Uncertainty estimates for surface nuclear magnetic resonance water content and relaxation time profiles from bootstrap statistics

Parsekian, Andrew D.; Grombacher, Denys

doi:10.1016/j.jappgeo.2015.05.005

Cited by 23 publications

(16 citation statements)

References 30 publications

(39 reference statements)

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“…Data processing and inversion of all surface NMR data was completed in the open‐source MRSMatlab software (Müller‐Petke et al, ). A smooth amplitude space inversion was used with a single T 2 * relaxation time parameter, and uncertainty was assigned by bootstrapping (Parsekian & Grombacher, ).…”

Section: Methodsmentioning

confidence: 99%

Why Are Some Rocky Mountain Lakes Ephemeral?

Shuman

Parsekian

Mercer

2018

Water Resources Research

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Small mountain lakes function as temporary storage basins for rain and snowmelt‐derived water. Many small lakes lose water seasonally, but questions remain about the processes involved and effects on watershed hydrology. Evaporation and groundwater outflow from lakes may influence baseflow in streams, hydrologic connections among lakes, and water fluxes from a watershed. To evaluate the role of small mountain lakes in watershed hydrology and the dominant pathways of water loss, we studied the water balances of four shallow, closed‐basin, subalpine lakes in southern Wyoming that lose up to 99% of their volumes between early summer and late fall. We tested the performance of seven evaporation models, compared observed rates of water loss with simulations of evaporation and drainage, and conducted geophysical surveys to evaluate the hydrologic environment between lakes. Our results show that groundwater outflow, rather than evaporation, can dominate water loss and cause closed‐basin mountain lakes to be ephemeral. Groundwater fluxes may contribute to varied rates and timing of water loss from the lakes. Evaporation accounted for 14% of water loss in a lake that overlays thin (<0.5 m) sediments and fractured bedrock and 83% in a lake underlain by >3 m of sediments and clay. Gradual recharge of groundwater (<18,000 m3·km−2·day−1) from each study lake likely helps sustain baseflow in streams once snowmelt has subsided. Total water loss from closed‐basin, subalpine lakes may therefore help to maintain baseflow of rivers in late summer, but their impact varies based on geological context and snowmelt availability.

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Section: Methodsmentioning

confidence: 99%

Why Are Some Rocky Mountain Lakes Ephemeral?

Shuman

Parsekian

Mercer

2018

Water Resources Research

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“…In the future, the resolution of the bottom can be improved using a fourfold more powerful MRS instrument (NUMIS POLY fabricated by IRIS Instruments (http://www.iris-instruments.com/) or GMR of VISTA CLARA, Inc. (http://www.vista-clara.com/)). However, even with a more powerful system, the accuracy of the MRS method to determine the water table depth is not as good as the one provided by GPR (Legchenko and Shushakov 1998;Weichman et al 2002;Müller-Petke and Yaramanci 2008;Parsekian and Grombacher 2015;Legchenko et al 2017).…”

Section: Discussionmentioning

confidence: 96%

“…However, even with a more powerful system, the accuracy of the MRS method to determine the water table depth is not as good as the one provided by GPR (Legchenko and Shushakov ; Weichman et al . ; Müller‐Petke and Yaramanci ; Parsekian and Grombacher ; Legchenko et al . ).…”

Section: Discussionmentioning

confidence: 99%

Investigating a firn aquifer near Helheim Glacier (South‐Eastern Greenland) with magnetic resonance soundings and ground‐penetrating radar

Legchenko

Miège

Koenig

et al. 2018

Near Surface Geophysics

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We apply the magnetic resonance sounding (MRS) method to investigate a firn aquifer in the south‐east region of the Greenland ice sheet. Our study aims to delineate and estimate the volume of the recently discovered water stored within the firn (compacted snow) that remains liquid throughout the year. We develop and test successfully a methodology for joint use of MRS and ground‐penetrating radar (GPR). This non‐invasive geophysical approach is particularly well‐adapted to glacier conditions and has a promising future for in situ investigation of water distribution in glaciers. At our field site, MRS showed an aquifer located at variable depths between 20 and 30 m beneath the ice‐sheet surface. At the monitoring site, both MRS and GPR show an increase in the water volume stored between April 2015 and July 2016. MRS estimates suggest that the volume increased by approximately 28%.

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“…; Chevalier et al . ; Parsekian and Grombacher ). However, in addition to the uncertainty of each parameter, it is also important to investigate parameter correlations.…”

Section: Introductionmentioning

confidence: 98%

“…Similarly, the simulated annealing approach of Mohnke and Yaramanci () provides a non‐deterministic inversion scheme where a suite of water content and decay time models fitting the data equally well are produced, providing uncertainty quantification. Approaches involving bootstrapping of surface NMR data have also been used to quantify uncertainty in the predicted water content and relaxation time profiles (Hertrich ; Parsekian and Grombacher ). Each of the previous approaches focuses primarily on the estimated model parameters’ uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Studies of parameter correlations in surface NMR using the Markov chain Monte Carlo method

Andersen

Wan

Grombacher

et al. 2017

Near Surface Geophysics

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Surface nuclear magnetic resonance is a technique capable of providing insight into subsurface aquifer properties. To produce estimates of aquifer properties (such as the spatial distribution of water content and parameters controlling the duration of the nuclear magnetic resonance signal), an inversion is required. Essential to the reliable interpretation of the estimated subsurface models is an understanding of the uncertainty and correlation between the parameters in the estimated models. To quantify parameter uncertainty and correlation in the surface nuclear magnetic resonance inversion, a Markov chain Monte Carlo approach is demonstrated. Markov chain Monte Carlo approaches have been previously employed to invert surface nuclear magnetic resonance data, but the primary focus has been on quantifying parameter uncertainty. The focus of this paper is to further investigate whether the parameters in the estimated models exhibit correlation with one another; equally important to building a reliable interpretation of the subsurface is an understanding of the parameter uncertainty. The utility of the Markov chain Monte Carlo approach is demonstrated through the investigation of three questions. The first question investigates whether the parameters describing the water content and thickness of a layer exhibit a strong correlation. This question stems from applying concepts known to electromagnetic surveys (that the layer thickness and layer resistivity parameters are strongly correlated) to the surface nuclear magnetic resonance inversion. A water content–layer thickness correlation in surface nuclear magnetic resonance would not have large effects for quantifying total water content but would affect the ability to identify layer boundaries. The second question examines whether the parameter controlling the duration of the nuclear magnetic resonance signal exhibits a correlation with the water content and layer thickness parameters. The resolution of surface nuclear magnetic resonance typically does not consider the duration of the signal and focuses primarily on the distribution of current amplitudes that form the suite of transmit pulses. It is common to treat regions with short‐duration signal with greater uncertainty, but it is important to understand whether the signal duration controls resolution for medium to long duration signals as well. The third question explores if the parameter uncertainty produced by the Markov chain Monte Carlo approach is consistent with that produced by an alternative approach based upon the posterior covariance matrix (for the linearised inversion). The ability of the Markov chain Monte Carlo approach to more thoroughly explore the model space provides a means to improve the reliability of surface nuclear magnetic resonance aquifer characterisations by quantifying parameter uncertainty and correlation.

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Uncertainty estimates for surface nuclear magnetic resonance water content and relaxation time profiles from bootstrap statistics

Cited by 23 publications

References 30 publications

Why Are Some Rocky Mountain Lakes Ephemeral?

Why Are Some Rocky Mountain Lakes Ephemeral?

Investigating a firn aquifer near Helheim Glacier (South‐Eastern Greenland) with magnetic resonance soundings and ground‐penetrating radar

Studies of parameter correlations in surface NMR using the Markov chain Monte Carlo method

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