A Method for Super-Resolution Reconstruction of Marine Geomagnetic Anomaly Reference Maps Based on an Improved Generative Adversarial Network

Abstract

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.

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