High-resolution marine geomagnetic anomaly maps are a prerequisite for accurate geomagnetic matching navigation. However, existing compilations are sparse and of low resolution, and conventional interpolation techniques fail to capture fine-scale anomalies. Although deep learning models have achieved remarkable success in natural-image super-resolution, they have rarely been tailored to geomagnetic grid data; their lack of physically motivated constraints frequently introduces geologically implausible structures. To address these limitations, we propose a physics-constrained generative adversarial network (PC-GAN) for the super-resolution reconstruction of marine geomagnetic anomaly maps. Building upon the Super-Resolution Generative Adversarial Network (SRGAN) backbone, we incorporate physics-informed loss terms for spatial continuity and edge preservation into the training objective, thereby endowing the data-driven architecture with geological consistency while maintaining numerical accuracy. Experiments were conducted on the NOAA EMAG2_V3 dataset across four representative marine regions. Over the Philippine Sea Plate, PC-GAN reduces the root-mean-square error (RMSE) by 28.0% and increases the peak signal-to-noise ratio (PSNR) by 2.85 dB relative to bicubic interpolation, and lowers RMSE by 27.5% while raising PSNR by 2.79 dB compared with PSO-Kriging. Ablation studies corroborate that the physics-based modules make a statistically significant contribution to reconstruction quality. PC-GAN furnishes a robust tool for generating high-fidelity geomagnetic reference maps and holds promise for high-precision geomagnetic matching navigation and related applications.

A shaft-rate electric field (SREF) is a weak characteristic signal generated by moving ships in the ocean, serving as a key physical feature in underwater target detection and identification. Based on the generation mechanism of shaft-frequency electric fields, the research progress of traditional detection and extraction methods was systematically reviewed. On this basis, the feasible paths for applying quantum technology in SREF detection were further elaborated by integrating the core characteristics of quantum technology. Results indicated that Rydberg atoms and diamond nitrogen-vacancy centers demonstrate promising feasibility for detecting SREFs, exhibiting significant advantages over conventional methods in sensitivity and precision. Meanwhile, addressing the detection frequency and measurement intensity constituted a crucial breakthrough for the development of quantum technology-based SREF detection. This study aims to provide theoretical foundations and technical feasibility analysis for the quantum detection of SREFs, promoting the paradigm shift in this field from traditional methods to highly sensitive quantum sensing.