Geostatistical Integration of Linear Coarse Scale and Fine Scale Data
Building numerical models requires to integrate all available data. In the earth sciences, data typically come from different sources with different volume supports. Some are fine scale quasi-point support, such as well or core data; others are coarse scale data averaged over large block support, such as remote sensing and seismic travel time data. Both point and block support data are valuable information and should be incorporated into the final models. In addition, prior information, such as spatial correlation and property statistics (mean, variance, etc), should also be considered. This paper aims at building high resolution models conditioned to both point and linear average block support data and accounting for prior structural information. Three algorithms are proposed. They are implemented as plug-ins of the freeware Stanford Geostatislical Modeling Software (SGeMS).
Yongshe Liu André G. Journel
Department of Energy Resources Engineering, Stanford University, CA 94305, USA
国际会议
The 12th Conference of the International Association for Mathematical Geology(第12届国际数学地质大会)
北京
英文
401-405
2007-08-26（万方平台首次上网日期，不代表论文的发表时间）