Acta Geophysica最新文献

In semi-arid regions like Wadi El-Madamud, Egypt, sustainable groundwater management is hindered by the intricate interplay of structural, lithological, and climatic controls on aquifer recharge and storage. Despite the hydrogeological importance of the Plio-Pleistocene aquifer, integrated assessments for delineating groundwater potential zones (GWPZs) remain limited. This study bridges this gap through a multi-source, GIS-based approach combining conventional (geology, soil, rainfall), remote sensing (Sentinel-2 for LULC, Landsat 8–9 for NDVI, ASTER-GDEM for topography), and geophysical data (aeromagnetic and DC resistivity) within an analytic hierarchy process (AHP) framework. Ten thematic layers—geology, soil, slope, elevation, drainage density, lineament density, rainfall, topographic wetness index (TWI), LULC, and NDVI—were integrated using AHP-weighted overlay (consistency ratio = 0.05). The region’s stratigraphy spans Cretaceous to Holocene, with soils (Lithosols, Calcaric Fluvisols, Eutric Regosols, Calcic Yermosols) exhibiting differential infiltration and retention. GWPZ mapping classified the area into five categories: excellent (0.16%), good (25.54%), moderate (21.01%), fair (52.17%), and poor (1.12%), with high-potential zones localized along the Nile Valley fringe due to permeable Quaternary–Holocene sediments, Calcaric Fluvisols, and favorable topography. Model accuracy was validated using hydrochemical data from 15 wells, revealing a fresh to slightly saline gradient (TDS: 366–1541 mg/L), and ROC-AUC of 0.72. Aeromagnetic analysis identified dominant structural trends (N–S, E–W, NE–SW, NW–SE) and basement depths (100–1250 m), while DC resistivity (31 VES points, Schlumberger array, AB ≤ 1000 m) revealed a four-layer subsurface: consolidated wadi deposits (> 1000 Ω·m), saturated sand aquifer (≤ 100 Ω m, 25–85 m thick, 15–40 m depth), dry compacted sand (10³–10⁴ Ω m), and Thebes Formation limestone (10⁴–10⁵ Ω m). The study recommends cross-validation with MIF and Fuzzy AHP and prioritizes drilling in north-central, southwestern, and northeastern zones. By integrating surface and subsurface datasets, this work advances hydrogeological modeling in structurally complex terrains and provides a replicable framework for groundwater exploration in arid and semi-arid regions.

This study presents an innovative approach for estimating permeability (K), a key reservoir property that influences fluid flow in natural gas hydrate (NGH) systems, which is essential for optimizing gas production from hydrocarbon reservoirs. In the NGH system, permeability is often significantly reduced due to the accumulation of hydrates within pore spaces, making the accurate estimation of permeability critical for evaluating reservoir quality and production. In this study, empirical correlations, regression analysis (RA), and artificial neural networks (ANNs) are integrated to enhance prediction accuracy. Comprehensive well-log datasets, including nuclear magnetic resonance (NMR), gamma ray (GR), P-wave sonic velocity, bulk density, and resistivity, were utilized to identify gas hydrate-bearing intervals, with a particular emphasis on NMR data for K estimation. The study evaluates the predictive efficacy of these models through absolute average relative error (AARE), normalized mean square error (NMSE), root mean square error (RMSE), and correlation coefficient (R²). The ANN model demonstrates superior performance, accurately predicting K values ranging from 0.01 to 100 mD in the gas hydrate zone (GHZ) at depths of 300–325 m below the seafloor (mbsf). For this study, the ANN model was trained solely on a single well dataset and still produced consistent permeability estimates, indicating its reliability for NGH assessment in data-scarce areas. This work provides novel insights by integrating advanced computational techniques for permeability prediction, strengthening the foundation for developing efficient production strategies in NGH resource exploitation. The proposed methodology offers a precise, data-driven solution for predicting permeability. It holds the potential for broader applications in similar geological settings, advancing the understanding and exploitation of gas hydrates.

The East Anatolian Fault Zone (EAFZ) is one of the most critical and active tectonic elements in Türkiye, and there are a significant number of high-magnitude earthquakes along with the EAFZ, mentioned in the historical documents and recorded in the instrumental periods in southeastern Anatolia. The latest devastating tectonic activity occurred on February 6, 2023 (Mw = 7.7), followed by another high-magnitude earthquake in the same day (Mw = 7.6) on this fault zone. More than 15,000 aftershocks (some of them are Mw ≥ 4.0) have been recorded since then. The EAFZ is composed of several sub-fault zones and their segments with different elongations. Although the majority of these segments indicate ruptures during the main shock and aftershocks, some of them (including the Malatya Fault) are still aseismic, including great potential to trigger high-magnitude earthquakes. In this study, we interpreted the magnetic data and the epicenter distributions of earthquakes to correlate the tectonic structures and active fault zones. The fault indicators (with maxspots) based on the different types of derivative transformations provided good correlations between the faults and magnetic discontinuities because almost all fault zones in the study area have been filled by the magmatic intrusions to create magnetic anomalies. The maxspots are also another practical tool to determine the possible segments of faults and/or exact locations of undefined magmatic intrusions. It is possible to claim that the faults have provided conduits for the intrusion of the causative bodies while triggering the earthquakes in this critical area. The earthquakes are generally recorded along the southern fault segments. As a result of these methods and correlations, we determined the exact location and the length of the Malatya Fault (approximately 220 km), which is represented with the low-magnitude earthquakes.

Accurate prediction of groundwater level (GWL) and its associated drought is crucial for sustainable water resources management, particularly in arid and semi-arid regions. In this study, a hybrid modeling framework was developed by integrating advanced data preprocessing techniques with artificial intelligence and deep learning (DL) models to predict GWL and groundwater drought (GWD) in the Nahavand aquifer, western Iran. Despite the critical role of the Nahavand region—one of the main tributary basins of the Karkheh watershed and a vital source of agricultural and domestic water supply—no comprehensive investigation has yet been conducted to assess its water resources and drought dynamics. This research gap is particularly concerning given the accelerating rate of groundwater extraction from the aquifer. Two signal decomposition methods including wavelet transform (WT) and complete ensemble empirical mode decomposition (CEEMD) were employed to decompose the time series into sub-signals, which were then used as inputs to the long short-term memory (LSTM) and group method of data handling (GMDH) models. Hybrid models (W-LSTM, W-GMDH, CEEMD-LSTM, and CEEMD-GMDH) were constructed and evaluated using statistical performance indicators. The results revealed that the W-GMDH hybrid model outperformed the others, achieving a coefficient of determination (R²) of 0.954 and a root mean square error (RMSE) of 0.027 m. The GWL forecasts generated by this model were used to compute the Groundwater Resource Index (GRI), indicating the occurrence of severe and prolonged droughts in the study area. Moreover, predictions for the first half of the 2024–2025 water year suggest continued GWD in the region. These findings highlight that combining signal decomposition techniques with AI-based models provides an efficient and reliable approach for groundwater prediction and drought assessment.

Previous studies on the Luohe Geothermal Field focused on resource exploitation and utilization without integrating systematic exploration and assessment, and the technical factors affecting reinjection efficiency were not thoroughly investigated. In this study, core and reinjection tests as well as numerical simulation were used to assess the geothermal reinjection efficiency and its influencing factors in the Luohe Geothermal Field. The division of reservoir–seal pairs was appropriate, and the reservoir had a higher thermal conductivity (2.546 W/mK), specific heat (1.94 MJ/m³·°C), permeability (20.67 mD), and porosity (23.64%) than the seal strata (1.143 W/mK, 1.67 MJ/m³·°C, 3 mD, and 20%, respectively). The tailwater extracted from the reservoir was more efficient as reinjection water than the Quaternary pore water. The reinjection efficiency in the Luohe Geothermal Field was most sensitive to the well spacing (weight: 0.606), followed by the extraction pressure (0.326), and was least sensitive to the reinjection temperature (0.042) and flow rate (0.026). The most appropriate extraction–reinjection parameters included a reinjection flow rate of 20 m³/h, a reinjection temperature of 20 °C°C, a well spacing of 400 m, and an extraction pressure of 1.013 × 10⁵ Pa. The optimization method is applicable to geothermal fields with geological conditions that are similar to those of the Luohe Geothermal Field in north China.

Precipitation is a crucial component of the hydrological cycle, with significant implications for agriculture, water resources, and environmental sustainability, particularly in climate-sensitive regions such as Pakistan. To enable informed decision-making and long-term planning, precipitation variability must be well understood and precisely modeled. We collected and evaluated seasonal precipitation data from numerous meteorological stations in Punjab, Pakistan. The precipitation concentration index (PCI) was calculated seasonally at each sampling station to analyze the concentration and timing of rainfall over the winter, spring, summer, and autumn seasons. To simulate the geographical distribution of seasonal PCI, we used four geostatistical methods: ordinary kriging, universal kriging, Bayesian ordinary kriging, and Bayesian universal kriging. As far as the proposed study is the first to use and compare both conventional and Bayesian kriging methods in mapping the seasonal precipitation concentration index (PCI) in the Punjab area. The seasonal orientation of PCI instead of an annual one gives us a complete knowledge of the intra-annual time distribution of precipitation. The evaluation of the temporal variability across seasons provides new information on spatial prediction accuracy and spatial variability, which can serve important functions in the planning of agricultural activities, as well as the water resource management and adapting to climate change within this climate-sensitive area. Before spatial interpolation, representative PCI values were prepared using the Gibbs sampling approach. Comparative performance according to the root mean squared error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe efficiency (NSE) indicated that Bayesian ordinary kriging was more effective than ordinary kriging in most of the seasons, and Bayesian universal kriging was more reliable and accurate than universal kriging. The results show that Bayesian geostatistical techniques can enhance the spatial modeling of seasonal precipitation indicators. The study’s findings are relevant to the Pakistan Meteorological Department and can serve as a scientific foundation for policymakers to develop improved water management, agricultural planning, and climate resilience measures.

Satellite data play a crucial role in understanding and monitoring numerous environmental processes, and their increasing accessibility has led to their use in various scientific disciplines, particularly those related to the hydrosphere. This includes the hydrosphere, with a wide range of applications related to lakes. In Poland, where there are several thousand lakes, they have become a subject of significant interest. The aim of this article is to review the current state of lake research in Poland using satellite data. The results indicate that data from the Landsat and Sentinel-2 satellite families have been most commonly used in research. The satellite-based research has covered a range of topics, including lake evolution and morphometry, water quality, water temperature, biodiversity, ice phenomena, and water levels. To broaden the use of satellite data in the future, it will be necessary to coordinate in situ studies, such as hydrological monitoring (water levels and temperature) and environmental monitoring (water quality and ecological status), with satellite overpasses. Considering the rapid development of satellite technology, this methodology is expected to gain importance, expanding the scope of knowledge into previously inaccessible areas.

Predicting crop yield is complex due to its dependence on multiple meteorological variables. Remote sensing (RS) has become the prime source of climate parameters due to detailed spatial and temporal resolutions; however, its product needs further quality enhancement due to associated errors. The primary aim of this study is to incorporate the EEMD, as a signal modification technique, with the LSTM model, aiming to anticipate crop yield of four strategic crops, namely barley, lentils, pea, and wheat in all provinces of Iran. The annual crop yield of these crops was extracted from Iran’s Ministry of Agriculture data over 15 years, spanning from 2005 to 2020. In the context of EEMD-LSTM prediction, four influential climate parameters, including minimum and maximum temperature, precipitation, and the SPEI, were considered as the input variables. The analysis shows that applying EEMD generally enhances the predictive performance of the LSTM model for most agricultural products. For barley, lentils, and peas, EEMD substantially improves accuracy compared with the baseline model. Although its impact on wheat is not statistically significant, EEMD still reduces RMSE by approximately 42%. For lentils, the method yields notable improvements, with reductions of 15.7, 13.8, and 8% in MAE, RMSE, and PCC, respectively. Additionally, when noisy wheat data are removed, the error distribution slightly increases, indicating that wheat exhibits the least improvement among all evaluated crops. The spatial assessment reveals clear geographic differences in the effectiveness of EEMD. The method substantially reduces prediction errors for barley in the northwestern region. A similar, though smaller, improvement is observed for lentils in the same area. Conversely, EEMD increases prediction errors for chickpea production in both the northwest and eastern regions, highlighting strong spatial variability in the model’s performance across the study area. The utilization of EEMD, as a noise removal tool, evidently leads to a reduction in model errors and a notable enhancement in the performance of the estimation model. The findings of this study can be utilized in the development of food security policies and enhancement of agricultural product performance across various locations, ultimately increasing productivity.

A set of new empirical relationships between modified Mercalli intensity (MMI) and synthetic peak ground acceleration (PGA) is developed for shallow crustal earthquakes in central and north-west Mexico. Few strong-motion recordings of shallow crustal earthquakes in these regions have led to uncertainties in estimating seismic risk even though they comprise some of the most densely populated urban sites in the country. We present relationships in which MMI is a function of PGA and its inverse form. No relationship for this type of events has been developed, although these earthquakes represent a high-risk potential for nearby highly populated urban regions. Ground motion data from 18 moderate-to-large earthquakes (4.5 < M_W < 7.5) that took place in the Basin and Range, Sierra Madre Oriental Fold-thrust belt, and Trans-Mexican Volcanic Belt provinces and the corresponding 531 MMI information reports were employed. Synthetic PGA data were generated using the finite-fault stochastic method, assuming different rupture scenarios to extend the limitations of the dataset. Linear and bilinear regression techniques were used, considering a binning averaging procedure and the whole dataset, respectively. As the first approach, a set of MMI predictive equations independent of moment magnitude (M_W) and hypocentral distance (R) was derived. Despite weak dependencies of the residuals on M_W and R terms, we also developed complementary predictive relationships that include these parameters as independent variables. The conversion equations between PGA and MMI, including all terms, show slightly less variability than simple linear equations in predicting intensity values. The proposed predictive equations are consistent with similar relationships in other regions of the world. The discrepancies among the different relationships may reflect variations in input data, particularly concerning the macroseismic intensity assignments, which are inherently subjective, and the tectonic regime. The conversion relationships that we have developed can be used to generate maps of estimated shaking intensities based on ground motion observations for crustal earthquakes in Mexico.