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Estimating the hydraulic conductivity at the south oyster site from geophysical tomographic data using Bayesian Techniques based on the normal linear regression model
Author(s) -
Chen Jinsong,
Hubbard Susan,
Rubin Yoram
Publication year - 2001
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2000wr900392
Subject(s) - ground penetrating radar , geology , hydraulic conductivity , attenuation , geophysics , bayesian probability , seismology , radar , soil science , statistics , mathematics , computer science , physics , telecommunications , optics , soil water
This study explores the use of ground penetrating radar (GPR) tomographic velocity, GPR tomographic attenuation, and seismic tomographic velocity for hydraulic conductivity estimation at the South Oyster Site, using a Bayesian framework. Since site‐specific relations between hydraulic conductivity and geophysical properties are often nonlinear and subject to a large degree of uncertainty such as at this site, we developed a normal linear regression model that allows exploring these relationships systematically. Although the log‐conductivity displays a small variation (σ 2 = 0.30) and the geophysical data vary over only a small range, results indicate that the geophysical data improve the estimates of the hydraulic conductivity. The improvement is the most significant where prior information is limited. Among the geophysical data, GPR and seismic velocity are more useful than GPR attenuation.