Pre-seismic geomagnetic fusion anomaly extraction based on Spatially Weighted Non-negative Tensor Factorization

Abstract. Earthquake preparation processes are known to generate geomagnetic anomalies in some cases. Existing methods for extracting pre-seismic geomagnetic anomalies from multi-station observations are limited by the lack of physically meaningful constraints. Considering that electromagnetic signal propagation is related to epicentral distance, we incorporate spatial relationships between observation stations and potential seismic source regions into Non-negative Tensor Factorization (NTF), and a Spatially Weighted Non-negative Tensor Factorization (SW-NTF) method is proposed to extract fused pre-seismic geomagnetic anomalies from multi-station data. The proposed method was applied to daily 1 Hz Z-component geomagnetic data recorded at seven stations from 90 d before to 30 d after the 2021 Ms 7.4 Madoi earthquake. Compared with traditional NTF, a more pronounced accelerated growth in the pre-seismic geomagnetic anomalies was captured by SW-NTF. The extracted anomalies exhibit two phases of S-shaped accelerated growth (day −85 to −60 and day −40 to −17). Spatially, anomalous signals are initially observed at stations farther from the epicenter and progressively migrate toward the epicentral region as the earthquake approaches. The potential influence of space weather activity was examined, confirming that the anomalies are not dominated by external geomagnetic disturbances. Moreover, the skin depth estimated from the dominant frequency of the anomalies is consistent with the focal depth. Temporal comparisons show that the two-phase acceleration of geomagnetic anomalies precedes similar acceleration in cumulative Benioff strain. The observed variation patterns are also consistent with magnetic field changes in rock loading experiments, and the spatiotemporal correspondence with seismological b values suggests that the anomalies likely reflect stress evolution in the crust during earthquake preparation.