HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Figures Only Full Text Free Full Text (PDF) Free Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in Web of Science Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Web of Science (3) Citing Articles via Google Scholar Google Scholar Articles by Cornacchiulo, D. Articles by Bagtzoglou, A. C. Search for Related Content PubMed Articles by Cornacchiulo, D. Articles by Bagtzoglou, A. C. GeoRef GeoRef Citation Agricola Articles by Cornacchiulo, D. Articles by Bagtzoglou, A. C. Related Collections Other Geophysical Methods Geostatistics Inverse Procedures/Parameter Estimation

SPECIAL SECTION: HYDROGEOPHYSICS

Geostatistical Reconstruction of Gaps in Near-Surface Electrical Resistivity Data

^a Department of Civil Engineering and Engineering Mechanics, Columbia University, New York, NY 10027
^b Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT 06269-2037
^* Corresponding author (acb{at}engr.uconn.edu)

Received 30 January 2004.

This study was motivated by the need to reduce the effects of various types of noise observed in geophysical field data. We focused on assessing the impact of noise and data gaps on electrical resistivity data and on evaluating whether geostatistical methods (in this case kriging) can be successfully used for restoring missing data before inversion. We used electrical resistivity forward and inverse modeling with a simple fault earth model to produce and invert synthetic datasets. We examined the effects of random background noise, data density deletion, and data gap and noise structure scenarios to study the influence of these factors on the inversion of resistivity data and the subsequent interpretability of the geologic structure. Our results suggest that geostatistical methods are potentially very useful for restoring data points deleted from noisy resistivity field data. Clearly, the efficacy of kriging depends on the level of noise and the amount of data deleted. The inversion RMSE of the kriged files is less than that of the original random background noise files containing all data. The magnitude of the improvement increases as random background noise increases. Even for cases where 80% of the original data were randomly eliminated and there was 10% random background noise, the kriging procedure resulted in significant improvement in the ability to resolve the basement and overburden structure, correctly place the orientation and location of the fault, and identify the downthrown block. At random background noise levels of 20 and 30%, kriging was effective at recovering the major geological features but to a lesser degree. The efficacy of the kriging procedure performed on the noisiest data appears to be a function of the location and magnitude of data gaps induced by editing or missing strings. Finally, the effect that coherent noise has on the efficacy of our approach was studied and contrasted to random deletion. Our study suggested that the geostatistical restoration approach improved the interpretability of electrical resistivity data that had been degraded by noise or data loss problems.

This article has been cited by other articles:

				A. M. Tartakovsky, D. Bolster, and D. M. Tartakovsky Hydrogeophysical Approach for Identification of Layered Structures of the Vadose Zone from Electrical Resistivity Data Vadose Zone J., November 1, 2008; 7(4): 1253 - 1260. [Abstract] [Full Text] [PDF]

				H. Vereecken, S. Hubbard, A. Binley, and T. Ferre Hydrogeophysics: An Introduction from the Guest Editors Vadose Zone J., November 1, 2004; 3(4): 1060 - 1062. [Full Text] [PDF]