PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
February 1, 2010Vadose Zone Journal73 citations

Inversion of Multiconfiguration Electromagnetic (DUALEM‐421) Profiling Data Using a One‐Dimensional Laterally Constrained Algorithm

View Full Paper
FSFernando A. Monteiro SantosJTJohn TriantafilisKBKira Bruzgulis

Key Points

Key points are not available for this paper at this time.

Abstract

The collection of apparent electrical conductivity (σ a ) data from electromagnetic (EM) instruments has been used widely to map the spatial variation of average soil properties. Soil consists of horizons, however, and often the vertical change in properties can be an impediment to agricultural productivity or land use. A commonly used approach to discern changes with depth is the use of EM inversion techniques, but large amounts of data are still required. Conventionally this has meant that multiple passes are made at different heights with various instruments. Technological advances have seen the development of the DUALEM‐421 (Dualem Inc., Milton, ON, Canada), however, which is designed to collect σ a at multiple coil spacing and orientations simultaneously. What is now required is an inversion technique. We have developed the DUALEM‐2D algorithm, which consists of a one‐dimensional inversion with two‐dimensional smoothness constraints between adjacent one‐dimensional models. Calculations are based on cumulative response functions. The algorithm was evaluated using data generated from three synthetic models. Two practical examples, using σ a data acquired with a DUALEM‐421 for environmental studies, were used to evaluate the practical usefulness of the algorithm. The general patterns of the inverted models were shown to compare favorably with the available information and existing knowledge at each site.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Santos et al. (2010) studied this question.

synapsesocial.com/papers/69ded661488ed2d92be941efhttps://doi.org/10.2136/vzj2009.0088
Ask AI
Helpful
Bookmark
Share
View Full Paper