On 6 December 2016 at 22:03 UTC, a devastating magnitude 6-class strike-slip earthquake occurred along an unidentified and unmapped fault in Pidie Jaya, northern Sumatra. We analysed the possible fault using continuous Global Positioning System (GPS) observation available in the region. In our investigation, we searched for the fault source parameters of the north- and south-dipping left-lateral faults and the west- and east-dipping right-lateral faults. We identified that the fault responsible for the earthquake was located offshore, with a southwest-northeast direction. We also computed the Coulomb failure stress and compared the result with the distribution of the aftershocks. In this study, we demonstrated that the result of the geological field survey conducted soon after the mainshock was attributed to the secondary effects of ground shaking and near-surface deformation, and not surface faulting. The newly identified offshore fault proposed by this study calls for further investigation of the corresponding submarine morphological attributes in this particular region.
In order to solve the problem that the moving span of basic local mean decomposition (LMD) method is difficult to choose reasonably, an improved LMD method (ILMD), which uses three cubic spline interpolation to replace the sliding average, is proposed. On this basis, with the help of noise aided calculation, an ensemble improved LMD method (EILMD) is proposed to effectively solve the modal aliasing problem in original LMD. On the basis of using EILMD to effectively decompose the data of GNSS deformation monitoring series, GNSS deformation feature extraction model based on EILMD threshold denoising is given by means of wavelet soft threshold processing mode and threshold setting method in empirical mode decomposition denoising. Through the analysis of simulated data and the actual GNSS monitoring data in the mining area, the results show that denoising effect of the proposed method is better than EILMD, ILMD and LMD direct coercive denoising methods. It is also better than wavelet analysis denoising method, and has good adaptability. This fully demonstrates the feasibility and effectiveness of the proposed method in GNSS feature extraction.
Typically, the extended Kalman filter (EKF) is used for tightly-coupled (TC) integration of multi-constellation GNSS PPP and micro-electro-mechanical system (MEMS) inertial navigation system (INS) to provide precise positioning, velocity, and attitude solutions for ground vehicles. However, the obtained solution will generally be affected by both of the GNSS measurement outliers and the inaccurate modeling of the system dynamic. In this paper, an improved robust adaptive Kalman filter (IRKF) is adopted and used to overcome the effect of the measurement outliers and dynamic model errors on the obtained integrated solution. A real-time IRKF-based TC GPS+Galileo PPP/MEMS-based INS integration algorithm is developed to provide precise positioning and attitude solutions. The pre-saved real-time orbit and clock products from the Centre National d’Etudes Spatials (CNES) are used to simulate the real-time scenario. The performance of the real-time IRKF-based TC GNSS PPP/INS integrated system is assessed under open sky environment, and both of simulated partial and complete GNSS outages through two ground vehicular field trials. It is shown that the real-time TC GNSS PPP/INS integration through the IRKF achieves centimeter-level positioning accuracy under open sky environments and decimeter-level positioning accuracy under GNSS outages that range from 10 to 60 seconds. In addition, the use of IRKF improves the positioning accuracy and enhances the convergence of the integrated solution in comparison with the EKF. Furthermore, the IRKF-based integrated system achieves attitude accuracy of 0.052°, 0.048°, and 0.165° for pitch, roll, and azimuth angles, respectively. This represents improvement of 44 %, 48 %, and 36 % for the pitch, roll, and azimuth angles, respectively, in comparison with the EKF-based counterpart.
Positioning integrity is crucial for Intelligent Transport Systems (ITS) applications. In this article, a method is presented for prediction of GNSS positioning integrity for ITS journey planning. This information, in addition to other route information, such as distance and time, can be utilized to choose the safest and economical route. We propose to combine the Advanced Receiver Autonomous Integrity Monitoring (ARAIM) technique, tailored for ITS, with 3D city models. Positioning is performed by GNSS Real-Time Kinematic (RTK) method, which can provide the accuracy required for ITS. A new threat model employed for computation of the protection levels (PLs) for RTK positioning is discussed. Demonstration of the proposed approach is performed through a kinematic test in an urban area in Tokyo. The comparison between the prediction method and the actual observations show that the two estimate close satellite geometry and PLs. The method produced PLs that bounds the actual position errors all the time and they were less than the preset alert limit.
Earth’s crust deforms in various time and spatial resolutions. To estimate them, geodetic observations are widely employed and compared to geophysical models. In this research, we focus on the Earth’s crust deformations resulting from hydrology mass changes, as observed by GRACE (Gravity Recovery and Climate Experiment) gravity mission and modeled using WGHM (WaterGAP Global Hydrological Model) and GLDAS (Global Land Data Assimilation System), hydrological models. We use the newest release of GRACE Level-2 products, i. e. RL06, provided by the CSR (Center for Space Research, Austin) analysis center in the form of a mascon solution. The analysis is performed for the European area, divided into 29 river basins. For each basin, the average signal is estimated. Then, annual amplitudes and trends are calculated. We found that the eastern part of Europe is characterized by the largest annual amplitudes of hydrology-induced Earth’s crust deformations, which decrease with decreasing distance to the Atlantic coast. GLDAS largely overestimates annual amplitudes in comparison to GRACE and WGHM. Hydrology models underestimate trends, which are observed by GRACE. For the basin-related average signals, we also estimate the non-linear variations over time using the Singular Spectrum Analysis (SSA). For the river basins situated on the southern borderline of Europe and Asia, large inter-annual deformations between 2004 and 2009 reaching a few millimeters are found; they are related to high precipitation and unexpectedly large drying. They were observed by GRACE but mismodelled in the GLDAS and WGHM models. Few smaller inter-annual deformations were also observed by GRACE between 2002-2017 for central and eastern European river basins, but these have been also well-covered by the WGHM and GLDAS hydrological models.
The outreach of Wi-Fi localization is extended in this study for urban wide applications as they provide the high potential to employ them for numerous applications for localization and guidance in urban environments. The selected application presented in this paper is the localization and routing of public transport smartphone users. For the conducted investigations, Received Signal Strength Indicator (RSSI) values are collected for users who are travelling from home in a residential neighbourhood to work in the city centre and return along the same route. Special tramway trains are selected which provide two on-board Wi-Fi Access Points (APs). Firstly, the availability, visibility and RSSI stability of the Wi-Fi signal behavior of these APs and the APs in the surrounding environment along the routes is analyzed. Then the trajectories are estimated based on location fingerprinting. A first analyses reveals that significant differences exists between the six employed smartphones as well as times of the day, e. g. in the morning at peak hours or at off-peak hours. From the long-time observations it is seen that the two on-board APs show a high stability of the RSSI signals at the same times of the day and along the whole route. It is therefore currently investigated how they can confirm and validate user localization along the route and if they can contribute to constrain the overall positioning solution in combination with the inertial smartphone sensors. Moreover, the railway track can serve as a further constraint. As an outlook on future work, the development of a Simultaneous Localization and Mapping (SLAM) solution with a fusion with the smartphone inertial sensors is proposed.
Mobile mapping vehicles, equipped with cameras, laser scanners (in this paper referred to as light detection and ranging, LiDAR), and positioning systems are limited to acquiring surface data. However, in this paper, a method to fuse both LiDAR and 3D ground penetrating radar (GPR) data into consistent georeferenced point clouds is presented, allowing imaging both the surface and subsurface. Objects such as pipes, cables, and wall structures are made visible as point clouds by thresholding the GPR signal’s Hilbert envelope. The results are verified with existing utility maps. Varying soil conditions, clutter, and noise complicate a fully automatized approach. Topographic correction of the GPR data, by using the LiDAR data, ensures a consistent ground height. Moreover, this work shows that the LiDAR point cloud, as a reference, increases the interpretability of GPR data and allows measuring distances between above ground and subsurface structures.
The paper concentrates on the iterative Getchell’s method (formulated in 1972) and its alternative Newtonian implementation for conversion of Cartesian geocentric coordinates into geodetic coordinates. The same basic equation formulated in the Getchell’s method is used in both cases. The equation has a stable form in the whole range of argument (latitude) variation . The original Getchell’s method (somehow “forgotten”) has a simple geometric interpretation and its applications turn out to be particularly effective. Many studies on iterative algorithms usually omit theoretical proofs of convergence replacing them with conclusions based on numerical examples. This paper presents theoretical proofs of algorithms convergence both for the Getchell’s method and the Newton procedure. The convergence parameter and numerical error of results were estimated in each case. Numerical tests were carried out for a set of points distributed on the Earth’s space, also for extreme h values. For typical practical applications of the Getchell’s method, sufficiently accurate results are obtained after 1–3 iterations, while in the Newton procedure already after one iteration, assuming the same numerical error and initial conditions. The accuracy of the geodetic coordinates determinations meets all practical requirements with some margin. For example an absolute numerical error for latitude is approx. [rad] i. e. about 0.00026 mm in the length of the meridian arc. The proposed methods were compared with other methods (algorithms), including in terms of stability and non-singularity in the entire usable space of the Earth, but excluding the near geocenter, which has no practical significance. Both the modification of the Getchell method and its Newtonian alternative are very good determined in this area (in the Earth’s poles, the final solution is directly the starting value of iterative algorithms). The discussed algorithms were implemented in the form of procedures in DELPHI language.