We propose a hierarchical clustering methodology for Global Navigation Satellite System (GNSS) data that is applicable from local to global scales. We first adapted the conventional 2D velocity clustering metric for global-scale applications by implementing parallel translation in differential geometry. Then, we combined it with an Euler pole-based metric to incorporate the physical nature of plate motions, achieving advantages in identifying tectonic structures. This hybrid metric approach is examined through two case studies at different spatial scales to determine whether it can accurately identify tectonic plate and crustal block boundaries; one study utilizes global-scale data from the ITRF2008 plate motion model, and the other focuses on a local-scale study in Taiwan. Results obtained by using the hybrid metric consistently align better with geological data than those using either the 2D or Euler vector-based metrics alone. The proposed method is computationally efficient, enabling us to conduct two types of stability assessments: examining the robustness of clusters with synthetic noise contamination and performing leave-one-out analysis. These stability tests are demonstrated to be feasible within practical time frames.  Key pointsWe propose a GNSS clustering method that integrates existing metrics and scales from local to global. Case studies at both global and local scales demonstrate that results of our method align more closely with geological information.  Computational efficiency enables various types of stability analyses for obtained clustering results.

We present a deep-learning approach for earthquake detection using waveforms from a seismic array consisting of multiple seismographs. Although automated, deep-learning earthquake detection techniques have recently been developed at the single-station level, they have potential difficulty in reducing false detections owing to the presence of local noise inherent to each station. Here, we propose a deep-learning-based approach to efficiently analyze the waveforms observed by a seismic array, whereby we employ convolutional neural networks in conjunction with graph partitioning to group the waveforms from seismic stations within the array. We then apply the proposed method to waveform data recorded by a dense, local seismic array in the regional seismograph network around the Tokyo metropolitan area, Japan. Our method detects more than $97$ percent of the local seismicity catalogue, with less than $4$ percent false positive rate, based on an optimal threshold value of the output earthquake probability of $0.61$. A comparison with conventional deep-learning-based detectors demonstrates that our method yields fewer false detections for a given true earthquake detection rate. Furthermore, the current method exhibits the robustness to poor-quality data and/or data that are missing at several stations within the array. Synthetic tests demonstrate that the present method has the potential to detect earthquakes even when half of the normally available seismic data are missing. We apply the proposed method to analyze 1-hour-long continuous waveforms and identify new seismic events with extremely low signal-to-noise ratios that are not listed in existing catalogs. (241words)

The discovery of slow slip events (SSEs) based on the installation of dense geodetic observation networks has provided important clues to understanding the process of stress release and accumulation in subduction zones. Because short-term SSEs (S-SSEs) do not often result in sufficient displacements that can be visually inspected, refined automated detection methods are required to understand the occurrence of S-SSEs. In this study, we propose a new method based on which S-SSEs can be detected in observations derived by a Global Navigation Satellite System (GNSS) array by using l1 trend filtering, a variation of sparse estimation, in conjunction with combined -value techniques. The sparse estimation technique and data-driven determination of hyperparameters are utilized in the proposed method to identify candidates of S-SSE onsets. In addition, combined -value techniques are used to provide confidence values for the detections. The results of synthetic tests demonstrated that almost all events can be detected with the new method, with few misdetections, compared with automated detection methods based on Akaike’s information criteria. The proposed method was then applied to daily displacements obtained at 39 GNSS stations in the Nankai subduction zone in western Shikoku, southwest Japan. The results revealed that, in addition to all known events, new events can be detected with the proposed method. Finally, we found the number of low-frequency earthquakes in the target region increased around at the onsets of potential events.