Acta Geophysica最新文献

As critical components of hydraulic structures, radial gates experience complex flow patterns during operation, inducing hydrodynamic loads that may threaten structural stability. This study investigates the flow characteristics around the radial gates under different conditions by using Particle Image Velocimetry (PIV) test in a laboratory flume. It is found that three key zones emerged behind the gate: a high-velocity jet zone, a shear layer marked by a velocity gradient, and a recirculation zone with reverse flow. The downstream water depth critically controls the evolution of these flow zones. Turbulence intensity peaks within the jet zone, decaying progressively across the shear layer. The flow self-similar is exhibited in the far-field region. Energy analysis reveals that large-scale vortex structures govern the kinetic energy distribution. These findings enhance our understanding of flow regimes near radial gates and support the optimization of gate designs for improved stability.

Three different ground motion prediction models (GMPMs) have been developed in this paper using machine learning (ML) methods to estimate the horizontal peak ground acceleration (HPGA) for Iran. Two of these models are based on artificial neural networks (ANNs) of the multilayer perceptron (MLP) type, while the third employs the support vector regression (SVR). Each model utilizes moment magnitude (M_w), fault type, epicentral distance, and soil type as features (predictors) to produce a numerical prediction for HPGA. The models have been trained, validated, and tested using a strong-motion dataset comprising 2472 corrected horizontal accelerograms from 1100 earthquakes recorded at 815 stations across Iran from 1974 to 2022. Given the significant imbalance in the number and magnitude of recorded accelerations for Iran, an algorithm called the Repeating function has been devised to mitigate this problem within the training dataset. Besides, we designed an innovative training loop that automatically trains a model multiple times until specified criteria for the model are confirmed. Notably, three developed ML models (DMLMs) accurately predict HPGA, even in cases where V_S30 is not defined. Although we have trained the three DMLMs to predict HPGA as the maximum value of the two horizontal components of the accelerogram (HPGA_max), they demonstrate a strong generalization in predicting the arithmetic and geometric means of the two mentioned components (HPGA_am and HPGA_gm). To evaluate the performance of the models, sensitivity and residual analyses, fitting curves, root-mean-square error (RMSE), and Pearson correlation coefficient (PCC) have been conducted.

This study uses deep learning models to present an advanced methodology for forecasting groundwater levels. The primary objective is to estimate monthly streamflow at various gauging stations, analyze long-term groundwater storage trends from 1986 to 2022, and predict future groundwater storage (GWS) for 2028. The majority of research relies on single-model forecasts, without considering regional hydrological variability or integrating minimal-data contexts, despite the increasing use of deep learning models in hydrology. By employing an ensemble deep learning (DL) architecture that combines Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), Stacked Long Short-Term Memory (SLSTM), and Gated Recurrent Unit (GRU), this study closes that gap by accurately predicting groundwater storage over the Middle Mahanadi Basin utilizing Hargreaves–Samani potential evapotranspiration (PET) estimate and SCS-CN runoff. Results reveal that the Ensemble DL model consistently outperforms individual models across all gauging stations, offering the most accurate predictions of GWS changes. This model’s integration of multiple techniques allows it to capture complex patterns and mitigate errors, particularly in regions with high variability. The analysis of seasonal trends reveals that the post-monsoon season exhibits increased groundwater storage, whereas the pre-monsoon and monsoon seasons display a declining trend. In 2004, there was a decrease in GWS across most stations out of 8 stations, likely due to reduced rainfall and increased water extraction, with slight recoveries observed in 2016 and 2022. In conclusion, the Ensemble DL model emerges as the region’s most reliable tool for groundwater forecasting, offering valuable insights for effective water resource planning and management, particularly in drought-prone areas. In drought-prone basins with limited data, the model provides a dependable tool for groundwater management and performs better than individual DL models at every station.

Adjustment of methane adsorption and desorption properties in coal is important for the high drainage rate and drainage effect of coalbed methane (CBM). The main characteristics of CBM reservoirs in the Qinshui coalfield of Shanxi Province are low pressure and low permeability of CBM. These conditions result in limited methane extraction and significant fluctuations in gas concentration. Revising the adsorption and desorption properties of CBM can improve both the extraction rate and efficiency. An attempt was made to revise the adsorption and desorption characteristics of methane in coal by adding an electric field. This study focuses on No. 3 anthracite in the southern Qinshui coalfield. Elemental analysis was conducted, and an electric field was used as a physical field to develop an experimental apparatus for electric field-revised CBM adsorption and desorption. This apparatus was used to test the adsorption properties of CBM, and X-ray photoelectron spectroscopy (XPS) was employed to examine the surface chemistry of coal. The types and relative contents of functional groups on the coal surface were analyzed. Additionally, the relationship between the electric field’s influence on CBM adsorption and the changes in functional groups on the coal surface was investigated. The experimental results indicate that the application of an electric field changes methane adsorption from coal, adhering to the Langmuir theory model. The impact of voltage on methane adsorption capacity and adsorption isotherms is greater than that of frequency. Moreover, as the intensity of the electric field increases, the maximum adsorbed quantity V_m demonstrates a linear decrease while the empirical adsorbed constant B exhibits an exponential decline. The functional groups on the coal surface primarily include C–C/C–H bonds, C-O bonds, C = O carbonyl groups, and COO- carboxyl or quinone groups. Under the influence of the electric field, the functional groups on the coal surface are modified. The relative content of C–C/C–H bonds decreases, resulting in an increase in the relative content of C–O bonds, C = O carbonyl groups, and COO- carboxyl or quinone groups. The findings revealed that electric field action diminished the methane adsorption capacity in coal.

Shanxi Reservoir is one of the most seismically active areas in Zhejiang Province, Eastern China, making it a natural experimental site for reservoir earthquake research. We apply the fluid substitution method to the study of reservoir earthquakes. Firstly, we estimate the distribution of seismic wave velocity and wave velocity ratio around Shanxi Reservoir based on the seismic phase observation reports. Then, based on the rock physics models and techniques, we estimate the porosity and saturation distribution around the reservoir using wave velocity and velocity ratio results, and analyze the permeability conditions of underground rocks. Further, using the earthquake catalog, we estimate the spatial distribution of b-value and stress field characteristics in the area. Finally, we discuss the influence of reservoir impoundment and underground lithology on seismicity and speculate on the mechanism of earthquake occurrence. The main conclusions are as follows: (1) When earthquakes occur, the rocks that rupture are essentially saturated; the earthquake magnitude of the region, where the rocks rupture before they are fully saturated, is relatively small. (2) The porosity of the SE segment on seismogenic fault F₁₁ (Shuangxi–Jiaoxiyang fault) is greater than that of the NW segment. (3) After the reservoir impoundment, the water first infiltrates in the middle to SE segment of the F₁₁ fault, which has large porosity, causing the pores within the rock to reach water saturation and inducing initial seismicity. The occurrence of initial earthquakes creates new infiltration channels, which makes the reservoir water infiltrate northwest along the fault. Therefore, the NW segment began to become active, resulting in the 2014 earthquake sequence. (4) The 2014 sequence started in the region with large porosity differences and small b-values. Large differences in porosity tend to result in large differences in local water pressure, coinciding with the large stress reflected by the b-value; this region became the most unstable location on the NW segment, and the underground rocks were the first to reach the yield limit and rupture.

Landslides are a major natural hazard in mountainous regions, often resulting in thousands of deaths and billions of dollars in property damage. Climate change is increasing the frequency and severity of these events. Seismic network monitoring has made it possible to detect landslides in real time. However, distinguishing seismic signals caused by landslides from those generated by earthquakes and background noise remains a key challenge. This study investigates the reliability of power spectral density (PSD) trends in seismic waveforms for identifying landslide-generated signals. By analysing seismic data from multiple stations within a 150-km radius of the event, we find a consistent PSD decay pattern across different landslides, regardless of their size, duration, or distance from the stations. We use the slope of the seismic waveform PSD in the frequency band 0.01–5 Hz and skewness of the spectral power distribution as scalable entities for landslide detection. This confirms that landslides have unique spectral features, enabling them to be distinguished from other seismic sources. Our analysis suggests that the landslides show PSD slope values between − 3 and − 9. We have also noticed steeper slopes that match the slope of seismic waveforms of background noise when the station distances are above 200 km. Although this limits landslide detection using distance stations, this can be ruled out when using the local networks for landslide monitoring. The study demonstrates that PSD analysis of seismic waveforms offers a stable and innovative method for real-time landslide detection in continuous seismic data. Utilizing these spectral signatures could greatly enhance landslide monitoring and early warning systems in high-risk areas.

This research examines the methods of controlling the surface evaporation rate of water reservoirs in drought conditions. Using physical and chemical coatings is one of the new methods to prevent evaporation. In this research, the combination of octadecanol and hexadecanol, as well as hexadecanol dissolved in ethanol, has been used as a chemical method to control evaporation on the surface of three ponds designed with dimensions of 2 × 2 × 2 square meters in the hydrometeorological research station of the Faculty of Natural Resources of Lorestan University. Quantitative (evaporation rate reduction) and qualitative (possible changes in some chemical and microbial parameters) effects were investigated in a 3-month period from 7/23/2021 to 10/22/2021. The monolayer combination of octadecanol and hexadecanol showed the highest reduction in evaporation rate (23%) with a significant difference of 5% compared to the control group. Hexadecanol monolayer ranked second with 17% reduction in evaporation. The chemical parameters of alkalinity (pH), total dissolved solids (TDS) and the presence of microbial biomass (turbidity) were investigated. The acidification of the environment due to the release of CO2 in the treatment ponds led to a decrease in pH. The faster growth of algae, bacteria and solid particles has increased the turbidity in the control pond compared to the treatments.

The operation of the Three Gorges Dam (TGD) since 2003 has induced a 60–90% reduction in suspended sediment concentrations in the Middle Yangtze River (MYR), which led to significant channel degradation and riverbed coarsening, and further affects the flood control and riverbank protection in the Middle Yangtze River. However, the influence of the riverbed adjustment on the bedload transport process in the near-dam reaches remains unclear. Based on the measured data in the MYR, a bedload transport formula was proposed that considered the coupled effects of riverbed topography and entrainment probability. The results showed that: (i) The proposed formula in this study can reasonably reflect the impact of cross-sectional morphology and bedload transport on the riverbed erosion and deposition process, and it is consistent well with the observations; (ii) after the TGD operation, the bedload transport rate in the Jingjiang Reach significantly decreased, accompanied by a reduction in sediment entrainment probability; the transport rate at Zhicheng station decreased from 163.66 to 2.55 kg/s, and the entrainment probability decreased from 0.994 to 0.001, but the transport rate and entrainment probability remained relatively stable at Shashi and Jianli stations; (iii) riverbed scouring in the Jingjiang Reach intensified, with reduced bank slope angles leading to weakened bedload transport intensity; and the transport rate revealed a negative correlation with riverbed coarsening and longitudinal riverbed stability. The results of this research are an enrichment to the sediment movement theory and can be used in the prediction of riverbed evolution in the alluvial rivers.

Grade control structures (GCS) are hydraulic structures often used in river regulation in the steep slope basins. Prediction of local scour dimensions is crucial for managing control structures and foundation design in water resources engineering. The present research experimentally investigates the influence of tailwater depth on the dimensions of the scour hole downstream of the GCS under steady flow and sediment mobility. Experiments were carried out for different discharges and tailwater depths, including three free tailwater depths of 1.5 and 2 times the initial tailwater depth in three different bed slopes of 0.05%, 0.2%, and 0.4%. The results indicated that increasing the tailwater depth while maintaining a constant flow rate and bed slope leads to a decrease in the dimensions of the scour hole downstream. On average, establishing the maximum tailwater depth for different discharges caused a 25% and 20% reduction in the downstream scour hole depth and length, respectively. An increase in bed slope from 0.05 to 0.4% at the maximum tailwater depth resulted in a 9% and 19.4% increase in the length and depth of the scour hole, respectively. Conversely, when the tailwater depth was doubled at the maximum bed slope and discharge, a reduction of 18.8% in depth, and 38.54% in scour length was observed. Finally, a general empirical relationship has been proposed to predict the depth and length of the scour hole downstream of GCS, incorporating the influence of the tailwater depth and bed slope.

This study applies the persistent scatterer interferometric synthetic aperture radar (PS-InSAR) technique to investigate surface deformation in Makkah City, Saudi Arabia, a region undergoing rapid urbanization and characterized by complex geological conditions. The rationale for this research stems from the extensive anthropogenic activities in Makkah City, such as groundwater over-extraction, large-scale construction projects, and significant land-use changes, which may lead to surface deformation and pose potential risks to infrastructure and public safety. Utilizing 16 C-band Sentinel-1 satellite images acquired from the European Space Agency (ESA) between December 2017 and January 2019, we employ the StaMPS code within MATLAB to measure deformation velocity and visualize the results using R Studio. The findings reveal that the deformation velocity in Makkah City ranges from − 19.1 to + 19.1 mm/year, indicating areas of both subsidence and uplift. Notably, certain regions exhibit substantial subsidence, while others show upward movement or an uplift. This study demonstrates the effectiveness of the PS-InSAR technique in detecting surface deformation in Makkah City, offering a valuable and economical tool for creating risk maps and implementing disaster mitigation strategies. The results stress the importance of continuous monitoring in areas exhibiting deformation signals and provide crucial insights for urban planning and infrastructure development in Makkah City, helping to mitigate potential future hazards and enhance the city’s resilience to geological risks.