Acta Geophysica最新文献

{"title":"Identification of homogeneous rainfall regions and long-term trends in Bankura and Purulia districts, West Bengal","authors":"Prasanya Sarkar,&nbsp;Sujit Kumar Roy,&nbsp;Debolina Pandit,&nbsp;Madhumita Mondal,&nbsp;Alok Sarkar,&nbsp;Shasanka Kumar Gayen","doi":"10.1007/s11600-026-02019-8","DOIUrl":"10.1007/s11600-026-02019-8","url":null,"abstract":"<div><p>The study examines the seasonal and annual rainfall patterns and variability of 20 Indian Meteorological Department (IMD) stations in the Bankura and Purulia districts using seasonal precipitation data compiled by the Indian Meteorological Department spanning 43 years (1980–2022) at a grid size of 0.25 latitude × 0.25 longitude. The southwest monsoon is the main source of seasonal rainfall, accounting for most of the rainfall each year. The winter and post-monsoon seasons bring only a little rain. There is significant seasonal variation in rainfall across the study area, with substantial differences from place to place. This is because of monsoonal patterns and the land’s topography. K-means and Fuzzy C-means clustering methods were employed to identify homogeneous rainfall regions. K-means clustering identified five distinct rainfall regions with varying characteristics, while Fuzzy C-means clustering provided a nuanced view of overlapping rainfall patterns. Annual rainfall trend analysis using the Innovative Trend Analysis (ITA) and Mann–Kendall tests indicates a predominantly decreasing trend across most stations, suggesting a long-term decline in rainfall. However, a few stations (G1, G7, G18, and G19) exhibit weak increasing trends in annual rainfall, highlighting localized variability within the region. These findings underscore a general decline in rainfall across the study area, with implications for water resource management and agricultural practices. The results highlight the need for adaptive strategies to manage the impacts of declining rainfall and inform future research and climate adaptation efforts.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148986858","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":"A comprehensive review of machine learning and deep learning applications in remote sensing for lithological mapping","authors":"Ilyes Salhi,&nbsp;Mourad El Koundi,&nbsp;Mohamed Aymen Ben Khalifa,&nbsp;Wafa Talhaoui,&nbsp;Abdessalem El Ghali","doi":"10.1007/s11600-026-02024-x","DOIUrl":"10.1007/s11600-026-02024-x","url":null,"abstract":"<div><p>Lithological mapping is critical for resource management, environmental assessment, and geohazard mitigation in regions where climate change and rapid land use require robust geoscientific tools. Integrating remote sensing (RS) with machine learning (ML) and deep learning (DL) provides objective and reproducible alternatives to traditional field surveys. This quantitative review synthesizes 444 independent case studies across 154 peer-reviewed publications to evaluate algorithm performance across diverse RS and geological settings. Kruskal–Wallis testing confirms statistically significant performance disparities among classification algorithms and architectures (H = 48.02, <i>p </i>&lt; 0.001, ε²= 0.11). Categorical meta-regression also reveals that geological context and sensor configurations significantly control the performance. For single-source RS data, conventional ML algorithms yield stable baseline metrics (mean overall accuracy (OA) ≈ 0.84—0.85). Conversely, hybrid DL architectures excel in high-dimensional settings, with multisource fusion (particularly multispectral + geophysical + geochemical data) achieving highest predictive accuracy (mean OA ≈ 0.90—0.95, kappa &gt; 0.90). Categorical analyses demonstrate that mixed sedimentary–igneous–metamorphic contexts impose statistically significant accuracy penalties (<i>p </i>&lt; 0.01) compared to igneous settings, though DL models better maintain accuracy in sedimentary settings (mean OA &gt; 0.86, kappa &gt; 0.81). Environmental constraints remain pervasive, with dense vegetation causing accuracy drops up to 5% in OA and 8% in kappa across all models. Egger’s regression (<i>p </i>= 0.6) confirms the absence of severe publication bias despite high methodological heterogeneity. Future research should prioritize label-efficient techniques, context-aware multimodal integration, lightweight edge deployment, and standardized multisensor benchmarks.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148986859","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":"Rainfall–runoff prediction in an ungauged catchment using an integrated HEC-HMS and machine learning framework","authors":"Mohamed Mehdi Djendli,&nbsp;Said Kechida,&nbsp;Fares Laouacheria,&nbsp;Salim Heddam,&nbsp;Renato Morbidelli,&nbsp;Hamza Bouchehed,&nbsp;Lotfi Zeghadnia","doi":"10.1007/s11600-026-02029-6","DOIUrl":"10.1007/s11600-026-02029-6","url":null,"abstract":"<div><p>Predicting discharge in ungauged catchments remains a major challenge because of the lack of observed discharge data. This study proposes an integrated framework combining a physically based model (HEC-HMS) with machine learning approaches (ANN, Random Forest, and XGBoost) for daily discharge prediction. The models were developed and evaluated in the gauged Kebir catchment and subsequently transferred to the ungauged Saf-Saf catchment. In the gauged catchment, all three machine learning models achieved strong predictive performance. ANN provided the best overall results, with R² = 0.962, RMSE = 8.168 m³/s, NSE = 0.962, and PI = 0.963, followed by Random Forest (R² = 0.959, RMSE = 8.450 m³/s, NSE = 0.959, and PI = 0.961) and XGBoost (R² = 0.953, RMSE = 9.084 m³/s, NSE = 0.953, and PI = 0.954). Random Forest achieved the lowest MAE (3.046 m³/s), compared with 3.137 and 3.238 m³/s for ANN and XGBoost, respectively. The positive PI values demonstrate that all three models outperformed the one-step persistence benchmark, indicating predictive skill beyond simply carrying forward the discharge observed at the previous day. In the ungauged catchment, model outputs were assessed indirectly using flow duration curves, seasonal regime, coefficient of variation, and uncertainty behavior. Random Forest exhibited the most stable behavior, with the lowest coefficient of variation (CV = 2.568), while all models reproduced plausible discharge dynamics consistent with the hydrological characteristics of the study area. Uncertainty analysis revealed a positive relationship between prediction interval width (ICwidth) and discharge, with correlation coefficients of 0.688, 0.716, and 0.676 for ANN, Random Forest, and XGBoost, respectively, indicating flow-dependent uncertainty. Overall, the proposed framework provides a physically informed and comparative approach for daily discharge estimation and one-step-ahead prediction in data-scarce environments, although direct validation in the ungauged catchment remains unavailable and sub-daily storm dynamics cannot be resolved from the available daily observations.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148987014","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":"Identification of homogeneous rainfall regions and long-term trends in Bankura and Purulia districts, West Bengal","authors":"Prasanya Sarkar,&nbsp;Sujit Kumar Roy,&nbsp;Debolina Pandit,&nbsp;Madhumita Mondal,&nbsp;Alok Sarkar,&nbsp;Shasanka Kumar Gayen","doi":"10.1007/s11600-026-02019-8","DOIUrl":"10.1007/s11600-026-02019-8","url":null,"abstract":"<div><p>The study examines the seasonal and annual rainfall patterns and variability of 20 Indian Meteorological Department (IMD) stations in the Bankura and Purulia districts using seasonal precipitation data compiled by the Indian Meteorological Department spanning 43 years (1980–2022) at a grid size of 0.25 latitude × 0.25 longitude. The southwest monsoon is the main source of seasonal rainfall, accounting for most of the rainfall each year. The winter and post-monsoon seasons bring only a little rain. There is significant seasonal variation in rainfall across the study area, with substantial differences from place to place. This is because of monsoonal patterns and the land’s topography. K-means and Fuzzy C-means clustering methods were employed to identify homogeneous rainfall regions. K-means clustering identified five distinct rainfall regions with varying characteristics, while Fuzzy C-means clustering provided a nuanced view of overlapping rainfall patterns. Annual rainfall trend analysis using the Innovative Trend Analysis (ITA) and Mann–Kendall tests indicates a predominantly decreasing trend across most stations, suggesting a long-term decline in rainfall. However, a few stations (G1, G7, G18, and G19) exhibit weak increasing trends in annual rainfall, highlighting localized variability within the region. These findings underscore a general decline in rainfall across the study area, with implications for water resource management and agricultural practices. The results highlight the need for adaptive strategies to manage the impacts of declining rainfall and inform future research and climate adaptation efforts.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148987010","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":"A comprehensive review of machine learning and deep learning applications in remote sensing for lithological mapping","authors":"Ilyes Salhi,&nbsp;Mourad El Koundi,&nbsp;Mohamed Aymen Ben Khalifa,&nbsp;Wafa Talhaoui,&nbsp;Abdessalem El Ghali","doi":"10.1007/s11600-026-02024-x","DOIUrl":"10.1007/s11600-026-02024-x","url":null,"abstract":"<div><p>Lithological mapping is critical for resource management, environmental assessment, and geohazard mitigation in regions where climate change and rapid land use require robust geoscientific tools. Integrating remote sensing (RS) with machine learning (ML) and deep learning (DL) provides objective and reproducible alternatives to traditional field surveys. This quantitative review synthesizes 444 independent case studies across 154 peer-reviewed publications to evaluate algorithm performance across diverse RS and geological settings. Kruskal–Wallis testing confirms statistically significant performance disparities among classification algorithms and architectures (H = 48.02, <i>p </i>&lt; 0.001, ε²= 0.11). Categorical meta-regression also reveals that geological context and sensor configurations significantly control the performance. For single-source RS data, conventional ML algorithms yield stable baseline metrics (mean overall accuracy (OA) ≈ 0.84—0.85). Conversely, hybrid DL architectures excel in high-dimensional settings, with multisource fusion (particularly multispectral + geophysical + geochemical data) achieving highest predictive accuracy (mean OA ≈ 0.90—0.95, kappa &gt; 0.90). Categorical analyses demonstrate that mixed sedimentary–igneous–metamorphic contexts impose statistically significant accuracy penalties (<i>p </i>&lt; 0.01) compared to igneous settings, though DL models better maintain accuracy in sedimentary settings (mean OA &gt; 0.86, kappa &gt; 0.81). Environmental constraints remain pervasive, with dense vegetation causing accuracy drops up to 5% in OA and 8% in kappa across all models. Egger’s regression (<i>p </i>= 0.6) confirms the absence of severe publication bias despite high methodological heterogeneity. Future research should prioritize label-efficient techniques, context-aware multimodal integration, lightweight edge deployment, and standardized multisensor benchmarks.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148986883","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":"Rainfall–runoff prediction in an ungauged catchment using an integrated HEC-HMS and machine learning framework","authors":"Mohamed Mehdi Djendli,&nbsp;Said Kechida,&nbsp;Fares Laouacheria,&nbsp;Salim Heddam,&nbsp;Renato Morbidelli,&nbsp;Hamza Bouchehed,&nbsp;Lotfi Zeghadnia","doi":"10.1007/s11600-026-02029-6","DOIUrl":"10.1007/s11600-026-02029-6","url":null,"abstract":"<div><p>Predicting discharge in ungauged catchments remains a major challenge because of the lack of observed discharge data. This study proposes an integrated framework combining a physically based model (HEC-HMS) with machine learning approaches (ANN, Random Forest, and XGBoost) for daily discharge prediction. The models were developed and evaluated in the gauged Kebir catchment and subsequently transferred to the ungauged Saf-Saf catchment. In the gauged catchment, all three machine learning models achieved strong predictive performance. ANN provided the best overall results, with R² = 0.962, RMSE = 8.168 m³/s, NSE = 0.962, and PI = 0.963, followed by Random Forest (R² = 0.959, RMSE = 8.450 m³/s, NSE = 0.959, and PI = 0.961) and XGBoost (R² = 0.953, RMSE = 9.084 m³/s, NSE = 0.953, and PI = 0.954). Random Forest achieved the lowest MAE (3.046 m³/s), compared with 3.137 and 3.238 m³/s for ANN and XGBoost, respectively. The positive PI values demonstrate that all three models outperformed the one-step persistence benchmark, indicating predictive skill beyond simply carrying forward the discharge observed at the previous day. In the ungauged catchment, model outputs were assessed indirectly using flow duration curves, seasonal regime, coefficient of variation, and uncertainty behavior. Random Forest exhibited the most stable behavior, with the lowest coefficient of variation (CV = 2.568), while all models reproduced plausible discharge dynamics consistent with the hydrological characteristics of the study area. Uncertainty analysis revealed a positive relationship between prediction interval width (ICwidth) and discharge, with correlation coefficients of 0.688, 0.716, and 0.676 for ANN, Random Forest, and XGBoost, respectively, indicating flow-dependent uncertainty. Overall, the proposed framework provides a physically informed and comparative approach for daily discharge estimation and one-step-ahead prediction in data-scarce environments, although direct validation in the ungauged catchment remains unavailable and sub-daily storm dynamics cannot be resolved from the available daily observations.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148986554","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":"Integrated geophysical and hydrogeochemical characterization of a fractured limestone aquifer in new Tuna El Gabel village, West El-Minia, Egypt","authors":"Mohamed Mourad,&nbsp;Eslam Abd Elrazek,&nbsp;Ahmed Abd El Gawad,&nbsp;Takeshi Tsuji,&nbsp;Ahmad B. Ahmad,&nbsp;Tarek Imam,&nbsp;Ahmed Shidied","doi":"10.1007/s11600-026-01990-6","DOIUrl":"10.1007/s11600-026-01990-6","url":null,"abstract":"<div><p>Groundwater is a vital component of water supply in arid regions, such as West El-Minia Governorate, Egypt, where water scarcity is projected to worsen, with per capita water supply expected to drop below 500 m³ by 2025. In response, Egypt’s One and a Half Million Feddan (≈630,000 hectares) project aims to reclaim the Western Desert and develop new communities, aligning with the country’s Vision 2030 for sustainable development. This study conducts a comprehensive qualitative and quantitative assessment of groundwater resources in the region, evaluating their suitability for various applications. Using 16 vertical electrical soundings (VES), we constructed geoelectric cross sections and maps that reveal five distinct geoelectric layers. The main water-bearing formation, a fractured limestone layer with clay intrusions, varies in thickness, increasing toward the southeastern portion, where groundwater potential is higher, owing to the influence of local structural features such as faults and fissures. Hydrogeochemical analysis of 12 water samples indicates that, although groundwater is generally fresh (TDS &lt; 1000 mg/L), all samples are classified as “very hard” (&gt; 180 mg/L), making them unsuitable for domestic use without prior treatment. However, evaluation using the US Salinity Laboratory (USSL) diagram, Sodium Adsorption Ratio (SAR), Salinity Index (SI), Sodium Percentage (Na %), Residual Sodium Carbonate (RSC), Kelly’s Ratio (KR), Magnesium Ratio (MR), and Permeability Index (PI) demonstrates that the majority of samples are highly suitable for irrigation and livestock purposes. The practical results of this study identify the eastern sector as having higher groundwater potential and confirm that the aquifer is a viable resource for irrigation-based land reclamation, provided appropriate water management and treatment strategies are applied.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11600-026-01990-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148986707","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":"Integrated geophysical and hydrogeochemical characterization of a fractured limestone aquifer in new Tuna El Gabel village, West El-Minia, Egypt","authors":"Mohamed Mourad,&nbsp;Eslam Abd Elrazek,&nbsp;Ahmed Abd El Gawad,&nbsp;Takeshi Tsuji,&nbsp;Ahmad B. Ahmad,&nbsp;Tarek Imam,&nbsp;Ahmed Shidied","doi":"10.1007/s11600-026-01990-6","DOIUrl":"10.1007/s11600-026-01990-6","url":null,"abstract":"<div><p>Groundwater is a vital component of water supply in arid regions, such as West El-Minia Governorate, Egypt, where water scarcity is projected to worsen, with per capita water supply expected to drop below 500 m³ by 2025. In response, Egypt’s One and a Half Million Feddan (≈630,000 hectares) project aims to reclaim the Western Desert and develop new communities, aligning with the country’s Vision 2030 for sustainable development. This study conducts a comprehensive qualitative and quantitative assessment of groundwater resources in the region, evaluating their suitability for various applications. Using 16 vertical electrical soundings (VES), we constructed geoelectric cross sections and maps that reveal five distinct geoelectric layers. The main water-bearing formation, a fractured limestone layer with clay intrusions, varies in thickness, increasing toward the southeastern portion, where groundwater potential is higher, owing to the influence of local structural features such as faults and fissures. Hydrogeochemical analysis of 12 water samples indicates that, although groundwater is generally fresh (TDS &lt; 1000 mg/L), all samples are classified as “very hard” (&gt; 180 mg/L), making them unsuitable for domestic use without prior treatment. However, evaluation using the US Salinity Laboratory (USSL) diagram, Sodium Adsorption Ratio (SAR), Salinity Index (SI), Sodium Percentage (Na %), Residual Sodium Carbonate (RSC), Kelly’s Ratio (KR), Magnesium Ratio (MR), and Permeability Index (PI) demonstrates that the majority of samples are highly suitable for irrigation and livestock purposes. The practical results of this study identify the eastern sector as having higher groundwater potential and confirm that the aquifer is a viable resource for irrigation-based land reclamation, provided appropriate water management and treatment strategies are applied.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11600-026-01990-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148986569","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":"Fault-controlled magmatic architecture beneath the Zacapú basin (central Trans-Mexican Volcanic Belt field) from 3D aeromagnetic modeling","authors":"Héctor López-Loera,&nbsp;José Rosas-Elguera,&nbsp;Avto Goguitchaichvili,&nbsp;Ulises Díaz-Ortega,&nbsp;Vadim Kravchinsky","doi":"10.1007/s11600-026-02004-1","DOIUrl":"10.1007/s11600-026-02004-1","url":null,"abstract":"<div><p>The Michoacán–Guanajuato Volcanic Field (MGVF), within the Trans-Mexican Volcanic Belt (TMVB), is one of the world’s largest monogenetic volcanic provinces. Here, we analyze SGM 1998 airborne aeromagnetic data acquired over the western Zacapú sector and integrate map-based filtering with 3D magnetic susceptibility inversion to constrain subsurface magmatic architecture and structural controls. Residual magnetic anomalies (up to ~+ 570 to − 412 nT) and reduction-to-the-pole maps upward continued to 3 km delineate three aeromagnetic domains (AMD I–III) with contrasting anomaly amplitudes and spatial organization. Analytic-signal processing and upward continuation to 10 km emphasize two dominant lineament sets (NW–SE and NE–SW) and a major NW–SE-trending discontinuity interpreted as a deep zone of crustal weakness. VOXI 3D inversion resolves multiple magnetized bodies: Body W1 is a large, irregular source with upper apophyses, extending from ~1887 to at least ~10,280 m.b.s.l. and remaining open at depth, whereas W2 is an elongate, N–S-trending body with a maximum length of ~25 km and a depth extent of ~11 km. Together, these results indicate that volcanism in the Zacapú sector reflects interaction between deep magma storage and regional structures that localize magma ascent and emplacement.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11600-026-02004-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148986604","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":"Fault-controlled magmatic architecture beneath the Zacapú basin (central Trans-Mexican Volcanic Belt field) from 3D aeromagnetic modeling","authors":"Héctor López-Loera,&nbsp;José Rosas-Elguera,&nbsp;Avto Goguitchaichvili,&nbsp;Ulises Díaz-Ortega,&nbsp;Vadim Kravchinsky","doi":"10.1007/s11600-026-02004-1","DOIUrl":"10.1007/s11600-026-02004-1","url":null,"abstract":"<div><p>The Michoacán–Guanajuato Volcanic Field (MGVF), within the Trans-Mexican Volcanic Belt (TMVB), is one of the world’s largest monogenetic volcanic provinces. Here, we analyze SGM 1998 airborne aeromagnetic data acquired over the western Zacapú sector and integrate map-based filtering with 3D magnetic susceptibility inversion to constrain subsurface magmatic architecture and structural controls. Residual magnetic anomalies (up to ~+ 570 to − 412 nT) and reduction-to-the-pole maps upward continued to 3 km delineate three aeromagnetic domains (AMD I–III) with contrasting anomaly amplitudes and spatial organization. Analytic-signal processing and upward continuation to 10 km emphasize two dominant lineament sets (NW–SE and NE–SW) and a major NW–SE-trending discontinuity interpreted as a deep zone of crustal weakness. VOXI 3D inversion resolves multiple magnetized bodies: Body W1 is a large, irregular source with upper apophyses, extending from ~1887 to at least ~10,280 m.b.s.l. and remaining open at depth, whereas W2 is an elongate, N–S-trending body with a maximum length of ~25 km and a depth extent of ~11 km. Together, these results indicate that volcanism in the Zacapú sector reflects interaction between deep magma storage and regional structures that localize magma ascent and emplacement.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11600-026-02004-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148986746","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}