Generalized aquifers are widely used in various fields, such as groundwater use, mine water prevention and control, and geothermal energy. This paper presents a transformer-model-based automatic aquifer generalization method using borehole logs in scenarios with scarce experimental parameters. Relying only on basic borehole data, the method used an agent-assisted approach to extract and clean key lithological and coordinate information, which was then fused using a dual embedding mechanism. The model leveraged multi-head self-attention to calculate attention weights between the target stratum and its adjacent strata, capturing the potential contextual correlations in aquifer potential across strata. The resulting deep feature vectors from the transformer’s encoder were fed into a classification head to predict aquifer potential labels. Evaluation results demonstrated a model accuracy of 0.86, significantly outperforming the random classification baseline in precision, recall, the F1-score, and the kappa coefficient.
Du et al. (Sun,) studied this question.