T245 Machine Learning for Advancing Data Analysis Toolkit in Geoscience

Wednesday, 13 October 2021
1:30 PM - 5:30 PM
B113/B114 (Hybrid Room)

INDEPENDENT, SEMI-AUTOMATIC CLASSIFICATION OF PETROGRAPHIC FEATURES IN VOLCANIC ROCKS USING FIJI AND WEKA

Paper number: T245-3
Program: Technical Sessions
Time: 1:50 PM – 2:05 PM

Authors

Abstract

Traditional methods of collecting quantitative petrographic data from thin sections (modal mineralogy, size distribution, shapes, etc.) are time- and labor-intensive, and rarely have sample sizes adequate to statistically describe complex rocks (i.e. volcanic rocks). Although manual counting and measurements are now routinely supplemented by digital image analysis, the majority of quantitative petrographic studies still go through a manual digitization stage where object classes are traced before further analyses. This is a major rate-limiting step that reproduces the same problems of small n-values resulting from significant effort. We have valuated the potential and limitations of using the Trainable Weka Segmentation (TWS) plugin within the commonly used ImageJ / Fiji digital image analysis and processing environment. Specifically, we have assessed their capacity to classify, segment, and threshold user-defined petrographic features from a suite of images of progressively more complex volcanic rocks to accelerate the collection of quantitative petrographic data.

TWS uses a fast-random-forest algorithm to classify an image based on a set of training pixels selected by the user - in this case different mineral phases, vesicles, etc. Training of the classifier is intuitive and fast. For example, three classes each with eleven training spots are classified in less than 1 minute for a medium to high-resolution image. Eight plane polarized light photomicrographs with increasing crystallinity and complexity were classified (i.e. trained) and automatically segmented using TWS. Samples where the assigned classes have distinct, homogeneous RGB values and sharp boundaries are successfully classified with TWS. However, samples where the classes are heterogeneous but similar, as a result of alteration for example, are not adequately classified. Once classified, two major efficiency gains are possible: (1) the classifier can be saved and applied again to any similar sample, and (2) the segmented image is immediately available for thresholding in ImageJ / Fiji (i.e. separating into class-specific images) without manual tracing or cut-and-paste. The thresholded images can then be measured using the image analysis tools in ImageJ / Fiji (e.g., dimensions, area, circularity, long-axis orientation, etc.).

Geological Society of America Abstracts with Programs. Vol. 53, No. 6

doi: 10.1130/abs/2021AM-367445

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