Preprints
https://doi.org/10.5194/egusphere-2026-317
https://doi.org/10.5194/egusphere-2026-317
11 Feb 2026
 | 11 Feb 2026

Pre-Seismic Geomagnetic Fusion Anomaly Extraction Based on Spatially Weighted Non-Negative Tensor Factorization

Baiyi Yang, Kaiguang Zhu, Ting Wang, Donghua Zhang, WenQi Chen, Yiqun Zhang, Pu Wang, Xingsu Li, and Yuqi Cheng

Abstract. Earthquake preparation processes are known to generate geomagnetic anomalies, 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 propose a Spatially Weighted Non-negative Tensor Factorization (SW-NTF) method to extract fused pre-seismic geomagnetic anomalies from multi-station data. SW-NTF was applied to daily 1 Hz Z-component geomagnetic data recorded at seven stations from 90 days before to 30 days after the 2021 Ms 7.4 Madoi earthquake. Compared with traditional NTF, SW-NTF captures a more pronounced accelerated growth in the pre-seismic geomagnetic anomalies. The extracted anomalies exhibit two phases of S-shaped accelerated growth (day −85 to −60 and day −40 to −17). Spatially, anomalous signals initially appear at stations farther from the epicenter and progressively migrate toward the epicentral region as the earthquake approaches. The potential influence of space weather activity is examined, suggesting that the detected anomalies are not dominated by external geomagnetic disturbances. Temporal comparisons show that the two-phase acceleration of geomagnetic anomalies precedes similar acceleration in cumulative Benioff strain. Observed variation patterns are also consistent with magnetic field changes in rock loading experiments, and the spatiotemporal correspondence with b-values indicates that the anomalies likely reflect stress evolution in the crust during earthquake preparation.

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Baiyi Yang, Kaiguang Zhu, Ting Wang, Donghua Zhang, WenQi Chen, Yiqun Zhang, Pu Wang, Xingsu Li, and Yuqi Cheng

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2026-317', Anonymous Referee #1, 26 Feb 2026
    • AC1: 'Reply on RC1', Baiyi Yang, 01 Mar 2026
      • RC2: 'Reply on AC1', Anonymous Referee #1, 01 Mar 2026
        • AC3: 'Reply on RC2', Baiyi Yang, 29 Mar 2026
  • RC3: 'Comment on egusphere-2026-317', Anonymous Referee #2, 03 Mar 2026
    • AC2: 'Reply on RC3', Baiyi Yang, 05 Mar 2026
      • RC4: 'Reply on AC2', Anonymous Referee #2, 05 Mar 2026
        • AC4: 'Reply on RC4', Baiyi Yang, 29 Mar 2026
Baiyi Yang, Kaiguang Zhu, Ting Wang, Donghua Zhang, WenQi Chen, Yiqun Zhang, Pu Wang, Xingsu Li, and Yuqi Cheng
Baiyi Yang, Kaiguang Zhu, Ting Wang, Donghua Zhang, WenQi Chen, Yiqun Zhang, Pu Wang, Xingsu Li, and Yuqi Cheng

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Short summary
We propose a spatially weighted non-negative tensor factorization method for extracting pre-seismic geomagnetic anomalies from multi-station data. By integrating spatial constraints between observation stations and the seismic source into the decomposition framework, the method improves the physical interpretability of the extracted anomalies and offers a new methodological approach for seismic anomaly analysis.
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