pure and applied geophysics最新文献

Water quality assessment is a critical component of sustainable development and water resource management. In this study, surface water quality across the Urmia Lake Basin was evaluated using 11 hydrochemical parameters measured at 30 hydrometric stations. The Entropy-Weighted Water Quality Index (EWQI) was calculated for each station, and spatial distribution of both individual parameters and EWQI was mapped using the Inverse Distance Weighted (IDW) interpolation method. According to the EWQI results, most regions of the basin exhibited favorable water quality for drinking purposes, except the northeastern sub-region, primarily located in East Azerbaijan Province. The poorest water quality was observed at Markid station, with an EWQI value of 946, followed by Akhola station (219.67); both were classified in class 5 (extremely poor). Arzanag station, with an EWQI of 127.63, fell into class 3 (medium). Stations Nazarabad, Ejvaj, and Orian were categorized as class 2 (good), while the remaining stations were classified in class 1 (excellent), indicating high-quality drinking water across the majority of the basin. The TOPSIS multi-criteria evaluation method was also applied, and the ranking results showed a very strong consistency with the EWQI outcomes, with a correlation coefficient of 0.984. Stations with poor water quality (high EWQI) were mainly located in salt flats and non-carbonate formations, influenced by agricultural and urban land uses. Except for the northeastern part, the rest of the Urmia Lake Basin had permissible drinking water.

The formation of large hail is often accompanied by increased lightning activity, and the density of lightning flashes may serve as a predictive indicator of hailfall. However, the quantitative relationship between hail characteristics and lightning events remains understudied in many regions, including Northern Eurasia. This study presents an analysis of lightning activity accompanying hail events in Russia. Our analysis is based on data from the World Wide Lightning Location Network (WWLLN) and a database containing over 3100 hail event reports in Russia for the 2016–2024 period. We estimate the number and density of lightning flashes in the surrounding areas of the reported hail events and analyze the spatial distribution of the lightning activity characteristics that accompany hail events. The distributions of number and density of lightning accompanying hail events are generally described by a power law and has two sections with different rates of change in the frequency, which can be associated with different types of convection organization. A nonlinear relationship is found between the characteristics of lightning activity and hail diameter. We calculate forecast skill scores using various values of lightning flash density as forecast for large hail (with a diameter of ≥ 20 mm), and then maximize these scores to determine the threshold values for lightning flash density that discriminate between non-hail and large hail events. Our findings could help refine information about the location and/or timing of large hail events that lack such information, by using lightning data. The obtained relationships between lightning and hail could potentially be used to estimate the true frequency of large hail events in low-populated regions with a sparse network of weather stations.

Relocated hypocenters (EHB) and the focal mechanisms reported by the Global CMT and Franco et al. (Geofísica Internacional 59(2):54–80, 2020) are used to study in detail the geometry and state of stress in the subducted Cocos plate and the maximum depth of the seismogenic zone. Ten profiles were plotted along the Mesoamerican Trench in the Oaxaca–Chiapas subduction zone, oriented in the direction of convergence. The seismicity and the focal mechanisms observed define three general segments of the plate geometry. They are defined as the Puerto Escondido–Huatulco, Tehuantepec, and Chiapas regions. The results suggest a gradual increase in the depth of seismogenic width from Oaxaca to the Chiapas subduction zone. Based on the width of the seismogenic plate contact and the length of this segment of the subduction zone, the potential largest magnitude earthquake in the Puerto Escondido–Huatulco region could be as large as M_w 8.5. In the Tehuantepec segment, a M_w 8.3 event could be expected and in the Chiapas region an earthquake M_w 8.1. However, a much larger earthquake could take place if the rupture were to extend to several of these segments, as it apparently happened in 1787.

Drought has been viewed as a climatic event of significant importance that hampers agricultural productivity, efficient management of water resources, and socio-economic development, especially in arid, semi-arid, and arid-semiarid regions. Even though improved approaches to modeling dry spells have been reported, there remains a substantial disparity in the forecasting ability of the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) for different climatic conditions. In response to the observed disparity, the current study utilized the Tuned Q-factor Wavelet Transform (TQWT), Variational Mode Decomposition, Empirical Mode Decomposition, and Empirical Wavelet Transform (EWT), together with Gaussian Process Regression (GPR), Support Vector Machines, and Adaptive Neuro-Fuzzy Inference System (ANFIS) models. The dataset included precipitation and temperature data collected from four synoptic instrument-equipped meteorological stations from 1990 to 2022—Tabriz and Shiraz corresponding to semi-arid, and Kerman and Yazd corresponding to arid regions—and included SPI and SPEI index predictions for temporal periods of 1, 3, and 6 months. Through the use of autocorrelation diagnostics, it was possible to identify the optimal input lags (t-1, t-2, and t-3) specifically allocated for the model development process, derived from 75% of the available dataset. For the case of the 1-month temporal period, the models using the TQWT revealed the best forecasting effectiveness; most importantly, the TQWT-ANFIS model recorded the highest accuracy at the Tabriz station, while the TQWT-GPR model showed the highest accuracy values at Shiraz, Kerman, and Yazd (R²≈0.996–0.997; RMSE≈0.05–0.07). For the 3- and 6-month temporal evaluations, the EWT-ANFIS model recorded the best performance among all allocated stations, marked by the lowest error metrics (RMSE≈0.01–0.03) together with nearly perfect goodness-of-fit values (R² and NSE≈0.999). The Shiraz and Kerman observation stations showed the best performance indices, reaching a Kling-Gupta Efficiency (KGE) of 0.99. By comparison, the report from Tabriz indicated a poorer KGE of about 0.93, while the Yazd station showed volatility in the 6-month Standardized Precipitation Index, reaching a KGE of about 0.60, suggesting a rising aridity trend. Overall, results demonstrate that while TQWT-based models dominate short-term drought prediction, EWT-ANFIS is the most robust for medium- and long-term forecasts. These findings emphasize the potential of hybrid decomposition–machine learning frameworks to improve drought monitoring and strengthen water resource management strategies in water-scarce regions.

During well planning, accurately determining geomechanical parameters is crucial to ensure wellbore stability during drilling, especially in complex reservoirs. A thorough assessment of these factors optimizes the drilling and well completion processes. Among the primary stresses, minimum horizontal stress plays a key role in hydraulic fracturing design and wellbore stability. However, directly measuring this stress is labor-intensive and costly, highlighting the need for efficient, alternative methods. This study focuses on the Niger Delta Basin, where unconsolidated reservoirs and overpressured shales are common, by presenting a well-log scale 1D geomechanical model and a data-driven approach to predict minimum horizontal stress in seven wells within the Eocene Agbada Formation. Using industry-standard equations, relevant geomechanical parameters were calculated, and six machine learning models were applied to conventional log data to predict minimum horizontal stress. Results showed that the sandstone sediments’ pore pressure gradient values ranged from 0.56 to 0.60 psi/ft, reaching 0.69 to 0.71 psi/ft in overpressured shales. The 1D model revealed a narrower mud window in overpressured zones. Among the models, gradient boosting achieved the highest accuracy, with an R² of 0.92 and the lowest MAE of 273.53 on test data. Blind testing on additional wells validated the model’s robustness and low error rate. These machine-learning results can significantly reduce the time, manpower, and resources typically required for direct measurements, enabling cost-effective pre-drilling evaluations of critical geomechanical properties, including stress and rock strength.

Absolute gravity time series from Antarctica are used to study the viscoelastic gravity change and deformation due to Glacial Isostatic Adjustment (GIA) after the Holocene deglaciation. Here we present the three-decades long absolute gravity (AG) time series at the Finnish Antarctic Research Station Aboa. A gravity increase of nearly 50 (upmu)Gal is observed. Comparisons of the gravity trend with the land uplift observed in the Aboa GPS station time series and with GIA model predictions show that GIA can’t explain the observed gravity increase. We use satellite gravimetry and altimetry, GPS measurements, and modelling to interpret the gravity increase. A regional mass increase around Aboa is observed with satellite gravimetry. Satellite altimetry shows positive surface elevation change in the region over the last three decades. GPS-based surface elevation change measurements in the vicinity of Aboa also point to increase snow and ice volume. Increased precipitation in Dronning Maud Land in the 2000s is noted in the literature. Modelling of the direct attraction due to added mass on the ice sheet around Aboa yields gravity change comparable to what is observed in the time series. Consequently the apparent explanation to the gravity increase is the positive mass balance of the seasonal snow close to the gravity laboratory and of the surrounding ice sheet. Increased direct attraction and elastic ground deformation overshadow the viscoelastic GIA signal in the absolute gravity time series. Conversely, absolute gravity time series at Aboa can be used as an independent observation of the mass increase.

Coal mining induces rock fracture and movement into the excavated space and alters the stress environment of the surrounding rocks. How to characterize mine pressure and control strata is fundamental to underground coal exploitation. However, few research concerns the relationship between overburden structure and dynamic disasters, spatiotemporal evolution of mining induced stress, causing mechanism, and joint warning model for dynamic disasters. To fill these gaps, this study proposes the use of the difference in support resistance to continuously detect the overburden stress of working faces. The concept of the dynamic pressure differential index of support (DPDIS) is introduced. A theoretical model for the DPDIS is constructed. Then the spatial evolution of DPDIS is simulated. A warning model for dynamic disasters based on the DPDIS is developed and applied in a coal mine. To well perfect the warning model, microseismic monitoring is integrated. The combination of DPDIS and microseismic monitoring could perfectly detect the rock fracture and near-field stress in the entire mining area and increase the warning reliability to dynamic disasters.

Accurately estimating evaporation in reservoir systems is an essential step in creating a water budget, as this information is crucial for the effective management of water resources, particularly in countries experiencing water stress. This investigation aims to test the success of tree-based (random forest, gradient boosting, extreme gradient boosting, adaptive boosting, and M5 prime) and neural network-based (multi-layer perceptron (MLP), Kolmogorov Arnold network (KAN), recurrent neural network, long short-term memory, and gated recurrent unit) methods, to estimation monthly evaporation at very important reservoir called Boukourdane Dam, which is located in a Mediterranean area in Algerian north. The KAN method was used for the first time in evaporation prediction. Data on minimum and maximum temperatures (T_max, °C, T_min, °C), wind speed (U, km/h), and relative humidity (H, %) between 1996 and 2016 were used as inputs to the models. Using lag values of the input data significantly increased the accuracy of the models. Although the applied machine learning models generally gave higher accuracy in predicting evaporation, neural network-based methods gave better results than tree-based ones. Although neural network-based methods give close results to each other, the MLP is the method that produces the best results for the test set. The most significant advantage of the KAN method, which consistently produces satisfactory results, is that it provides a clear and straightforward equation. Explainable artificial intelligence graphs showed that T_max is the most effective parameter in evaporation estimation. The study results will provide convenience to decision-makers for efficient dam operation.

Non-Tidal Atmospheric Loading (NTAL) plays a crucial role in the precision and reliability of GNSS-based positioning and geophysical interpretations, particularly in high-latitude regions, sensitive to atmospheric dynamics. This investigation examines the influence of non-tidal atmospheric loading on GNSS time series and velocities derived from them for high-latitude regions. With a dataset from 2020 to 2023, we process a GNSS network across northern Europe, focusing on the Finnish permanent GNSS network (FinnRef). Using GAMIT/GLOBK software, where corrections are applied at the observation level, we incorporate a new atmospheric grid model derived from the European Centre for Medium-Range Weather Forecasts (ECMWF) numerical weather data. This model provides higher spatial resolution compared to previously available models in GAMIT/GLOBK. Temporal variability of NTAL-corrected GNSS time series is reduced by 17% in the vertical component, and by 8% and 2% in the north and east components, respectively, across the FinnRef network. Additionally, our results highlight that NTAL correction lowers vertical trend uncertainty by an average of 33.5%. Besides evaluating metrics such as spectral power density (PSD) and annual amplitude variation, we observe that the spectral index of the vertical component drops from − 1.44 to − 0.9, indicating reduced long-term noise correlation. We also compare this observation-level approach with an alternative method that applies NTAL corrections at the raw-data level and find that the observation-level correction shows slightly better performance. These results demonstrate that significant improvements in the stability of GNSS time series can be expected after NTAL application, especially in the vertical component.

Drought is a critical climate hazard that threatens agriculture, ecosystems, and water security, particularly in climatically sensitive regions such as the Tibetan (TP) Plateau. Accurate projection of future drought characteristics is essential for effective mitigation and adaptation strategies. However, existing approaches often suffer from uncertainties due to variability among climate models and inadequate representation of precipitation extremes. To address these challenges, we propose the Kling–Gupta hybrid weighted ensemble (KG-HWE), a novel two-phase ensemble weighting framework. The framework integrates historical model performance with divergence-based weights using the Kling–Gupta efficiency (KGE) metric, enhancing the reliability of drought projections from a multi-model ensemble of eighteen Coupled Model Coupled Model Intercomparison Project Phase 6(CMIP6) General Circulation Models (GCMs). Additionally, steady-state probabilities are estimated using a Markov chain approach to evaluate the long-term likelihood of different drought classes under three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5). Results indicate that the KG-HWE consistently outperforms traditional methods, achieving a lower average Normalized root mean square error (NRMSE = 1.990), reduced normalized relative absolute error (NRAE = 1.484), and higher correlation (0.670) with observed data compared to equal weighted averaging and mutual information weighting. Probabilistic analysis further reveals a marked increase in severe and near-extreme drought probabilities under the high-emission SSP5- 8.5 scenario, highlighting heightened long-term drought risks. Overall, the proposed KG-HWE framework provides a robust tool for improved drought characterization and prediction, supporting climate adaptation and sustainable water resource management in regions with complex hydro-climatic conditions.