MPMSE: A Multitask Deep Learning Framework for Earthquake Source Characterization Using High-Rate GNSS, Strong-Motion Data, and Magnitude-Derived Priors

Abstract Accurate and rapid estimation of earthquake source parameters is essential for postevent emergency response and cascading hazard assessment. Multisource observations provide complementary constraints on the rupture process, but their heterogeneous data characteristics make effective data fusion a key challenge for rapid source characterization. Here, we propose the multisource prior-constrained magnitude and slip estimator (MPMSE), a multitask deep learning framework that integrates high-rate Global Navigation Satellite System (GNSS) displacement waveforms, strong-motion velocity records, and magnitude-derived prior constraints. The framework combines parallel waveform encoders, station-geometry conditioning, and scaling-law-based priors to jointly estimate earthquake magnitude, rupture area, maximum slip, and spatial slip distribution. Synthetic earthquake tests show that MPMSE achieves stable magnitude estimation using a 90 s input window, with a mean absolute error of approximately 0.03. When applied to Japanese subduction-zone earthquakes and the 2024 Hualien earthquake, MPMSE rapidly recovered the main source characteristics of these real events, with GNSS displacement variance reductions exceeding 66%. Overall, MPMSE improves the reliability and observational consistency of rapid earthquake source characterization by integrating multisource observations and introducing magnitude-derived prior constraints, providing a new technical pathway for post-event rapid response and cascading hazard (e.g., tsunami) assessment.