Acta Geophysica最新文献

{"title":"Spatial analysis of seismic parameters and their correlation with active faults in northwest Zagros","authors":"Narges Afsari,&nbsp;Zohreh Sheikhhosseini,&nbsp;Fataneh Taghizadeh-Farahmand","doi":"10.1007/s11600-026-01974-6","DOIUrl":"10.1007/s11600-026-01974-6","url":null,"abstract":"<div><p>The scientific basis for seismic risk assessment, safe engineering designs, and disaster risk reduction policymaking is the determination of seismicity parameters, and inaccuracy in these parameters can lead to overestimation or underestimation of risk. In Iran, which is a seismically active country, determining these parameters is essential. In this research, seismicity parameters in the northwest of the Zagros were first calculated for the entire region using three methods: CUVI, maximum curvature, and Kijko and Sellevoll (1992). Subsequently, their spatial variations were examined with a focus on the latter two methods within a geographic grid. The results indicated that the spatial distribution of seismic activity is strongly influenced by the location of the region’s major faults, including the Main Recent Fault, Mountain Front Flexural Fault, High Zagros Fault, and Zagros Front Fault. Seismic productivity (a-value) is significantly higher in areas near the Mountain Front Flexural Fault and the Zagros Front Fault, reaching over 3.8 in some central and western grids (maximum curvature method), while in sections farther from these faults and closer to the High Zagros Fault, this value sometimes drops to around 2.1. This pattern aligns with the number of complete earthquakes, identifying areas adjacent to the frontal faults as the most active zones. The b-value parameter also shows a close relationship with the type of faulting; very low values (less than 0.55) are mainly observed near the flexural and frontal faults, indicating a higher likelihood of large earthquakes in these mature compressive belts, whereas western areas influenced by secondary faulting and the Main Recent Fault exhibit higher b-values (around 0.85–0.90). These spatial heterogeneities, which are directly dependent on the position and behavior of the Zagros's major faults, highlight the need to revise uniform seismic hazard models. Zones with a combination of high a-value and low b-value along the frontal faults were identified as the highest-risk areas, requiring priority in GPS monitoring, paleoseismological studies, and immediate updates to probabilistic seismic hazard assessments (PSHA) for cities such as Kermanshah and its surroundings.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148752001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Regularized fruit fly optimization for robust inversion of self-potential data","authors":"Khalid S. Essa,&nbsp;Eid R. Abo-Ezz,&nbsp;Yves Géraud,&nbsp;Marc Diraison","doi":"10.1007/s11600-026-01984-4","DOIUrl":"10.1007/s11600-026-01984-4","url":null,"abstract":"<div><p>Self-potential surveying constitutes a widely applied passive geophysical technique in environmental and near-surface investigations, including groundwater assessment, mineral exploration, and subsurface fluid characterization. Quantitative interpretation of self-potential data requires solving nonlinear inverse problems that are inherently ill-conditioned and highly sensitive to measurement uncertainty, often leading to unstable or non-unique parameter estimates. This study presents a reproducible inversion framework that integrates Tikhonov regularization with the fruit fly optimization algorithm (FOA) to achieve stable and computationally efficient parameter recovery. The proposed scheme embeds the swarm-based search process within a regularized objective function, thereby explicitly addressing ill-posedness and noise amplification while preserving global exploration capability. The framework is rigorously evaluated through synthetic benchmarks involving single and multiple subsurface sources under variable noise contamination (0%, 5%, and 15), achieving NRMSE values as low as 0.000391 under noise-free conditions and maintaining stable recovery up to 15% noise (NRMSE = 0.1117). In multi-source scenarios, the framework successfully resolves a ten-parameter search space with an NRMSE of 0.0005681 under noise-free conditions and 0.0741 under 10% noise. Comparative benchmarking against particle swarm optimization (PSO), genetic algorithm, and the bat algorithm confirms that FOA consistently achieves superior convergence precision and lower misfit values, with an average execution time of 0.64 s and NRMSE of 0.00408 under noise-free conditions compared to PSO (NRMSE = 0.00768). Application to three field datasets—the Bavarian Woods graphite deposit (Germany) and two copper ore bodies in Turkey—further validates the framework, with recovered source depths (e.g., 31.39 m) aligning closely with independent drilling constraints (~ 30 m) and dynamically estimated shape factors (<i>q</i> = 0.74–1.07) enabling accurate morphological classification of ore bodies. The resulting system provides a transparent, uncertainty-aware, and transferable modeling tool suitable for automated SP inversion and broader environmental geophysical applications.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148751698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial analysis of seismic parameters and their correlation with active faults in northwest Zagros","authors":"Narges Afsari,&nbsp;Zohreh Sheikhhosseini,&nbsp;Fataneh Taghizadeh-Farahmand","doi":"10.1007/s11600-026-01974-6","DOIUrl":"10.1007/s11600-026-01974-6","url":null,"abstract":"<div><p>The scientific basis for seismic risk assessment, safe engineering designs, and disaster risk reduction policymaking is the determination of seismicity parameters, and inaccuracy in these parameters can lead to overestimation or underestimation of risk. In Iran, which is a seismically active country, determining these parameters is essential. In this research, seismicity parameters in the northwest of the Zagros were first calculated for the entire region using three methods: CUVI, maximum curvature, and Kijko and Sellevoll (1992). Subsequently, their spatial variations were examined with a focus on the latter two methods within a geographic grid. The results indicated that the spatial distribution of seismic activity is strongly influenced by the location of the region’s major faults, including the Main Recent Fault, Mountain Front Flexural Fault, High Zagros Fault, and Zagros Front Fault. Seismic productivity (a-value) is significantly higher in areas near the Mountain Front Flexural Fault and the Zagros Front Fault, reaching over 3.8 in some central and western grids (maximum curvature method), while in sections farther from these faults and closer to the High Zagros Fault, this value sometimes drops to around 2.1. This pattern aligns with the number of complete earthquakes, identifying areas adjacent to the frontal faults as the most active zones. The b-value parameter also shows a close relationship with the type of faulting; very low values (less than 0.55) are mainly observed near the flexural and frontal faults, indicating a higher likelihood of large earthquakes in these mature compressive belts, whereas western areas influenced by secondary faulting and the Main Recent Fault exhibit higher b-values (around 0.85–0.90). These spatial heterogeneities, which are directly dependent on the position and behavior of the Zagros's major faults, highlight the need to revise uniform seismic hazard models. Zones with a combination of high a-value and low b-value along the frontal faults were identified as the highest-risk areas, requiring priority in GPS monitoring, paleoseismological studies, and immediate updates to probabilistic seismic hazard assessments (PSHA) for cities such as Kermanshah and its surroundings.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148751697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Regularized fruit fly optimization for robust inversion of self-potential data","authors":"Khalid S. Essa,&nbsp;Eid R. Abo-Ezz,&nbsp;Yves Géraud,&nbsp;Marc Diraison","doi":"10.1007/s11600-026-01984-4","DOIUrl":"10.1007/s11600-026-01984-4","url":null,"abstract":"<div><p>Self-potential surveying constitutes a widely applied passive geophysical technique in environmental and near-surface investigations, including groundwater assessment, mineral exploration, and subsurface fluid characterization. Quantitative interpretation of self-potential data requires solving nonlinear inverse problems that are inherently ill-conditioned and highly sensitive to measurement uncertainty, often leading to unstable or non-unique parameter estimates. This study presents a reproducible inversion framework that integrates Tikhonov regularization with the fruit fly optimization algorithm (FOA) to achieve stable and computationally efficient parameter recovery. The proposed scheme embeds the swarm-based search process within a regularized objective function, thereby explicitly addressing ill-posedness and noise amplification while preserving global exploration capability. The framework is rigorously evaluated through synthetic benchmarks involving single and multiple subsurface sources under variable noise contamination (0%, 5%, and 15), achieving NRMSE values as low as 0.000391 under noise-free conditions and maintaining stable recovery up to 15% noise (NRMSE = 0.1117). In multi-source scenarios, the framework successfully resolves a ten-parameter search space with an NRMSE of 0.0005681 under noise-free conditions and 0.0741 under 10% noise. Comparative benchmarking against particle swarm optimization (PSO), genetic algorithm, and the bat algorithm confirms that FOA consistently achieves superior convergence precision and lower misfit values, with an average execution time of 0.64 s and NRMSE of 0.00408 under noise-free conditions compared to PSO (NRMSE = 0.00768). Application to three field datasets—the Bavarian Woods graphite deposit (Germany) and two copper ore bodies in Turkey—further validates the framework, with recovered source depths (e.g., 31.39 m) aligning closely with independent drilling constraints (~ 30 m) and dynamically estimated shape factors (<i>q</i> = 0.74–1.07) enabling accurate morphological classification of ore bodies. The resulting system provides a transparent, uncertainty-aware, and transferable modeling tool suitable for automated SP inversion and broader environmental geophysical applications.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148752002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Linearized gravity inversion for layer-constrained 3D density estimation using a series expansion-based discretization approach","authors":"Ali A. Mohieldain,&nbsp;Mihály Dobróka,&nbsp;Norbert P. Szabó","doi":"10.1007/s11600-026-01971-9","DOIUrl":"10.1007/s11600-026-01971-9","url":null,"abstract":"<div><p>Geophysical inversion has become a powerful tool for accurately estimating the model parameters that describe subsurface geological structures. In this study, we performed 3D inversion of gravity data to reveal the subsurface density distribution. Both the classical Gaussian least squares and the newly developed series expansion-based inversion methods were employed to solve the inverse problem, separately. Both techniques were evaluated using a comprehensive suite of synthetic experiments designed to investigate inversion performance under varying model complexities, regularization parameters, observation densities, and noise conditions in both the model and data spaces. In both cases, the inversion methods demonstrated stability and effectiveness in minimizing the misfit between observed and calculated data, ensuring reliable estimation of the model parameters, provided the problem is overdetermined. However, in an underdetermined inverse problem which is the most common case in practical applications where the number of model parameters exceeds the observed data, the Gaussian least squares method suffers from non-uniqueness often accompanied by numerical instability. Hence, reliable estimation of model parameters in such cases requires additional physical and mathematical constraints. In contrast, the series expansion-based inversion method, which reduces the need for explicit regularization constraints, approximates the spatial distribution of model parameters using polynomial coefficients of known basis functions. Instead of high number of density values, much less expansion coefficients are to be determined, which can significantly increase the data-to-unknowns ratio and help to transform the problem into a more stable overdetermined system. The proposed methodology was further applied to gravity data from the Shendi–Atbara Basin, Sudan, under both overdetermined and underdetermined inversion scenarios. These findings highlight the feasibility of the series expansion-based inversion method as a robust alternative to the Gaussian least squares method, particularly in case of fewer and sparsely distributed measured data points.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11600-026-01971-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148751149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Precipitation forecasting in Thailand with machine learning technique","authors":"Peeranat Longsombun,&nbsp;Anantaporn Hanskunatai","doi":"10.1007/s11600-026-01972-8","DOIUrl":"10.1007/s11600-026-01972-8","url":null,"abstract":"<div><p>The current weather forecasting in Thailand relies on numerical weather prediction (NWP) techniques, with the Thai Meteorological Department (TMD) implementing the Weather Research and Forecasting (WRF) model. However, the accuracy of weather forecasting in tropical regions remains limited, particularly for 24-h accumulated precipitation, due to geographical factors. This paper proposes an ensemble forecasting methodology for 24-h accumulated precipitation using supervised machine learning techniques. Several regression models were evaluated, including decision tree, random forest, support vector regression, and multilayer perceptron. The dataset covers Thailand from January 1, 2018, to December 31, 2022, resulting in 214,243 records. The results indicate that the support vector regression model achieved the best performance, with an average mean absolute error (MAE) of 8.7747, an average root-mean-square error (RMSE) of 20.3170, and an average correlation coefficient of 0.7211. Compared with the best-performing individual WRF parameterization scenario (Scenario 10), the proposed SVR-based ensemble forecasting approach reduced RMSE and MAE by approximately 43% and improved agreement with observed precipitation. These findings demonstrate the potential of machine learning as an effective post-processing approach for precipitation forecasting over Thailand.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148751148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Precipitation forecasting in Thailand with machine learning technique","authors":"Peeranat Longsombun,&nbsp;Anantaporn Hanskunatai","doi":"10.1007/s11600-026-01972-8","DOIUrl":"10.1007/s11600-026-01972-8","url":null,"abstract":"<div><p>The current weather forecasting in Thailand relies on numerical weather prediction (NWP) techniques, with the Thai Meteorological Department (TMD) implementing the Weather Research and Forecasting (WRF) model. However, the accuracy of weather forecasting in tropical regions remains limited, particularly for 24-h accumulated precipitation, due to geographical factors. This paper proposes an ensemble forecasting methodology for 24-h accumulated precipitation using supervised machine learning techniques. Several regression models were evaluated, including decision tree, random forest, support vector regression, and multilayer perceptron. The dataset covers Thailand from January 1, 2018, to December 31, 2022, resulting in 214,243 records. The results indicate that the support vector regression model achieved the best performance, with an average mean absolute error (MAE) of 8.7747, an average root-mean-square error (RMSE) of 20.3170, and an average correlation coefficient of 0.7211. Compared with the best-performing individual WRF parameterization scenario (Scenario 10), the proposed SVR-based ensemble forecasting approach reduced RMSE and MAE by approximately 43% and improved agreement with observed precipitation. These findings demonstrate the potential of machine learning as an effective post-processing approach for precipitation forecasting over Thailand.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148751147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Linearized gravity inversion for layer-constrained 3D density estimation using a series expansion-based discretization approach","authors":"Ali A. Mohieldain,&nbsp;Mihály Dobróka,&nbsp;Norbert P. Szabó","doi":"10.1007/s11600-026-01971-9","DOIUrl":"10.1007/s11600-026-01971-9","url":null,"abstract":"<div><p>Geophysical inversion has become a powerful tool for accurately estimating the model parameters that describe subsurface geological structures. In this study, we performed 3D inversion of gravity data to reveal the subsurface density distribution. Both the classical Gaussian least squares and the newly developed series expansion-based inversion methods were employed to solve the inverse problem, separately. Both techniques were evaluated using a comprehensive suite of synthetic experiments designed to investigate inversion performance under varying model complexities, regularization parameters, observation densities, and noise conditions in both the model and data spaces. In both cases, the inversion methods demonstrated stability and effectiveness in minimizing the misfit between observed and calculated data, ensuring reliable estimation of the model parameters, provided the problem is overdetermined. However, in an underdetermined inverse problem which is the most common case in practical applications where the number of model parameters exceeds the observed data, the Gaussian least squares method suffers from non-uniqueness often accompanied by numerical instability. Hence, reliable estimation of model parameters in such cases requires additional physical and mathematical constraints. In contrast, the series expansion-based inversion method, which reduces the need for explicit regularization constraints, approximates the spatial distribution of model parameters using polynomial coefficients of known basis functions. Instead of high number of density values, much less expansion coefficients are to be determined, which can significantly increase the data-to-unknowns ratio and help to transform the problem into a more stable overdetermined system. The proposed methodology was further applied to gravity data from the Shendi–Atbara Basin, Sudan, under both overdetermined and underdetermined inversion scenarios. These findings highlight the feasibility of the series expansion-based inversion method as a robust alternative to the Gaussian least squares method, particularly in case of fewer and sparsely distributed measured data points.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11600-026-01971-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148751385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mechanical degradation of limestone under thermo-chemical–mechanical coupling","authors":"Han Zihao,&nbsp;Meldi Suhatril,&nbsp;Huzaifa Hashim","doi":"10.1007/s11600-026-01978-2","DOIUrl":"10.1007/s11600-026-01978-2","url":null,"abstract":"<div><p>Limestone, a representative carbonate rock, plays a crucial role in the safety and long-term stability of deep energy exploitation, geothermal extraction, and underground storage. In engineering environments, it is often subjected to thermo–chemical–mechanical (T–C–M) coupling conditions involving mechanical disturbance, thermal stress, and acidic fluid erosion, leading to progressive deterioration. To elucidate the degradation mechanisms and dominant factors, an L₁₆ orthogonal experiment was conducted, with pre-stress, temperature, and pH at four levels each. Variations in porosity, <i>P</i>-wave velocity, and uniaxial compressive strength (UCS) were analyzed to quantify the coupling effects. The results show that limestone evolves from a dense to a fissured structure under multi-field coupling. Temperature is the primary factor controlling degradation, followed by pre-stress, while acid erosion has a minor effect. UCS decreases by approximately 36% with increasing temperature and slightly with higher pre-stress. The degradation follows a “mechanical-induction–thermal driving–chemical synergy” pattern. A multiple-regression model (R² = 0.986) accurately predicts UCS variations under coupled conditions, providing an effective tool for strength assessment. This study offers new insights into the multi-field deterioration behavior of limestone and supports stability evaluation of surrounding rocks in geothermal reservoirs, deep tunnels, and CO₂ sequestration projects.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148661901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of global ionospheric TEC disturbances during the October 2024 G5 geomagnetic storm","authors":"Huihui Fan,&nbsp;Shuhui Li,&nbsp;Yu Wang,&nbsp;Junsheng Wu,&nbsp;Ruihao Luo,&nbsp;Lihua Li","doi":"10.1007/s11600-026-01965-7","DOIUrl":"10.1007/s11600-026-01965-7","url":null,"abstract":"<div><p>Using multi-source observational data from GPS receivers, magnetometers, Swarm satellites, and the GUVI/TIMED satellite, this study analyzed the ionospheric total electron content (TEC) response to a G5 geomagnetic storm of October 2024 across the American, European–African, and East Asian–Australian longitudinal sectors. The results reveal significant longitudinal-sector differences and north–south asymmetry in the global ionospheric response. Positive storms predominated in the American sector, associated with strong eastward prompt penetration electric field (PPEF), whereas the European–African and East Asian–Australian sectors were characterized primarily by negative storms driven by thermospheric O/N₂ depletion. The irregularity activity exhibited a clear latitudinal dependence: at high latitudes, the Rate of TEC Index (ROTI) reached values 5–6 times those of quiet periods, with the occurrence rate of ROTI &gt; 0.5 exceeding 70%. Combined analysis of GUVI/TIMED satellite and geomagnetic observation data indicated that the PPEF was the key driver of the initial TEC enhancement in the equatorial anomaly region. In contrast, thermospheric composition changes, specifically a decrease in the O/N₂ ratio, were the primary cause of the subsequent negative storms at middle and high latitudes. Thus, the spatiotemporal evolution of the ionospheric storm is attributed to the nonlinear coupling of multiple processes, including the global ring current, regional current systems, and background thermospheric circulation, which collectively produce pronounced longitudinal-sector variability.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148661902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}