Studia Geophysica et Geodaetica最新文献

Spectral inversion, based on the odd-even decomposition principle of reflectivity, used the relationship between seismic data and wavelet amplitude spectrum to establish the inversion equation and achieve resolution-enhancement processing. Compared with deconvolution based on the L₂ norm, the odd and even components of reflectivity using spectral inversion can weaken the tuning effect, identify thin layers, and obtain data with higher resolution. However, most post-stack seismic data are non-stationary, i.e., attenuation of amplitude, phase, and frequency with time exists. We derived a resolution-enhancement algorithm of non-stationary seismic data with quality factor Q based on the short-time Fourier transform. Due to the instability of the spectral inversion algorithm, the lateral continuity of the obtained result is poor. Therefore, we proposed a multichannel spectral inversion algorithm with lateral constraints. The algorithm inherits the high-resolution characteristics of spectral inversion and effectively enhances lateral continuity. Applications to model and field data sets show that the proposed L₂ norm-based non-stationary multichannel spectral inversion method can be effectively applied to the resolution-improvement processing of non-stationary seismic data.

The earthquake of 9 November 1880 was one of the most important moments in the seismic history of Zagreb (Croatia). It is the strongest earthquake to have occurred in the greater Zagreb area, and as such it defines the seismic hazard in northwestern Croatia, the most populated part of the country. The main objective of this study was to reanalyze the location and magnitude of the earthquake, the input parameters which are crucial for a better assessment of seismic hazard, as there were macroseismic indications that the previous assessments should be revised. In addition, the strongest aftershock occurred two days after the main event, so it can be assumed that the observed intensities were caused by the cumulative effect of these two events. Therefore, a new isoseismal map was created, synthetic macroseismic modelling was performed and additional geophysical and microtremor measurements were taken. Based on all the information collected, the attempt to separate the effects of the strongest aftershock from the effects of the mainshock (to avoid a cumulative effect), a new assessment of the location of the epicentre of the main 1880 earthquake in Zagreb and its magnitude was made. When it comes to historical earthquakes, from a seismological point of view, even small improvements in the definition of the main seismological parameters of an earthquake significant for a given area are very important - for a better understanding of the geodynamics of the area, the earthquake mechanism and the spatial distribution of damage after the earthquake, as well as for the consistent assessment of seismic hazard and thus risk.

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.

Global Geopotential Models (GGMs) provide valuable information about Earth’s gravity field functionals, such as geoid heights and gravity anomalies. However, ground-based datasets are required to validate these GGMs at the regional and local scales. In this study, we validated the accuracy of GGMs by comparing them with ground-based Global Navigation Satellite System (GNSS)/levelling data for the first time in Nigeria. We employed two validation scenarios: with and without considering spectral consistency using the spectral enhancement method (SEM) to incorporate high and very high frequencies of the gravity field spectrum from the combined global gravity field model (XGM2019e_2159) and the residual terrain model (RTM) derived from the Shuttle Radar Topography Mission (SRTM) data, respectively. The results of this evaluation confirmed that the application of SEM improved the assessment of the GGM solutions in an unbiased manner. Integrating XGM2019e_2159 and SRTM data to constrain the high-frequency component of geoid heights in Gravity Field and Steady-State Ocean Circulation Explorer (GOCE)-based GGMs led to an improvement of approximately 10% in reducing the standard deviation (STD) relative to when SEM was not applied. GO_CONS_GCF_2_TIM_R6 at spherical harmonics (SH) of up to degree and order 260 demonstrated the lowest STD when compared to GO_CONS_GCF_2_DIR_R6 and GO_CONS_GCF_2_SPW_R5, with a reduction from 0.380 m without SEM application to 0.342 m with SEM implementation. In addition, four transformation models, namely, linear, four-parameter, five-parameter, and seven-parameter models, were evaluated. The objective is to mitigate the reference system offsets between the GNSS/levelling data and the GGMs and to identify the particular parametric model with the smallest STD across all GGMs. This effort reduced the GGMs misfits to GNSS/levelling to 0.30 m, representing a 15.3% decrease in STD. Notably, the XGM2019e_2159 model provides this improvement.

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.