Lithology identification by adaptive feature aggregation under scarce labels

Lithology identification plays an important role in petroleum exploration. However, due to the high cost of labeling by cores and cuttings, the application of semi-supervised method in lithology identification should be considered. There is a main problem in the past graph-based semi-supervised methods, that is, the connection strengths (i.e., edges) among samples are calculated before training and therefore fixed. However, these pre-handcrafted connections strengths determine the graph structure and therefore affect the performance of the trained model. Obviously, there inevitably exist inappropriate connection settings, which motivates us to consider the dynamic adjustment of these connection strengths via training. First, the graph attention network is used to complete the semi-supervised task in the process of lithology identification. This is the first time to our knowledge that the graph attention network is applied to lithology identification. This method can learn edge weights and mitigate interference due to the error of initial edge weights. Second, two methods are used in graph neural network to establish the connection between logging data and transforming them into graph structure data. Third, through a lot of experiments, the graph attention network using depth similarity (GAT-D) outperforms other algorithms on two metrics (macro-avg-f1 score of 73.73% and macro-avg-recall of 80.54% in dataset A). Hence, converting logging data into graph data and then using graph neural network to realize semi-supervised classification could improve the performance of lithology identification.