Linear discontinuous ground deformation detection based on coherence analysis of pre and post event radar image pairs

The article presents information on how to use satellite interferometry to detect linear discontinuous ground deformation [LDGD] caused by underground mining. Assumptions were made based on the properties of the SAR signal correlation coefficient (coherence). Places of LDGD have been identified based on these assumptions. Changes taking place on the surface between two acquisitions lead to worse correlation between two radar images. This results in lower values of the SAR signal correlation coefficient in the coherence maps. Therefore, it was assumed that the formation of LDGD could reduce the coherence value compared to the previous state. The second assumption was an increase in the standard deviation of coherence, which is a classic measurement of variability. Therefore any changes in the surface should lead to increasing standard deviation of coherence compared to the previous state. Images from the Sentinel-1 satellite and provided by the ESA were used for analysis. The research is presented on the basis of two research areas located in the Upper Silesian Coal Basin in the south of Poland. The area in which LDGD could occur was limited to 6 % of the total area in case 1 and 36 % in case 2 by applying an appropriate methodology of satellite image coherence analysis. This paper is an introduction to the development of a method of detecting LDGDs caused by underground mining and to study these issues further.

GNSS time and frequency transfers through national positioning, navigation and timing infrastructure

GNSS signals have been a practical time transfer tool to realise a Coordinated Universal Time (UTC) and set civilian clocks around the world with respect to this atomic time standard. UTC time scale is maintained by the International Bureau of Weights and Measurements (BIPM) adjusted to be close to a time scale based on the Earth’s rotation. In Thailand, the official atomic time clocks are maintained by the National Institute of Metrology Thailand (NIMT) to produce UTC(NIMT) and Thailand standard time which is always 7 hours ahead of UTC(NIMT) because of the time zone differences between Greenwich and Bangkok. National Positioning, Navigation and Timing (PNT) infrastructure comprises of GNSS geodetic receivers uniformly distributed to continually observe GNSS signals, mainly for geodetic survey applications both real-time and post-processing services. NIMT is involved in order to provide time link to UTC and to determine the characteristics of GNSS receiver internal clocks; namely, fractional frequency offset and frequency stabilities by applying the GNSS time transfer techniques of common-view algorithms. Monitored time differences with respect to UTC(NIMT) are achieved from selected 4 ground stations in different parts of the country with observations of 21 days in order to determine the frequency stability at 1-day and 7-day modes. GNSS standard log files; in RINEX format, at these receivers are transformed into a time transfer standard format; CGGTTS, used to compute the time differences between two stations, the fractional frequency offset and the frequency stability. Averaged fractional frequency offsets are 2.8 × 10 − 13 Hertz/Hertz 2.8\times {10^{-13}}\hspace{2.38387pt}\text{Hertz/Hertz} and computed Allan deviation is around 1.5 × 10 − 13 Hertz/Hertz 1.5\times {10^{-13}}\hspace{2.38387pt}\text{Hertz/Hertz} for an averaging time of 1 day. The comparison of the national time scale and receiver clock offsets of every receivers in this national GNSS PNT infrastructure could be accomplished through common-view time transfer using GNSS satellites to maintain the time link of geodetic active control points to UTC as well as to determine receiver internal clock characteristics.

Target-based terrestrial laser scan registration extended by target orientation

In almost all projects, in which terrestrial laser scanning is used, the scans must be registered after the data acquisition. Despite more and more new and automated methods for registration, the classical target-based registration is still one of the standard procedures. The advantages are obvious: independence from the scan object, the geometric configuration can often be influenced and registration results are easy to interpret. When plane black-and-white targets are used, the algorithm for estimating the target center fits a plane through the scan of a target, anyway. This information about the plane orientation has remained unused so far. Hence, including this information in the registration does not require any additional effort in the scanning process. In this paper, we extend the target-based registration by the plane orientation. We describe the required methodology, analyze the benefits in terms of precision and reliability and discuss in which cases the extension is useful and brings a relevant advantage. Based on simulations and two case studies we find out that especially for registrations with bad geometric configurations the extension brings a big advantage. The extension enables registrations that are much more precise. These are also visible on the registered point clouds. Thus, only a methodological change in the target-based registration improves its results.

Validating the impact of various ionosphere correction on mid to long baselines and point positioning using GPS dual-frequency receivers

Due to the ionosphere delay, which has become the dominant GPS error source, it is crucial to remove the ionospheric effect before estimating point coordinates. Therefore, different agencies started to generate daily Global Ionosphere Maps (GIMs); the Vertical Total Electron Content (VTEC) values represented in GIMs produced by several providers can be used to remove the ionosphere error from observations. In this research, An analysis will be carried with three sources for VTEC maps produced by the Center for Orbit Determination in Europe (CODE), Regional TEC Mapping (RTM), and the International Reference Ionosphere (IRI). The evaluation is focused on the effects of a specific ionosphere GIM correction on the precise point positioning (PPP) solutions. Two networks were considered. The first network consists of seven Global Navigation Satellite Systems (GNSS) receivers from (IGS) global stations. The selected test days are six days, three of them quiet, and three other days are stormy to check the influence of geomagnetic storms on relative kinematic positioning solutions. The second network is a regional network in Egypt. The results show that the calculated coordinates using the three VTEC map sources are far from each other on stormy days rather than on quiet days. Also, the standard deviation values are large on stormy days compared to those on quiet days. Using CODE and RTM IONEX file produces the most precise coordinates after that the values of IRI. The elimination of ionospheric biases over the estimated lengths of many baselines up to 1000 km has resulted in positive findings, which show the feasibility of the suggested assessment procedure.

Automated near-field deformation detection from mobile laser scanning for the 2014 Mw 6.0 South Napa earthquake

Quantifying off-fault deformation in the near field remains a challenge for earthquake monitoring using geodetic observations. We propose an automated change detection strategy using geometric primitives generated using a deep neural network, random sample consensus and least squares adjustment. Using mobile laser scanning point clouds of vineyards acquired after the magnitude 6.0 2014 South Napa earthquake, our results reveal centimeter-level horizontal ground deformation over three kilometers along a segment of the West Napa Fault. A fault trace is detected from rows of vineyards modeled as planar primitives from the accumulated coseismic response, and the postseismic surface displacement field is revealed by tracking displacements of vineyard posts modeled as cylindrical primitives. Interpreted from the detected changes, we summarized distributions of deformation versus off-fault distances and found evidence of off-fault deformation. The proposed framework using geometric primitives is shown to be accurate and practical for detection of near-field off-fault deformation.

Artificial neural network for improving the estimation of weighted mean temperature in Egypt

One of the most important parameters in meteorological data is the Precipitable Water Vapor (PWV). It can be measured by radiosonde stations (RS), but the fact is that RS are not available in all times. Therefore, GNSS satellite signals are considered an accurate function to compute it within a conversation factor. The conversation factor depends on the weighted mean temperature ( T m {T_{m}} ) which is non-measurable. In this research, a new idea to estimate T m {T_{m}} is provided, which can potentially contribute to the GNSS meteorology. The T m {T_{m}} was designed, including six RS, over one year in Egypt as input parameters. The machine learning (ML) model has been utilized in the design (IBM SPSS Statistics 25 package). The new model needs to collect the day of year (DOY), site location information and surface temperature to predict the T m {T_{m}} . The results of ML model and four other empirical models (Bevis et al., Wayan and Iskanda, Yao and Elhaty et al. models) are compared. The validation work is carried out, using the radiosonde data, and results indicate that the new T m {T_{m}} model can achieve the best performance with RMS of 1.7 K.

Recursive Gauss-Helmert model with equality constraints applied to the efficient system calibration of a 3D laser scanner

Sensors for environmental perception are nowadays applied in numerous vehicles and are expected to be used in even higher quantities for future autonomous driving. This leads to an increasing amount of observation data that must be processed reliably and accurately very quickly. For this purpose, recursive approaches are particularly suitable in terms of their efficiency when powerful CPUs and GPUs are uneconomical, too large, or too heavy for certain applications. If explicit functional relationships between the available observations and the requested parameters are used to process and adjust the observation data, complementary approaches exist. The situation is different for implicit relationships, which could not be considered recursively for a long time but only in the context of batch adjustments. In this contribution, a recursive Gauss-Helmert model is presented that can handle explicit and implicit equations and thus allows high flexibility. This recursive estimator is based on a Kalman filter for implicit measurement equations, which has already been used for georeferencing kinematic multi-sensor systems (MSS) in urban environments. Furthermore, different methods for introducing additional information using constraints and the resulting added value are shown. Practical application of the methodology is given by an example for the calibration of a laser scanner for a MSS.