Recently, deep learning has become more and more widely used in geology. An important topic in geological modeling is building a subsurface model according to sparse spatial observation data. Deep learning-based geological modeling has been explored through conditional generative adversarial networks, which results in realistic geological images in line with spatial measurements. However, most methods are only conditioned on spatial observations, ignoring the adjustment of geological attributes in images. This study proposes a method to adjust geological images by introducing geological attribute labels on the basis of spatial measurements. The method introduces label data representing a geological attribute category as one of the generation conditions and expands an attribute classifier to cooperate with the label to adjust the generated image, achieving more controllable images. Considering the high cost of manual labeling, this study adopts semi-supervised clustering to automatically assign labels to unlabeled data using a small amount of labeled data. In addition, clustering may produce noise labels that affect the modeling results. In response, the symmetric cross-entropy loss is used to improve the classful network to enhance the robustness of the network against noise labels. Experiments are carried out on a geological dataset in the Yellow River. Results show that the method achieves realistic geological images featuring different geological patterns and conforming to spatial observations for different attribute labels, which proves the effectiveness of the method.