Acta Geophysica最新文献

The present study deals with investigating the spatial variation of suspended sediment properties in the Sutlej river basin (SRB). Twenty sampling locations were selected in the Sutlej river and its tributaries, and sediment sampling was carried out during monsoon season. The suspended sediment properties, namely suspended sediment concentration (SSC), particle size distribution (PSD), shape, and mineralogy, were measured through dynamic imaging, laser diffraction, and x-ray diffraction techniques. It was found that there is a wide variation of SSC, whereas there is an insignificant variation of PSD, shape, and mineralogy in the SRB. The highest SSC (2841 mg/l) was observed in the Kiran tributary, whereas the lowest (131 mg/l) was in the Titang tributary. The highest SSC (1749 mg/l) in the Sutlej main stem was observed at Khab, the topmost sampling location. The mean particle size varies from 6.00 to 25.36 µm (5.53–7.53 Φ). Shape analysis showed high degree of sphericity varying from 0.81 to 0.88 and slight elongation varying from 0.70 to 0.8. Mineralogical analysis depicts the dominance of quartz (66.73–82.25%) in the SRB. International Electrotechnical Commission IEC 62364 (IEC 62364 (2019) Hydraulic machines—Guidelines for dealing with hydro-abrasive erosion in Kaplan, Francis, and Pelton turbines, 2nd edn. Geneva, Switzerland) based average shape and hardness factors for SRB were estimated as 1.36 and 0.85, respectively. The maximum particle load for SRB was estimated as 0.014 kg/m³.

The moderate-sized M_w 5.5 Rukwa earthquake of 21 March 2019 in southwestern Tanzania caused strong shaking in the vicinity of towns. Results from the relocation suggest the earthquake is associated with the Chisi shear zone and the fault plane solution reveals dextral strike-slip motion. The association of the event with shear zone and dextral strike-slip faulting likely represents reactivation of the Paleoproterozoic dextral shear faults caused by transfer of strain energy accommodated in the previously north-eastern active Lupa border fault into the previously less-active south-western Ufipa border fault. The orientations of lineaments around the epicenter and the macroseismic intensity distribution indicate the ruptured plane of the event propagated along SE direction. The intensity map shows significant ground shaking extending further southwest, up to ~ 450 km, affecting buildings in towns along the Western Rift. The notable strong vibration is associated with the amplification of the ground movement caused by soil effects. Most of the buildings in towns along the Western Rift cannot withstand strong shaking when a big event occurs. This implies that a future, bigger event in the Western Rift would result in large casualties and destruction of properties. The occurrence of a big event can also result in major volcanic hazards by triggering an eruption within the Rungwe Volcanic Province. The event provides the most useful first-hand input data for the estimation of hazards and also assists in better planning against earthquake hazards in near-field cities and towns along the Western rift.

Crop yield prediction is one of the burgeoning research areas in the agriculture domain. The crop yield forecasting models are developed to enhance productivity with improved decision-making strategies. The highly efficient crop yield forecasting model assists farmers in determining when, what and how much to plant on their cultivable land. The main objective of the proposed research work is to build a high efficacious crop yield prediction model based on the data available for the period of 21 years from 1997 to 2017 using machine learning and hybrid deep learning approaches. Two prediction models have been proposed in this research work to predict the crop yield accurately. The first model is a machine learning-based model which uses the CatBoost regression model and its hyperparameters are tuned which improves the performance of the yield prediction using the Optuna framework. The second model is the hybrid deep learning model which uses spatio-temporal attention-based convolutional neural network (STACNN) for extracting the features and the bidirectional long short-term memory (BiLSTM) model for predicting the crop yield effectively. The proposed models are evaluated using the error metrics and compared with the latest contemporary models. From the evaluation results, it is shown that the proposed models significantly outperform all other existing models and CatBoost regression model slightly performs better than the STACNN-BiLSTM model, with the R-squared value of 0.99.

Quantitative analysis of the slip rate of active faults and their seismic parameters is important for seismic hazard analysis. In this study, we first construct an elastic block model to obtain the slip rate of boundary faults based on the distribution characteristics of active faults, seismicity, and global navigation satellite system (GNSS) observations in Sichuan–Yunnan, China. Then, the long-term seismic risks of the boundary faults are quantitatively evaluated based on the principle of seismic moment balance. The Sichuan–Yunnan region can be divided into 17 relatively independent and stable subblocks. There is clear zoning in the distribution and mechanisms of boundary fault movement and deformation. The boundary faults exhibit an alternating dextral–sinistral–dextral–sinistral strike-slip pattern from northeast to southwest. Among these boundary faults, the Xianshuihe–Xiaojiang fault zone has a high sinistral strike-slip rate, and the Jinshajiang fault plays an important role in accommodating the movement and deformation of the subblocks in the Chuandian block. The dextral strike-slip rate is approximately 10 mm/yr, which is diffusely transferred to the secondary boundary faults in the Chuandian block. Comparison of the rates of moment accumulation and release reveals that the southern segment of the Xiaojiang fault, the Longriba fault, the Daliangshan fault, and the Yuanmou fault exhibit significant moment deficits, with corresponding moment magnitudes exceeding Mw 7.5. More attention should be given to the strong earthquake risks of these faults. The Xianshuihe–Xiaojiang, Jiali–Lancangjiang, and Red River faults, which are arc shaped, dominate the regional deformation and determine the motion and deformation model of the subblocks and secondary boundary faults within the Chuandian block and the area southwest of the Red River fault.

To gain insight into the organization of the underlying structures in the northern Tunisia region, detailed gravity data in combination with geological information have been investigated.

Many techniques have been used, including residual, upward continuations and derivative. Results from the edge detection technique based on the tilt angle map generated from the first vertical gradient were discussed and compared with results obtained by Euler deconvolution. The compilation and comparison of gravity maps and geology maps enable the determination of major structural trends. It validates specific structural elements acquired from outcrops and specifies new ones. According to the gravity data interpretation, the area of study is impacted by several underlying structural trends. The main trend direction is NE–SW, which is closely linked to the J Chehid and Bled Tejra Akseb trends. NW–SE direction is the second significant trend that borders Bled El Ghorfa to the north. Additional determined trends include: the N–S direction, which is associated with the Lakouat fault. A strong correlation was noted between results from tilt angle method and Euler deconvolution, showing that they may both be used to delineate the main structural framework of the region.

The 4th Atlas Georesources International Congress (AGIC), convened in Hammamet, Tunisia in March 2023, provided a significant forum for scholars, scientists, and practitioners to converge and deliberate on the intricate challenges surrounding resource management. Themed “Geoscience Innovations For Resource Management: Socio-Economic Challenges In An Environmentally Constrained World,” the congress spotlighted five pivotal domains: hydrology and water resource management, applied geophysics in geological exploration and structural analysis, environmental pollution, climate and atmospheric studies, and renewable energy and climate modeling. These thematic areas serve as focal points for grappling with the multifaceted challenges posed by contemporary resource management paradigms.

Accurate evaluation of aquifer potential is essential for groundwater assessments. Aquifer potential is mainly evaluated by aquifer parameters. Hydraulic conductivity (K) is the main aquifer parameter extensively measured in groundwater studies. Traditionally, boreholes are used to determine K. However, the conventional approaches have several limitations, can only provide point-scale K measurements, and cannot be performed in the high topography areas. On the other hand, geophysical approaches are less time-consuming and non-invasive, more cost-effective, faster, and can assess the subsurface hydrogeological conditions over large areas. In the past, several empirical-based geophysical studies were carried out to estimate K. However, in such studies, VES (vertical electrical sounding) method was used to estimate 1D K mostly in the homogeneous setting. Given the natural heterogeneity of hard rock terrains, the borehole/VES-based K causes uncertainty in accurate assessment of aquifer potential related to the weathered layers and fractures/faults. To this end, this contribution uses ERT (electrical resistivity tomography) method to predict 2D K for more reliable and thorough evaluation of groundwater potential in the heterogeneous sites. The proposed approach assesses the water-bearing ability of geological layers via definite ranges of K and resistivity, i.e., high potential aquifer enclosed by completely weathered/fractured rock via resistivity below 400 Ωm and K between 1.0 × 10^–1–9.5 × 10 m/d, medium potential aquifer of partly weathered/fractured rock with resistivity from 400 to 1000 Ωm and K from 3.5 × 10^–5 to 1.0 × 10^–1 m/d, negligible-potential aquifer of integral rock having resistivity variations between 1000 and 3000 Ωm and K between 6.5 × 10^–8 and 3.5 × 10^–5 m/d, and no potential aquifer of fresh rock with resistivity above 3000 Ωm and K from 1.5 × 10^–10 to 6.5 × 10^–8 m/d. Two deeper zones of weathered rocks were evaluated by the localized fractures/faults namely F1 and F2. This is the rare study, which estimates 2D K using ERT or any geophysical method. The obtained results, compared with the traditional approaches, offer much better evaluation of aquifer potential for groundwater assessment in the complex geological settings of hard rock.

Rajgir thermal spring field in Bihar, Eastern India, holds international significance due to its historical and multi-religio-cultural importance. Present study attempted to understand the aquifer system to arrest its diminishing discharge in the backdrop of climate change and urbanization. The study identified three aquifer systems, namely fractured quartzite, basement granite, and overlying alluvium, that are working in tandem toward the delicate balance in recharge and discharge in the thermal spring field controlled by the regional fault system. Geological study along with geophysical surveys through gradient resistivity profiling, vertical electrical sounding, and electrical resistivity tomography is carried out to identify local distribution of the fault system. Based on the study, a geological map of the area is prepared followed by preparation of water table contour map. Study of exploratory borehole tapping the fracture zone reveals that the fracture system is under thermo-artesian condition capable of 5 m³/h yield with 40 m drawdown. This translates into transmissivity of about 1 m²/day. Historical rainfall, spring discharge, and groundwater hydrograph of the area are correlated and show that an average time lag of about 20–30 days exists between peak rainfall and peak groundwater level and about 30 days between peak rainfall and peak discharge. It highlights the importance of local recharge for survival of the thermal spring field. Rainfall and potential evapotranspiration data analysis shows that recharge occurs only when annual rainfall exceeds 850 mm, making the variability in rainfall due to climate change a potential threat to adequate recharge. The area is also witnessing growing urbanization which is altering the local groundwater regime. Analysis of historical water level shows that groundwater table declined by about 2 m in the last decade. Comparison with earlier studies is used to identify the changes in groundwater flow pattern in the area. Spatial variation in groundwater temperature is mapped and statistically analyzed. Results show that temperature above 30.8 °C may be considered as samples having signature of thermal water component. It is also observed that thermal water is restricted in some fixed pathways. Discharge measurements are carried out to study diurnal variation and effect of pumping. A set of private borewells, where thermal water is reported, is also studied. Typically, these wells are limited to the weathered part of the rock formation and do not tap fractures. Based on hydrogeological scenario, a local hydrodynamic model is conceived. Further, a management plan is suggested for sustenance of the thermal spring field with potential solution involving establishment of a carefully regulated zone with prohibition of energized pumping. Based on the present study, Government of Bihar has accepted the recommendation and is in the process of implementation.

The Chang 8 formation of the Yanchang Group, located in Yuancheng area of the Ordos Basin, is a typical tight oil reservoir in China. This reservoir is characterized by low porosity, low permeability, strong non-homogeneity, and significant difficulty in evaluating the reservoir parameters. To examine and investigate the microscopic pore structure characteristics of the Chang 8 formation, cast thin section and scanning electron microscopy techniques were utilized in this study, Moreover, tests on the core physical properties were conducted and the data from these tests were integrated into the analysis of basic characteristics of the reservoir rock mineralogy, pore permeability, and other fundamental characteristics. The shapes of piezomercury curves were systematically examined to study the characteristics and features of pore structures for the 17 samples. In accordance with the fractal dimension of the NMR T₂ spectrum, the reservoir was classified into four categories, and a conversion model delineating the correlation between the NMR T₂ spectrum and the capillary pressure curve was formulated through the application of the segmented power function method. This model was then implemented in the interpretation of NMR logging, facilitating the acquisition of a seamless pseudo-capillary pressure curve spanning the entire well section. Three essential parameters reflecting the microscopic pore structure, namely the expulsion pressure, median pressure, and sorting coefficient of the core samples, were extracted. The association between reservoir parameters and reservoir categorization was then determined through the application of a generalized regression neural network. The pseudo-capillary pressure curve reservoir parameters of the whole well section were processed to derive the classification profile of the reservoir, and the classification results demonstrated a strong alignment with those of the mercury injection experiments. This study highlights that the proposed method can provide crucial foundation for the investigations on pore structures in tight oil reservoirs and the evaluation of reservoir classification.

Understanding how precipitation fluctuates geographically and temporally over a specific place due to climate change is critical. Generally, simulations of general circulation models (GCM) under different scenarios are downscaled to the local scale to study the impact of climate change on precipitation. However, selecting suitable GCMs for the given study area is one of the most hectic tasks, as the performance of GCMs may vary with respect to space and timescale. Therefore, the current study ranks twenty-seven CMIP 6 (Coupled Modelled Intercomparison Project Phase 6) GCMs in simulating precipitation over India for nine times series, including daily, monthly, yearly, and six extreme series extracted with annual maximum and peak over threshold methods. The gridded daily rainfall data provided by the India Meteorological Department (IMD) are used as the observed data. The GCMs' outputs are corrected for the systematic bias using the linear scaling method. The performance of a GCM is assessed with three statistical performance metrics, namely NSE, RMSE, and R². The GCMs' ranks are determined using a multi-criterion decision-making technique named the modified technique of order preference by similarity to an ideal solution (mTOPSIS) for every grid point and nine timescales (i.e., daily, monthly, yearly, and six extreme series). From the results, for the entire India, the top ten recommended CMIP 6 GCMs are FGOALS-g3, HadGEM3-GC31-MM, EC-Earth3, BCC-CSM2-MR, CNRM-CM6-1-HR, CanESM5, AWI-ESM-1-1-LR, MPI-ESM-1-2-HR, IITM-ESM, and INM-CM5-0. The identified best-performing models provide insightful information for better regional climate projections and underscore the necessity of considering multiple model outputs for reliable climate change impact assessments and adaptation strategies in the region.