Method for predicting geological features from thin section images using a deep learning classification process

A method for predicting an occurrence of a geological feature in an image of a thin section, the method comprising the steps of:

• (a) providing a trained backpropagation-enabled classification process, the backpropagation-enabled classification process having been trained by
• i. providing a training set of thin section images;
• ii. determining the scale of each of the thin section images in the training set;
• iii. extracting training image fractions from the training set of thin section images, each extracted training image fraction having substantially the same absolute horizontal and vertical length;
• iv. defining a set of geological features of interest, wherein the set of geological features comprises a plurality of classes;
• v. selecting a class for labeling each of the training image fractions;
• vi. inputting the extracted training image fractions with associated labeled classes into the backpropagation-enabled classification process;
• and
• vii. iteratively computing a prediction of the probability of occurrence of the class in the extracted training image fractions and adjusting parameters in the backpropagation-enabled classification model accordingly, thereby producing the trained backpropagation-enabled classification process;
• and
• (b) using the trained backpropagation-enabled classification process to predict the occurrence of the class in a non-training thin section image by
• i. providing a non-training thin section image;
• ii. determining the scale of the non-training thin section image;
• iii. extracting a non-training image fraction from the non-training thin section image, the extracted non-training image fractions having substantially the same absolute horizontal and vertical length as the extracted training image fractions used to train the backpropagation-enabled classification process;
• and
• iv. inputting the extracted non-training image fractions to the trained backpropagation-enabled classification process;
• v. predicting a probability of occurrence of the class on the extracted non-training image fractions and;
• vi. combining the probabilities for the extracted non-training image fractions to produce an inference for the occurrence of the class in the non-training thin section image.