Acta Geodaetica et Geophysica最新文献

Global navigation satellite system (GNSS) ultra-rapid clock offsets and orbit products are essential for near-real-time and real-time uses. To meet the requirements of accuracy and timeliness in high-accuracy applications, the issuing rates of ultra-rapid products are increased to six or three hours. However, there is an appreciable fluctuation of positioning residuals during the period of updating of products. To improve the performance of GNSS rapid services including the GPS, GLONASS, GALILEO and BeiDou, this paper proposes a method for updating satellite ultra-rapid clock offsets and orbits based on the covariance intersection algorithm, where kernel tricks are used to model the series in the position domain. Moreover, the parameter characteristics of the clock and orbit and the unknown inter-series correlation are considered in the model of satellite ultra-rapid products. Meanwhile, a sparse strategy is used in solving the model coefficients; i.e., the least absolute shrinkage and selection operator (LASSO) strategy. Several experimental schemes show that 1) jumps and gross errors in the GNSS ultra-rapid clock offset affect the modeling and services and should be detected and repaired before high-accuracy applications; 2) the updating of clock offsets and orbits reduces the steadiness of product services, while position residual fluctuations are introduced using GNSS precise point positioning solutions; 3) improved clock offset and orbit series can be obtained using the covariance intersection algorithm and kernel tricks; 4) the LASSO strategy can automatically and effectively choose and estimate the model coefficients of clock offset and orbit series; and 5) the proposed method can smooth the short-term arcs of GNSS products and positioning services by 29.8–99.5% for Multi-GNSS Experiment stations compared with original series. It is thus concluded that the proposed updating strategy is meaningful for improving GNSS satellite ultra-rapid products.

The evaluation of physico-mechanical characteristics of rocks after thermal treatment is a key issue in underground rock engineering projects such as exploitation of geothermal resources and geological disposal of nuclear waste. In this research, the lengths of cylindrical Nanan granite specimens were obtained before, during and after thermal treatment (up to 1000 °C) to investigate their linear thermal expansion coefficients, and the variation mechanisms were revealed by optical microscopic observations. According to the experimental results collected from the extensive corresponding literature, the relationships between the linear thermal expansion coefficients of various granites were also elaborated. The experimental results demonstrated that the linear thermal expansion coefficients of the granite in this study both under and after thermal treatment increase with temperature. Meanwhile, the linear thermal expansion coefficients increase rapidly above 500 °C, which is because of the quartz phase transition from α–phase to β–phase. The increase of linear thermal expansion coefficients of granite under and after thermal treatment closely relates to the thermal expansion of mineral crystals and the development and coalescence of intergranular and transgranular microcracks. The experimental results are expected to provide a reference to analytical calculations of thermophysical processes in granite.

This work discusses the application of the magnetotelluric (MT) method to observing and delineating a local fault in the Majalengka Regency, West Java, Indonesia. This fault is part of the well-known Baribis fault segment. Phase tensor and induction vector analysis were applied to all MT data to reveal the dimensionality, geoelectric strike, and geological conditions of the study area, with 12 MT sites composing the studied profile. The estimated skew angle (β) value is − 3º < β < 3º enabling the subsurface structure modeling using the 2-D inversion. The calculated geoelectric strike of the study area of N15^oE was used to rotate the impedance tensor of all MT observation points before modeling. The induction vector analysis revealed that the vectors did not lie in a particular direction. It can be possibly related to the volcanic products which dominate the surroundings of the study area. The 2-D subsurface electrical resistivity model suggested the presence of a very conductive zone (C1 ≤ 10 Ωm), which may be related to the existence of the targeted fault. The subsurface model also showed the resistivity contrasts between C1 (≤ 10 Ωm) and R1 (ρ ≥ 500 Ωm) as well as C1 (≤ 10 Ωm) and R2 (ρ = 50–100 Ωm). These notable contrasts are represented by the models’ block boundaries, and it is suggested that these may become a future earthquake epicenter.

Ionospheric irregularities can severely degrade radio communication and navigation systems. Geomagnetic storms may affect the generation of these irregularities in a way that is not yet fully understood. To improve the forecasting of this phenomenon, we need to study the ionosphere in different regions of the world, and in particular in the equatorial ionization anomaly (EIA) where irregularities are usually more intense. This study analyses the effect of geomagnetic storms on ionospheric irregularities. We examined the occurrence of irregularities at the southern crest of the EIA in Argentina (Tucumán, 26.9°S, 294.6°E, dip latitude 15.5°S) during three intense and one moderate geomagnetic storm of different solar sources, between 2015 and 2018. We used data from an ionosonde, a Global Positioning System receiver and magnetometers. Ionogram spread-F, the F-layer bottom side (h'F), the critical frequency of the F2-layer (foF2), the rate of TEC index and the S4 scintillation index were analysed. The data show irregularities were present as range spread-F and moderate TEC fluctuations in one storm: 27 May 2017 (a coronal mass ejection CME-driven storm occurred on local winter), and were absent in the other events. We suggest that eastward disturbance dynamo electric field and over-shielding prompt penetration electric fields may create favourable conditions for developing these irregularities, whereas westward storm time electric fields might inhibit the growth of irregularities during the other storms considered. During co-rotating interaction region CIR-driven storms, the westward disturbance dynamo electric field may be associated with the non-occurrence of irregularities.

The theoretical magnetotelluric responses of a vertical inhomogeneous and anisotropic resistivity structure with a transitional layer in which the resistivity is a linear function of depth are investigated. The expressions of the tangential electric and magnetic fields at the surface of the Earth model and the corresponding impedance have been evaluated. The influence of some model parameters such as the anisotropic dipping angles, the anisotropic coefficients and the resistivity contrast as well as the thickness of the transitional layer on the apparent resistivity and impedance phase are treated in detail. The results are graphically illustrated in the form of apparent resistivity and impedance phase curves, and they may be used in the interpretation of magnetotelluric sounding data in some specified geologic situations.

The number of devices equipped with global satellite positioning has exceeded seven billion recently. There are a wide variety of receivers regarding their accuracy and reliability. Low cost, multi-frequency units have been released on the market latterly; however, the number of single-frequency receivers is still significant. Since their measurements are influenced by ionospheric delay, accurate ionosphere models are of utmost importance to reduce the effect. This paper summarizes how Gauss process regression (GPR) can be applied to derive near real-time regional ionosphere models using raw Global Navigation Satellite System (GNSS) observations of permanent stations. While Gauss process is widely used in machine learning, GPR is a nonparametric, Bayesian approach to regression. GPR has several benefits for ionosphere monitoring since it is quite robust and efficient to derive a grid model from data available in irregular set of ionospheric pierce points. The corresponding instrumental delays are estimated by a parallel Kalman filter. The presented algorithm can be applied near real-time, however the results are offline calculated and are compared to two high quality TEC map products. Based on the analysis, the accuracy of the GPR modell is in 2 TECu range. The developed methods could be efficiently applied in the field of autonomous vehicle navigation with meeting both accuracy and integrity requirements.

Since ionospheric variability changes dramatically before the major earthquakes (EQ), the detection of ionospheric anomalies for EQ forecasting has been a hot topic for modern-day researchers for the last couple of decades. Therefore, there is a need to identify highly accurate, advance, and intelligent models to identify these anomalies. In the present study, we have discussed artificial intelligence techniques e.g. autoregressive integrated moving average (ARIMA), and long short-term memory (LSTM) network, to detect ionospheric anomalies using the total electron content (TEC) time series over the epicenter of Mw 7.0 Haiti EQ on January 12, 2010. We have considered 20 days of TEC data with a daily 2-h interval and trained the models with an accuracy of 1.28 and 0.07 TECU for ARIMA and LSTM, respectively. Both ARIMA and LSTM results showed that the negative anomalies are recorded 5 days before the EQ (January 7), while strong positive anomalies are recorded 1–2 days before the EQ (January 11–12) that are consistent with the findings of previous studies. Moreover, the quiet space weather conditions during the analyzed period indicate that the observed variations could be considered precursors to the impending Haiti EQ. Our analysis suggests that the performance of the LSTM model is more robust as compared to the ARIMA model in terms of detection of seismoionospheric anomalies.

Previous studies have shown the feasibility of point-mass modellings for deriving terrestrial water storage (TWS) from the harmonic solutions of the Gravity Recovery And Climate Experiment (GRACE) mission at regional scales (e.g., Greenland and Antarctica). However, a thorough assessment of point-mass modelling approaches at the global and river basin levels is still necessary. Therefore, this study’s objective is to assess the implementation and performance of the point-mass modelling approaches based on simulations using as inputs the TWS from Global Land Data Assimilation System (GLDAS). First, the approximate solutions of Newton’s integral using the Taylor series expansion, such that the zeroth-order approximation is equivalent to the “original point-mass” (OPM) and the third-order approximation to the “improved point-mass” (IPM) modellings are presented. Second, numerical comparisons of the gravitational potential forwarded by the IPM and OPM are carried out at which both approaches show errors smaller than the GRACE uncertainties for the potential differences ((sim 7.6times 10^{-4}) (hbox {m}^2)/(hbox {s}^2)). Nevertheless, the spatial patterns of the OPM’s errors still assemble the TWS’s spatial variations. Finally, simulations showed that considering OPM’s deviations from IPM improves the root-mean-square-difference (RMSD) of the inverted TWS up to 50% at the global and basin scales if the edge effects are neglected. After accounting for the edge effects, the IPM solution presented an RMSD of 6.44 mm with an enhancement of about only 20% regarding the OPM. Although the present study confirms the suitability of point-mass approaches for recovering TWS, further investigations regarding its advantages compared to GRACE spherical harmonic synthesis are still necessary.

In this paper, we present a new ridge estimation method for solving rank-deficient least squares problems, in which a rank-deficient matrix is regarded as an almost rank-deficient. First, we give an algebraic derivation that the optimal solution can in fact be obtained by solving a related regularized problem on the optimal worst-case residual. Second, we give a new iterative algorithm to solve ridge parameter and prove its convergence. Finally, examples are given to demonstrate the efficiency of our new method. It is shown that the proposed algorithm can not only assess the stability of solution but also use additional prior information to guarantee the uniqueness of solutions to the problem of rank-deficient free-network adjustment.

This contribution presents the Tikhonov regularized weighted total least squares (TRWTLS) solution in an errors-in-variables (EIV) model. The previous attempts had solved this problem based on the hybrid approximation solution (HAPS) within a nonlinear Gauss-Helmert model. The present formulation is a generalized form of the classical nonlinear Gauss-Helmert model, having formulated in an EIV general mixed observation model. It is a follow-up to the previous work throughout the WTLS problems formulated rely on a standard least squares (SLS) theory. Two cases, namely the EIV parametric model and the classical nonlinear mixed model, could be considered special cases of the general mixed observation model. These formulations are conceptually simple; because they are formulated based on the SLS theory, and subsequently, the existing SLS knowledge can directly be applied to the ill-posed mixed EIV model. Two geodetic applications have then adopted to illustrate the developed theory. As a first case, 2D affine transformation parameters (six-parameter affine transformation) for ill-scattered data points are adeptly solved by the TRWTLS method. Second, the circle fitting problem as a nonlinear case is not only tackled for well-scattered data points but also tackled for ill-scattered data points in a nonlinear mixed model. Finally, all results indicate that the Tikhonov regularization provides a stable and reliable solution in an ill-posed WTLS problem, and hence an efficient method applicable to many engineering problems.