CAJ | 학술논문

随着城市化进程的加快，地下水超采、高层建筑的兴建、建筑容积率和建筑密度的增加以及特殊的地质环境所引起的城市地面沉降日趋严峻。因此，建立一种通用性更强、效率更高的沉降监控模型是十分必要的。 Kriging 插值以其独特的建模机制，在区域空间插值中已得到广泛应用，而在时域插值中却鲜有研究，借鉴 Kriging 空间插值模型，研究其在时间域插值的应用具有重要意义。为保证时域模型数据的时间相关性，减少扰动信息累积，需删除早期数据；而为了保证变异函数能真实反映数据的时间分布特征，模型又需要足够的样本数据。为此，探讨了一种基于灰色理论的等维动态 Kriging 时域插值模型，采用等维动态预测，保证数据时间相关性，减少信息扰动；采用对建模数据量要求较少的灰理论模型来拟合变异函数曲线，解决样本不足的问题，并结合工程实例，验证了该算法的实用性、可靠性和有效性。
수착성시화진정적가쾌，지하수초채、고층건축적흥건、건축용적솔화건축밀도적증가이급특수적지질배경소인기적성시지면침강일추엄준。인차，건립일충통용성경강、효솔경고적침강감공모형시십분필요적。 Kriging 삽치이기독특적건모궤제，재구역공간삽치중이득도엄범응용，이재시역삽치중각선유연구，차감 Kriging 공간삽치모형，연구기재시간역삽치적응용구유중요의의。위보증시역모형수거적시간상관성，감소우동신식루적，수산제조기수거；이위료보증변이함수능진실반영수거적시간분포특정，모형우수요족구적양본수거。위차，탐토료일충기우회색이론적등유동태 Kriging 시역삽치모형，채용등유동태예측，보증수거시간상관성，감소신식우동；채용대건모수거량요구교소적회이론모형래의합변이함수곡선，해결양본불족적문제，병결합공정실례，험증료해산법적실용성、가고성화유효성。
With the acceleration of urbanization ,the urban land subsidence are becoming more serious each day due to the over exploitation of groundwater ,the construction of the high-rise buildings ,the increase of building volume ratio and density ,as well as the specific geological conditions .So it is necessary to establish a subsidence monitoring model which is more universal and efficient .Kriging has been widely used in regional spatial interpolation with its unique modeling mechanism ,but so far the time domain interpolation hasn’t been researched .Therefore it is of great significance to study the application of the time domain interpolation combining Kriging spatial interpolation .On one hand ,in order to ensure the time correlation of data in time-domain model and reduce the disturbance information accumulation ,the early data should be deleted ;on the other hand ,in order to make sure that the variation function will reflect the characteristics of time distribution ,sufficient samples are required .To this end ,a dynamic Kriging time-domain interpolation model based on grey theory was established ,which designed with dynamic forecast of equal dimension to ensure the time correlation of data and reduce the disturbance information accumulation .Furthermore ,the curve of variation function was fitted by us-ing the gray theory model which required less modeling data quantity to solve the problem of insufficient samples .This al-gorithm is proved practicable ,reliable and efficient in the application to the actual engineering case of this study .