Research on outlier detection in marine magnetic data based on Hampel Filtering

Marine magnetic data are susceptible to interference from factors such as navigation errors, instrument malfunctions, and transcript mistakes, leading to frequent outliers. These outliers not only distort the magnetic anomaly patterns but also disrupt the continuity of magnetic stripes, severely affecting data quality and the reliability of subsequent interpretations. Therefore, outlier detection and removal are crucial steps in marine magnetic data processing. However, traditional methods often fail to effectively distinguish between different types of outliers, especially contextual outliers. Additionally, manual detection is time-consuming, prone to errors, and inefficient. To address this issue, this study proposes a weighted Hampel filter based on a local median weighting strategy. This method dynamically adjusts the weights of data points to more accurately identify and remove outliers in marine magnetic data, especially performing well in regions with significant data heterogeneity. Compared to other methods such as autoregression, isolation forest, and autoencoder, weighted Hampel filter not only effectively detects and removes global and contextual outliers but also better preserves the original features of the data, significantly improving detection accuracy. In validation with real data from the Magellan Rise in the Central Pacific Ocean, weighted Hampel filter consistently achieved higher F1 scores than other methods, demonstrating its superiority in outlier detection. This method provides important technical support for improving the quality and interpretability of marine magnetic data and lays a foundation for the future automated processing of large-scale data.