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AAPG Bulletin


AAPG Bulletin, V. 105, No. 4 (April 2021), P. 631-638.

Copyright ©2021. The American Association of Petroleum Geologists. All rights reserved.

DOI: 10.1306/08192019051

Applying deep learning for identifying bioturbation from core photographs

Eric Timmer,1 Calla Knudson,2 and Murray Gingras3

1Department of Earth and Atmospheric Sciences, University of Alberta, Edmonton, Alberta, Canada; [email protected]
2Department of Earth and Atmospheric Sciences, University of Alberta, Edmonton, Alberta, Canada; [email protected]
3Department of Earth and Atmospheric Sciences, University of Alberta, Edmonton, Alberta, Canada; [email protected]


Advances and availability of deep learning (DL) software have recently allowed the development, testing, and deployment of automated image classification schemes for sedimentary features from core images. The development of these methods is especially relevant for extracting useful geological features from otherwise unused core photographs. This paper demonstrates and tests the use of a DL workflow for the automated extraction of bioturbation data from a core photograph data set.

The proposed workflow includes extracting image tiles from core photographs along a grid and referencing each tile with collected sedimentary data. Each labeled image tile is then used as a training and testing input for a machine learning algorithm. This method allows users to quickly generate thousands of labeled training images.

To demonstrate and test this workflow, a data set was collected using PyCHNO™, an open-source software specifically designed to collect sedimentary data from core photographs. The resulting data set comprising 13,545 tiles of 128 × 128 pixel resolution is used to train a DL algorithm to automatically predict if a core photograph contains evidence of bioturbation. The trained model was able to predict whether or not an image demonstrated evidence of bioturbation with up to 88% accuracy.

The workflow demonstrates one of many possible applications for automatically extracting biogenic or physical sedimentary structure data from core photographs. Models built using this approach can be used to “seed” wells from a given area or interval, which can therefore significantly increase the value of core photograph data sets with relative ease.

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