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Summary Deep Learning technologies are increasingly used to automate the processing of unstructured data, such as images, in many industrial and scientific areas. They have notably proven their efficiency to optimize routine tasks and let expert staff focus on activities of higher added value. Some applications on geological objects have produced promising results, and systems have been designed for assisted lithofacies characterization on core images. However only few practical use cases have been documented so far. In this paper, we confront Deep Learning workflows for image classification with actual core data. To do so, we use a dataset from an IODP expedition in the Gulf of Corinth, consisting in core images from 3 drilling sites in the Gulf, and an expert interpretation in terms of 17 facies associations. From this experience, we highlight the main challenges to expect in the assisted interpretation of core images with Artificial Intelligence and share some good practices. Notably, we describe potential solutions to handle situations where only little training data is available and techniques to choose and tune a model through Transfer Learning. |
