Acta Geophysica最新文献

The Oued Gueniche aquifer system, represented by plio-quaternary silico-clastic layers, is considered one of the most important groundwater reservoirs in the Bizerte region (northern Tunisia). It is the only source of water supply for a variety of purposes, particularly agriculture. Unfortunately, overexploitation of these resources has led to their gradual depletion, which has been exacerbated by years of deficient rainfall. This situation has prompted decision makers to explore possible resources in deeper layers, especially those of the Mio-Pliocene. Although some of these have proved to be good aquifers, the available data is insufficient to develop a strategy for their rational exploitation. It is against this backdrop that this study aims to characterize, for the first time, the geometry of the deep Mio-Pliocene aquifer via a multidisciplinary approach integrating surface and subsurface data. Existing geological maps, lithological logs from four oil wells, and twenty-eight seismic lines were used to track variations in sedimentary facies both laterally and vertically and to highlight subsurface structures such as folds, horsts, grabens, and controlling faults. As a result, isobath and isopach maps of the Mio-Pliocene formations were produced. These clearly show the geometry (thickness, depth, lateral extensions, etc.) of the aquifers and aquitards. For greater precision, a 3D image of the Mio-Pliocene aquifer system was constructed. Above all, it highlighted the various groundwater flow paths. The results obtained, which can be improved by further studies, underline the importance of integrating various data sources and analytical techniques to characterize the Oued Gueniche aquifer system, providing valuable insights for developing long-term solutions to address the water scarcity in this region of Tunisia.

This study proposes using a two-dimensional nonlinear gravimetric inversion methodology based on the regularized Quasi-Newton method to infer the geometry of the boundary between the sedimentary deposit and its basement and to define the simulated lateral density contrast in a sedimentary basin environment. The interpretation model consists of a set of rectangular prisms in 2D space. The parameters estimated in this inversion proposal are the thickness of each prism and its density contrast, interpreted in terms of the depth of the basement and the lateral variation of contrast between the basement and the basin sediment. While most commonly used inversion programs neglect lateral density contrast to estimate basement depth, resulting in algorithms that overlooks much of the lateral geological complexity present in the subsurface, the proposal of this work encompasses such complexities more generically. In this research, the initial analysis involved tests with gravimetric anomalies generated by two synthetic models that exhibited variations in basement depth and lateral density contrast. Subsequently, the methodology developed was applied to data from the high-resolution Earth gravity field model, SGG-UGM-2, along a profile crossing the central part of the Tucano Basin, Bahia, Brazil.

The World Stress Map is a global compilation of data on crustal stress designed to assess worldwide stress patterns and identify stress sources. However, its effectiveness is limited due to inadequate coverage in Africa. This research project aimed to model crustal stress patterns and understand the correlations with contemporary seismicity. To this end, these stresses were modelled from gravitational potential resulting from contrasts in the density structure and the topographic elevations of the African tectonic plate (TAP). Accordingly, the data set of crustal parameters from Crust1.0 and elevations of GEBCO-2022 (general bathymetric chart of the oceans) were used as inputs to determine the lateral forces on elements from gravitational potential. An elastic finite element analysis in plane stress was then employed to determine the present-day stress field of TAP. Subsequently, the simulation results were compared to the recent studies of stress indicators. The stress distributions obtained from the finite element model (FEM) are correlated and found to be in good agreement with the contemporary seismicity of Africa from empirical relations. High stresses were observed in eastern, northern, and southern Africa. At the same time, central Africa and western Africa exhibited low-stress levels. Maximum horizontal stress (SHmax) direction in the East Africa Rift System is roughly parallel to the rift. Generally, the observed stress orientations are in harmony with World Stress Map, with the errors in azimuths ranging from 5.4° to 21.5°.

In this work, numerical modeling was performed using a dynamic self-consistent (SC) anisotropic micromechanics approach. This study examines how porosity, pore aspect ratio, and seismic anisotropy affect the velocity dispersion and attenuation patterns of P-waves and polarized S-waves (SH and SV) traveling through oil- and brine-saturated carbonate rocks (3D body) at seismic, sonic, and ultrasonic frequencies. Here, the carbonate rock was idealized as a mineral carbonate matrix embedded with aligned ellipsoidal fluid inclusions of aspect ratio (gamma). These ellipsoidal inclusions represent the porosity (phi), which is saturated with fluid. The studied rock was assumed to have a vertical axis of symmetry that is perpendicular to the plane of alignment and therefore will show vertical transverse isotropy (VTI). Wave velocities were modeled considering a variety of porosities ((phi =)5, 10, 15, and 20%), aspect ratios (ranging from (gamma = 0.2) to (gamma = 5)), and angles of incidence ((theta = 0^circ ,) (30^circ ,)(45^circ), (60^circ), and (90^circ)), where (theta) is the angle between the symmetry axis and the direction of wave propagation through the rock. Overall, results showed that the largest changes in P- and SH-wave velocities are due to variations in (phi) (up to 15%), followed by changes in (theta) (up to 6%), and then by changes in (gamma) (up to 4.3%). By contrast, for SV-wave velocities the order changes in the last two parameters. Finally, the variations in P- and S-wave velocity dispersion and their maximum attenuation are greater for oblate ellipsoids (left(gamma <1right)) than for prolate ellipsoids(left(gamma >1right)), regardless of porosity, angle of incidence, and aspect ratio.

Vegetation plays a pivotal role in mitigating soil erosion in China’s Loess Plateau, but the management of soil moisture is equally crucial. This study reviewed 112 relevant research papers on the correlation between vegetation and soil moisture in the Loess Plateau. A meta-analysis systematically evaluates the impact of different vegetation types (woodland, shrubland, and grassland) on soil moisture since the initiation of the grain for green project in 1999, a large-scale government initiative. The results showed that in the vegetation construction of the Loess Plateau, the degree of soil moisture declined from large to small was woodland, shrubland, grassland, and became more serious with the increase in soil depth. Through subgroup analysis, it was found that the soil moisture of woodland and shrubland decreased with increasing rainfall; in various altitudes, the soil moisture of all vegetation decreases with increasing altitude; in different slope ranges, the decrease in soil water content of all vegetation construction was the greatest at 25° and the smallest at 15–25°; throughout the growth stages of vegetation, soil moisture consumption by trees increased with vegetation age, but the degree of water depletion did not further increase at 30 years for shrubland and 20 years for grassland. Therefore, from the perspective of soil moisture alone, shrubs and herbs are more suitable for establishment in the Chinese Loess Plateau than tree vegetation.

Upon conducting simulated fault friction tests on granite specimens under various normal stresses utilizing CNL and CNS rock joint shear testers (RJST-616), we aimed to comparatively analyze the stick–slip behavior characteristics during fault sliding. Our results reveal that the sliding behavior of granite simulated faults under different normal stresses is primarily marked by stick–slip features. Both the static shear stress drop and the stick–slip time interval exhibit an exponential growth relationship with the normal stress, whereas the spatial frequency of stick–slip events demonstrates a negative correlation with the normal stress. Furthermore, we observed an exponential growth relationship between the critical slip weakening displacement and normal stress, with the growth rate showing a rapid and then slow trajectory. Additionally, we established an intrinsic connection between different seismic source parameters using normal stress as a bridge. Finally, we unveiled the occurrence and recovery mechanisms of fault stick–slip under varying normal stresses from the perspective of the friction cone. These findings not only enhance our understanding of the behavioral characteristics of fault stick–slip but also provide novel insights into the seismic evolution of natural fault systems.

This study addresses the substantial terrestrial gamma radiation exposure and associated radiological risk in the Amsoi region, located in the seismically active Kopili Fault Zone (KFZ) on the periphery of Shillong Plateau’s gneissic complex. A portable monitoring device highly sensitive to gamma radiation, equipped with a NaI (Tl) scintillator, was used to quantify the terrestrial gamma dose rates in indoor and outdoor air. The recorded dose rates varied among house patterns, with mud houses having the highest. The calculated absorbed dose rates indoors and outdoors were found to be in the range of 157.9–362.5 nGy h⁻¹ and 163.7–336.2 nGy h⁻¹, respectively, which are much higher than the reported population-weighted global averages of 84 nGy h⁻¹ and 59 nGy h⁻¹. The indoor-to-outdoor ratio was also calculated and found to be in the range of 0.7–1.4. The elevated terrestrial gamma radiation could be attributed to the geological setting of the study area, located in the seismically active KFZ. The annual effective dose equivalents for indoor and outdoor environments were calculated and found to be in the ranges of 0.8–1.8 mSv and 0.2–0.4 mSv, respectively. The excess lifetime cancer risk was assessed by calculating the lifetime effective dose and was found to be in the range of 3.4 × 10^–3–7.3 × 10^–3, which is considerably higher than the global average of 1.45 × 10^–3. This study has revealed that the populations residing in this seismically active fault zone are living precariously under high terrestrial gamma radiation.

Research in earthquake engineering heavily relies on strong motion observation. The quality of strong motion records directly affects the reliability of earthquake disaster prevention, rapid reporting of seismic magnitude, earthquake early warning, and other areas. Currently, basic mathematical methods, such as zero-line adjustment and filtering, are commonly employed to ensure the quality of strong motion records. However, these methods often rely on subjective judgment based on human experience when dealing with abnormal waveforms in strong motion records, leading to relatively low efficiency. To address this challenge, this paper proposes an innovative Transformer model based on Bayesian optimization to efficiently identify baseline drift anomalies in strong motion records. By partitioning the strong motion record data from the 1999 Chi-Chi earthquake in Taiwan, China, into two categories: high-quality records (with minimal baseline drift) and low-quality records (with significant baseline drift), we extracted data with distinct features and inputted them into the proposed model for training. Data with distinct features were extracted and input into the proposed model for training. Finally, the model was used to predict whether strong motion records exhibited baseline drift abnormalities. The experimental results show that the optimized Transformer model achieves a performance exceeding 85% in key evaluation metrics such as accuracy and F1 scores. It is capable of efficiently identifying a substantial volume of strong motion records with baseline drift within a short period of time. The model effectively performs the baseline drift classification task for strong motion records and can be used for subsequent identification of abnormalities after baseline drift correction, enabling automation in handling abnormal data related to baseline drift.

To investigate the effect of time-series decomposition on the error rate and accuracy of simulations, two random forest, and hybrid CEEMD-RF models were used in simulating river discharge in the Mahabadchai, Nazlochai, Siminehrud, and Zolachai sub-basins over the Lake Urmia catchment from 1971 to 2019. The results showed that the simulated values by the random forest model are within the 95% confidence intervals, and the model's efficiency is deemed acceptable according to the Nash–Sutcliffe criterion. Besides, the average values of simulated flow are slightly higher than observed discharges. To simulate the river flow values using the CEEMD-RF model, the river flow data was first decomposed into nine empirical mode function values and a residual series. By choosing the best lag for each of the nine values, these values were simulated using the random forest algorithm in the training and testing phases, and the cumulative values were compared with the observed discharges. The research findings showed that the model's efficiency in the decomposed state had increased significantly. Based on the RMSE criterion, the simulation results of the river flow values on a monthly scale showed that the decomposition of the river flow values reduced the error rate in Mahabadchai, Nazlochai, Siminehrud, and Zolachai sub-basins in the training phase to about 36, 94, 29 and 20 percent, respectively, and in the testing phase, improve about 54, 86, 40, and 36 percent. The research results showed that by decomposing the observational values and choosing the best lag, it is possible to cover the range of observational data changes and simulate the minimum and maximum points of the data well.