Acta Geophysica最新文献

Extracting angle domain common-image gathers (ADCIGs) form wave equation migration is an essential process for geophysical inversion techniques such as migration velocity analysis and amplitude-variation-to-angle inversion. Efficiency and robustness remain two major issues when developing wave equation based ADCIGs extraction methods. Among many other wave equation based ADCIGs generation methods, the extended imaging condition based approach is target-oriented and computationally efficient, makes this method promising for large-scale 3D imaging problem. However, the classical extended imaging condition based ADCIGs extraction method introduce some smearing artifacts appear as crossing events, which will decrease signal to noise level of the generated ADCIGs In this paper, we propose a modification to the conventional extended imaging condition based angle gather generation method. We reformulate the process for calculating ADCIGs as a regularized least square inversion problem. We illustrate that by replacing the traditional extended imaging condition mapping method with least square regularized inversion, the mentioned smearing artifacts in calculated ADCIGs is significantly attenuated and higher signal to noise level can be achieved. Numerical results with both synthetic and field data validate the effectiveness and efficiency of the present method.

Least-squares reverse time migration (LSRTM) has become a popular research topic and has been practically applied in recent years. LSRTM can generate preferable images with high signal-to-noise ratio (SNR), high resolution, and balanced amplitude. However, LSRTM faces substantial computational challenges when dealing with large amounts of data. Anderson acceleration (AA) is recognized for its simplicity in implementation and its potential to reduce computational costs. By incorporating QR factorization into AA, computational efficiency can be further enhanced. We propose the use of AA with QR factorization (AA-QR) for LSRTM in the frequency domain to accelerate convergence and reduce computational cost. Numerical experiments utilizing the sunken model, the salt model, and the Marmousi model indicate that an optimal memory size for AA-QR is 10, and the step length can be set to five times the initial iteration step length of the steepest descent (SD) method. Compared to the SD method, conjugate gradient (CG) method, limited-momory Broyden–Fletcher–Goldfarb–Shanno (LBFGS) method, and AA, the AA-QR approach not only converges faster but also delivers superior imaging quality. Additionally, AA-QR remains robust under noisy conditions, producing high-resolution images. As such, AA-QR presents a viable alternative to LBFGS for gradient update in LSRTM.

A comprehensive groundwater (GW) monitoring approach is necessary for the long-term sustainability of regional economies and livelihoods, especially with the threats of population explosion, rapid urbanization, and climate change. By using modern technologies like integrating of machine learning, geographic information systems (GIS), and remote sensing (RS) data, climate-resilient monitoring strategies can be developed. This study aims to estimate groundwater levels using records from the TerraClimate dataset (1958–2020) and predict future GW patterns up to 2050 using climate model data. The focus is on the Mahanadi River basin in India, utilizing GIS, RS, and Google Earth Engine cloud. Future climate trend analysis (2021–2050) was conducted using the mean ensemble CMIP6 models (i.e., EC-Earth3 and MIROC6) historical, SSP2-4.5, and SSP5-8.5 datasets. Additionally, spatiotemporal vegetation indices were analyzed using MODIS data (2010–2017). This research employs an innovative ensemble boosting of six machine learning algorithms to predict groundwater levels and develop climate-resilient agricultural strategies in the river basin. The approach uses six nature-inspired wrapper algorithms to identify the best features contributing to groundwater level predictions for pre-monsoon and post-monsoon seasons. Validation was done using regional groundwater level data and various machine-learning classification matrices. The Boruta algorithm and SHapley Additive exPlanations methods were applied to select features for delineating hydrometeorological conditions in the study area. The slope of linear regression results showed a negative trend in precipitation in the lower part of the basin (around − 0.371 mm/year) and a positive trend in the upper part (around 0.238 mm/year). In the pre-monsoon period, the Extreme Gradient Boosting and Adaptive Boosting models achieved the best accuracy of 92% based on the area under the receiver operating characteristics curve. Based on the ensemble of two CMIP6 GCMs, under SSP5 8.5, 33.25% of the area is classified as having very high GWL exposure during the pre-monsoon period, compared to 30.58% in historical data. Additionally, under SSP5 8.5, 23.80% of the area is classified as having very high GWL exposure during the post-monsoon period, compared to 18.73% in historical data. The heavy reliance on groundwater for irrigation is a major cause of groundwater depletion in the catchment area. These results can inform sustainable agriculture planning, policymaking, and management in the future.

Studying the radiation hazards in volcanic environments is of paramount importance due to the often-present thermal activity, exemplified by phenomena like hot springs, fumaroles, and geysers. These features contribute to the release of radioactive elements and heavy metals, presenting significant threats to both human health and the environment. This study aims to assess the concentration of natural radiation and heavy metals and the associated environmental hazards in the Al-Lisi volcanic mountain in Dhamar city, Yemen. Nineteen different samples, including hot spring water, soil, plants, and groundwater, were collected. These samples were detected using a high-purity germanium detector. The concentrations of ²²⁶Ra in these samples were measured at 185.91 Bq L⁻¹ in water, 373.95 Bq kg⁻¹ in soil, 111.10 Bq kg⁻¹ in plants, and 1.44 Bq L⁻¹ in groundwater; for ²³²Th, concentrations were 138.62 Bq L⁻¹, 507.08 Bq kg⁻¹, 141.5 Bq kg⁻¹, and 1.77 Bq L⁻¹; and for ⁴⁰K, concentrations were 448.47 Bq L⁻¹, 1084.45 Bq kg⁻¹, 928.07 Bq kg⁻¹, and 13.08 Bq L⁻¹, respectively. Radium equivalent activity and absorbed gamma dose in the air due to these nuclides, as well as external hazard index, and annual effective dose, were calculated for all samples, the overall results indicated that the study area poses higher radiological hazards than the globally recommended values. Moreover, variations in environmental pollution levels due to heavy metal concentrations were observed. The results are concerning, and we suggest detailed geological and radiological analyses to understand, address, and devise appropriate solutions.

We studied seismic features of the 2023 Kahramanmaraş Türkiye earthquake sequence by analyzing the spatiotemporal behavior of the b- and -p values and source characteristics of the mainshocks. We complemented our study by determining the regional stress field. The b-values in the region varied from 0.45 to 1.15. We observed a slight b-value decrease (Δb≈0.2) months before the two mainshocks. Our results exhibited complex b-value patterns on the fault planes and regular aftershock productivity rates (1.14 < p < 1.25). We compare static stress drop estimates derived from effective fault dimensions to those of finite-fault dimensions. Total effective stress drops (the sum of the stress drops of all fault segments) for the earthquake doublet were almost identical (~ 2.05 MPa), while those from finite-fault dimensions are somewhat lower (0.35 and 0.96 MPa). Based on a complete stress drop case, effective seismic efficiency was 0.65 and 0.43 for both mainshocks. The amount of partial stress drop was used to discriminate between different stress drop models. No clear model is discerned for the first mainshock, but a partial or even complete stress drop, assuming finite-fault dimensions, is supported by our measurements. Stress drop estimations derived from spectral analysis (25.46 and 34.39 MPa) agreed with global studies but larger than finite and effective fault estimates. Stress inversion results indicated that in the fault segments where the mainshocks occurred, the orientation of principal axes was consistent with a strike-slip regime. Conversely, the normal-faulting regime dominates adjacent areas of the main fault system.

Groundwater contamination risk mapping constitutes an important component of groundwater management and quality control. In the present study, we describe a method for such mapping that is more suitable for arid regions than other methods developed in previous work. Specifically, we integrate machine learning tools, interpolation and process-based models with a modified version of DRASTIC-AHP to evaluate groundwater vulnerability to nitrate contamination, and to map this contamination in Jiroft plain, Iran. The DRASTIC model provides a tool for evaluating aquifer vulnerability by using seven parameters related to the hydrogeological setting (depth to water, net recharge, aquifer media, soil media, topography, impact of vadose zone and hydraulic conductivity), while the criteria ratings and weights of these parameters are evaluated by means of an analytic hierarchy process (AHP). However, to obtain the risk map, the model predictions related to groundwater vulnerability are combined here with a contamination hazard map, which we estimate by applying ensemble modeling. This modeling builds on the occurrence probability predicted by means of a modeling framework that is based on generalized linear modeling (GLM), flexible discriminant analysis (FDA) and support vector machine (SVM). We find that the application of our ensemble modeling to predicting groundwater contamination in Jiroft plain leads to better results (AUC = 0.916, Kappa = 0.89, MSE = 0.18 and RMSE = 0.11) compared to the separated employment of the various machine learning (ML) methods, i.e., either SVM (AUC = 0.847, Kappa = 0.86, MSE = 0.19 and RMSE = 0.29), GLM (AUC = 0.829, Kappa = 0.81, MSE = 0.23 and RMSE = 0.37) or FDA (AUC = 0.816, Kappa = 0.8, MSE = 0.26 and RMSE = 0.42). Our integrated modeling framework provides an assessment of both regional patterns of groundwater contamination and an estimate of contamination impacts based on socio-environmental variables, being particularly suitable for applications in which the amount of available data is scarce. The groundwater contamination risk map obtained from our case study shows that the central and southern regions of the Jiroft plain display high and very high contamination risk, respectively. This result is associated with the high production rate of urban waste in residential lands and an overuse of nitrogen fertilizers in agricultural lands throughout the study area. Therefore, while the present work introduces a new model which is applicable to arid regions in situations of scarce data availability, our results both provide insights for the future assessment of groundwater contamination in Jiroft plain and have potential impacts for the management and control of water resources in arid and semiarid environments.

Current study attempts to comprehend the complex interplay between wetland hydrological regime and balance between Methane (CH₄) emission and Carbon dioxide (CO₂) sequestration in the rain fed floodplain wetlands. A mapping of wetland was done based on consistency of water presence using water presence frequency (WPF) of ten years (2013–2022). Ground truth validation ensured the accuracy of the wetland mapping. To obtain an estimation of spatial balance, satellite image-based estimation was adopted. In this task, first, annual gross CH₄ emission and CO₂ sequestration were estimated separately by the estimation of seasonal emission and cycle-wise sequestration. The masses of gross annual emission and sequestration were then compared to determine net balance of gas regulation. The study also considered the atomic mass balance of carbon and the global warming potential (GWP) to standardize the greenhouse effects of these gases. The yearly total emission and sequestration were estimated 0.3 × 10³ ton and 82.2 × 10³ ton. Overall the wetland contributed positively in this balance by absorbing 81.9 × 10³ ton surplus amount of CO₂. As a result of such a surplus amount of CO₂ sequestration, if the global warming potentiality (GWP) of the emitted CH₄ is considered, then also the yearly sequestration remains surplus by 72.8 × 10³ ton. This amount saves 7.3 million USD of fixation cost per year. In terms of atomic mass of C, yearly 22.2 × 10³ ton was additionally sequestrated in wetland. This balance was investigated to have negatively associated with water presence consistency of the wetland. The water presence frequency (WPF) which portrays the consistency of water presence in the wetland, exhibited coefficient of correlation (r) value −0.5 with yearly balance CH₄ emission and CO₂ sequestration, indicating a negative association. This association of WPF was respectively positive and negative with CH₄ emission and CO₂ sequestration. Since the peripheral parts of wetland are often left behind from the focus of wetland restoration program and face massive encroachment. The present findings signify the vital role of hydrologically poor peripheral wetland parts in the balance and suggest to put emphasis on the conservation these parts for immense ecological and environmental importance.

The Nahavand polymetallic deposit, located within the metamorphic-magmatic Sanandaj–Sirjan zone, is one of the most important metallogenic zones in the Hamedan Province of Iran. Different ore mineralization has been found in the study area, including Au, Cu, Fe and Zn. Due to trenches lithological sampling analysis, the type of mineralization for this ore deposit is podiform, which has high expansion in all three dimensions (length, width and depth). In this paper, to approach a realistic interpretation, the parameters integration of geophysical surveys including magnetic susceptibility, electrical resistivity, and induced polarization has been simultaneously carried out in the study area. We applied the Li–Oldenburg algorithm and the smoothness-constrained least-squares method to invert magnetic, resistivity data, and induced polarization data in three dimensions. The efficiency of these inversion methods and programs is demonstrated with 3D inversion modeling of both synthetic and field data. The 3D inversion results of whole different geophysical methods determine the depth of the top and bottom of a polymetallic ore deposit and also show that the mineralization controlled by the geological faults and most of the slopes of the mineralization have a direction to the south.

Intensity prediction equations (IPEs) are critical for quickly obtaining the macroscopic intensity of a site post-earthquake, with regional dependencies influencing their design. Historically, most IPEs in China have focused primarily on the factors of distance and magnitude. This study develops site-specific IPEs for Western China, using data from 53 seismic events since 1970, to address the previously overlooked importance of site conditions and overcome the limitations of past models. The data were categorized into three site groups, with IPEs derived through multiple nonlinear regression methods. Our findings reveal that macroscopic intensities at category III and IV sites are notably higher than those at categories I and II, with this disparity increasing alongside the magnitude. Unlike conventional IPEs, the IPEs proposed in this paper incorporate local geological and seismological characteristics, enhancing prediction accuracy across varied site conditions. This methodology distinctly contrasts with prior approaches by providing a nuanced assessment that integrates comprehensive site categorization, resulting in more precise intensity predictions. This advancement is particularly crucial for effective emergency management and disaster mitigation strategies in seismically active regions.

The fact that on a live bed, boulders tend to sink during scouring is usually ignored, weakening the true understanding of hydrodynamics in boulder beds. In this paper, flume experiments were conducted to investigate the hydrodynamics around a boulder over a movable bed with a particle tracking velocimetry (PTV) system. By measuring the velocity field, the major flow characteristics, such as velocity distribution, turbulent kinetic energy (TKE) and bed shear stress, were analyzed. The results show that the sinking boulder apparently mediates the local flow structure and turbulence pattern. The near wake region is located in the range of 2D (D is the particle size of the boulder) downstream of the boulder. There is a near-bed countercurrent in the near wake region, the TKE increase sharply, and the velocity distribution deviates from the logarithmic distribution. Compared with the flat bed, the turbulent kinetic energy extreme point of the boulder riverbed in the near wake area deviate from the bed surface to the water depth at the top of the boulder, and the direction reversal and extreme point appear at the top of the boulder. The bed shear stress increases sharply in the near wake region of 1.5 ~ 2D.