Optimized Spatio-Temporal Aware Seismic Signal Reconstruction

Seismic signal reconstruction in (micro)seismic monitoring presents a persistent challenge, as contemporary research has predominantly focused on temporal correlations while neglecting the spatial distribution of seismic stations. This study formulates the reconstruction task as an optimization problem by modeling seismic signals through time-varying graphs, introducing an auxiliary weight matrix and an L-order temporal difference matrix to effectively capture spatio-temporal dynamics and enhance signal reconstruction. To address this optimization challenge, we propose an iterative solution method and implement deep algorithm unrolling (DAU) techniques for parameter optimization. Empirical validation across four heterogeneous datasets, encompassing both earthquake and landslide-induced microseismic signals, demonstrates substantial improvements in reconstruction accuracy, as evidenced by reduced root mean square error (RMSE) compared to existing methodologies. The framework exhibits robust performance under varying noise conditions, with comprehensive waveform analyses confirming high-fidelity signal reconstruction across diverse seismic events. This research advances seismic signal reconstruction through the introduction of a spatio-temporal awareness component, thereby enhancing the reliability and precision of geophysical monitoring.