1. A cautionary tale: How phase compensation during surface nuclear magnetic resonance inversion conceals forward modelling errors
- Author
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Denys Grombacher, Esben Auken, Jens Haldrup, Jakob Juul Larsen, and Gordon Osterman
- Subjects
Complex data type ,Physics ,Offset (computer science) ,Hydrogeology ,010504 meteorology & atmospheric sciences ,Inversion ,Inversion (meteorology) ,Surface nuclear magnetic resonance ,010502 geochemistry & geophysics ,01 natural sciences ,Synthetic data ,Geophysics ,Amplitude ,Nuclear magnetic resonance ,Phase ,Electromagnetic coil ,Exponential decay ,0105 earth and related environmental sciences - Abstract
Surface nuclear magnetic resonance (NMR) data are sensitive to key hydrogeological parameters including water content and pore size. The measured data are modelled as a complex sinusoidal exponential decay where the phase is a function of the physics of the experiment and instrumental factors, parameters that are difficult to decouple. When inverting surface NMR data, practitioners typically account for these phases either by considering only the amplitudes of the complex signals, thus eliminating the influence of the phase, or by iteratively rotating the complex data during inversion so the data phase matches the theoretical phase generated during forward modelling. Each of these approaches assumes the user has an accurate forward model; if not, the data will be incorrectly rotated and forced to fit an erroneous forward model. Additionally, this rotation will artificially reduce the total data misfit, thus masking the effect of the erroneous forward model. We demonstrate the pitfalls of using inverse methods that correct for the phase by inverting synthetic data with three types of deliberate modelling errors that may occur during a surface NMR experiment: errors in the offset between the Larmor and transmit frequencies, errors in the subsurface resistivity model, and errors in the relative positioning of a separated transmitter-receiver coil pair. The inverted water content profiles show that the modelling errors can introduce inversion artifacts. However, the amplitude inversions and complex inversions with iterative phase correction frequently produce χ2 misfit values close to unity, showing that these inverse methods will fail to “raise the alarm” when an incorrect forward model is implemented.
- Published
- 2020
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