Acta Geodaetica et Geophysica最新文献

This article consolidates and organizes modern methods for calibrating relative gravimeters, aiming to streamline the selection and implementation of effective calibration systems, particularly in Kazakhstan. The concept of the calibration function and various methods for its determination are presented. Practical implementations of laboratory methods relevant to modern high-precision relative gravimeters are discussed, including the tilt, moving mass, artificial acceleration, and line calibration methods. Data processing through least squares adjustment at the calibration line and an overview of existing software packages for gravity observation equalization are explored. The article also covers existing horizontal and vertical calibration systems in different countries, detailing their main characteristics and schematics. Finally, an estimation of scale factors for five years of measurements with Scintrex CG-5 gravimeters at the Zhetygen calibration line in Kazakhstan is provided.

With the Beidou navigation system's fast expansion in China, it is popular in military and civilian aspects. However, since the satellite orbit operates at an extremely high position and there is energy loss during the propagation process, the receiver only picks up a very faint signal, which makes the Beidou receiver very vulnerable to interference. The interference of the receiver is divided into natural interference and human interference, of which the human interference is particularly serious. Deception is commonly used in human interference. The deception interference detection technology in Beidou navigation system is studied in this research. Firstly, the signal in the signal capture stage is detected by multi-peak detection algorithm to determine the signal type. If it cannot be determined, the signal is detected by the half-peak full-width algorithm, so as to determine the signal type. In the stage of signal tracking, the Doppler shift of the spoofing signal is applied to determine whether the signal is spoofed or not. When the spoofing signal forwarding delay is set to 0.5 and 1 chip respectively, the full width of half peak is 8.56 and 11.35 after fitting the main peak. If the half-peak full width exceeds the normal navigation signal, it indicates spoofing interference. The constructed model can effectively track downspoofing signals and improve the Beidou navigation system’s detection performance.

This study applies machine learning techniques to improve petrographic classification in India's Bokaro coalfield's Barakar Formation, using conventional geophysical well logs from three wells. We analysed natural gamma ray, true resistivity, bulk density, neutron porosity, and photoelectric factor data using k-nearest neighbor (kNN), support vector machine (SVM) and random forest (RF) classifiers. A master well provided initial reference log measurement cut-off values for typical lithologies like shale, sandstone, carbonaceous shale, and coal, forming the basis of our training dataset. We assessed model accuracy using precision, recall, and F1-score metrics, finding the random forest model to be the most effective in litho-type discrimination. During the training phase, the computed overall accuracy of the predicted ML modes exceeded 89% and model accuracy hierarchy was RF>SVM>kNN. These classifiers were then applied to other well locations to predict lithological sequences, aiding in lithofacies sequence identification and potential fault extension detection. The study demonstrates the random forest model's superior precision and efficiency in lithological discrimination. Our findings enhance automated processes for identifying missing lithology during well correlation, offering valuable insights for geological interpretation in resource exploration and development. This machine learning-driven approach marks a significant advancement in subsurface geological studies.

Graphical abstract

The spatial–temporal evolution of terrestrial water storage anomalies (TWSA) is crucial in monitoring floods and sustainable water management. Unlike monthly gravity models, daily models can obtain TWSA at daily resolution, which demonstrates advantages in monitoring short-term floods. Moreover, with sufficient observations it is possible to capture the temporal characteristics of TWSA. In this paper the TWSA of nine major drainage basins in China spanning from January 2003 to August 2016 are estimated. The spatial variations of the Yangtze drainage basin which is taken as example accurately reflect the 15 July, 2010 flood. The variation of Wetness Index (WI) agrees well with that of discharge of DaTong gauging station. Meanwhile, WI shows four days lead-time prior to the flood, which can be regarded as early warning indictor in ungauged basin. For the temporal analysis, noise characteristics of TWSA are assessed, which show that the optimal noise model is autoregression moving average noise (ARMA) but with different orders for different basins. With the optimal ARMA noise, the uncertainties of estimated parameters can reach up to 28 times that considering only white noise. Therefore, to get the comprehensive temporal features of daily TWSA, its time-correlated characteristics cannot be neglected.

Landslide inventory maps (LIMs) are a map type that provides information to the user about the landslides. The high accuracy of this map enables outputs derived from it to be produced more realistically and accurately. LIMs are generally produced by the requests from the user, taking into account the regions where access is easy, where settlements are located and using traditional techniques. LIMs produced for areas where access is limited or settlements are absent generally have distant, low accuracy, and unreliable data because landslides in these regions are often not considered. This study aims to investigate the landslides using multi-temporal SAR imagery and to develop a methodology to improve the LIM by new landslides. The results show that the velocities of PS points falling on the landslides are between − 11.2 and 14.9 mm/y. Then, the regions defined as possible landslides were identified by analyzing the velocity model produced with the help of the kriging interpolation. The potential landslides overlapped with those recorded by public institutions and organizations, and eight new landslides consisting of approximately 8.32 km² were identified. Finally, these new landslides were examined regarding slope degree and soil groups by overlapping with land use capability maps. Results indicated that all new landslides were located in landslide-prone areas. The detection of new landslides and monitoring of landslides were carried out in a practical, highly accurate, and cost-effective manner using the developed methodology.

Vertical electrical sounding (VES) and magnetic data were analyzed to infer the groundwater system in the Belesa area within the central Main Ethiopian Rift. The analysis is aimed at aiding in determining the groundwater potential for current and future development of groundwater resources within the Belesa area. The analysis of two-dimensional magnetic forward and one-dimensional electrical inverse models aided in locating faults, fractures, and lithological units that may contain groundwater. Lithological logs from nearby boreholes where lithological units had been determined were used to constrain the magnetic and VES models. The VES models and a magnetic derivative (tilt and horizontal) analysis mapped the existence of several possible fractures and faults that may act as either barriers or conduits for groundwater movement. The magnetic and electrical resistivity models indicated a potential aquifer associated with low electrical resistivity values occurring between 120 and 240 m in depth within the weathered and fractured ignimbrites which are bounded by faults, plus an additional potential deeper aquifer within a sandy pumice layer. Low electrical resistivity horizons provide targets for future drilling for water and a base for geophysical surveys that would further delineate the groundwater system in the Belesa area.

The interpretation of the CHAMP and Swarm-A satellites magnetic anomalies is discussed. The vertical gradient of the Swarm-A anomalies is presented. The vertical gradient shows in detail the magnetic sources. The inversion of the CHAMP magnetic anomalies is also presented. The inversion shows the parameters of the selected forward model. The inversion is solved by numerical procedures: the nonlinear Simplex and Simulated Annealing methods. Possible origin of upper crust magnetization is summarized. The calculation of the error propagation is estimated.

The results obtained through GRACE/GRACE Follow-On spherical harmonic coefficient model exhibit noticeable north–south strip noise. While applying low-pass filtering along the latitude circle direction can effectively eliminate the north–south strip error, it unavoidably introduces east–west strip noise. This paper introduces a method that utilizes the cosine function to determine the cutoff frequency for the low-pass filtering in the latitude direction. Additionally, a low-pass filtering step is added in the meridian circle direction. This combined approach successfully mitigates the north–south strip error and simultaneously suppresses the east–west strip noise. The analysis results highlight the improvement in signal to noise ratio of global surface quality anomaly estimation achieved by utilizing the cosine function to determine the cutoff frequency. The use of data from five institutions reveals that the dual low-pass filtering (DLP) improves the signal to noise ratio by more than 2% compared to the traditional efficient low-pass-filtering (ELP). When analyzing the uncertainty in the Yangtze River Basin using the three-cornered hat method, it is observed that the uncertainties of the DLP solution, traditional ELP solution, and mascon solution are comparable. Furthermore, the DLP solution exhibits the smallest uncertainty, measured at 4.89 cm. In terms of root mean square error, the DLP almost consistently yields the lowest values across various regions, with the difference from the smallest root mean square error value remaining within 2 mm in certain regions.

We provide a key magnetotelluric section, composed of archived magnetotelluric data along a NW-SE profile in Transdanubia, Hungary. For the interpretation of the key section, observations from raw magnetotelluric data and inversion results were used. In addition, other geophysical-geological information was also considered to confirm the conclusions based on the electrical resistivity sections. All this information was combined to identify the main structural lines and geologic units along the profile. Main structural lines observed on the resistivity sections are the Alpokalja line, Rába line, Balaton line, Kapos line, and Mecsekalja line. Geologic units that can be delineated due to their resistivity contrast include the Lower and Upper Austroalpine Units, the Transdanubian Range Unit, the Mid-Hungarian Megaunit, the Tisza Megaunit and sedimentary rocks filling the sub-basins of the Miocene Pannonian back-arc basin. The inversion results of the transverse magnetic (TM) polarization mode and the phase-depth sections of the raw data were found to be the most suitable for detecting the morphology and identifying the depth of the Pre-Cenozoic basement along the profile.