Acta Geophysica最新文献

{"title":"A fractal approach to daily streamflow simulation in Indian river basins using visibility graphs","authors":"L. Akhila,&nbsp;Aayisha Salim,&nbsp;Shamseena Vahab,&nbsp;S. Adarsh,&nbsp;Thomas Plocoste,&nbsp;Meenakshi S. Pillai","doi":"10.1007/s11600-026-01989-z","DOIUrl":"10.1007/s11600-026-01989-z","url":null,"abstract":"<div><p>Understanding the fractal behavior is one of the efficient alternative modeling practices for the accurate simulation and forecasting of streamflows. This study explores the fractal characteristics of daily streamflows in five Indian river basins through a novel framework based on visibility graphs (VG). First, visibility matrices were constructed using a complex network-based VG approach. Then, the fractal features of these matrices were extracted using the sandbox method (SBM), enabling the estimation of both the fractal dimension and the multifractal spectrum of the streamflow time series. Building on this, a new fractal regression modeling (FRM) approach was proposed for simulating daily streamflows at selected stations. The FRM approach involves segmenting the time series along the time domain, estimating the fractal dimensions of each segment, and using the average scaling exponents to simulate streamflows. The results indicate that the fractal-based model performs competitively compared to traditional models such as autoregressive integrated moving average (ARIMA) and linear regression. In most cases, it yields higher coefficient of determination (<i>R</i>²) values and lower root mean square error (RMSE), demonstrating stronger agreement with observed data. These findings offer valuable insights for advancing hydrological modeling by effectively capturing the complex, multifractal patterns of streamflow dynamics. The proposed methodology holds significant promise for enhancing the accuracy of streamflow predictions under changing environmental conditions.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149035345","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 fractal approach to daily streamflow simulation in Indian river basins using visibility graphs","authors":"L. Akhila,&nbsp;Aayisha Salim,&nbsp;Shamseena Vahab,&nbsp;S. Adarsh,&nbsp;Thomas Plocoste,&nbsp;Meenakshi S. Pillai","doi":"10.1007/s11600-026-01989-z","DOIUrl":"10.1007/s11600-026-01989-z","url":null,"abstract":"<div><p>Understanding the fractal behavior is one of the efficient alternative modeling practices for the accurate simulation and forecasting of streamflows. This study explores the fractal characteristics of daily streamflows in five Indian river basins through a novel framework based on visibility graphs (VG). First, visibility matrices were constructed using a complex network-based VG approach. Then, the fractal features of these matrices were extracted using the sandbox method (SBM), enabling the estimation of both the fractal dimension and the multifractal spectrum of the streamflow time series. Building on this, a new fractal regression modeling (FRM) approach was proposed for simulating daily streamflows at selected stations. The FRM approach involves segmenting the time series along the time domain, estimating the fractal dimensions of each segment, and using the average scaling exponents to simulate streamflows. The results indicate that the fractal-based model performs competitively compared to traditional models such as autoregressive integrated moving average (ARIMA) and linear regression. In most cases, it yields higher coefficient of determination (<i>R</i>²) values and lower root mean square error (RMSE), demonstrating stronger agreement with observed data. These findings offer valuable insights for advancing hydrological modeling by effectively capturing the complex, multifractal patterns of streamflow dynamics. The proposed methodology holds significant promise for enhancing the accuracy of streamflow predictions under changing environmental conditions.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149035344","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":"Integrating spectroscopy and remote sensing to assess soil salinization under treated wastewater irrigation: a case study in northern Tunisia","authors":"Ines Smaani,&nbsp;Sonia Gannouni,&nbsp;Salma Boussen,&nbsp;Noamen Rebai","doi":"10.1007/s11600-026-02020-1","DOIUrl":"10.1007/s11600-026-02020-1","url":null,"abstract":"<div><p>Several perimeters have been developed in Tunisia for the reuse of treated wastewater (TWW) in agriculture as part of the national strategy for the reuse of TWW under water scarcity conditions. The aim of this study is to assess long-term soil salinization in the irrigated perimeter of Borj Touil (northern Tunisia) by combining multi-date Landsat imagery (1987–2023), in situ Vis–NIR spectroscopy (using an ASD spectroradiometer), and geochemical analysis. The spatiotemporal distribution of evaporite minerals was mapped from the processed Landsat data using the classification method Spectral Angle Mapper (SAM) and laboratory measurements (pH, electrical conductivity, X-ray diffraction, and major and trace elements) were used for independent characterization of soil salinity and mineral composition. The electrical conductivity of the soil was moderate to high from 4.3 mS/cm in the control parcel to 8.6 mS/cm in the longest-irrigated parcels. Linear regressions between measured salinity, mean ASD reflectance and the Landsat-derived SAM fraction (<i>n</i> = 11) resulted in significant relationships (<i>R</i>²≈ 0.7, <i>p </i>&lt; 0.01), indicating the capacity of spectral indicators to capture salinization gradients. The progressive accumulation of gypsum, anhydrite and halite was confirmed by time-series analysis and the onset of mineral precipitation was detected around 10–15 years after the start of irrigation, and mineral precipitation increased with irrigation duration. The mineral fractions were highest for parcels with over 30 years of irrigation. The findings here suggest that the combination of multispectral remote sensing, field spectroscopy and geochemistry offers a coherent approach to tracking long-term changes in salinization in TWW-irrigated systems. The predictive performance is still moderate, though, and the method should be used in conjunction with laboratory analyses. It is limited by the spatial resolution of Landsat imagery, presence of mixed pixels in heterogeneous agricultural areas, sensitivity to soil moisture and vegetation cover, and limited capability to detect subsurface salinity. Integrated approach is helpful for the long-term salinization risk evaluation and for sustainable soil and water management in semiarid irrigated areas.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149035300","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":"Integrating spectroscopy and remote sensing to assess soil salinization under treated wastewater irrigation: a case study in northern Tunisia","authors":"Ines Smaani,&nbsp;Sonia Gannouni,&nbsp;Salma Boussen,&nbsp;Noamen Rebai","doi":"10.1007/s11600-026-02020-1","DOIUrl":"10.1007/s11600-026-02020-1","url":null,"abstract":"<div><p>Several perimeters have been developed in Tunisia for the reuse of treated wastewater (TWW) in agriculture as part of the national strategy for the reuse of TWW under water scarcity conditions. The aim of this study is to assess long-term soil salinization in the irrigated perimeter of Borj Touil (northern Tunisia) by combining multi-date Landsat imagery (1987–2023), in situ Vis–NIR spectroscopy (using an ASD spectroradiometer), and geochemical analysis. The spatiotemporal distribution of evaporite minerals was mapped from the processed Landsat data using the classification method Spectral Angle Mapper (SAM) and laboratory measurements (pH, electrical conductivity, X-ray diffraction, and major and trace elements) were used for independent characterization of soil salinity and mineral composition. The electrical conductivity of the soil was moderate to high from 4.3 mS/cm in the control parcel to 8.6 mS/cm in the longest-irrigated parcels. Linear regressions between measured salinity, mean ASD reflectance and the Landsat-derived SAM fraction (<i>n</i> = 11) resulted in significant relationships (<i>R</i>²≈ 0.7, <i>p </i>&lt; 0.01), indicating the capacity of spectral indicators to capture salinization gradients. The progressive accumulation of gypsum, anhydrite and halite was confirmed by time-series analysis and the onset of mineral precipitation was detected around 10–15 years after the start of irrigation, and mineral precipitation increased with irrigation duration. The mineral fractions were highest for parcels with over 30 years of irrigation. The findings here suggest that the combination of multispectral remote sensing, field spectroscopy and geochemistry offers a coherent approach to tracking long-term changes in salinization in TWW-irrigated systems. The predictive performance is still moderate, though, and the method should be used in conjunction with laboratory analyses. It is limited by the spatial resolution of Landsat imagery, presence of mixed pixels in heterogeneous agricultural areas, sensitivity to soil moisture and vegetation cover, and limited capability to detect subsurface salinity. Integrated approach is helpful for the long-term salinization risk evaluation and for sustainable soil and water management in semiarid irrigated areas.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149035032","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":"Investigating the effects of aerosol optical properties, meteorology and morphology on radiative forcing and climate change","authors":"Pallavi Pradeep Khobragade,&nbsp;Ajay Vikram Ahirwar,&nbsp;Royal Madan","doi":"10.1007/s11600-026-02015-y","DOIUrl":"10.1007/s11600-026-02015-y","url":null,"abstract":"<div><p>The presence of aerosol has a significant impact on radiation and cloud formation. Aerosol radiative forcing denotes the imbalance in the Earth's radiation budget induced by human-generated aerosol. Despite substantial progress in monitoring, interpreting and modeling these processes over the last four decades, considerable uncertainties persist. This paper examines several crucial factors, such as aerosol optical properties and composition, contributing to a novel perspective on aerosol radiative forcing and heating rates throughout the industrial era. The estimation of forcing and heating rates over urban industrial areas aims to discern the specific contribution of industrial aerosol. The constraints on our understanding of aerosol absorbance and the factors influencing trends in ground radiative fluxes limit the assessment of forcing from aerosol–radiation interactions. Disturbances in radiative equilibrium prompt the evolution of the system's state, and human activities, by altering atmospheric chemistry, can potentially induce such disturbances. Raipur, the state capital of Chhattisgarh, is selected as the study area located in the eastern central portion of India. AOD, AE and SSA values were retrieved from satellite and model-derived data using OMI and MERRA-2. RF estimation was performed using COART model. The annual mean value of RF during 2016–2021 is found to be 10.92 W/m² with 11.85 W/m², 11.03 W/m², 10.51 W/m² and 10.29 W/m² during autumn, winter, spring and summer, respectively. RF is found to be dependent on SSA. Heating rate in the study area is found to be 0.328 K/day, indicating strong localized warming. This is significantly higher than typical tropospheric or stratospheric heating rates in the study region, indicating contribution from anthropogenic sources and a potential for atmospheric instability. This work studies the regional effect of optical properties radiative forcing studying the meteorological and morphological properties on air quality.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149034512","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":"Investigating the effects of aerosol optical properties, meteorology and morphology on radiative forcing and climate change","authors":"Pallavi Pradeep Khobragade,&nbsp;Ajay Vikram Ahirwar,&nbsp;Royal Madan","doi":"10.1007/s11600-026-02015-y","DOIUrl":"10.1007/s11600-026-02015-y","url":null,"abstract":"<div><p>The presence of aerosol has a significant impact on radiation and cloud formation. Aerosol radiative forcing denotes the imbalance in the Earth's radiation budget induced by human-generated aerosol. Despite substantial progress in monitoring, interpreting and modeling these processes over the last four decades, considerable uncertainties persist. This paper examines several crucial factors, such as aerosol optical properties and composition, contributing to a novel perspective on aerosol radiative forcing and heating rates throughout the industrial era. The estimation of forcing and heating rates over urban industrial areas aims to discern the specific contribution of industrial aerosol. The constraints on our understanding of aerosol absorbance and the factors influencing trends in ground radiative fluxes limit the assessment of forcing from aerosol–radiation interactions. Disturbances in radiative equilibrium prompt the evolution of the system's state, and human activities, by altering atmospheric chemistry, can potentially induce such disturbances. Raipur, the state capital of Chhattisgarh, is selected as the study area located in the eastern central portion of India. AOD, AE and SSA values were retrieved from satellite and model-derived data using OMI and MERRA-2. RF estimation was performed using COART model. The annual mean value of RF during 2016–2021 is found to be 10.92 W/m² with 11.85 W/m², 11.03 W/m², 10.51 W/m² and 10.29 W/m² during autumn, winter, spring and summer, respectively. RF is found to be dependent on SSA. Heating rate in the study area is found to be 0.328 K/day, indicating strong localized warming. This is significantly higher than typical tropospheric or stratospheric heating rates in the study region, indicating contribution from anthropogenic sources and a potential for atmospheric instability. This work studies the regional effect of optical properties radiative forcing studying the meteorological and morphological properties on air quality.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149034573","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":"From raw signals to decomposed insights: boosting models enhanced by EMD variants for multi-depth soil temperature and moisture prediction","authors":"Khaled Merabet,&nbsp;Ozgur Kisi,&nbsp;Sungwon Kim,&nbsp;Salim Heddam,&nbsp;Mohammad Zounemat-Kermani","doi":"10.1007/s11600-026-02025-w","DOIUrl":"10.1007/s11600-026-02025-w","url":null,"abstract":"<div><p>The integration of signal decomposition with machine learning to improve predictive accuracy has become of great importance. Prediction of soil temperature and soil moisture is becoming an important area of research aiming at improving water resource management. In the present paper, four machine learning (ML) models were developed in order to predict two agronomic variables: (i) soil temperature (<i>T</i>_s) and (ii) soil moisture (SM) measured at 05 and 20 cm depths, using real in situ data. The proposed ML models are, respectively: (i) eXtreme gradient boosting (XGBoost), (ii) light gradient boosting machine (LightGBM), (iii) categorical boosting (CatBoost), and (iv) random forest regression (RFR). The ML was developed with and without the integration of signal decomposition, and in total four scenarios were analyzed in the present paper: (i) prediction of <i>T</i>_s and SM without the integration of signal decomposition, (ii) prediction of <i>T</i>_s and SM by integrating the empirical mode decomposition (EMD) signal decomposition, (iii) prediction of <i>T</i>_s and SM by integrating the ensemble empirical mode decomposition (EEMD), and (iv) prediction of <i>T</i>_s and SM by integrating complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Furthermore, our modeling approach was oriented toward the use of air temperature (<i>T</i>_a) and soil temperature (<i>T</i>_s) as the single input variable; these signals are themselves decomposed into multiple intrinsic mode functions (IMFs) using the EMD, EEMD, and CEEMDAN, which are subsequently adopted as new input features. The modeling framework was developed using data collected from two USGS stations and measured at an hourly time step. The obtained results revealed that, for soil temperature prediction, the EMD, EEMD, and CEEMDAN significantly contributed to the improvement of prediction accuracy, and the XGBoost model exhibited high performance with root-mean-square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE), and correlation coefficient (R) of 1.396 °C, 0.667 °C, 0.906, and 0.952, for 05 cm depth, and 1.039 °C, 0.477 °C, 0.921, and 0.960 for 20 cm depth. For soil moisture prediction, all models based on signal decomposition exhibited very high performance, with XGBoost-EMD providing the lowest validation errors across the evaluated soil depths. Furthermore, the effectiveness of our proposed hybrid approach was confirmed, and the results are very promising.</p><h3>Graphical abstract</h3>\u0000<div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149034983","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":"From raw signals to decomposed insights: boosting models enhanced by EMD variants for multi-depth soil temperature and moisture prediction","authors":"Khaled Merabet,&nbsp;Ozgur Kisi,&nbsp;Sungwon Kim,&nbsp;Salim Heddam,&nbsp;Mohammad Zounemat-Kermani","doi":"10.1007/s11600-026-02025-w","DOIUrl":"10.1007/s11600-026-02025-w","url":null,"abstract":"<div><p>The integration of signal decomposition with machine learning to improve predictive accuracy has become of great importance. Prediction of soil temperature and soil moisture is becoming an important area of research aiming at improving water resource management. In the present paper, four machine learning (ML) models were developed in order to predict two agronomic variables: (i) soil temperature (<i>T</i>_s) and (ii) soil moisture (SM) measured at 05 and 20 cm depths, using real in situ data. The proposed ML models are, respectively: (i) eXtreme gradient boosting (XGBoost), (ii) light gradient boosting machine (LightGBM), (iii) categorical boosting (CatBoost), and (iv) random forest regression (RFR). The ML was developed with and without the integration of signal decomposition, and in total four scenarios were analyzed in the present paper: (i) prediction of <i>T</i>_s and SM without the integration of signal decomposition, (ii) prediction of <i>T</i>_s and SM by integrating the empirical mode decomposition (EMD) signal decomposition, (iii) prediction of <i>T</i>_s and SM by integrating the ensemble empirical mode decomposition (EEMD), and (iv) prediction of <i>T</i>_s and SM by integrating complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Furthermore, our modeling approach was oriented toward the use of air temperature (<i>T</i>_a) and soil temperature (<i>T</i>_s) as the single input variable; these signals are themselves decomposed into multiple intrinsic mode functions (IMFs) using the EMD, EEMD, and CEEMDAN, which are subsequently adopted as new input features. The modeling framework was developed using data collected from two USGS stations and measured at an hourly time step. The obtained results revealed that, for soil temperature prediction, the EMD, EEMD, and CEEMDAN significantly contributed to the improvement of prediction accuracy, and the XGBoost model exhibited high performance with root-mean-square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE), and correlation coefficient (R) of 1.396 °C, 0.667 °C, 0.906, and 0.952, for 05 cm depth, and 1.039 °C, 0.477 °C, 0.921, and 0.960 for 20 cm depth. For soil moisture prediction, all models based on signal decomposition exhibited very high performance, with XGBoost-EMD providing the lowest validation errors across the evaluated soil depths. Furthermore, the effectiveness of our proposed hybrid approach was confirmed, and the results are very promising.</p><h3>Graphical abstract</h3>\u0000<div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149034906","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":"Comment on “mapping subsurface structural lineaments using the tilt angle map from gravity gradient data and Euler deconvolution, Lakouat–Aksab region, Northwestern Tunisia” by Hamdi Nasr et al. (2024)","authors":"Mohamed Montassar Ben Slama,&nbsp;Mohamed Ghanmi","doi":"10.1007/s11600-026-02022-z","DOIUrl":"10.1007/s11600-026-02022-z","url":null,"abstract":"<div><p>This comment challenges the petrophysical and geometric interpretation of salt structures proposed by Hamdi Nasr et al. (2024) in the Lakhouat–Aksab region. The original study attributes positive gravity anomalies to dense Triassic salt intumescences. However, the integration of laboratory density measurements and field observations (reworking and dissolution) suggests that the Triassic complex exhibits a mass deficit compared to the surrounding sedimentary series. Furthermore, the simple vertical diapirism model employed overlooks the geometry of allochthonous “salt glaciers” documented in regional literature (Ghanmi et al. 2001; Talbot 2005; Chelbi et al. 2006; Ben Slama et al. 2009; Amri et al. 2020, 2022). This reassessment implies that the observed positive anomalies may originate from the basement or deep magmatic bodies rather than the salt mass itself.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149034957","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":"Comment on “mapping subsurface structural lineaments using the tilt angle map from gravity gradient data and Euler deconvolution, Lakouat–Aksab region, Northwestern Tunisia” by Hamdi Nasr et al. (2024)","authors":"Mohamed Montassar Ben Slama,&nbsp;Mohamed Ghanmi","doi":"10.1007/s11600-026-02022-z","DOIUrl":"10.1007/s11600-026-02022-z","url":null,"abstract":"<div><p>This comment challenges the petrophysical and geometric interpretation of salt structures proposed by Hamdi Nasr et al. (2024) in the Lakhouat–Aksab region. The original study attributes positive gravity anomalies to dense Triassic salt intumescences. However, the integration of laboratory density measurements and field observations (reworking and dissolution) suggests that the Triassic complex exhibits a mass deficit compared to the surrounding sedimentary series. Furthermore, the simple vertical diapirism model employed overlooks the geometry of allochthonous “salt glaciers” documented in regional literature (Ghanmi et al. 2001; Talbot 2005; Chelbi et al. 2006; Ben Slama et al. 2009; Amri et al. 2020, 2022). This reassessment implies that the observed positive anomalies may originate from the basement or deep magmatic bodies rather than the salt mass itself.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149034958","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}