Acta Geophysica最新文献

The study presents the results of the investigation of the behavior of long-range correlations and self-similarity in solar energetic particles (SEPs). SEPs are high-energy, charged particles that originate from the solar atmosphere and solar wind. They consist of protons, electrons, and heavy ions with energies ranging from a few tens of keV to several GeV. The correlations and persistency have been examined for two distinctly different phases of Solar Cycle 23, one corresponding to solar maximum while the other solar minimum. The global scaling exponent ((boldsymbol{alpha })-exponent) using a method called robust detrended fluctuation analysis (r-DFA) is calculated for proton flux measured at various energy levels (ranging from > 1 MeV to > 100 MeV) for different years, which compare the features of SEP flux during solar maximum and minimum years. This analysis establishes a relationship between the behavior of SEPs at different energy levels and the corresponding phases of solar activity, gaining insight into the dynamics of energetic particle transport and modulation in the interplanetary medium.

The study investigates climate–agriculture interactions by integrating machine learning models with participatory household surveys, a novel dual approach in the context of climate change. We combined wheat crop statistics, vegetation indices, and climatic parameters from 2001 to 2021 with survey data from 292 farming households in Aligarh district, Uttar Pradesh, India. The temporal analysis revealed increasing trends in wheat yields and vegetation indices, with December–January being the months when EVI and NDVI showed strong positive correlations with wheat yields. Furthermore, vegetation indices were negatively associated with the diurnal temperature range but positively correlated with precipitation. March emerged as a crucial month, as rising temperatures, vapor pressure, and potential evapotranspiration had an adverse effect on vegetation indices. Among the models tested, random forest (RF) outperformed support vector machine (SVM) and multiple linear regression (MLR), demonstrating its robustness for predicting nonlinear crop–climate relationships. Household surveys revealed a high awareness of climate change, but significant barriers to adaptation were also reported, including a lack of capital and small landholdings. It indicated a need to connect awareness with adoption, which is crucial for successful adaptation interventions. By combining top-down (machine learning) and bottom-up (participatory) approaches, the research contributes a novel framework for climate-resilient policy planning. To the best of our knowledge, this is one of the first studies in India to integrate quantitative modeling with qualitative social insights at the localized district scale for a region underrepresented in climate–agriculture studies. The findings offer actionable insights for informed agrarian policy-making and lay a foundation for future integrative research on climate adaptation in rural areas.

This paper presents a CSA-DLSQ hybrid inversion that uses Cuckoo Search Algorithm (CSA) which provides better estimation for petrophysical properties. This hybrid technique significantly reduces computational costs while preserving the global convergence properties of CSA and the fast convergence of DLSQ. Inversion is realized of depth-local petrophysical parameters and zone-dependent global variables for facilitating geological consistency in layered formations. A single-layer synthetic model is first used for investigating CSA's hyperparameter sensitivity (population size, discovery rate Pa, Lévy exponent β) and to analyze the algorithm’s dynamic search behavior. An extension to a four-layer model with global cementation exponent is then used to reduce parameter oscillations at layer boundaries. The hybrid algorithm successfully stabilizes the inversion process and converges faster than stand-alone CSA. The method is used for well-log data observed in a Hungarian borehole, resulting in a 56.6% reduction in total misfit and ~ 87 × per-point speedup, generating petrophysical parameter distributions conforming with lithological interpretation. The hybrid inversion is more accurate and efficient than stand-alone CSA, especially for zones of complex geology or noisy measurements. The results confirm that the algorithm developed offers a robust and scalable framework for petrophysical interpretation within conventional hydrocarbon reservoirs.

The slip models for the 1993 (Mw 7.2) and 2012 (Mw 7.4) events are determined in this study. The rupture of both events indicates that they propagated toward up-dip and do not overlap. In the case of the 1993 event, two asperities ruptured, suggesting complex faulting, similar to the rupture of the 1970 event (Mw 7.3), proposed by Yamamoto and Mitchell (1988). The 1993 and 2012 events broke adjacent segments of the subduction zone. The Tehuantepec gap has been recognized as an area of high seismic potential, because no large or great interplate earthquakes have occurred in the last 120 years. To the southeast of the Tehuantepec gap, where the 1970 and 1993 earthquakes took place, the Chiapas and northwestern segments of the Guatemala subduction zone have not experienced earthquakes larger than 7.4 in instrumental times. The Chiapas interplate contact to the southeast of the Tehuantepec gap has been broken only by three relatively moderate events in 1970, 1993, and 2012. Presumably, this region is highly coupled according to GPS studies. In this complex region of the Mesoamerican subduction zone, it is possible that the Tehuantepec gap, as we know it today, has the potential to produce a very large earthquake if the two adjacent regions in Chiapas and Guatemala are considered part of the same gap.

The determination of a precise geoid model still represents a chief demand for the geodetic communities worldwide. It necessitates the collection of as much as terrestrial gravity measurements with a homogenous distribution over a spatial region. The current research aims to collect several gravity datasets from several organizations and to unify their geodetic datums and gravity references. Such a task has been carried out within a geographic information systems (GIS) framework. A number of 9724 terrestrial gravity stations have been analyzed to construct a unique integrated gravimetric database of Egypt. That database has been referenced to the World Geodetic System 1984 (WGS84) and has been related to the Egyptian National Gravity Standardization Network 1997 (ENGSN-97) framework. GIS analysis tools have been performed to determine common points between gravity datasets and to construct correction surfaces to relate each category to the ENGSN-97 reference. Accordingly, an enhanced free-air gravity anomaly dataset has been developed. Such a modified gravimetric dataset is slightly more precise than the original one and performs a little better when compared to a global gravitational model (GGM). It has been found that the unification process performed herein has slightly enhanced the gravitational field of Egypt by almost 6.6%. Furthermore, comparing the terrestrial gravity data against the global GGM model shows that an improvement of 3.1% has been attained in terms of standard deviation. On another scene, the proposed approach might be valuable to the undergoing project for developing a precise geoid model of Egypt where different datasets have been collected from different sources with large variations with respect to geodetic datums and gravity references.

This study investigates the complex interplay between seasonal temperature anomalies and extreme rainfall patterns in Kerala, India, using daily gridded temperature and rainfall data from the India Meteorological Department (IMD) for 1951–2023. The analysis employs eight Expert Team on Climate Change Detection and Indices (ETCCDI), including Rx1-day (maximum 1-day rainfall), PRCPTOT (total wet-day rainfall), TXx (hottest days), TXm (mean maximum temperature), TXn (coldest days), TNx (warmest nights), TNm (mean minimum temperature), and TNn (coldest nights), aggregated seasonally across Kerala’s North, Central, and South regions. The objectives are to evaluate long-term trends in these indices, identify seasons with extreme climatic events or transitions, examine regional variations, and explore the relationship between temperature anomalies and subsequent extreme rainfall. A consistent warming trend is observed across all regions, with the South exhibiting the highest increase (0.0217°C/season during the Southwest Monsoon). PRCPTOT shows a decline in the Central and North regions, with significant shifts in the mid-1980s and early 1970s, while the South experiences increased Rx1-day events during the Southwest and Northeast Monsoons, with notable transitions in 1991 and 1992. Warmer winters and summers amplify extreme rainfall during the Southwest Monsoon, particularly in the South, as evidenced by the 2019 floods, where high winter TNm anomalies (+ 1.2°C) preceded extreme Rx1-day values. The North shows high monsoon rainfall variability (~ 2311 mm PRCPTOT) and flood risk, while the South’s stable, warmer temperatures (TNm ~ 23.3°C in summer) highlight distinct vulnerabilities. A positive correlation between winter/summer temperature anomalies and subsequent monsoon rainfall extremes (Spearman’s ρ = 0.22–0.26, p < 0.05) underscores temperature’s role in intensifying rainfall. These findings inform climate adaptation strategies, emphasizing heat-tolerant crops and energy-efficient systems in the South, drought-resistant crops in the Central and North, and enhanced flood management across regions to address Kerala’s increasing climate risks.

Accurate characterization of subsurface thermal regimes is critical for geothermal resource assessment and basin thermal history reconstruction. In Tunisia, existing borehole temperature corrections were often adapted from methods calibrated for non-local geological settings, limiting their applicability to the Tunisian sedimentary basins. For that, a review of novel correction framework tailored to the Gulf of Gabes and Gulf of Hammamet regions in Eastern Tunisia is proposed. It integrates 2690 temperature measurements from 240 boreholes—including Bottom Hole Temperatures (BHT), Drill Stem Tests (DST), and Modular Formation Dynamics Tester (MDT) data. Multiple correction techniques were evaluated and the cross-plot method of BHT versus DST emerged as the most robust, enabling derivation of two domain-specific correction equations that account for the distinct structural and thermal evolution of each basin and closely approximate formation temperatures. In addition, a new approach is used to adjust thermal conductivity based on porosity measurements, resulting in the conception of the first thermal conductivity map for Eastern Tunisia. The combined corrections significantly refine the spatial distribution of heat flow, yielding average values of 68 mW/m² in the northern Sahel–Gulf of Hammamet block and 71 mW/m² in the southern Gulf of Gabes. The corrected heat flow map of Eastern Tunisia demonstrates strong correlation with regional tectonic structures and geodynamic processes, reflecting crustal thinning and mantle dynamics influencing the thermal regime. This updated thermal model provides an enhanced framework for interpreting geothermal gradients and heat flow patterns, in Eastern Tunisia, serving as a valuable tool for both geothermal and petroleum explorations.

Accurate imaging the anisotropic multi-component field data is still a challenging task especially that acquired in areas with complex rugged surface topography. To address these challenges, we propose a new topography anisotropic wave separation elastic reverse time migration (ERTM) method with high efficiency. We first introduce a robust and efficient surface-adaptive modeling scheme based on a traditional finite difference (FD) operator to eliminate the influence of complex irregular surface topography on ERTM. We then develop a new approach for wave-mode separation in vertical transverse isotropic (VTI) media using anisotropic pseudo-decoupled wavefield equations. The vector source and receiver anisotropic P- and S-waves for anisotropic wave separation elastic reverse time migration (AWSERTM) can be efficiently obtained by numerically solving the proposed anisotropic pseudo-decoupled wave equations with the FD method. Synthetic examples demonstrate that the proposed topography anisotropic wave separation elastic reverse time migration (TAWSERTM) is both efficient and stable. It can not only obtain anisotropic P- and S-waves with high efficiency, correct the anisotropy effect, but also effectively eliminate the influence of surface topography on migration results.

In this study, crustal discontinuities in Central Anatolia were investigated using 2D weighted compact gravity inversion (2DWCGI) and gravity gradient-based techniques (HGM, TA, SI). Spectral analysis identified key structural boundaries at depths of 4.5 km (basement), 17 km (Conrad), 34 km (Moho), and 116 km (LAB). Inversion tests on two profiles yielded a maximum RMS error of 0.00074 mGal with a depth uncertainty of ~ 2 km. Density models reveal high-density zones ranging between 2.68 and 2.74 g/cm³, particularly along the CAFZ and NFZ, extending to depths of 15–20 km. The westward motion of the Anatolian block at a rate of 20–25 mm/yr imposes regional stress, which controls the development of the newly proposed left-lateral Gözlükuyu Fault and other crustal discontinuities. This fault is supported by HGM, TA, SI, and focal mechanism solutions. Earthquake hypocenters from the ISC catalog are mostly concentrated between 10 and 18 km, indicating that deformation is focused within the crust. Furthermore, this study demonstrates, for the first time, the applicability of the SI method for lineament mapping, with 0-contours effectively delineating major faults with significant vertical components. These findings provide quantitative insights into the complex tectonic framework of Central Anatolia and offer new input for seismic hazard assessment.