The well-described river courses in geological sedimentary models are essential for identifying oil and gas reservoirs. However, numerous noises are generated around the river course, including irregular noises and regions due to errors in the geological data, as well as traces left over from the printed model. The cluttered distributions of noises make the complete river course unobtainable, which interferes with reservoir prediction. To the best of our knowledge, deep learning-based methods for automatic noise detection and removal have not been explored in the field of processing geological sedimentary model images. In this paper, we present Multi-scale Contrastive Learning Networks (CLGAN) for detecting noise in geological sedimentary model images. A multi-scale contrastive training strategy is proposed to capture noise locations and river discontinuities by comparing the distributions with and without noise in image space and feature space. A paired dataset that consists of images with noise and images without noise is constructed for contrastive training. Moreover, cyclic denoising is proposed to completely denoise by constantly modifying the pixel values at the noise, which prevents missed detections by calling the model multiple times. Subsequently, pixel-level denoising is designed to initially remove a portion of the noise by contouring non-river regions, which reduces the pressure on the cyclic denoising method. The detection results demonstrate the effectiveness of CLGAN. The denoising results with excellent river connectivity and integrity highlight the superiority of the proposed denoising methods.