인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Abstract Spatio-temporal characteristics in the Global Navigation Satellite System (GNSS) time series provide fundamental insights into data properties. Owing to its ease of use, the F5 solution, which is the most frequently used dataset from the GNSS Earth Observation Network System (GEONET), a nationwide GNSS network in Japan, is frequently employed in a wide range of geodetic, seismic, and volcanic analyses related to crustal deformation. However, comprehensive analyses of the spatio-temporal characteristics of F5 solutions have rarely been reported. Therefore, we aimed to model the spatio-temporal characteristics of these time series in the frequency domain over a few decades. The power spectral densities were estimated from each sliding 4-year time window for each component and station and modeled using the two terms representing low- and high-frequency contributions. The model parameters were robustly estimated against the presence of outliers by maximizing the spectral Rényi divergence. The model parameters in the low-frequency components generally corresponded to the occurrence of transient deformations, such as postseismic movements, slow slip events, and movements related to volcanic phenomena. In contrast, the parameters in the high-frequency components were related to the positioning strategy itself, such as improvements in the accuracy of the GNSS orbits until 2003, and might be related to the deactivation of Selective Availability in 2000. The frequency dependence of the spectra indicated temporally correlated observation noise, even in the high-frequency components. All the estimated model parameters are publicly available and can be utilized for various research applications, including the generation of synthetic time series with realistic noise, which is particularly useful in machine learning studies, modeling or correcting transient deformation, discovering previously undetected slow slip events, and detecting anomalies related to the local environment of stations. A robust estimation method for spectral modeling can be applied to any GNSS time series, and it provides essential baseline information on the properties of the observed data. Graphical Abstract
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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