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the ARC Training Centre for Multiscale 3D Imaging, Modelling and Manufacturing

P6-2. Neural Network Analysis of Biostratigraphically Important Conodonts

Project leader: Tim Denham (SoAA-CASS, ANU)
Industry partner: Dr. Patrick Mark Smith, Australian Museum/UNSW
Fig. 1: Samples of conodonts (Mathieson et al. 2016)
Objective:
  1. Conodonts are extremely useful biostratigraphic markers of rich mineral bearing fossil sequences.
  2. MicroCT scanning of diverse conodont taxa in Australian Museum collections provide dataset for training of neural network to discriminate taxa.
  3. First step in designing a new system to use computer-derived biostratigraphic data to correlate and identify important rock units.
Alignment within M3D Innovation:
  1. Use of multiscale 3D imaging and neural network to design new computer-aided technologies for industry.
  2. Development of technology and trained personnel.
Approach:
  1. Current methods to discriminate significant conodont species labour-intensive and 19th century technology.
  2. MicroCT scan, visualise and analyse taxonomically identified conodonts in Australian Museum collection.
  3. Train neural network to discriminate conodont taxa, with a focus on mineralogically significant biostratigraphic markers.
  4. Develop computer-aided and potentially semi-automated workflows to inform mineral exploration.
Key Milestones:
  1. Bring study of conodont taxonomy and biostratigraphy into the 21st century.
  2. Create digital database of taxonomically identified conodonts from existing collections.
  3. Train neural network in interspecific discrimination.
  4. Design computer-aided workflow to rapidly and reliably correlate economically important rock units using microCT scanning, data visualization and neural network.