Remote sensing observations, products, and simulations are fundamental sources of information to monitor our planet and its climate variability. Uncovering the main modes of spatial and temporal varia...
Principal component analysis (PCA) is a powerful tool for extracting common mode errors from the position time series of a regional station network determined by global navigation satellite system (GN...
Principal component analysis (PCA) is a good method to be used in spatiotemporal filtering for regional GPS network. As an extension of PCA, independent component analysis(ICA) is also widely concerde...
Our understanding of plate boundary deformation has been enhanced by transient signals observed against the backdrop of time‐independent secular motions. We make use of a new analysis of displacement ...
Online Material: Synthetic datasets, the parameters used to generate them, and MATLAB scripts for parsing data provided through the testing center described in the text. Over the past decade the numb...
On 24 August 2014, the M 6.0 South Napa earthquake shook much of the San Francisco Bay area, leading to significant damage in the Napa Valley. The earthquake occurred in the vicinity of the West Napa ...
Numerous shallow earthquakes, including a multitude of small shocks and three moderate mainshocks, i.e., the Amatrice earthquake on 24 August, the Visso earthquake on 26 October and the Norcia earthqu...
Multipath, a highly autocorrelated signal is observable phenomena during time periods longer than the sidereal period of Global Positioning System (GPS) satellites in their constellations. Multipath p...
Mass redistribution of the atmosphere, oceans, and terrestrial water storage generates crustal displacements which can be predicted by environmental loading models and observed by the Global Positioni...
Landslide displacement time series can directly reflects landslide deformation and stability characteristics. Hence, forecasting of the non-linear and non-stationary displacement time series is necess...
In the previous chapters we have discussed various methods to estimate the parameters of the trajectory models for geodetic time series. The observations were written as the sum of a signal plus noise...
In the daily operation of regional GNSS (Global Navigation Satellite System) networks, the formal errors of all stations’ coordinate components are calculated. However, spatiotemporal filtering based ...
In geodesy and geophysics, continuous GNSS observations have been used globally. As the number of GNSS observing stations increases, GNSS time series analysis software should be developed with more fl...
In general, high-rate GPS data sets are subject to common mode error (CME), multipath error, and high-frequency random noise, which adversely affect the GPS positioning accuracy. In order to improve t...
High-rate Global Positioning Systems (GPS) observations can record ground motions from moderate to strong earthquakes at distances of a few kilometers up to thousands of kilometers (Larson et al., ...
Here we present a new mathematical tool, the localized Hartley (HL) transform (Hartley, 1942; Bracewell, 1990), that allows for the filtering of 1-D time series through the identification of the power...
Global navigation satellite systems (GNSS) techniques, such as GPS, can be used to accurately record vertical crustal movements induced by seasonal terrestrial water storage (TWS) variations. Converse...
Global navigation satellite system (GNSS) datasets suffer from common mode error (CME) and other unmodeled errors. To decrease the noise level in GNSS positioning, we propose a new data-driven adaptiv...
Geodesy, the oldest science, has become an important discipline in the geosciences, in large part by enhancing Global Positioning System (GPS) capabilities over the last 35 years well beyond the satel...
GPS has been widely used in the field of geodesy and geodynamics thanks to its technology development and the improvement of positioning accuracy. A time series observed by GPS in vertical direction u...