High-resolution aeromagnetic map through Adapted-SRGAN: A case study in Québec, Canada

• Published in: Computers & geosciences Vol. 176; p. 105363
• Main Authors: Bavandsavadkoohi, Mojtaba, Cedou, Matthieu, Blouin, Martin, Gloaguen, Erwan, Tirdad, Shiva, Giroux, Bernard
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.07.2023
• Subjects: Airborne magnetic; Convolutional Neural Networks (CNNs); Deep learning; High-resolution map; Super-resolution GAN; Deep learning; Super-resolution GAN; High-resolution map; Airborne magnetic; Convolutional Neural Networks (CNNs)
• ISSN: 0098-3004; 1873-7803
• DOI: 10.1016/j.cageo.2023.105363

Due to their cost-effectiveness, aeromagnetic data have been acquired for decades to guide mineral exploration. Aeromagnetic map enhancement is immensely useful as it allows dykes, faults, and other geological structures to be highlighted more clearly, therefore assisting the geologist with a better understanding of the geological process. Over the last years, technological improvements allowed increasing the sensitivity of airborne magnetic data acquisition systems and the accuracy of navigation instrumentation, which resulted in higher resolution (HR) maps. Such higher resolution implies close lines of flight, which typically implies smaller area coverage. On the other hand, the vintage aeromagnetic surveys have high coverage but a much lower resolution. Geological interpretation for regions where only low-resolution aeromagnetic data (LR) are available remains challenging for geologists. Hence, we adapted and trained a deep neural network to address this problem by learning the statistical relations between collocated LR and HR airborne magnetic data. First, we trained a Super-Resolution (SR) network based on an Adapted-SRGAN (ASRGAN) in the source (training) set for mapping LR images to HR images. Second, given this trained network, we generate HR images in the target (test) set where only LR images are available, with a 4 × times resolution increase. We validated the generalization of our model using aeromagnetic maps from several regions of the Québec province, by computing Peak Signal to Noise Ratio (PSNR) and Structural Similarity index (SSIM) between super-resolved GAN outputs with ground truth HR aeromagnetic images. The performance of the proposed approach is compared to bicubic interpolation and conventional SRGAN, with a slight improvement in terms of the SSIM and PSNR but with about 35 percent reduction in the computational time required to train the network. •A new and cost-effective deep learning-based super resolution to enhance low-resolution aeromagnetic map.•Improve performance of conventional SRGAN by an effective training strategy.•Super-resolved adapted SRGAN (ASRGAN) model presents very good generalization in test (unseen) area.