MRSmatlab — A software tool for processing, modeling, and inversion of magnetic resonance sounding data

ABSTRACTMRSmatlab is a manufacturer-independent software tool for processing, modeling, and inversion of surface nuclear magnetic resonance data. Intended as an open platform, MRSmatlab has been growing over the past few years by connecting researchers and making the latest research available to the community. We have developed insights into yet unpublished numerical implementations of signal processing and complex inversion. In addition, we have evaluated a field example demonstrating the need to carefully inspect all the steps of handling surface nuclear magnetic resonance data. MRSmatlab can be obtained by contacting the authors.

[1]  Ugur Yaramanci,et al.  QT inversion — Comprehensive use of the complete surface NMR data set , 2010 .

[2]  Burkhard Buttkus,et al.  Spectral Analysis and Filter Theory in Applied Geophysics , 2000 .

[3]  T. Günther,et al.  Three‐dimensional modelling and inversion of dc resistivity data incorporating topography – II. Inversion , 2006 .

[4]  Rosemary Knight,et al.  The impact of off-resonance effects on water content estimates in surface nuclear magnetic resonance , 2015 .

[5]  Esben Auken,et al.  Efficient full decay inversion of MRS data with a stretched-exponential approximation of the T2* distribution , 2012 .

[6]  Ugur Yaramanci,et al.  Evaluation of surface nuclear magnetic resonance-estimated subsurface water content , 2011 .

[7]  Rosemary Knight,et al.  The inversion of surface-NMR T1 data for improved aquifer characterization , 2013 .

[8]  Andrew D. Parsekian,et al.  Uncertainty estimates for surface nuclear magnetic resonance water content and relaxation time profiles from bootstrap statistics , 2015 .

[9]  Thomas Günther,et al.  Assessment of the potential of a new generation of surface nuclear magnetic resonance instruments , 2011 .

[10]  Ugur Yaramanci,et al.  Study on complex inversion of magnetic resonance sounding signals , 2005 .

[11]  Jun Lin,et al.  Statistical stacking and adaptive notch filter to remove high-level electromagnetic noise from MRS measurements , 2011 .

[12]  Thomas Günther,et al.  Hydraulic properties at the North Sea island of Borkum derived from joint inversion of magnetic resonance and electrical resistivity soundings , 2012 .

[13]  Marian Hertrich,et al.  Imaging of groundwater with nuclear magnetic resonance , 2008 .

[14]  Ugur Yaramanci,et al.  Inversion of resistivity in Magnetic Resonance Sounding , 2008 .

[15]  Stephan Costabel,et al.  Comparison and optimal parameter settings of referencebased harmonic noise cancellation in time and frequency domains for surface-NMR , 2014 .

[16]  M. Levitt Spin Dynamics: Basics of Nuclear Magnetic Resonance , 2001 .

[17]  Ugur Yaramanci,et al.  Reliability and limitations of surface NMR assessed by comparison to borehole NMR , 2011 .

[18]  Clifford H. Thurber,et al.  Parameter estimation and inverse problems , 2005 .

[19]  J. Dugundji,et al.  Envelopes and pre-envelopes of real waveforms , 1958, IRE Trans. Inf. Theory.

[20]  Alan G. Green,et al.  Off-resonance effects in surface nuclear magnetic resonance , 2011 .

[21]  Malcolm H. Levitt,et al.  The Signs of Frequencies and Phases in NMR , 1997 .

[22]  Stephan Costabel,et al.  Despiking of magnetic resonance signals in time and wavelet domains , 2014 .

[23]  Thomas Günther,et al.  AQUIFER CHARACTERIZATION USING COUPLED INVERSION OF DC/IP AND MRS DATA ON A HYDROGEOPHYSICAL TEST-SITE , 2010 .

[24]  Stephen J. Wright,et al.  Numerical Optimization , 2018, Fundamental Statistical Inference.

[25]  A. T. Basokur,et al.  Joint parameter estimation from magnetic resonance and vertical electric soundings using a multi‐objective genetic algorithm , 2014 .

[26]  Esben Auken,et al.  Noise cancelling of MRS signals combining model-based removal of powerline harmonics and multichannel Wiener filtering , 2014 .

[27]  Eiichi Fukushima,et al.  Experimental pulse NMR : a nuts and bolts approach , 2018 .

[28]  Simon Haykin,et al.  Digital communication systems , 2014 .

[29]  T. Radic,et al.  Improving the Signal-to-Noise Ratio of Surface NMR Data Due to the Remote Reference Technique , 2006 .

[30]  Thomas Günther,et al.  Two-dimensional distribution of relaxation time and water content from surface nuclear magnetic resonance , 2014 .

[31]  Anatoly Legchenko,et al.  Removal of power-line harmonics from proton magnetic resonance measurements , 2003 .

[32]  Anatoly Legchenko,et al.  A review of the basic principles for proton magnetic resonance sounding measurements , 2002 .

[33]  Stewart A. Greenhalgh,et al.  Three-Dimensional Magnetic Field and NMR Sensitivity Computations Incorporating Conductivity Anomalies and Variable-Surface Topography , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[34]  Esben Auken,et al.  Adaptive noise cancelling of multichannel magnetic resonance sounding signals , 2012 .

[35]  David O. Walsh,et al.  Multi-channel surface NMR instrumentation and software for 1D/2D groundwater investigations , 2008 .

[36]  Jochen Kamm,et al.  Simultaneous inversion of magnetic resonance sounding in terms of water content, resistivity and decay times , 2009 .

[37]  Ugur Yaramanci,et al.  Magnetic resonance soundings with separated transmitter and receiver loops , 2005 .

[38]  Anatoly Legchenko,et al.  Processing of surface proton magnetic resonance signals using non-linear fitting , 1998 .

[39]  Rosemary Knight,et al.  Frequency cycling for compensation of undesired off-resonance effects in surface nuclear magnetic resonance , 2016 .

[40]  P. Weichman,et al.  Theory of surface nuclear magnetic resonance with applications to geophysical imaging problems , 2000, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[41]  Maciek W. Lubczynski,et al.  Exploiting the MRS-phase information to enhance detection of masked deep aquifers: examples from the Netherlands , 2014 .