Acta Geophysica最新文献

Understanding long-term rainfall variability is critical for hydrological planning and disaster mitigation, particularly in monsoon-dependent regions like Hyderabad, India. This study utilizes India Meteorological Department gridded data (1981–2023) to analyze seasonal rainfall trends, frequency-domain characteristics, anomalous precipitation events, regime transition pathways, extreme rainfall probabilities, lag-based forecasting accuracy, and clustering-based seasonal regimes. Using Fourier transform analysis, dominant low-frequency magnitudes were detected, confirming seasonal signal stability and intensity across South West Monsoon (SWM), North East Monsoon (NEM), Hot Weather Period, and Cold Weather Period. Anomaly detection methods highlighted extreme precipitation events occurring in 1988, 1996, and 2020, aligning with atmospheric disturbances and ENSO impacts. Statistical probability estimations revealed the highest likelihood of extreme rainfall (> 200 mm) during SWM (62.79%), while NEM exhibited greater variability for rainfall exceeding 300 mm (32.55%). Lag-based forecasting demonstrated superior accuracy using LSTM models with a 7-day history, improving RMSE by 18%, while clustering methods identified distinct low, moderate, and extreme rainfall regimes within seasonal classifications. Advanced regime metrics such as the Rainfall Regime Acceleration Index and Multi-Year Regime Momentum Grid further revealed intra-seasonal volatility and persistence patterns. These findings contribute to regional hydrological planning and flood risk mitigation strategies, emphasizing the importance of sequence-aware, data-driven forecasting in climate variability assessment.

Lithological and physical property variations in subsurface sand bodies lead to highly complex seismic phase information. Accurately extracting, analyzing, and utilizing phase information from seismic data remains one of the challenging issues in identifying and predicting subsurface reservoirs using seismic data. Previous phase domain reconstruction methods rely on single-trace time–frequency analysis and neglect the time-varying characteristics of seismic wavelets during propagation, resulting in insufficient accuracy for identifying reservoir boundaries and special geological bodies. To address this issue, this paper proposes a phase domain reconstruction method driven by time-varying wavelet deconvolution: First, high-precision time–frequency analysis is achieved using the generalized S-transform, and the time-varying wavelet spectrum is extracted by optimizing time–frequency resolution parameters. Second, phase shifting over θ ∈ [ − π, π] is applied to the zero-phase time-varying wavelet to generate an arbitrary-phase wavelet w(t, θ). Finally, the reflection coefficient r(t, θ) is obtained by deconvolving w(t, θ) with the original seismic data. Convolution is then performed to generate specific phase component data s(t, θ), which are combined into phase gathers. The breakthrough of this method lies in overcoming the limitation of conventional methods that assume wavelet stationarity. In practical application, it enables multi-phase collaborative characterization of concealed channel sand bodies, effectively separates reservoir response characteristics, eliminates mixed phase interference, and provides a novel approach for the multi-scale characterization of tight sandstone reservoirs.Query

Uniaxial compressive strength (UCS) is one of the most fundamental parameters in rock mechanics, widely used in the design and stability assessment of geotechnical and mining structures. However, its direct determination requires high-quality samples, sophisticated laboratory facilities, and significant time and cost, which often limit its applicability in practice. As a result, a broad spectrum of indirect estimation techniques has been developed, ranging from simple empirical correlations to advanced artificial intelligence (AI) models. This review provides a comprehensive synthesis of the methods employed in UCS estimation, with a particular focus on both conventional index tests and machine learning approaches. Traditional methods such as the Schmidt rebound hammer (SRH), ultrasonic pulse velocity (UPV), point load test (PLT), and Brazilian tensile strength (BTS) have demonstrated considerable utility, though their predictive accuracy is highly dependent on lithology, rock anisotropy, and site-specific conditions. On the other hand, AI-based techniques, including artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), and optimization-enhanced hybrid models, have achieved superior predictive performance by capturing nonlinear and multivariate relationships, often yielding coefficients of determination (R²) above 0.95. Despite their promise, AI methods require large and representative datasets, and issues of model interpretability and overfitting remain challenges. The comparison highlights that no single approach is universally applicable; rather, the integration of empirical knowledge with computational intelligence appears to be the most effective strategy. The study concludes that future research should prioritize the development of hybrid models and standardized open-access databases to enhance the accuracy, robustness, and practical applicability of UCS prediction in diverse geological settings.

Understanding historical variations in sub-daily rainfall extremes is critical for anticipating their future evolution under changing climate conditions. Using long-term hourly rainfall records from Meenambakkam, Chennai (1969–2023), this study investigates the behavior and evolving trends of short duration (1–3 h) rainfall extremes. Although total annual and seasonal rainfall exhibited no pronounced long-term trend, notable shifts were detected in rainfall intensity, frequency, and duration. During the region’s dominant rainfall season, the northeast monsoon (NEM), the rainfall centroid shifted later by approximately 5.9 days, suggesting a delayed seasonal concentration. About 90% of the NEM rainfall (mean = 735.37 mm) occurred within just 4.6 days, revealing a markedly concentrated rainfall regime. Remarkably, 53.03% of daily rainfall occurred within a single hour, underscoring the dominance of intense, short-lived events. Extreme rainfall during the NEM increasingly occurs during afternoon-night hours and has become more variable over time, with higher short-duration intensities observed in the recent decades. Transition probability analysis revealed that a one-hour rainfall extreme had a 0.732 likelihood of persisting to three hours, but only a 0.482 likelihood of extending to six hours, reinforcing the short-lived yet severe character of the NEM storms. Event frequencies of intense 1–3 h rainfall have also risen, signaling a strengthening of sub-daily extremes. Moreover, the monsoon extension beyond December into January has become increasingly evident in the past decade (2015–2023), with mean daily rainfall nearly doubling to 36.82 mm and the maximum recorded intensity surging from 86.80 to 216 mm. Collectively, these findings highlight a transition toward more intense, temporally concentrated, and variable rainfall extremes, underscoring the growing need for enhanced localized flood forecasting, improved drainage design, and robust urban resilience strategies.

Drought is among the most severe natural disasters, with its frequency and intensity increasing due to global climate change. Accurate drought characterization is essential for ensuring environmental sustainability. As precipitation is a key variable in drought assessment, many recent studies utilize precipitation data from global climate models (GCMs). However, inconsistencies among GCM outputs limit the effectiveness of individual models. To address this, multi-model ensemble (MME) approaches combine outputs from multiple GCMs, though traditional methods often rely on linear metrics like Pearson correlation, which fail to capture nonlinear dependencies. This study introduces the standardized dual Ddivergence-correlation (SDDC) index, which incorporates a novel Dual Divergence-Correlation Weighting (DDCW) scheme. The DDCW method integrates distance correlation and divergence-based weighting to effectively capture both nonlinear associations and distributional differences between observed and modeled data. Using precipitation data from 22 CMIP6 GCMs, the DDCW method is compared with Simple Model Averaging (SMA) and a recent Weighted Ensemble (WE) approach. The comparison is performed using quality assessment measures, including the correlation coefficient and mean absolute error (MAE), to evaluate the accuracy and reliability of the projected precipitation outcomes. Results demonstrate that DDCW consistently outperforms over traditional methods, achieving a higher mean correlation (0.5175) compared to SMA (0.4676) and WE (0.5140), and a lower mean MAE (18.458) compared to SMA (19.164) and WE (18.906). For future drought characterization, steady state probabilities (StSP) were computed under three shared socio-economic pathway (SSP) scenarios, revealing that "No Drought" conditions are most probable, while extreme events remain less frequent. This outcome likely reflects the regional hydroclimatic behavior of Pakistan, where projected precipitation increases under CMIP6 models moderate drought severity even in high-emission scenarios.

Climate change increasingly threatens water resources in semi‑arid, rain‑fed regions such as Nigeria’s Benue River Basin. This study evaluates climate‑induced variability in river discharge and groundwater recharge within the basin using the physically based ArcSWAT model to assess climate-driven variability in monthly river discharge from 1990 to 2024, integrating terrain, land cover, soils, and multi-source climate data, supported by Google Earth Engine remote sensing. Rigorous calibration (1990–2000) and validation (2001–2012) using the SUFI-2 algorithm confirmed strong model performance (R² = 0.99/0.85, NSE = 0.98/0.82), effectively capturing topography-driven runoff, soil–water interactions, and evapotranspiration dynamics. Results reveal pronounced seasonal contrasts, with peak discharges in August (145 m³/s) and extremely low flows (< 2 m³/s) during February–May, intensifying dry-season water stress. A marked January–July flow decline indicates shifts in atmospheric–hydrologic linkages. Spatial analysis shows greater discharge losses in upstream forested sub-basins than in downstream zones. Climate projections under RCP4.5 and RCP8.5 suggest mean annual streamflow reductions of 11.1% and 18.5%, with dry-season declines reaching 25%. Coupling CA-Markov land use simulations with CMIP6 ensemble projections enhanced ArcSWAT’s forecasting accuracy under future scenarios. Combined land climate impacts led to up to 30% dry-season flow reduction and increased hydrological variability across sub-basins. As one of the few physically based long-term assessments in West Africa, the study underscores the compounded effects of land use and climate change on water resources. Urgent adaptive strategies such as aquifer recharge, climate-smart irrigation, and decentralized water storage are recommended. Future research should integrate groundwater and socio-economic water-use modeling to better inform resilient, sustainable basin-scale water management.

Fluid escape pipes are critical irregular 3D structures with complex internal conduits and typically identified from high-resolution 3D seismic volumes. Their significant association with various environmental and geological aspects raises broad concern, yet they are constrained by geophysical limits, resulting in compromised imaging quality, particularly for their internal mixture complexity. Previous seismic findings have struggled to obtain clear imaging of the internal structure and capture distinct seismic signatures, particularly affected by varying illumination, thereby resulting in poorly constrained seismic interpretation. To improve the geological understanding, we apply point-spread function (PSF)-based convolution modeling to simulate fluid pipe structures containing internal mixtures, drawing insights from exemplary seismic data through reasoned interpretation, analogs, and properties. This way can help produce a geology–seismic bridge that allows to explore how seismic signatures are controlled by various illumination-related scenarios (high, intermediate, low) in seismic reflection data. The modeling results demonstrate that: (1) Imaging quality within internal structural mixtures is poor under interpretation-driven velocity models due to inadequate illumination of complex internal features; (2) The adverse impact of insufficient maximum-dip illumination intensifies progressively with decreasing dip angle, generating significant uncertainties, particularly at low angles (e.g., 10°); (3) Limited illumination induces substantial imaging artifacts in internal structures (e.g., discontinuities, disruptions, layer merging, pseudo-stratified layering), showing strong correlation with severely constrained acquisition geometries; (4) Enhanced maximum-dip illumination via optimized industrial-scale acquisition is recommended to improve detailed imaging of this complex structural setting.

With timely monitoring of drought phenomenon and proper management of existing water resources, especially groundwater, the adverse effects of destructive factors can be reduced. Satellite data such as the Gravity Recovery and Climate Experiment (GRACE) mission can be used in monitoring drought over different areas. This research aims to evaluate the applicability of the GRACE data from 2002 to 2017 to calculate drought indices in arid and semi-arid regions, especially in a small area such as the Rafsanjan Plain, Iran. Our findings indicate that Modified Total Storage Deficit Index (MTSDI) outperforms Total Storage Deficit Index (TSDI) and Total Water Storage Deficit Index (TWSDI), because it removes the effect of changes due to human activities from the Total Water Storage Anomaly (TWSA) time series. Also, the traditional meteorological drought indices including Standardized Precipitation Index (SPI), Z-Score Index (ZSI), China-Z index (CZI), and Modified CZI (MCZI) had a weaker relationship with GRACE-derived indices (except for MTSDI), which suggests that TSDI and TWSDI might not be the best choice for evaluating droughts that affect groundwater. Meanwhile, the effect of subtracting components modeled by the Global Land Data Assimilation System (GLDAS) from GRACE data for estimating groundwater storage was investigated. The results demonstrated that the water-level observation data had a strong correlation with the GRACE data, and subtracting GLDAS components did not significantly improve GRACE estimations of groundwater changes. In fact, in most observation wells, the correlation values slightly decreased, which was not statistically significant. Moreover, exploring time lags ranging from 0 to 11 months in both GRACE data and GRACE minus GLDAS data did not lead to any notable improvement in correlation across the observation wells. Therefore, GRACE-derived TWSA can be effectively used to support groundwater resource assessment and drought monitoring in arid and semi-arid regions.

This study presents a comprehensive numerical investigation of transient seepage behavior and slope stability in the Mahabad earth-fill dam under four reservoir drawdown rates (0.15, 0.3, 0.6, and 1.2 m/day), utilizing site-specific geotechnical and hydraulic properties. Simulations were performed in the SEEP/W and SLOPE/W modules of GeoStudio 2018R2, which employ mesh-independent finite element modeling for transient unsaturated flow and limit equilibrium stability analyses. The analysis revealed that rapid drawdown initially induces elevated seepage discharges and hydraulic gradients; however, as the upstream shell transitions to unsaturated conditions, both seepage rates and exit gradients decline sharply, remaining well below critical safety thresholds in all cases. Quantitatively, the minimum recorded seepage rate following drawdown decreased from 5.74 × 10⁻⁵ to 2.17 × 10⁻⁶ m³/s/m (at 0.15 m/day over 400 days), and corresponding exit gradients dropped from 0.731 to 0.063, illustrating effective dissipation of hydraulic forces. Stability analysis showed that the upstream slope experiences a transient decline in factor of safety (FoS) after drawdown initiation, reaching a minimum of 1.662 (for 0.15 m/day) and as low as 1.62 for the fastest scenario, but recovery follows as pore pressures dissipate. The downstream slope exhibited minimal FoS fluctuation, consistently maintaining values above 1.69 across all drawdown rates, underscoring the effectiveness of the dam’s material zoning and drainage systems. Despite model simplifications, the findings confirm Mahabad Dam’s resilience under varying drawdown scenarios and offer a solid basis for safety evaluation and future advanced analyses.