Application of time-lapse ERT imaging to watershed characterization
Author(s) -
Carlyle R. Miller,
Partha S. Routh,
Troy R. Brosten,
J. P. McNamara
Publication year - 2008
Publication title -
geophysics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.178
H-Index - 172
eISSN - 1942-2156
pISSN - 0016-8033
DOI - 10.1190/1.2907156
Subject(s) - inversion (geology) , electrical resistivity tomography , watershed , geology , subtraction , remote sensing , synthetic data , computer science , data mining , algorithm , structural basin , computer vision , electrical resistivity and conductivity , geomorphology , mathematics , engineering , electrical engineering , arithmetic
Time-lapse electrical resistivity tomography ERT has many practical applications to the study of subsurface properties and processes. When inverting time-lapse ERT data, it is useful to proceed beyond straightforward inversion of data differences andtakeadvantageofthetime-lapsenatureofthedata.Weassess various approaches for inverting and interpreting time-lapse ERTdataanddeterminethattwoapproachesworkwell.Thefirst approachismodelsubtractionafterseparateinversionofthedata from two time periods, and the second approach is to use the in- verted model from a base data set as the reference model or prior information for subsequent time periods. We prefer this second approach. Data inversion methodology should be considered when designing data acquisition; i.e., to utilize the second ap- proach, it is important to collect one or more data sets for which the bulk of the subsurface is in a background or relatively unper- turbed state.Athird and commonly used approach to time-lapse inversion,invertingthedifferencebetweentwodatasets,localiz- es the regions of the model in which change has occurred; how- ever, varying noise levels between the two data sets can be prob- lematic. To further assess the various time-lapse inversion ap- proaches,weacquiredfielddatafromacatchmentwithintheDry Creek Experimental Watershed near Boise, Idaho, U.S.A. We combined the complimentary information from individual static ERTinversions,time-lapseERTimages,andavailablehydrolog- icdatainarobustinterpretationschemetoaidinquantifyingsea- sonalvariationsinsubsurfacemoisturecontent.
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