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oa Using Data from Multiple Loop Sizes Simultaneously in a 1D Surface Nuclear Magnetic Resonance Inversion
- Publisher: European Association of Geoscientists & Engineers
- Source: Conference Proceedings, 24rd EEGS Symposium on the Application of Geophysics to Engineering and Environmental Problems, Apr 2011, cp-247-00117
Abstract
While the surface nuclear magnetic resonance (SNMR) method holds enormous promise, the technique has not been widely adopted due to its challenges. to be useful to hydrologists reliable Inversions for both porosity and decay parameters must be developed, and then relationships between these decay parameters and hydraulic permeability established. using field data collected around Lexington, Nebraska we first illustrate that the variability of the SNMR data is sufficient, even over short time intervals, to lead to dramatically different Inversion results with currently available Inversion schemes. these field sites are unique because a battery of geophysical and hydro-geologic tests have been done in the area, providing insight into the true aquifer characteristics. the same sites were returned to for SNMR data collection several times a year between 2007 and 2010. Most of the variability in Inversion results can be attributed to changing noise levels. often smaller diameter figure-eight loops were deployed to suppress noise and better illuminate the very near surface. However, these data are currently inverted independently of the deeper sensing large diameter loop data. We present a new 1D Inversion scheme that uses the complete dataset to simultaneously invert for T2* and partial water content in the Fourier domain. Electrical conductivity effects are taken into account and arbitrarily shaped transmitter and receiver wires are incorporated. the scheme has several advantages over existing Inversion algorithms in that the NMR signal is demodulated in this domain, and is narrow band. As such a generalized Inversion in this domain delivers significant compression compared to a time-domain formulation. Due in part to this compression, it becomes practical to combine multiple SNMR datasets into a single Inversion. By combining datasets from multiple loop sizes, more consistent results are realized as the sensitivity of each loop configuration contributes to the solution.