ma soja fs2021 - ethz.chprof. dr. benedikt soja ([email protected]) advisors / further supervisors (incl....

Contents Impact of massive satellite constellations (Starlink) on geodetic Very Long Baseline Interferometry (VLBI) ............................................................................................................................ 2

Predicting Very Long Baseline Interferometry (VLBI) observation accuracies using Machine Learning ................................................................................................................................................. 3

The quality assessment of VTEC products derived from observations with the next-generation VLBI system ....................................................................................................................... 4

Detection of ionospheric disturbances with machine learning ....................................................... 5

Prediction of Earth orientation parameters with machine learning ............................................... 6

Super resolving space geodetic data ................................................................................................ 7

Co-location of VLBI in space on board Galileo satellites: quality of the frame tie parameters..8

MSc Geomatics – Master’s theses FS 2021

2

Chair: Space Geodesy

Supervisor (incl. e-mail):

Prof. Dr. Benedikt Soja ([email protected])

Advisors / further supervisors (incl. e-mail):

Matthias Schartner ([email protected])

Thesis Title: Impact of massive satellite constellations (Starlink) on geodetic Very Long Baseline Interferometry (VLBI)

Abstract: VLBI measures the difference in arrival time from signals of extra-galactic radio sources between globally distributed telescopes based on cross-correlation. Due to the large distance between the radio source and the Earth, the received signal is very weak and one needs large radio telescopes combined with a high sampling rate to be able to detect it. Although VLBI is relatively tolerant to local radio-interference (RFI), unless it is so strong that it drives the receiver to saturation, the situation is different with space born RFI since this might affect multiple telescopes simultaneously and thus might affect the cross-correlation. So far, this effect was neglected, because there were only few satellites broadcasting at VLBI frequencies. However, the magnitude of the problem is going to grow dramatically due to the deployment of new satellite internet constellations, such as Starlink with up to 42.000 satellites. With Starlink broadcasting at Ku-band, there will be interferences with the VLBI global observing system (VGOS) band D resulting in reduced signal to noise ratio (SNR) and potential loss of observations. In this work, the student will investigate the impact of different Starlink satellite constellations on VGOS observations. First, the student will compute the azimuth and elevation angles from the VLBI telescopes to potential Starlink satellites and compare them with the azimuth and elevation angles to radio sources that are planned to be observed based on real and artificial schedules. Next, the student will investigate how many observations or radio sources will be close to Starlink satellites and thus will be affected. By assuming a loss of these observations, the impact on the accuracy of the geodetic parameters will be investigated using Monte-Carlo simulations.

© SpaceX Illustration © M Schartner

Particularities (e.g. comments on group work etc.):

Group work:

no


3

Chair:

Space Geodesy




Matthias Schartner ([email protected])

Thesis Title: Predicting Very Long Baseline Interferometry (VLBI) observation accuracies using Machine Learning

Abstract: VLBI is one of the pillars of space geodesy with a wide range of applications, such as providing highly accurate station coordinates and scale information for a terrestrial reference frame (TRF), defining the celestial reference frame (CRF) and providing a full set of Earth orientation parameters (EOP). In order to derive observations usable by these applications, a global network of VLBI telescopes is necessary as well as many auxiliary facilities such as correlators and analysis centres. Thus, performing VLBI observations is quite expensive in terms of money and labour. To mitigate costs, new approaches and developments are first tested by using simulations. Although theoretical concepts exist that model the observation accuracy, the difference between reality and simulation is still significant in many cases. Within this work, the potential of Machine Learning (ML) algorithms will be investigated to derive the accuracy information based on features such as telescope sensitivity, azimuth and elevation angles, meteorological parameters and source flux densities. The student will first collect and prepare these features and later use them with different state of the art ML techniques and potentially ensemble methods. The performance and accuracy of the individual ML algorithms will be compared, and observation accuracies for different VLBI sessions will be predicted and compared with reality. Finally, the improvement in terms of simulation accuracy will be investigated by comparing the derived simulated precision of the geodetic parameters by using a classical approach and the ML approach with real observations.

© M Gaylard / HartRAO IVS live (http://ivslive.astrophy.u-bordeaux.fr)


Group work:

no


4

Chair:

Space Geodesy




Dr. Grzegorz Kłopotek ([email protected])

Thesis Title: The quality assessment of VTEC products derived from observations with the next-generation VLBI system

Abstract: Space geodesy is a scientific discipline dealing with the measurement and representation of the Earth as well as investigation of numerous global-scale geodynamical phenomena. In order to achieve that, space geodesy utilizes observations of multiple Earth satellites and extragalactic point-like radio sources, commonly referred to as quasars. The latter form the basis of very long baseline interferometry (VLBI), a space-geodetic technique. The next-generation VLBI system is known as the VLBI Global Observing System (VGOS). As it traverses the Earth atmosphere, electromagnetic radiation from artificial or natural radio sources is subject to various propagation effects. In the case of the ionosphere, this results in additional signal contributions that are proportional to the slant total electron content (STEC). The latter is a measure of the electron density along the signal propagation path. STEC can be projected to the vertical direction, i.e., as VTEC values. Space-geodetic techniques operating in the microwave regime, such as geodetic VLBI, are able to derive VTEC parameters, quantities of high importance for scientific studies and society at large. Project goals: Derive time series of VTEC values at VGOS stations based on publicly available VGOS databases. Perform a comprehensive quality analysis of the obtained VTEC values, also in relation to the external ionosphere products. Develop an automatic processing pipeline for obtaining and analyzing VGOS-derived VTEC products. Relevant to the project: Basic knowledge on space geodesy and geodetic VLBI; Matlab or Python or C/C++ programming skills; code versioning;


Group work: no


5

Chair:

Space Geodesy




Thesis Title: Detection of ionospheric disturbances with machine learning

Abstract: The ionosphere is part of the Earth’s atmosphere and reaches from about 50 km to 1000 km altitude. The ionospheric effect on electromagnetic waves is dispersive and can hence be determined by dual-frequency measurements. GNSS is a suitable technique to determine several parameters characterizing the ionosphere. Strong variations can occur in the ionospheric total electron content (TEC). For example, space weather events like geomagnetic storms due to coronal mass ejections can cause significant ionospheric disturbances. Tsunamis induced by strong earthquakes produce gravity waves in the atmosphere that are detectable as traveling ionospheric disturbances (see figure). Rocket launches and even earthquakes have been shown to impact ionospheric parameters. The goal of this master thesis is to detect such events based on TEC time series measured by GNSS. The student will be provided GNSS data from regions of interest, e.g. Japan, and will derive TEC values of high spatio-temporal resolution. The detection of ionospheric disturbances will be performed with machine learning techniques. Ideally, both temporal and spatial patterns will be considered using a combination of recurrent and convolutional neural networks. Improved detection of ionospheric disturbances would be very beneficial for monitoring space weather, earthquakes, and tsunamis. Heki and Ping, 2005


Group work: no


6

Chair:

Space Geodesy




Thesis Title: Prediction of Earth orientation parameters with machine learning

Abstract: Earth orientation parameters (EOP) connect the terrestrial and celestial reference frames and are needed, inter alia, for satellite operations and navigation tasks. For several applications, in particular real-time positioning, accurate predictions of EOP are needed. The prediction of EOP has been an important task performed by multiple geodetic institutions, mostly using classical statistical methods. The goal of this master thesis is to precisely predict EOP into the future using machine learning techniques. The student will be provided with EOP time series of different institutions, based on a combination of several space-geodetic techniques (e.g., GNSS). As a baseline, statistical methods, such as ARMA (Autoregressive-Moving-Average) will be implemented. Next, machine learning algorithms, including recurrent neural networks (in particular, LSTM), will be used to predict the EOP. The machine learning architectures will be refined and the results compared to the statistical methods. Machine learning is very promising for this task, since it is able to capture non-linear dependency structures well. In addition to this purely data driven approach, geophysical models (e.g., related to the atmosphere) will be considered and integrated into the machine learning scheme in order to improve the predictions.

Vondrak, 2008 Modiri et al., 2020


Group work: no


7

Chair:

Space Geodesy




Thesis Title: Super resolving space geodetic data

Abstract: Super resolution is the process of increasing the resolution of an image, e.g. sharpening a fuzzy image or increasing visible details. Various machine learning techniques have emerged to enhance images this way. State-of-the-art machine learning methods for super resolution are based on Generative Adversarial Networks (GAN). Adversarial training strategies have not only been successfully used on images, but also on climate models, which resulted in a significant increase in the spatial resolution of the model (c.f. the figure below). In this study, super resolution based on GAN architectures will be applied to data and products from space geodetic techniques. We will start with models from geodetic data that are available with global coverage, such as GRACE gravity fields or global ionospheric maps from GNSS observations. We will consider these data in different spatial resolutions. The goal is to enhance the lower-resolution models with super resolution and evaluate the performance with higher-resolution models. Most of the space geodetic data is available in the form of time series from individual observing stations, for example time series of station positions or tropospheric parameters. A second goal of the study is to apply super resolution methods on time series data. For this, it is necessary to convert one-dimensional time series to two-dimensional data, for example by computing spectrograms or wavelets. This way, super resolution could increase the temporal resolution of the time series. Improvements in spatial or temporal resolution achieved with super resolution would be very beneficial for many geodetic applications, making highly-resolved data and models cheaper to create.

GAN architecture for super resolution (Stengel et al., 2020)


Group work: no


8

Chair:

Space Geodesy




Dr. Grzegorz Kłopotek ([email protected])

Thesis Title: Co-location of VLBI in space on board Galileo satellites: quality of the frame tie parameters

Abstract: In space geodesy, geodetic instruments co-located at core sites are usually complemented with local surveys. Such local measurements provide three-dimensional distances (local ties) between the reference points of the nearby instruments and are utilized at the analysis stage to relate independent technique-specific solutions in a multi-technique combination. The concept of local ties can be extended with Earth satellites that are observed by several space-geodetic techniques. In this case, a set of such satellites is equipped with technique-specific instruments. Vectors between the reference points of those instruments are in this case known to a high degree of accuracy and are commonly referred to as space ties. Nowadays, such an approach can be in principle realized with satellite laser ranging (SLR) and Global Navigation Satellite Systems (GNSS), where SLR observations to Galileo satellites are carried out and both observation types are utilized at the data analysis stage. Recently, there are also ideas of including an additional space-geodetic technique: very-long-baseline interferometry (VLBI). Project goals: Investigate the concept of GNSS-VLBI space ties on board Galileo satellites in terms of the quality of transformation parameters between VLBI and GNSS frames to be expected from this type of the co-location approach. This simulation study shall involve a different quantity of satellites and participating VLBI stations, technique-specific biases, diverse scenarios and co-location satellite configurations as well as various quality of the related a priori information and different observation noise levels. Relevant to the project: Basic knowledge on precise orbit determination, space geodesy, GNSS and geodetic VLBI; Matlab or Python or C/C++ programming skills; code versioning


Group work: no

ma soja fs2021 - ethz.chprof. dr. benedikt soja ([email protected]) advisors / further supervisors (incl....

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