The horizontal to vertical spectral ratio(HVSR)methodology is used here to characterize pumice soils and to image the three-dimensional surface geometry of Guadalajara,Mexico.Similar to other Latin American cities,Gua...The horizontal to vertical spectral ratio(HVSR)methodology is used here to characterize pumice soils and to image the three-dimensional surface geometry of Guadalajara,Mexico.Similar to other Latin American cities,Guadalajara is exposed to high seismic risk,with the particularity of being the largest urban settlement in Latin America built on pumice soils.Methodology has not yet been tested to characterize subsoil depths in pumice sands.Due to the questionable use of traditional geotechnical tests for the analysis of pumice soils,HVSR provides an alternative for its characterization without altering its fragile and porous structure.In this work,resonance frequency(F0)and peak amplitude(A0)are used to constrain the depth of the major impedance contrast that represents the interface between bedrock and pumice soil.Results were compared with borehole depths and other available geotechnical and geophysical data and show good agreement.One of the profiles estimated on the riverbanks that cross the city,reveals different subsoil thickness that could have an impact on different site responses on riverine areas to an eventual earthquake.Government and academic efforts are combined in this work to characterize depth sediments,an important parameter that impacts the regulations for construction in the city.展开更多
Landslide susceptibility mapping is an integral part of geological hazard analysis.Recently,the emphasis of many studies has been on data-driven models,notably those derived from machine learning,owing to their aptitu...Landslide susceptibility mapping is an integral part of geological hazard analysis.Recently,the emphasis of many studies has been on data-driven models,notably those derived from machine learning,owing to their aptitude for tackling complex non-linear problems.However,the prevailing models often disregard qualitative research,leading to limited interpretability and mistakes in extracting negative samples,i.e.inaccurate non-landslide samples.In this study,Scoops 3D(a three-dimensional slope stability analysis tool)was utilized to conduct a qualitative assessment of slope stability in the Yunyang section of the Three Gorges Reservoir area.The depth of the bedrock was predicted utilizing a Convolutional Neural Network(CNN),incorporating local boreholes and building on the insights from prior research.The Random Forest(RF)algorithm was subsequently used to execute a data-driven landslide susceptibility analysis.The proposed methodology demonstrated a notable increase of 29.25%in the evaluation metric,the area under the receiver operating characteristic curve(ROC-AUC),outperforming the prevailing benchmark model.Furthermore,the landslide susceptibility map generated by the proposed model demonstrated superior interpretability.This result not only validates the effectiveness of amalgamating mathematical and mechanistic insights for such analyses,but it also carries substantial academic and practical implications.展开更多
Due to associated uncertainties,modelling the spatial distribution of depth to bedrock(DTB) is an important and challenging concern in many geo-engineering applications.The association between DTB,the safety and econo...Due to associated uncertainties,modelling the spatial distribution of depth to bedrock(DTB) is an important and challenging concern in many geo-engineering applications.The association between DTB,the safety and economy of design structures implies that generating more precise predictive models can be of vital interest.In the present study,the challenge of applying an optimally predictive threedimensional(3D) spatial DTB model for an area in Stockholm,Sweden was addressed using an automated intelligent computing design procedure.The process was developed and programmed in both C++and Python to track their performance in specified tasks and also to cover a wide variety of diffe rent internal characteristics and libraries.In comparison to the ordinary Kriging(OK) geostatistical tool,the superiority of the developed automated intelligence system was demonstrated through the analysis of confusion matrices and the ranked accuracies of different statistical errors.The re sults showed that in the absence of measured data,the intelligence models as a flexible and efficient alternative approach can account for associated uncertainties,thus creating more accurate spatial 3D models and providing an appropriate prediction at any point in the subsurface of the study area.展开更多
基金Consejo Nacional de Ciencia y Tecnología of Mexico(CONACyT)under Grant No.1000473。
文摘The horizontal to vertical spectral ratio(HVSR)methodology is used here to characterize pumice soils and to image the three-dimensional surface geometry of Guadalajara,Mexico.Similar to other Latin American cities,Guadalajara is exposed to high seismic risk,with the particularity of being the largest urban settlement in Latin America built on pumice soils.Methodology has not yet been tested to characterize subsoil depths in pumice sands.Due to the questionable use of traditional geotechnical tests for the analysis of pumice soils,HVSR provides an alternative for its characterization without altering its fragile and porous structure.In this work,resonance frequency(F0)and peak amplitude(A0)are used to constrain the depth of the major impedance contrast that represents the interface between bedrock and pumice soil.Results were compared with borehole depths and other available geotechnical and geophysical data and show good agreement.One of the profiles estimated on the riverbanks that cross the city,reveals different subsoil thickness that could have an impact on different site responses on riverine areas to an eventual earthquake.Government and academic efforts are combined in this work to characterize depth sediments,an important parameter that impacts the regulations for construction in the city.
基金funded by the Sichuan Transportation Science and Technology Project(Grant No.2018-ZL-01)High-end Foreign Expert Introduction program(Grant No.G2022165004L)Science and Technology Research Program of Chongqing Municipal Education Commission(Grant No.HZ2021001).
文摘Landslide susceptibility mapping is an integral part of geological hazard analysis.Recently,the emphasis of many studies has been on data-driven models,notably those derived from machine learning,owing to their aptitude for tackling complex non-linear problems.However,the prevailing models often disregard qualitative research,leading to limited interpretability and mistakes in extracting negative samples,i.e.inaccurate non-landslide samples.In this study,Scoops 3D(a three-dimensional slope stability analysis tool)was utilized to conduct a qualitative assessment of slope stability in the Yunyang section of the Three Gorges Reservoir area.The depth of the bedrock was predicted utilizing a Convolutional Neural Network(CNN),incorporating local boreholes and building on the insights from prior research.The Random Forest(RF)algorithm was subsequently used to execute a data-driven landslide susceptibility analysis.The proposed methodology demonstrated a notable increase of 29.25%in the evaluation metric,the area under the receiver operating characteristic curve(ROC-AUC),outperforming the prevailing benchmark model.Furthermore,the landslide susceptibility map generated by the proposed model demonstrated superior interpretability.This result not only validates the effectiveness of amalgamating mathematical and mechanistic insights for such analyses,but it also carries substantial academic and practical implications.
基金funded through the support of the Swedish Transport Administration through Better Interactions in Geotechnics(BIG)the Rock engineering Research Foundation(BeFo)Tyrens AB。
文摘Due to associated uncertainties,modelling the spatial distribution of depth to bedrock(DTB) is an important and challenging concern in many geo-engineering applications.The association between DTB,the safety and economy of design structures implies that generating more precise predictive models can be of vital interest.In the present study,the challenge of applying an optimally predictive threedimensional(3D) spatial DTB model for an area in Stockholm,Sweden was addressed using an automated intelligent computing design procedure.The process was developed and programmed in both C++and Python to track their performance in specified tasks and also to cover a wide variety of diffe rent internal characteristics and libraries.In comparison to the ordinary Kriging(OK) geostatistical tool,the superiority of the developed automated intelligence system was demonstrated through the analysis of confusion matrices and the ranked accuracies of different statistical errors.The re sults showed that in the absence of measured data,the intelligence models as a flexible and efficient alternative approach can account for associated uncertainties,thus creating more accurate spatial 3D models and providing an appropriate prediction at any point in the subsurface of the study area.
