Estimation of offsets in GPS time-series and application to the detection of earthquake deformation in the far-field

Extracting geophysical signals from Global Positioning System (GPS) coordinate time-series is a well-established practice that has led to great insights into how the Earth deforms. Often small discontinuities are found in such time-series and are traceable to either broad-scale deformation (i.e. earthquakes) or discontinuities due to equipment changes and/or failures. Estimating these offsets accurately enables the identification of coseismic deformation estimates in the former case, and the removal of unwanted signals in the latter case which then allows tectonic rates to be estimated more accurately. We develop a method to estimate accurately discontinuities in time series of GPS positions at specified epochs, based on a so-called ‘offset series’. The offset series are obtained by varying the amount of GPS data before and after an event while estimating the offset. Two methods, a mean and a weighted mean method, are then investigated to produce the estimated discontinuity from the offset series. The mean method estimates coseismic offsets without making assumptions about geophysical processes that may be present in the data (i.e. tectonic rate, seasonal variations), whereas the weighted mean method includes estimating coseismic offsets with a model of these processes. We investigate which approach is the most appropriate given certain lengths of available data and noise within the time-series themselves. For the Sumatra–Andaman event, with 4.5 yr of pre-event data, we show that between 2 and 3 yr of post-event data are required to produce accurate offset estimates with the weighted mean method. With less data, the mean method should be used, but the uncertainties of the estimated discontinuity are larger

Empirical modelling of site-specific errors in GPS observations

GPS is an essential element of the global geospatial information infrastructure, it is free, open and dependable. Precise positioning and navigation enabled by GPS has led to the development of hundreds of applications affecting every aspect of modern life and is now found in everything from mobile phones to bulldozers. Underpinning the day-to-day operation of GPS is the International Terrestrial Reference Frame (ITRF). Without an accurate earth-centred, earth-fixed reference frame, such as ITRF, it would not be possible to accurately determine station location and position as a function of time. To achieve an accurate reference frame precise models of all aspects of the GPS system are required, including; the satellites, their orbits, the signal propagation medium, the ground receivers and antennas, and the orientation and motion of the Earth's crust. For more than two decades GPS observations have been integral to the determination of the ITRF. GPS is the critical technique that provides the connection, through collocation, between other terrestrial observation systems, SLR, and VLBI necessary to define accurately the origin, orientation and scale of the ITRF. GPS solutions provide the most precise and accurate estimates of polar motion and is the geodetic technique most commonly used to access the ITRF. The main weaknesses of GPS observations today are due to unmodelled site-specific errors, particularly at collocated stations, orbit mismodelling errors (such as solar radiation pressure), errors in the conventional model for diurnal and semi-diurnal variations in Earth orientation due to ocean tides (griffiths2013), and an under-determined TRF scale due to uncalibrated satellite antenna phase centre offsets Analysis and modelling techniques have continuously been refined and improved. Despite these advances, there has been little progress on addressing site-specific biases in GPS processing. In this thesis, we are mainly concerned with site-specific biases due to reflections of the incoming GPS signal, as well as errors in the antenna model. These site-specific errors can alias into the GPS station position time series producing time-correlated errors which do not average out over time. The result is a GPS time series which will have unmodelled biases that can affect the interpretation of geophysical signals. This is particularly a problem for reference frames if there are site-specific biases at GPS stations used to collocate the different observation techniques. This thesis presents a methodology that can account for site-specific errors at the observational level, which is applicable to historic and future data sets. The technique relies on using carrier phase residuals obtained from the processing of a large network of GPS stations. These residuals are then used to model the errors at individual stations, and those associated with individual satellites. We have investigated the applicability of carrier phase residuals to model site-specific biases, through the use of simulations. The technique has then been tested and verified by applying the models to short-baseline kinematic solutions for 3 different collocation stations. We also investigate the impact of applying the model to large global solutions, in particular, we investigate the impact upon coordinate and velocity estimates as well as orbit and clock products, key products used to access and determine the reference frame

The ANU GRACE visualisation web portal

The launch of the Gravity Recovery and Climate Experiment (GRACE) space gravity mission opened new horizons to the scientific community for environmental monitoring. Through the provision of estimates of temporal changes in the Earth's gravity field, th

Avaliação das variações temporais nos sistemas de referência verticais na América do Sul baseada em observações GPS e GRACE

Orientador : Prof Dr. Silvio R. C. de FreitasTese (doutorado) - Universidade Federal do Paraná, Setor de Setor de Ciências da Terra , Programa de Pós Graduação em Ciências Geodésicas. Defesa : Curitiba, 23/02/2018Inclui referênciasÁrea de concentração : GeodésiaResumo

Model of the western Laurentide Ice Sheet, North America

The Laurentide Ice Sheet reached its maximum extent at the Last Glacial Maximum, 26 500-19 000 years before present. It is responsible for a large portion of the approximately 130 m of eustatic sea level fall since that time. During its retreat, meltwater from the Laurentide Ice Sheet caused rapid changes in sea level, and affected global climate by changing ocean circulation. However, previous estimates of the absolute volume of the Laurentide Ice Sheet through time have been limited due to deficiencies in the chronology of margin retreat and information on glacial-isostatic adjustment (GIA). In this study, I present a new numerical ice sheet model of the western portion of the Laurentide ice sheet. I constrain the model using GIA indicators, including the tilts of well dated glacial lake strandlines, tilt rates of contemporary modern lakes, uplift rates from GPS, and relative sea level indicators. I also present a new margin history based on the minimum timing of retreat. All data used in the modelling exercise are carefully assessed to ensure they are reliable. At the Last Glacial Maximum, the ice sheet model has a broad dome that extended from the Cordillera to the area west of Great Slave Lake, Northwest Territories. The southern portion of the ice sheet is modelled to have a shallow gradient, with thickness values less than 2000 m south of 56 degrees north. This is in contrast to previous ice sheet models of the Laurentide Ice Sheet based on GIA modelling, such as ICE-5G (Peltier, 2004), that have over 5000 m of ice in this region. During deglaciation, the largest decrease in volume happened between 16,000 and 13,000 years before present, coinciding with margin retreat in Alberta and Northwest Territories. From 13 000 to 11 500 years before present, ice sheet retreat slowed, corresponding to Younger Dryas cooling. After 11 500 years before present, ice sheet retreat was more rapid, and by 6500 years before present, no ice remained in the study area. Glacial lake tilt observations support a thick elastic lithosphere, with values greater than 120 km providing the best fit to the data. A wide range of mantle viscosity values were investigated, and the calculated GIA matched observations within the range of 3-5×10 20 Pa s for the upper mantle and > 5 × 10^21 Pa s for the lower mantle for the majority of observations