Acta Geophysica最新文献

This study develops a wave propagation model for predicting seismic P-wave dispersion and attenuation that considers multiscale (micro-, meso-, and macroscopic) attenuation mechanisms. The modeling approach comprises the following steps: First, substitute the rock physics parameters into the Biot–Rayleigh model to calculate the compressional wave velocity. Subsequently, the bulk modulus of the rock skeleton is inferred from the velocity using Gassmann’s equation, and thus, the mesoscopic-scale attenuation mechanism of seismic waves is considered. Next, substitute the rock physics parameters into Gurevich’s model considering the impact of differential pressure, i.e., the pressure difference between compliant pores/microcracks and the surrounding stiff pore framework, to calculate the bulk modulus of the rock skeleton directly to integrate into the mechanism of attenuation occurred at microscopic scale. Then, the two bulk moduli are combined via a weighted summation, thereby obtaining an effective bulk modulus of rock skeleton that considers cross-scale attenuation mechanism. Finally, the effective modulus is incorporated into a dynamic system, where the framework of Biot’s equations is employed. Based on plane wave analysis, dispersion and attenuation are predicted. The results reveal that the mesoscopic effect saturates quickly at low pressures, while the microscopic mechanism contributes more gradually over a broader pressure range. However, when both mechanisms are strongly activated, their interaction may suppress attenuation at high pressures, indicating a nonlinear coupling between the mesoscopic and microscopic attenuation mechanisms. This feature highlights the physical interpretability and adaptability of this approach in predicting the seismic wave attenuation characteristics at different scales, laying a foundation for high-precision seismic modeling and inversion in complex geological porous media.

Rockburst, as a typical dynamic disaster in deep underground engineering, poses significant challenges to construction safety control. To achieve real-time and accurate rockburst prediction, this study proposes a rockburst grade prediction method based on microseismic parameter optimization and improved XGBoost. First, feature mining was conducted on 136 sets of multi-source engineering case data, and four microseismic parameters—cumulative event count, energy, apparent volume, and microseismic event rate—were selected as prediction indicators through mutual information analysis and information gain calculation. To address the parameter sensitivity issue of the XGBoost model, the whale optimization algorithm (WOA) was introduced for adaptive hyperparameter tuning, establishing a WOA-XGBoost prediction model that integrates feature selection and parameter optimization. The accuracy and reliability of the model were validated from three aspects: model evaluation, model comparison, and engineering verification. The results demonstrate that the improved model achieves a prediction accuracy of 89.28%, representing a 7.15% improvement over the unoptimized XGBoost model, while also outperforming other benchmark algorithms in key metrics such as precision and recall. Engineering validation using four case studies, including the Gaofeng Mine and Qinling Tunnel, showed highly consistent predictions. This method provides a scientific reference for short-term rockburst prediction in deep underground engineering projects.

Seismic wavefields propagating through real Earth media are subject to viscoacoustic phenomena such as amplitude attenuation and phase dispersion, which inevitably degrade imaging resolution. However, the widely used constant-Q model fails to accurately describe wavefield characteristics under high-temperature and high-pressure conditions, where related imaging reports remain scarce. To address this limitation, we introduce the power-law frequency-dependent Q model, which provides a more realistic description of viscoacoustic behavior in such environments. Building upon this model, we develop a power-law frequency-dependent Q-compensated frequency-domain reverse time migration (power-law QRTM). To mitigate the instability introduced by high-frequency components during Q-compensation, we propose a stabilized reflectivity-based imaging condition. Comparisons of wavefields simulated in acoustic, constant-Q viscoacoustic, and power-law viscoacoustic media reveal distinct differences in velocity dispersion and amplitude attenuation, underscoring the necessity of conducting imaging in power-law viscoacoustic settings. Numerical imaging experiments further demonstrate that the proposed power-law QRTM effectively compensates for power-law frequency-dependent viscoacoustic effects. Compared with imaging references obtained by acoustic RTM applied to acoustic data, our method yields more accurate amplitude and spectral characteristics than that of acoustic RTM applied to power-law viscoacoustic data. Importantly, it also avoids the interpretational errors that arise when applying acoustic RTM to viscoacoustic data.

In the present era, climate change coupled with population explosion, industrial advancement and upsurge in agricultural activities has put a tremendous pressure on freshwater resources leading to water crisis. To manage this ongoing issue, rainwater harvesting (RWH) at potential sites is emerging as an ecofriendly strategy for sufficing the agricultural and other domestic requirements. In this perspective, it is imperative to identify the potential sites for harvesting the excess rainwater which otherwise flows as a surface runoff. In the present study, suitable sites for rainwater harvesting have been identified in block Balachaur of District SBS Nagar Punjab (India) using GIS and multi-criteria decision-making techniques. The analytical hierarchy process (AHP) and Fuzzy-AHP techniques have been used in GIS environment to identify the most suitable sites for RWH. This study advances current GIS-based rainwater harvesting (RWH) assessment approaches by integrating both AHP and Fuzzy-AHP within a multi-criteria geospatial decision framework. Eight criterion layers including runoff depth, soil type, slope, stream order, drainage density, geology, LULC and distance to roads have been used. AHP and Fuzzy-AHP have been used to assign the weight to each layer and then weighted overlay analysis (for AHP) and fuzzy overlay (for Fuzzy-AHP) was done in ArcGIS to generate the RWH site suitability maps. The AHP-based RWH site suitability map revealed that about 7.34% (23.04 km²) of the total area is extremely suitable for RWH. About 33.69% (105.78 km²) and 38.29% (120.23 km²) is highly and moderately suitable for RWH, respectively. Similarly, Fuzzy-AHP-based map showed that around 13.10% (41.13 km²) of the area is extremely suitable for RWH in the study area. Similarly, the area under highly suitable and moderately suitable is 29.43% (92.41 km²) and 36.82% (115.61 km²), respectively. The area under less suitable category is almost as AHP analysis (20.65%; 65.0 km²). The validation of developed map has been done by obtaining the ground truth points of various RWH structures and by using the ROC-AUC technique. The ROC-AUC of 0.89 for AHP and 0.90 for Fuzzy-AHP indicates very good predictive accuracy rate. The comparative evaluation of AHP- and F-AHP–derived suitability maps provides new scientific insights on how deterministic and fuzzy decision structures influence RWH zone delineation. The findings of the present study shall be beneficial for policy makers including soil conservation officers and other line departments to implement proper rainwater harvesting measures in the study area.

The Tarim Basin hosts numerous fault-controlled fracture-cavity reservoirs, which represent a distinctive type of reservoir with substantial hydrocarbon potential. However, due to their deep burial depth, conventional methods often fail to accurately characterize these reservoirs, falling short of the precision required for effective exploration and development. To address this challenge, this study proposes a novel method for identifying the core zones of fracture-cavity reservoirs. The proposed approach begins by verifying the advantages of angle-domain seismic data as a superior data source for delineating fracture-cavity systems. A deep learning model incorporating both spatial and channel attention mechanisms is then employed to extract the contours of the fracture-cavity bodies. Through waveform separation processing, a refined dataset devoid of stratigraphic interference is obtained. Subsequently, the first eigenvalue of the structure tensor is extracted to enhance the energy response associated with the fracture-cavity features. Finally, energy tracking is applied to identify the core zones, resulting in a comprehensive characterization of the core–belt architecture of the fracture-cavity system. Field applications demonstrate that the proposed method significantly outperforms conventional techniques in accurately identifying the core zones of fracture-cavity reservoirs. This approach provides an effective and reliable tool that can be widely applied in other regions with similar geological conditions.

This paper focuses on the development of a robust numerical model designed for predicting sporadic-E layer occurrences. The developed model computes the maximum ion convergence (VIC(_{text {max}})) and takes into account meridional and zonal winds, magnetic field orientation (inclination angle), and ion–neutral collision frequency profiles. The horizontal wind model (HWM) serves to provide neutral wind parameters. For magnetic field and neutral density data, we incorporate the International Geomagnetic Reference Field (IGRF) and the Naval Research Laboratory Mass Spectrometer and Incoherent Scatter radar (NRLMSIS) models. VIC(_{text {max}}) at altitudes ranging from 100 to 120 km is analyzed. The time evolution of VIC(_{text {max}}) for both July and January, corresponding to the northern and southern summer hemispheres, respectively, is assessed. We utilize foEs data from 11 ionosonde stations worldwide to validate our empirical numerical findings. The impact of active geomagnetic conditions is integrated through variations in the A(_p) index. Notably, we observe a distinct signature of solar wind influence on sporadic-E layer formation in both hemispheres. Furthermore, we investigate the potential correlation between active geomagnetic conditions and global sporadic-E distribution. The enhanced occurrence rate in both hemispheres aligns with our model’s predictions. We examine the solar radiation index and the differences in sporadic-E intensity noted in the radio occultation (RO) data for July and January of 2008 and 2014. The results from our numerical model show a strong correlation with the occurrence rates observed between July 2008 and July 2014, and the model also replicates similar trends observed in the RO data for July 2014.

Imaging methods offer a computationally efficient alternative to physical property inversion by directly transforming observed data into interpretable spatial distributions for rapid subsurface mapping. However, most existing approaches neglect the influence of rugged terrain on imaging results and suffer from limited resolution. To address these challenges, this study innovatively proposes a focusing-constrained wavenumber-domain imaging method for gravity anomalies in rugged terrain. In this method, a recursive data fitting technique is applied to terrain-distorted observations, generating optimized inputs for imaging calculations. And a terrain-adaptive constraint matrix is developed to mitigate topographic artifacts on the results. Additionally, a focus constraint is implemented during the iterative computation process to enhance spatial resolution. Collectively, these strategies improve both physical accuracy and structural delineation. The method’s effectiveness is validated using two sets of synthetic gravity data simulated with varying terrain complexities and field data from the Sullivan, north Missouri area. Comparative results demonstrate its superiority over conventional 3D iterative imaging under rugged terrain conditions.

Natural hazards like thunderstorms or lightning can cause loss to the economy and lives, especially in major urban centers. A long-term analysis of pre-monsoon thunderstorm rainfall and lightning, as well as thermodynamic indices like Convective Available Potential Energy, George’s K-Index, and Total Totals Index, has been performed over the major urban hubs of India, i.e., Delhi, Hyderabad, Chennai, Bangalore, Mumbai, Pune, Kolkata, and Ahmedabad. For this purpose, the study utilized IMDAA and ERA5 datasets correspondingly for rainfall and thermodynamic indices for the period from 1980 to 2020, and TRMM and WGLC datasets for lightning observations for periods from 1999–2009 (PRE10) to 2010–2020 (POST10) respectively. For most cities, the quadri-decadal trend of pre-monsoon precipitation increased except for Kolkata and Ahmedabad, where a decrease was noticed. However, the trend over Chennai was realized to be significant through the Mann-Kendal test. There is an east–west contrast in the amount of pre-monsoon precipitation between the cities located in the eastern and western parts of the country. The spatial distribution indicates that rainfall has considerably increased in the boundary regions of the cities and the associated non-urban neighborhoods in recent times. This urban boundary contrast is more prominent in the case of Hyderabad, Delhi, and Pune, since rainfall shows an increase of > 25 mm in the boundary regions in the recent years. The spatiotemporal analysis of lightning shows an increasing trend in most of the cities, where it is statistically significant over Delhi, Hyderabad, and Kolkata in the POST10 period. For Kolkata, the trend is more profound with a considerable Sen’s slope value (~ 0.04) compared to other cities. Most of the flashes are observed in the boundary regions of the cities rather than their center. The analysis also advocates KI and TTI to play a considerable role in the initiation of pre-monsoon thunderstorms compared to CAPE.

The Gravity Recovery and Climate Experiment (GRACE) and GRACE follow-on have enabled basin- to continental-scale monitoring of global water storage, cryospheric mass change, and sea-level variation since 2002. Yet key challenges remain in coarse spatial resolution, signal leakage, and dependence on auxiliary models. This review synthesizes research progress and frontiers by tracking thematic evolution, methodological innovation, and interdisciplinary integration. Analyzing 2417 publications from Web of Science Core Collection (2015–2025) with CiteSpace, we mapped publication patterns, research hot spots, collaboration networks, and emerging frontiers. Three main findings stood out: (1) a progression from “mission milestones” to “methodological innovation” and “cross-disciplinary expansion,” with terrestrial water storage, gravity inversion, and water balance as core themes, while machine learning and multi-source fusion as recent foci; (2) a global collaboration network dominated by the National Aeronautics and Space Administration, the Chinese Academy of Sciences, and the German Research Centre for Geosciences, showing strong cooperation among North America, Europe, and China but limited engagement elsewhere; and (3) expansion in interdisciplinary areas, such as carbon cycle–hydrology coupling and the water–energy–food nexus, underscoring GRACE’s role in sustainability. This study outlines priorities to advance GRACE applications through mission continuity, AI-based modeling, and high-resolution regional analysis, contributing to global change research and water resource management.

This study investigates the combined effects of hydraulic and soil parameters on scour hole dimensions caused by symmetric crossing water jets impinging on a cohesive bed, using controlled laboratory flume experiments. The examined dimensionless variables include Froude number (Fr_j), jet crossing angle (α_c), vertical jet angle (α_v), relative tailwater depth (T/D_eq), relative jet distance from the water surface (S/D_eq), and dimensionless undrained bed cohesion (C^*). Results indicate that increasing Fr_j and decreasing C^* elevate scour volume, while larger α_c reduces erosion by promoting energy dispersion and hole elongation. Enhanced moisture and sand content increase the effective cohesion, thereby mitigating scour. Global sensitivity analysis revealed that α_c, T/D_eq, and Fr_j are the most influential parameters on scour hole volume, respectively. Increasing α_c and T/D_eq reduce scour volume by 90% and 73%, respectively, whereas increasing Fr_j leads to a 62% increase in scour volume. Conversely, α_v is the least influential parameter, reducing scour volume by an average 15%. A novel nonlinear empirical model was developed to predict relative scour volume (R² = 0.94, RMSE = 0.67, MRE = 6%). This model demonstrates a high level of accuracy in predicting scour volume and provides the first integrated analysis of crossing jet hydrodynamics and cohesive bed mechanics for hydraulic structure design.