The journal publishes original research papers in the field of geodesy and geophysics under headings: aeronomy and space physics, electromagnetic studies, geodesy and gravimetry, geodynamics, geomathematics, rock physics, seismology, solid earth physics, history. Papers dealing with problems of the Carpathian region and its surroundings are preferred. Similarly, papers on topics traditionally covered by Hungarian geodesists and geophysicists (e.g. robust estimations, geoid, EM properties of the Earth’s crust, geomagnetic pulsations and seismological risk) are especially welcome.
Acta Geodaetica et Geophysica
Description
Identifiers
| ISSN | 2213-5812 |
| e-ISSN | 2213-5820 |
Publisher
Springer International Publishing
Additional information
Data set: Springer
Articles
article
Acta Geodaetica et Geophysica > 2019 > 54 > 4 > 513-543
Ultra-rapid clock products provide the main parameters for real-time or near real-time precise point positioning services. However, it has been found that BeiDou ultra-rapid clock offsets do not meet the requirements for high-accuracy applications because of their low accuracy, especially regarding the prediction parts. This study proposes an improved model for BDS satellite ultra-rapid clock offset...
article
Acta Geodaetica et Geophysica > 2019 > 54 > 4 > 483-497
This paper proposes a heuristic singular spectrum analysis (SSA) approach to extract signals from suspended sediment concentration (SSC) time series contaminated by multiplicative noise, in which multiplicative noise is converted to approximate additive noise by multiplying with the signal estimate of the time series. Therefore both the signal and noise components need to be recursively estimated...
article
Acta Geodaetica et Geophysica > 2019 > 54 > 4 > 557-566
The Least-squares extrapolation of harmonic models and autoregressive (LS + AR) prediction is currently considered to be one of the best prediction model for polar motion parameters. In this method, LS fitting residuals are treated as data to train an AR model. But it is readily known that using too many data will result in learning a badly relevant AR model, implying increasing the model bias. It...
