Data-driven magnetic anomaly data gap filling

SUMMARY As a critical category of geophysical data, magnetic anomalies play vital roles in geological interpretation, resource exploration and target detection. For most applications involving magnetic anomaly data, the ideal data set should have uniformly distributed data points, high resolution and completeness without gaps. However, because of the environmental constraints and measurement limitations, magnetic anomaly data obtained from real-world measurements often fail to meet these requirements. Thus, interpolation techniques present effective and cost-efficient technical approaches for processing measured magnetic anomaly data to meet the aforementioned criteria. To our knowledge, current research on magnetic anomaly data interpolation has primarily focused on gridding methods for interpolating irregularly sampled data into gridded data and super-resolution interpolation methods aimed at enhancing spatial resolution. Meanwhile, studies on interpolation methods specifically designed to fill large-area data gaps remain relatively scarce. To address the challenge of reconstructing large-area missing magnetic anomaly data, we propose a data-driven method for magnetic anomaly data gap filling. First, based on the analysis of the characteristics of magnetic anomaly data, we construct an open-source magnetic anomaly interpolation data set (MAID) specifically designed for magnetic anomaly data interpolation tasks. Subsequently, we develop a magnetic anomaly data gap-filling generative adversarial network (MADGF-GAN) tailored for magnetic anomaly data gap filling. Upon sufficient training on the MAID training set, MADGF-GAN can directly fill gaps in given magnetic anomaly data. Finally, the effectiveness of MADGF-GAN is validated using four test samples from the MAID test set and Afghan aeromagnetic data. Compared with four existing interpolation methods, MADGF-GAN demonstrates considerable advantages in terms of interpolation accuracy, computational efficiency and practicality. This study demonstrates the potential of data-driven approaches in magnetic anomaly data processing, providing crucial technical support for related geoscientific applications.