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A cautionary tale: How phase compensation during surface nuclear magnetic resonance inversion conceals forward modelling errors

机译:警示故事:表面核磁共振反演期间的相位补偿如何隐藏正向建模误差

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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 chi(2) misfit values close to unity, showing that these inverse methods will fail to "raise the alarm" when an incorrect forward model is implemented. (C) 2019 Elsevier B.V. All rights reserved.
机译:表面核磁共振(NMR)数据对关键水文地质参数敏感,包括含水量和孔径。测量的数据被建模为复杂的正弦指数衰减,其中相位是实验和乐器因素的物理学的函数,难以解耦的参数。当反转表面NMR数据时,从业者通常通过仅考虑复杂信号的幅度来占这些阶段,从而消除了相位的影响,或者通过在反转期间迭代地旋转复杂数据,因此数据阶段与在期间产生的理论相位匹配正向建模。这些方法中的每一种都假定用户具有准确的前进模型;如果不是,则数据将被错误地旋转并强制拟合错误的前向模型。另外,这种旋转将人为地减少总数据不足,从而掩盖了错误前向模型的效果。我们展示了使用在表面NMR实验期间可能发生的三种类型的故意建模误差反转界面来校正阶段的逆方法的缺陷,这些误差是在表面NMR实验期间发生的误差:传播频率之间的偏移中的误差,地下电阻率模型中的误差和分离发射机 - 接收器线圈对的相对定位中的错误。倒置的水分谱表明建模误差可以引入反转伪影。然而,具有迭代相位校正的幅度反转和复杂的反向频繁地产生接近统一的CHI(2)错配值,显示在实现不正确的前向模型时,这些逆方法将无法“提高警报”。 (c)2019年Elsevier B.V.保留所有权利。

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