Challenges in stratigraphic modeling arise from underground uncertainty.While borehole exploration is reliable,it remains sparse due to economic and site constraints.Electrical resistivity tomography(ERT)as a cost-eff...Challenges in stratigraphic modeling arise from underground uncertainty.While borehole exploration is reliable,it remains sparse due to economic and site constraints.Electrical resistivity tomography(ERT)as a cost-effective geophysical technique can acquire high-density data;however,uncertainty and nonuniqueness inherent in ERT impede its usage for stratigraphy identification.This paper integrates ERT and onsite observations for the first time to propose a novel method for characterizing stratigraphic profiles.The method consists of two steps:(1)ERT for prior knowledge:ERT data are processed by soft clustering using the Gaussian mixture model,followed by probability smoothing to quantify its depthdependent uncertainty;and(2)Observations for calibration:a spatial sequential Bayesian updating(SSBU)algorithm is developed to update the prior knowledge based on likelihoods derived from onsite observations,namely topsoil and boreholes.The effectiveness of the proposed method is validated through its application to a real slope site in Foshan,China.Comparative analysis with advanced borehole-driven methods highlights the superiority of incorporating ERT data in stratigraphic modeling,in terms of prediction accuracy at borehole locations and sensitivity to borehole data.Informed by ERT,reduced sensitivity to boreholes provides a fundamental solution to the longstanding challenge of sparse measurements.The paper further discusses the impact of ERT uncertainty on the proposed model using time-lapse measurements,the impact of model resolution,and applicability in engineering projects.This study,as a breakthrough in stratigraphic modeling,bridges gaps in combining geophysical and geotechnical data to address measurement sparsity and paves the way for more economical geotechnical exploration.展开更多
Macroeconomic situation is the overall performance of a country’s and regional economic situation.At present,the vast majority of macroeconomic indicators are obtained through sampling surveys,step-by-step reporting,...Macroeconomic situation is the overall performance of a country’s and regional economic situation.At present,the vast majority of macroeconomic indicators are obtained through sampling surveys,step-by-step reporting,statistical calculations,and other processes,which are publicly released by the Statistical Bureau.There are some shortcomings,such as lag and non-authenticity.Timely forecasting and early warning of macroeconomic trends are the important needs of government affairs.However,the timeliness of data has a direct impact on government decision-making.In this paper,the high frequency and relatively accurate big data sources are adopted to construct a multivariate regression prediction model for traditional national economic accounting indicators(such as industrial value added above the scale of Hefei),which is different from the traditional time series prediction model such as ARIMA model.Based on the macroeconomic prediction model of time series big data,multi-latitude data sources,sequential update,verification set screening model and other strategies are used to provide more reliable,timely,and easy-to-understand forecasting values of national economic accounting indicators.At the same time,the potential influencing factors of macroeconomic indicators are excavated to provide data and theoretical basis for macroeconomic analysis and decision-making.展开更多
基金the financial support from the National Key R&D Program of China(Grant No.2021YFC3001003)Science and Technology Development Fund,Macao SAR(File No.0056/2023/RIB2)Guangdong Provincial Department of Science and Technology(Grant No.2022A0505030019).
文摘Challenges in stratigraphic modeling arise from underground uncertainty.While borehole exploration is reliable,it remains sparse due to economic and site constraints.Electrical resistivity tomography(ERT)as a cost-effective geophysical technique can acquire high-density data;however,uncertainty and nonuniqueness inherent in ERT impede its usage for stratigraphy identification.This paper integrates ERT and onsite observations for the first time to propose a novel method for characterizing stratigraphic profiles.The method consists of two steps:(1)ERT for prior knowledge:ERT data are processed by soft clustering using the Gaussian mixture model,followed by probability smoothing to quantify its depthdependent uncertainty;and(2)Observations for calibration:a spatial sequential Bayesian updating(SSBU)algorithm is developed to update the prior knowledge based on likelihoods derived from onsite observations,namely topsoil and boreholes.The effectiveness of the proposed method is validated through its application to a real slope site in Foshan,China.Comparative analysis with advanced borehole-driven methods highlights the superiority of incorporating ERT data in stratigraphic modeling,in terms of prediction accuracy at borehole locations and sensitivity to borehole data.Informed by ERT,reduced sensitivity to boreholes provides a fundamental solution to the longstanding challenge of sparse measurements.The paper further discusses the impact of ERT uncertainty on the proposed model using time-lapse measurements,the impact of model resolution,and applicability in engineering projects.This study,as a breakthrough in stratigraphic modeling,bridges gaps in combining geophysical and geotechnical data to address measurement sparsity and paves the way for more economical geotechnical exploration.
基金The work is supported by the NSF of China(No.11871447)Anhui Initiative in Quantum Information Technologies(AHY150200).
文摘Macroeconomic situation is the overall performance of a country’s and regional economic situation.At present,the vast majority of macroeconomic indicators are obtained through sampling surveys,step-by-step reporting,statistical calculations,and other processes,which are publicly released by the Statistical Bureau.There are some shortcomings,such as lag and non-authenticity.Timely forecasting and early warning of macroeconomic trends are the important needs of government affairs.However,the timeliness of data has a direct impact on government decision-making.In this paper,the high frequency and relatively accurate big data sources are adopted to construct a multivariate regression prediction model for traditional national economic accounting indicators(such as industrial value added above the scale of Hefei),which is different from the traditional time series prediction model such as ARIMA model.Based on the macroeconomic prediction model of time series big data,multi-latitude data sources,sequential update,verification set screening model and other strategies are used to provide more reliable,timely,and easy-to-understand forecasting values of national economic accounting indicators.At the same time,the potential influencing factors of macroeconomic indicators are excavated to provide data and theoretical basis for macroeconomic analysis and decision-making.
