scholarly journals Study on the Spatially Variable Saturated Hydraulic Conductivity and Deformation Behavior of Accumulation Reservoir Landslide Based on Surface Nuclear Magnetic Resonance Survey

2018 ◽  
Vol 2018 ◽  
pp. 1-15
Author(s):  
Shu Zhang ◽  
Yunshan Xiahou ◽  
Huiming Tang ◽  
Lei Huang ◽  
Xiao Liu ◽  
...  
Keyword(s):  
Nuclear Magnetic Resonance ◽  
Finite Element ◽  
Magnetic Resonance ◽  
Hydraulic Conductivity ◽  
Stress Analysis ◽  
Deformation Behavior ◽  
Saturated Hydraulic Conductivity ◽  
Three Gorges Reservoir Area ◽  
Baishuihe Landslide ◽  
Nuclear Magnetic

Saturated hydraulic conductivity (Ks) is spatially variable in accumulation landslide sites that exert significant effort onto landslide seepage and deformation behavior. To better understand spatial variability and the effect of Ks on the slide mass of an accumulation landslide, this study introduced the surface nuclear magnetic resonance (SNMR) technology to study a representative reservoir accumulation landslide field in the Three Gorges Reservoir area (TGRA), the Baishuihe landslide, to obtain a series of relative reliable spatial measurements of Ks effectively on the basis of calibration in terms of the field tests measurements. The estimated Ks values were distributed log-normally for the overall landslide mass site with a wide range of 3.00 × 10−6∼7.80 × 10−3 cm/s, which reaches about 3 orders of magnitude. Variogram analysis indicated that the Ks values have the range (A) of 295.89 m and 65.56 m for the overall site and major cross-sectional analysis, respectively. A finite-element seepage-stress analysis associated with a Kriging-interpolated spatial Ks variable calculation model based on the best-fitted theoretical variogram was subsequently performed to study the seepage and deformation behavior of the landslide. The available monitored data and simulated results of the finite-element seepage-stress analysis indicated that the Baishuihe landslide is a progressive landslide, and the main factor influencing the deformation is rainfall and reservoir water fluctuation. This study provides an unconventional framework for studying the heterogeneous geomaterial and contributes to a better understanding of the spatial variation of the hydraulic property of accumulation reservoir landslides at a field scale.

ESTIMATION OF HYDRAULIC CONDUCTIVITY IN FORMERLY SATURATED ALLUVIUM USING NUCLEAR MAGNETIC RESONANCE

2017 ◽  
Author(s):  
Krishangi Devi Groover ◽  
◽  
John Izbicki ◽  
Katherine L. Pappas ◽  
Carole D. Johnson
Keyword(s):  
Nuclear Magnetic Resonance ◽  
Magnetic Resonance ◽  
Hydraulic Conductivity ◽  
Nuclear Magnetic

Models and methods for predicting hydraulic conductivity in near-surface unconsolidated sediments using nuclear magnetic resonance

Geophysics ◽  
2016 ◽  
Vol 81 (5) ◽  
pp. D503-D518 ◽  
Author(s):  
Jeremy Maurer ◽  
Rosemary Knight
Keyword(s):  
Nuclear Magnetic Resonance ◽  
Magnetic Resonance ◽  
Hydraulic Conductivity ◽  
Uncertainty Quantification ◽  
Physical Parameters ◽  
Data Set ◽  
Near Surface ◽  
Nmr Data ◽  
Uncertainty Estimates ◽  
Nuclear Magnetic

Nuclear magnetic resonance (NMR) logging provides a relatively new approach for estimating the hydraulic conductivity [Formula: see text] of unconsolidated aquifers. We have evaluated results from model validation and uncertainty quantification using direct-push measurements of NMR mean relaxation times and [Formula: see text] in sands and gravels at three field sites. We have tested four models that have been proposed for predicting [Formula: see text] from NMR data, including the Schlumberger-Doll research, Seevers, and sum-of-echoes equations, all of which use empirically determined constants, as well as the Kozeny-Godefroy model, which predicts [Formula: see text] from several physical parameters. We have applied four methods of analysis to reanalyze NMR and [Formula: see text] data from the three field sites to quantify how well each model predicted [Formula: see text] from the mean log NMR relaxation time [Formula: see text] given the uncertainties in the data. Our results show that NMR-estimated porosity does not improve prediction of [Formula: see text] in our data set for any model and that all of the models can predict [Formula: see text] to within an order of magnitude using the calibrated constants we have found. We have shown the value of rigorous uncertainty quantification using the methods we used for analyzing [Formula: see text]-NMR data sets, and we have found that incorporating uncertainty estimates in our analysis gives a more complete understanding of the relationship between NMR-derived parameters and hydraulic conductivity than can be obtained through simple least-squares fitting. There is little variability in our data set in the calibrated constants we find, given the uncertainty present in the data, and therefore we suggest that the constants we find could be used to obtain first-order estimates of hydraulic conductivity in unconsolidated sands and gravels at new sites with NMR data available.

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