Acta Geodaetica et Geophysica最新文献

The aim of this paper is to present the skills of two statistical models in anticipating the development of El Niño based on sea level anomaly (SLA) forecasts with lead time up to 12 weeks. The models are: (1) the polynomial-harmonic model (PH) combined with the threshold autoregressive model (TAR), known as the PH+TAR, and (2) PH integrated with the multivariate autoregressive model (MAR), referred to as PH+MAR. Five powerful El Niño events are considered: 1997/1998, 2002/2003, 2006/2007, 2009/2010, 2015/2016. The performance of the prediction models is calculated in specific locations in the equatorial Pacific, i.e. centres of Niño 1+2, Niño 3, Niño 3.4 and Niño 4 regions. It is found that the SLA predictions hitting the El Niño peaks reveal different accuracy for dissimilar El Niño events, with the most skillful prognoses for El Niño 1997/1998. Two specific regions are identified in which the model performance fulfils the assumed accuracy limit of 5 cm, namely the Niño 1+2 and Niño 4 regions. In addition, the PH+MAR model performed better than the PH+TAR solution.

When separate inversion of geophysical data set cannot result in desirable reconstruction of subsurface anomalies, joint inversion of geophysical methods is one of the most common ways to overcome the problem. Cross gradient based joint inversion is the only technique allowing to execute joint inversion of magnetometry and DC resistivity data. In this paper, joint inversion of magnetometry and DC resistivity, based on nonlinear equations, is carried out. Smoothness and product of depth weighting and compactness are manipulated as model weighting functions for DC resistivity and magnetometry methods, respectively. The joint inversion is first applied to noise-free synthetic data of a thin dyke to evaluate its productivity which shows that separate inversion of DC resistivity leads to poor reconstruction of the thin dyke, while magnetometry recovers the dyke model very well. After the joint procedure, the magnetic method plays the role of an efficient constraint through cross gradient to improve the resistivity model about satisfactory recovery of the thin dyke. Then, usefulness of the joint inverse algorithm is tested in the presence of the noise demonstrating its productivity for noise levels up to 5%. Ultimately, the joint inversion algorithm is utilized for the real data collected over a relatively thin dyke in Morgenzon Farm in South Africa successfully.

Equatorial ionospheric irregularities have the potential to disrupt radio communications and satellite-based navigation. The influence of geomagnetic storms on these disturbances remains not fully understood, particularly in how they may initiate or inhibit the irregularities (also called Equatorial Plasma Bubbles, EPBs). Enhanced predictive capabilities require comprehensive studies of the ionosphere’s behavior, especially within the equatorial ionization anomaly (EIA) zone, where such irregularities are more pronounced. This study examines the effect of the November 3, 2021, geomagnetic storm on equatorial ionospheric irregularities in the F region over South America. We used data from multiple instruments, including GPS, GOLD, ICON, ionosondes, SuperDARN, and AMPERE, to analyze the role of the storm-time electric fields in the day-to-day dynamics of EPBs. We found that irregularities were suppressed during the storm’s main and recovery phases. Our results suggest that the inhibition of ionospheric irregularities in the F region may be associated with a combination of westward under-shielding penetration electric field and disturbance dynamo electric fields. The present study may contribute to advancing our understanding of the ionospheric response to geomagnetic storms and to develop better predictive models for space weather impacts.

This study outlines the planning, emphases, and proposed implementation of a gravimetric control network for Israel. The establishment of a comprehensive and accurate gravimetric control network is crucial for various geodetic applications, including geoid determination, gravity field modelling, vertical datum establishment, and geophysical studies. This study aims to provide a framework for the development and implementation of such a network in Israel. The proposed network is divided into two orders. The first order, also known as the absolute order or zero order, comprises four stations situated in geologically stable and tranquil locations. The second order, referred to as the first order, is established through relative gravimetric measurements. This order will encompass 29 stations positioned in areas that are both stable and secure. The zero and first order stations will be evenly distributed throughout the country to ensure homogeneous coverage. The proposed gravimetric network is in conjunction with the requirements to improve the geoid undulation model of Israel. Through the establishment of a small gravimetric network and the implementation of relative gravimetric measurements, we were able to determine the optimal control point density and establish a well-structured methodology for measurement, analysis, and the adjustment process.

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.