Studia Geophysica et Geodaetica最新文献

The oblique collision zone of Arabia-Eurasia is a seismically active region with complex crustal deformation patterns. While GPS measurements provide valuable data, their sparse distribution limits our understanding of the full extent of deformation. This study addresses this limitation by using a robust interpolation method for GPS velocity data in the collision zone. We utilized biharmonic splines to interpolate horizontal components of sparse GPS velocity data independently and in a coupled manner by altering Poisson ratio. This method is an effective means of interpolating sparse vector data in cases where deformation mechanics can be explained by elasticity principles. The interpolation process included fitting trends to the input data, calculating residuals, and analyzing them. The prediction process consisted of trend and spline fitting stages. We interpolate horizontal GPS velocities onto a standard geographic grid with a 30-minute interval, excluding data points with significant deviation. The data was partitioned into training and testing subsets, with the training set used for calibration and the testing set for evaluation of the interpolation method. Our analysis revealed an irregular spatial distribution of crustal movement. The northern component of the velocity field consistently points towards Eurasia and is greater than the eastern component. The amplitude of the northern component decreases from south to north and from west to east, indicating variations in deformation intensity. The eastern component exhibits a change in direction, moving westward in the western half of Iran and eastward in the eastern half, with a reversed trend in the north. This change in direction highlights the presence of solid blocks within the collision zone. Undeformed regions, major faults, convergence deformation, and compressing high-elevation regions are also observed in the collision zone. These findings provide a detailed picture of present-day crustal deformation in the Arabia-Eurasia collision zone, enhancing our understanding of the collision process.

The formula for the area of a rhumb polygon, a polygon whose edges are rhumb lines on an ellipsoid of revolution, is derived and a method is given for computing the area accurately. This paper also points out that standard methods for computing rhumb lines give inaccurate results for nearly east- or west-going lines; this problem is remedied by the systematic use of divided differences.

Earth’s surface velocities are routinely extracted from Global Navigation Satellite System (GNSS) position time series. In addition to velocity estimates, acceleration may be a crucial parameter for modeling non-linear motion. Typically, a statistical hypothesis test is employed to evaluate the significance of the involved parameters and guide the selection of the appropriate model. In this contribution, we formulate a statistical test procedure from the generalized likelihood ratio test to analyze the significance of the acceleration in the model. The proposed procedure is compared with results obtained using the Akaike Information Criterion and Bayesian Information Criterion. Additionally, Minimal Detectable Horizontal Acceleration is provided as an indicator of the sensitivity of the acceleration detection. The GNSS time series of position estimates from the Nevada Geodetic Laboratory were used for this study. The experiments demonstrated a good agreement between the statistical test proposed and the information criteria approach. Therefore, the proposed statistical test may be another criterion to help the user in the important task of model selection.

Compared to gravity method, the gravity gradient has multi-component advantages and can emphasize short wavelength features. By providing more detailed features in the image display, it could present a more accurate determination of the spatial distribution of the underling anomaly body. In our study, based on the gravity gradient data that was synthesized from the high-precision gravity model from Gravity Recovery and Interior Laboratory mission, we analyzed the tectonic structure of the Moon’s shallow layers in Oceanus Procellarum region. Bouguer anomalies of gravity gradient are used for geological boundary recognition and three-dimensional density inversion. Theta Map method is adopted for the edge identification of geological structures. It fully utilizes the characteristics of multi-components combination of gravity gradient to sharpen the boundaries identification of abnormal bodies. During the density inversion, in order to decrease the non-unique solution problem, the depth weight constraint is added to the inversion equation. Furthermore, the method of wavelet coefficient compression and Least Squares QR-decomposition is applied to accelerate the inverse calculation of large ill-conditioned equations. According to the result illustration, we found that: 1) The combination of gravity tensors has a strong boundary recognition ability in the horizontal direction. There is close consistency with the results of density inversion. 2) Our study supports the expansion and intrusion hypothesis of lunar magma in the research zone of Oceanus Procellarum, since the distribution of density tomography results at different depths is higher than the normal assumed crust density of the Moon in the majority of areas. Moreover, the intrusion source in our research area is concentrated at a direction depth of 30–45 km below the mean lunar radius surface approximately.

The seismic event of the 2016 Amatrice earthquake and its subsequent aftershocks have provided a significant opportunity to study seismic waves and their effects on the Apennine región, a very active seismic area. This research utilizes GPS data from three geodetic networks (ItalPos, NetGEO, and RING) to analyze the seismic waves generated by the earthquake, capturing seismic effects with great precision and resolution. By examining data from various GPS stations within the region, the study demonstrates the efficacy of GPS in providing detailed and accurate representations of ground motion. The study identifies co-seismic displacements and accelerations at monitoring stations by processing and analyzing GPS data, including precise point positioning strategies and wavelet adjustments. Moreover, a least squares adjustment method is employed to optimize the estimation of temporal parameters associated with seismic event detection across the spatial network of stations. The results obtained from GPS data are validated against seismic equipment, affirming their reliability in characterizing seismic events. Furthermore, the study elucidates the propagation of surface waves and path effects over the affected region, contributing to a comprehensive understanding of the earthquake impact. This research underscores the potential of GPS data as a valuable tool for rapid and precise characterization of seismic events, offering insights into ground motion dynamics and facilitating timely response and mitigation efforts.

Seismic data with a high signal-to-noise ratio is beneficial in the inversion and interpretation. Thus, denoising is an indispensable step in the seismic data processing. Traditional denoising methods based on prior knowledge are susceptible to the influence of the hypothesis model and parameters. In contrast, deep learning-based denoising methods can extract deep features from the data autonomously and generate a sophisticated denoising model through adaptive learning. However, these methods generally learn a specific model for each noise level, which results in poor representation ability and suboptimal denoising efficacy when applied to seismic data with different noise levels. To address this issue, we propose a denoising method based on a two-stage convolutional neural network (TSCNN). The TSCNN comprises an estimation subnet (ES) and a denoising subnet (DS). The ES employs a multilayer CNN to estimate noise levels, and the DS performs noise suppression on noisy seismic data based on the ES estimation of the noise distribution. In addition, attention mechanisms are implemented in the proposed network to efficiently extract noise information hidden in complex backgrounds. The TSCNN also adopts the L₁ loss function to enhance the generalization ability and denoising outcome of the model, and a residual learning scheme is utilized to solve the problem of network degradations. Experimental results demonstrate that the proposed method can preserve event features more accurately and outperforms existing methods in terms of signal-to-noise ratio and generalization ability.

As one of the main components of the Earth orientation parameters, short-term prediction of the geodetic polar motion series is crucial in the field of deep-space exploration, high-precision positioning, and timing services, which require high real-time performance. Additionally, its middle- and long-term prediction is equally important in climate forecasting and geodynamics research. In this study, we propose the combined BiLSTM+ARIMA model, which is based on bidirectional long- and short-term memory (BiLSTM) and autoregression integrated moving average (ARIMA). First, ensemble empirical mode decomposition (EEMD) is performed as a filter to decompose the polar motion time series to obtain low- and high-frequency signals. The EOP14 C04 time series provided by International Earth Rotation and Reference Systems Service and decomposed by EEMD includes low-frequency signals like the long-term trend, decadal oscillation, Chandler wobble, and prograde annual wobble, along with shorter-period high-frequency signals. Second, low- and high-frequency signals are predicted using BiLSTM and ARIMA models, respectively. Finally, the low- and high-frequency signal forecast components are reconstructed to obtain geodetic polar motion predictions. In middle- and long-term polar motion prediction, the results show that the proposed model can improve the prediction accuracy by up to 42% and 17%, respectively. This demonstrated that the BiLSTM+ARIMA model can effectively improve the accuracy of polar motion prediction.

Red clay is widely distributed globally and is closely related to human production and life. The middle reaches of the Yellow River basin in China are characterized by complex geological structures, concentrated rainfall periods. The soluble salts such as sodium sulfate enter the red clay particles along with the infiltrating water, forming a red clay-like saline soil. In order to study the effects of water and salt on red clay soils, this paper uses red clay in the Heyang of Weinan with different ratios of distilled water (10–20%) and Na₂SO₄ (0–4%), and obtains the resistivity of red clay soils at different frequencies (100 Hz–100 kHz) using an inductance, capacitance and resistance digital bridge tester. The results show that the resistivity of red clay is negatively correlated with water and salt content. With the increase of water content, the increase of conductive paths in the pore water improved the electrical conductivity of the red clay; while when the concentration of Na₂SO₄ increased, the free moving anions and cations in the pore water increased, the electrical conduction efficiency increased and the resistivity decreased. The high frequency increased the conductivity of red clay by contributing to electrical double layer deformation, whereas the electrode polarization led to inhibition of conductivity under low frequency. A negative power exponential relationship exists between the resistivity of red clay and the test frequency. This study may provide a valuable reference for the rapid identification of the physical properties of red clay and its internal structure.