Acta Geophysica最新文献

This study demonstrates that applying spatially variable density correction (VDC) to eliminate terrain effects in airborne gravity gradients enables the estimation of surface density distribution and improves the extraction of structural boundaries through a semi-automatic analysis. Because gravity gradients are more susceptible to nearby density anomalies than gravity, they are expected to have a high correspondence with rock-type distribution near the surface. In this study, airborne gravity gradients acquired using FALCON airborne gravity gradiometry system in the Yuzawa–Kurikoma area, Japan, were used to evaluate the validity of VDC. FALCON system measures only two horizontal components of the gradient tensor, which have anisotropic sensitivity to density anomalies. Hence, we derived the vertical gradients from these components and applied VDC. The estimated densities were generally consistent with the rock-type distribution shown in the geological map and with those obtained from actual rock samples. Subsequently, Euler deconvolution, a semiautomatic analysis technique, was conducted on the VDC-corrected data to extract lineaments based on the continuity of the solutions. The extracted structures agreed well with previously identified or inferred faults, and several new lineaments were found along the extensions of these faults. Additionally, deconvolution was applied to the data after terrain correction with a unique assumed density, and the variability and structural characteristics of the solution distributions were compared between the two correction methods based on information entropy. The VDC-corrected data yielded lower entropy than the conventionally corrected data, indicating that VDC can improve the detectability of structural boundaries when using Euler deconvolution.

An effective earthquake early warning system (EWS) should quickly identify destructive earthquakes and provide enough time for emergency responses before strong and damaging waves reach vulnerable areas. Accurate magnitude estimation is essential for issuing reliable alerts. This study examines the impact of site effects on earthquake magnitude estimation using the traditional predominant period parameter, ({tau }_{P}^{max}). Since horizontal components of ground motion carry richer information but are highly affected by the site effects, we show that removing site effects enables the effective use of horizontal components for accurate magnitude estimation. For this purpose, we first applied a traditional frequency-domain approach—Fourier transform, deconvolution, and inverse Fourier transform—to remove site effects from the seismograms data at each station. To improve computational efficiency for real-time applications, we also designed an IIR filter based on the inverse of the site response function estimated using the horizontal-to-vertical (H/V) spectral ratio, to remove site effects directly in the time domain. The method preserves phase information and achieves results comparable to traditional frequency-domain deconvolution but with significantly faster computation, making it practical for real-time applications. Application of the proposed approach to K-NET strong motion records from Japan (1998–2022) showed that removing site effects improved magnitude estimates by approximately 0.1–0.2 units. Additionally, correcting vertical components also improved magnitude estimates by about 0.1 units.

Precise atmospheric parameter profiles are indispensable for calculating atmospheric radiation transmission. Considering that traditional atmospheric parameter models failed to account for variations in atmospheric parameters with horizontal direction, we developed a method for generating inhomogeneous atmospheric parameter profiles along the line-of-sight path based on the fifth-generation reanalysis product ERA5, released by the European Centre for Medium-Range Weather Forecasts (ECMWF). This method employs the target ray tracing technique and utilizes spatial rapid interpolation methods such as horizontal inverse distance weighting and vertical spline interpolation. According to this method, we investigated the distribution of atmospheric temperature profiles, water vapor density profiles, and total water vapor content along slant paths in three regions of China: Yushu, Hefei, and Maoming. Compared with the single-point vertical atmospheric parameter profile models in the above regions, the results show that: As the observation elevation angle decreases, the deviation between this two atmospheric models tends to increase gradually. Specifically, at an observation elevation angle of 10°, the average absolute deviations of atmospheric temperature profiles in three regions are 0.36, 0.45, and 0.32K, respectively; the average relative deviations of the slant water vapor content are 1.23, 1.25 and 1.01%, respectively. Further research reveals that when the observation elevation angle ranges from 0.1° to 4°, the relative deviation of total water vapor content in Hefei under the two models can reach up to approximately positive 7% in the south and negative 7% in the north during spring and winter, while at observation elevation angles between 6°and 10°, the relative deviation is around 1% to 3%, suggesting that the influence of observation elevation angle in different horizontal directions should be considered. In addition, the total water vapor content in three regions generally exhibits a trend of being higher in the south and lower in the north at different azimuth angles.

Flooding in arid urban regions is an increasingly pressing concern due to the compounded effects of climate change, rapid urbanisation, and hydrologically underprepared infrastructure systems. These environments, often characterised by impervious surfaces and poor drainage, face heightened exposure to short-duration, high-intensity rainfall events. Makkah, a rapidly growing arid city in western Saudi Arabia with significant topographical variability, typifies such risk and necessitates a robust, infrastructure-specific flood risk modelling framework. This study aims to develop a scientifically rigorous and spatially detailed flood susceptibility and damage assessment framework tailored for critical infrastructure specifically, healthcare facilities and road networks. The methodological approach integrates advanced remote sensing, machine learning, and probabilistic simulation. A comprehensive flood inventory was prepared using historical satellite imagery, ground-truthing, and official flood records. Flood conditioning parameters including topographic (e.g. slope, curvature, elevation), hydrological (e.g. drainage density, rainfall), and anthropogenic (e.g. proximity to roads/rivers) were derived and validated. To address multicollinearity and ensure data integrity, correlation and variance inflation factor (VIF) analyses were conducted. Four machine learning models such as Random Forest, Support Vector Machine, Gradient Boosting Machine, and Categorical Boosting (CatBoost) were trained using optimised hyperparameters and validated through stratified k-fold cross-validation. Among these, CatBoost yielded the highest accuracy and reliability (AUC = 0.90), mapping approximately 282.02 km² under ‘very high’ and 156.55 km² under ‘high’ flood susceptibility zones. Sensitivity analysis further revealed Support Vector Machine to be the most robust against input perturbations. Infrastructure-specific exposure analysis, coupled with Monte Carlo-based probabilistic economic modelling, estimated potential damages at SAR 28.1 billion for hospitals, SAR 15.9 billion for buildings, and SAR 3.36 billion for roads. Critical vulnerability clusters were identified in Aziziyah, Al-Haram, and Al Misfalah districts. This integrated framework offers a replicable model for infrastructure resilience planning in arid urban environments.

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.