fracture orientations and the spatial configurations for progressively larger (or smaller) scale of analysis. Because of its inherited anisotropy, the Morlet wavelet is proved to be an excellent choice for detecting directional linear features, i.e. regions where the amplitude of the signal is regular along one direction and has sharp variation along the perpendicular direction. Performances of the Morlet wavelet are tested against the 'classic' Mexican hat wavelet, deploying a complex synthetic fracture network. When applied to a natural fracture network, formed triaxially (σ1>σ2=σ3) deforming a core sample of the Hopeman sandstone, the combination of 2D Morlet wavelet and wavelet coefficient maps allows for the detection of characteristic scale orientation and length transitions, associated with the shifts from distributed damage to the growth of localised macroscopic shear fracture. A complementary outcome arises from the wavelet coefficient maps produced by increasing the wavelet scale parameter. These maps can be used to chart the variations in the spatial distribution of the analysed entities, meaning that it is possible to retrieve information on the density of fracture patterns at specific length scales during deformation.

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Original languageEnglish
Publication statusPublished - Dec 2017
EventAGU Fall Meeting 2017 - New Orleans, United States
Duration: 11 Dec 2017 → …

Conference

ConferenceAGU Fall Meeting 2017
Country/TerritoryUnited States
CityNew Orleans
Period11/12/17 → …

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