Abstract
A calibration procedure that fits the observed modeled data is used to determine the parameters of a hydrological model. As a result, the model parameters are highly uncertain. Estimation and the impact of uncertainty on model parameters have long been a source of debate. The bootstrap statistics method assesses uncertainty in surface nuclear magnetic resonance (surface NMR) water content and transverse relaxation time. The fundamental issue associated with the surface NMR data is that the quality of the surface NMR data is reduced in the presence of ambient electromagnetic and environmental noise. The bootstrap statistics is particularly well suited for estimating the uncertainty of the data set. We demonstrate that a bootstrap resampling of the observed synthetic data can provide an uncertainty estimate that closely fits the known uncertainty using synthetic forward modeled data with various noise levels, i.e., 5nV, 15nV, 30nV, and 50nV. The thickness of bootstrapped profile represents the uncertainty in the water content and relaxation time profiles. The thickness of the bootstrapped water content profile increases with an increase in noise level in the synthetic NMR data sets. Also, the thickness of the profiles increases along with the subsurface depth. Finally, we present seasonal field surface NMR data sets collected during the pre-monsoons and post-monsoon seasons under realistic ambient noise conditions. The surface NMR model was run for a 500–500 bootstrap to assess the pre-monsoon and post-monsoon uncertainty. This method is computationally extensive but straightforward to apply, and it provides valuable uncertainty estimates for both relaxation time and water content results for smooth-mono surface NMR models.
Similar content being viewed by others
Availability of Data and Materials
Data and material will be made available on reasonable request to the authors.
References
Andersen KR, Wan L, Grombacher D (2018) Studies of parameter correlations in surface NMR using the Markov chain Monte Carlo method. Near Surf Geophys 16:206–217. https://doi.org/10.3997/1873-0604.2017064
Bashir A, Shehzad MA, Hussain I (2019) Reservoir inflow prediction by ensembling wavelet and bootstrap techniques to multiple linear regression model. Water Resour Manag 33:5121–5136. https://doi.org/10.1007/s11269-019-02418-1
Bickel PJ, Freedman DA (1981) Some asymptotic theory for the bootstrap. Ann Stat 1196–1217. https://www.jstor.org/stable/2240410
Braun M, Yaramanci U (2008) Inversion of resistivity in magnetic resonance sounding. J Appl Geophys 66:151–164. https://doi.org/10.1016/j.jappgeo.2007.12.004
Chu H, Wei J, Jiang Y (2021) Middle- and long-term streamflow forecasting and uncertainty analysis using lasso-DBN-bootstrap model. Water Resour Manag 35:2617–2632. https://doi.org/10.1007/s11269-021-02854-y
Efron B, Tibshirani R (1986) Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Stat Sci 1:54–75. https://doi.org/10.1214/ss/1177013815
Ferré T, Bentley L, Binley A (2009) Critical steps for the continuing advancement of hydrogeophysics. Eos (washington DC) 90:200. https://doi.org/10.1029/2009EO230004
Grombacher D, Liu L, Larsen JJ, Auken E (2018) Practical considerations for small receive coils in surface NMR. J Appl Geophys 154:81–92. https://doi.org/10.1016/j.jappgeo.2018.04.005
Grunewald E, Knight R (2011) The effect of pore size and magnetic susceptibility on the surface NMR relaxation parameter T*2. Near Surf Geophys 9:169–178. https://doi.org/10.3997/1873-0604.2010062
Guillen A, Legchenko A (2002) Inversion of surface nuclear magnetic resonance data by an adapted Monte Carlo method applied to water resource characterization. J Appl Geophys 50:193–205. https://doi.org/10.1016/S0926-9851(02)00139-8
Hertrich M (2008) Imaging of groundwater with nuclear magnetic resonance. Prog Nucl Magn Reson Spectrosc 53:227–248. https://doi.org/10.1016/j.pnmrs.2008.01.002
Hirsch RM, Archfield SA, De Cicco LA (2015) A bootstrap method for estimating uncertainty of water quality trends. Environ Model Softw 73:148–166. https://doi.org/10.1016/j.envsoft.2015.07.017
Irons TP, McPherson BJOL, Kass MA (2018) Bootstrapping reliable noise measure in time-gated nuclear magnetic resonance data. ASEG Ext Abstr 2018:1–6. https://doi.org/10.1071/aseg2018abt6_2h
Kremer T, Juul Larsen J, Nguyen F (2019) Processing harmonic em noise with multiple or unstable frequency content in surface NMR surveys. Geophys J Int 219:753–775. https://doi.org/10.1093/gji/ggz307
Larsen JJ, Behroozmand AA (2016) Processing of surface-nuclear magnetic resonance data from sites with high noise levels. Geophysics 81:WB75–WB83. https://doi.org/10.1190/GEO2015-0441.1
Mazzilli N, Boucher M, Chalikakis K (2016) Contribution of magnetic resonance soundings for characterizing water storage in the unsaturated zone of karst aquifers. Geophysics 81:WB49–WB61. https://doi.org/10.1190/GEO2015-0411.1
Mohnke O, Yaramanci U (2002) Smooth and block inversion of surface NMR amplitudes and decay times using simulated annealing. J Appl Geophys 50:163–177. https://doi.org/10.1016/S0926-9851(02)00137-4
Mueller-Petke M, Yaramanci U (2010) QT inversion - Comprehensive use of the complete surface NMR data set. Geophysics. https://doi.org/10.1190/1.3471523
Müller-Petke M, Braun M, Hertrich M (2016) MRSmatlab — A software tool for processing, modeling, and inversion of magnetic resonance sounding data. Geophysics 81:WB9–WB21. https://doi.org/10.1190/geo2015-0461.1
Müller-Petke M, Dlugosch R, Yaramanci U (2011) Evaluation of surface nuclear magnetic resonance-estimated subsurface water content. New J Phys. https://doi.org/10.1088/1367-2630/13/9/095002
Pan J, Lu K, Wang Z (2021) Advantages of the optimum pulse moment in surface NMR and application in groundwater exploration. Groundwater 59:199–213. https://doi.org/10.1111/gwat.13046
Parsekian AD, Dlubac K, Grunewald E (2015) Bootstrap calibration and uncertainty estimation of downhole NMR hydraulic conductivity estimates in an unconsolidated aquifer. Groundwater 53:111–121. https://doi.org/10.1111/gwat.12165
Parsekian AD, Grombacher D (2015) Uncertainty estimates for surface nuclear magnetic resonance water content and relaxation time profiles from bootstrap statistics. J Appl Geophys 119:61–70. https://doi.org/10.1016/j.jappgeo.2015.05.005
Razmi A, Mardani-Fard HA, Golian S, Zahmatkesh Z (2022) Time-varying univariate and bivariate frequency analysis of nonstationary extreme sea level for New York City. Environ Process 9:1–27. https://doi.org/10.1007/s40710-021-00553-9
Sacchi MD (1998) A bootstrap procedure for high-resolution velocity analysis. Geophysics 63:1716–1725. https://doi.org/10.1190/1.1444467
Sehgal V, Tiwari MK, Chatterjee C (2014) Wavelet bootstrap multiple linear regression based hybrid modeling for daily river discharge forecasting. Water Resour Manag 28:2793–2811. https://doi.org/10.1007/s11269-014-0638-7
Simar L, Wilson PW (2010) A general methodology for bootstrapping in non-parametric frontier models. J Appl Stat 27:779–802. https://doi.org/10.1080/02664760050081951
Tao Y, Yang T, Faridzad M, Jiang L, He X, Zhang X (2018) Non-stationary bias correction of monthly CMIP5 temperature projections over China using a residual-based bagging tree model. Int J Climatol 38(1):467–482. https://doi.org/10.1002/joc.5188
Trushkin DV, Shushakov OA, Legchenko AV (1994) The potential of a noise-reducing antenna for surface NMR groundwater surveys in the Earth's magnetic field. Geophys Prospect 42(8):855–862. http://www.kinetics.nsc.ru/comp/comp2009/shush4.pdf
Walsh DO (2008) Multi-channel surface NMR instrumentation and software for 1D/2D groundwater investigations. J Appl Geophys 66:140–150. https://doi.org/10.1016/j.jappgeo.2008.03.006
Weichman PB, Lavely EM, Ritzwoller MH (2000) Theory of surface nuclear magnetic resonance with applications to geophysical imaging problems. Phys Rev E - Stat Physics Plasmas Fluids Relat Interdiscip Top 62:1290–1312. https://doi.org/10.1103/PhysRevE.62.1290
Xia X, Chen X, Zhang Y, Wang Z (2008) Grey bootstrap method of evaluation of uncertainty in dynamic measurement. Meas J Int Meas Confed 41:687–696. https://doi.org/10.1016/j.measurement.2007.10.008
Yaramanci U, Lange G, Knödel K (1999) Surface NMR within a geophysical study of an aquifer at Haldensleben (Germany). Geophys Prospect 47:923–943. https://doi.org/10.1046/j.1365-2478.1999.00161.x
Funding
There is no funding agency to support this research work.
Author information
Authors and Affiliations
Contributions
Singh, U: conceptualization, methodology, formal analysis, data curation, software, writing-original draft; Sharma, P. K.: Investigation, visualization, writing-review, and editing.
Corresponding author
Ethics declarations
Ethical Approval
Not applicable.
Consent to Participate
Not applicable.
Consent to Publish
Both authors agree to publish.
Competing Interest
The authors declared no potential conflict of interest concerning authorship, research, etc.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Key Points
• The uncertainty of the subsurface water content and relaxation time is proportional to the thickness of bootstrapped profiles.
• Ambient noise condition influences the uncertainty of the surface NMR data.
• Uncertainty is proportional to the subsurface depth irrespective of the noise level.
Rights and permissions
About this article
Cite this article
Singh, U., Sharma, P.K. Seasonal Uncertainty Estimation of Surface Nuclear Magnetic Resonance Water Content using Bootstrap Statistics. Water Resour Manage 36, 2493–2508 (2022). https://doi.org/10.1007/s11269-022-03155-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11269-022-03155-8