Slow Slip Events (SSEs) are critical for understanding subduction zone tectonics and earthquake prediction; however their detection is challenged by low-magnitude-offsets and data gaps. To address these challenges, this paper introduces an optimization-based signal decomposition (OSD) framework capable of automatically processing signals with missing data. We applied and validated this framework with GNSS coordinate time series in the Cascadia subduction zone, benchmarking its performance against the existing SSEs catalog. The proposed high-magnitude-offset detection method achieved an accuracy of 67.21% in single-station SSE detection, significantly outperforming traditional methods such as the Relative Strength Index (RSI; 32.24%) and deep learning methods like bidirectional Long Short-Term Memory (bi-LSTM; 44.41%). Additionally, we proposed a complementary velocity-based screening strategy that successfully identified low-magnitude-offset SSEs and events obscured by data gaps. Through cluster analysis of single-station detection results, we successfully identified the spatiotemporal boundary of the majority of SSEs. Finally, we established an anomaly catalog for uncataloged period from 2018 to 2024, which further demonstrates the method's efficacy in characterizing the spatiotemporal features of SSEs. The OSD-based SSEs detection framework identified SSEs with diverse kinematic patterns using raw geodetic data, facilitating the construction of high-quality SSEs catalogs. These advancements enhance our understanding of subduction zone dynamics and provide a robust technical foundation for seismic hazard assessment.

Global Navigation Satellite System (GNSS) imaging method (GIM) has been successfully applied to global regions to investigate vertical land motion (VLM) of the Earth's surface. GNSS images derived from conventional GIM method may present fragmented patches and encounter problems caused by excessive smoothing of velocity peaks, leading to difficulty in short-wavelength deformation detection and improper geophysical interpretation. Therefore, we propose a novel GNSS imaging method based on Gaussian process regression with velocity uncertainty considered (GPR-VU). Gaussian processing regression is introduced to describe the spatial relationship between neighboring site pairs as a priori weights and then reweight velocities by known station uncertainties, converting the discrete velocity field to a continuous one. The GPR-VU method is applied to reconstruct VLM images in the southwestern United States and the eastern Qinghai-Xizang Plateau, China, using the GNSS position time series in vertical direction. Compared to the traditional GIM method, the root-mean-square (RMS) and overall accuracy of the confusion matrix of the GPR-VU method increase by 5.0 % and 14.0 % from the 1° × 1° checkerboard test in the southwestern United States. Similarly, the RMS and overall accuracy increase by 33.7 % and 15.8 % from the 6° × 6° checkerboard test in the eastern Qinghai-Xizang Plateau. These checkerboard tests validate the capability to effectively capture the spatiotemporal variations characteristics of VLM and show that this algorithm outperforms the sparsely distributed network in the Qinghai-Xizang Plateau. The images from the GPR-VU method using real data in both regions show significant subsidence around Lassen Volcanic in northern California within a 30 km radius, slight uplift in the northern Sichuan Basin, and subsidence in its central and southern sections. These results further qualitatively illustrate consistency with previous findings. The GPR-VU method outperforms in diminishing the effect by fragmented patches, excessive smoothing of velocity peaks, and detecting potential short-wavelength deformations.