2011
DOI: 10.1190/geo2010-0378.1
|View full text |Cite
|
Sign up to set email alerts
|

Inversion of low-induction number conductivity meter data to predict seasonal saturation variation

Abstract: Our research introduced a method to monitor saturation in the near surface. In agricultural settings, methods measuring electrical conductivity can provide useful information about soil type, moisture content, and salinity extent. Electrical conductivity meters have been used in a number of studies to determine soil properties in a qualitative sense. We examined the range of structures in which the use of low-induction number instruments can be used successfully to determine layered-earth electrical conductivi… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
2
0

Year Published

2016
2016
2022
2022

Publication Types

Select...
3
1

Relationship

0
4

Authors

Journals

citations
Cited by 4 publications
(2 citation statements)
references
References 23 publications
0
2
0
Order By: Relevance
“…Soil electrical conductivity is closely related to soil properties. Soil electrical conductivity can re ect soil salinity, moisture, porosity and other parameters (Smiarowski et al 2011).…”
Section: Correlation and Response Of Individual Pah-degrading Fungi T...mentioning
confidence: 99%
“…Soil electrical conductivity is closely related to soil properties. Soil electrical conductivity can re ect soil salinity, moisture, porosity and other parameters (Smiarowski et al 2011).…”
Section: Correlation and Response Of Individual Pah-degrading Fungi T...mentioning
confidence: 99%
“…As a result, r has been successfully employed to predict a variety of related soil properties including clay [Triantafilis et al, 2013a], salinity [Zara et al, 2016], water content [Huang et al, 2016a]. A number of different inversion approaches have also been attempted over the past decade, including using the cumulative function under the low induction number (LIN) [e.g., Smiarowski et al, 2011;Triantafilis et al, 2013b;Viganotti et al, 2013;Jadoon et al, 2015], local and global optimizations [e.g., Mester et al, 2011;Kamm et al, 2013;von Hebel et al, 2014], regularizations [e.g., Borchers et al, 1997;Vervoort and Annen, 2006;P erez-Flores et al, 2012;Li et al, 2013], conditional simulations [e.g., Minsley, 2011;Dafflon et al, 2013], and joint-inversion in combination with other geophysical data sets [e.g., Farzamian et al, 2015]. However, compared with the well-established geostatistical space-time models used in other fields of geoscience [Kyriakidis and Journel, 1999], the continuity and the spatiotemporal variations in EMI data have little been explored.…”
Section: Introductionmentioning
confidence: 99%