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Automatic image analysis applied to the recognition of quartz surface microtextures using neural network.

Pavel Sikora1, Martin Kiac1, Pedro J M Costa2

  • 1Brno University of Technology, Faculty of Electrical Engineering and Communications, Dept. of Telecommunications, Technicka 12, Brno 616 00, Czech Republic.

Micron (Oxford, England : 1993)
|April 25, 2024
PubMed
Summary

This study introduces DeepGrain, a new software for automatically identifying quartz grain microtextures. It significantly reduces analysis time and improves accuracy in sediment provenance studies.

Keywords:
Artificial intelligenceDeepGrainMachine learningQuartz microtexturesSEMSegmentation

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Area of Science:

  • Geology
  • Sedimentology
  • Geomorphology

Background:

  • Quartz grain surface microtextures offer insights into sedimentary processes and environmental conditions.
  • Microtextural analysis is crucial for sediment provenance studies.
  • Subjectivity in manual microtextural recognition can limit accuracy.

Purpose of the Study:

  • To develop an automated software solution for identifying quartz grain microtextures.
  • To minimize subjectivity and enhance the efficiency of microtextural analysis.
  • To introduce DeepGrain, a novel software tool for sediment provenance research.

Main Methods:

  • Utilized the DeepLabV3 model for image analysis.
  • Applied advanced techniques to improve the DeepLabV3 model's performance.
  • Developed the DeepGrain software for automatic microtexture identification.

Main Results:

  • Achieved 99% accuracy in identifying the area of tested quartz grains.
  • Reached 63% accuracy in identifying mechanical features on grain surfaces.
  • Reduced the average analysis time per SEM image to 3.10 seconds.

Conclusions:

  • DeepGrain offers a highly accurate and efficient automated method for quartz microtexture analysis.
  • The software significantly streamlines the process of sediment provenance studies.
  • DeepGrain provides a valuable tool for researchers in sedimentology and geology.