Surface Nuclear Magnetic Resonance Signal Research Articles

An investigation of factors affecting the depths of steady-state surface nuclear magnetic resonance signals

Recent developments in surface nuclear magnetic resonance (NMR) data transmit schemes, called steady-state measurements, involve the acquisition of the NMR signal during a train of closely spaced identical pulses, and indicate great promise to enhance the measurement’s signal-to-noise ratio. The steady-state signal displays a complex dependence on a range of experimental parameters, such as the strength of the individual pulse and the separation between the pulses, as well as subsurface parameters, such as the relaxation times controlling the time dependence of the NMR signals. It is imperative to understand the role that each of these parameters plays in controlling the depth of origin for steady-state signals so as to enable optimization of a suite of measurements consisting of the fewest possible measurements delivering satisfactory resolution. A range of synthetic studies are conducted to gain insights into controls on steady-state signals’ depths of origin. Relaxation times, duty cycle, and pulse train timing are all observed to influence the signal’s depth, in addition to the pulse duration and current strength. A discussion of whether high-duty cycle steady-state sequences may enhance depth penetration is given, along with the presentation of a field data set composed of a nontraditional depth sounding approach, where repetition times are varied to encode depth sensitivity.

Acquisition and correction for early surface nuclear magnetic resonance signal based on multi-turn small air-core coil sensors

Surface nuclear magnetic resonance (SNMR) with multi-turn small air-core coil sensor (ACS) has considerable potential for the detection of shallow soil water or oil. However, loss of the early free induced decay signal and the distortion of the recorded signal arising from the ACS, make it difficult to quantitatively evaluate hydrological information. To address this problem, we propose an early signal acquisition and correction method. A fast-switching unit is applied to acquire the early signal. And a correction method based on the mathematical model of the ACS is proposed. Finally, the performance of the proposed method is evaluated by simulation experiments. Results show that the proposed method overcomes the signal attenuation caused by the ACS, while the fitting error of key parameters decreases from about 15% to 2%. Comparative test results of the existing method in an electromagnetically shielded laboratory further demonstrate the effectiveness of the proposed method.

Signal Processing Steady-State Surface NMR Data

Surface nuclear magnetic resonance (NMR) is a unique technique to study groundwater as it provides direct insights into water content and pore-scale properties. However, surface NMR suffers from an extremely weak signal that limits mapping speeds and can prohibit measurements in high ambient noise environments. It has recently been demonstrated that steady-state sequences can deliver orders of magnitude signal-to-noise ratio enhancement in comparison to standard methods. Most notably, the steady-state approach collapses the surface NMR signal an extremely narrow frequency band, which leads to vastly improved noise rejection. In this paper, we develop the signal processing chain for steady-state surface NMR data. Each step in the signal processing chain, transient culling, pulse windowing, de-spiking, powerline harmonics subtraction and spectral analysis are explained in detail. We demonstrate how error estimation can be efficiently performed using the power spectral density in the vicinity of the transmitting frequency. We also demonstrate how off-resonance excitation can be determined by locating the peak spectrum of surface NMR signal with an array of phase-corrected discrete Fourier transforms. The presented signal processing methods can be easily implemented and applied to extract signal features for quality control and inversion procedures. We give a field example where data collected with steady-state pulse sequences are processed and inverted.

Frequency Shifting Steady‐State Surface NMR Signals to Avoid Problematic Narrowband‐Noise Sources

AbstractRecent developments in surface nuclear magnetic resonance (NMR) based on steady‐state sequences show great enhancements in signal quality and mapping speeds. We demonstrate how manipulating the timing and phase of these sequences can shift the NMR signal away from problematic narrow‐band noise sources, for example, co‐frequency powerline harmonics, or shift narrowband noise sources away from the NMR signal. The spectral separation of the NMR signal from co‐frequency noise sources enables production of high‐quality data under one of the most challenging noise scenarios in surface NMR. We demonstrate the feasibility with surface NMR measurements at a site where the Larmor frequency coincides with a powerline harmonic. We separate the NMR signal and powerline noise by up to 5 Hz and obtain high‐quality data, which are readily inverted. The approach is straightforward to implement, well suited for field processing, and shows great potential to expand the range of conditions allowing surface NMR measurements.

Modeling of Surface Nuclear Magnetic Resonance Based on Prepolarization and Its Application in Urban Shallow Measurements

Surface nuclear magnetic resonance (SNMR) technology is a geophysical method to directly measure the water content and saturated porosity of aquifers by exciting the nuclear magnetic resonance (NMR) phenomenon of hydrogen nuclei in groundwater. With the natural Earth magnetic field B0 as the background detection field, the SNMR signals, which usually exist only within a range of tens of nanovolts, are very likely to be submerged in widespread environmental noise. The prepolarization (PP) method with artificial application of an active field to enhance NMR signals has been gradually applied to the SNMR field, and this method is expected to overcome existing problems and to gain further development in more high-noise detection environments, e.g., urban engineering detection. However, when PP is introduced to SNMR, difficulties in data interpretation focus on the theoretical formula derivation and model construction. In this paper, we studied the state of the detected target under a PP field. Based on currently available measurement processes, we established the corresponding PP-SNMR forward equations and modeling for groundwater. It was proven by simulation and actual measurements that the research in this study can interpret actual field detection data. In addition, compared with the conventional PP-SNMR theory, the proposed improved method can effectively avoid detected aquifer misestimation. The results gained in this study compensate for the shortcomings of the current PP-SNMR theory, which is of significance to the development and application of high-power PP-SNMR technology.

An Improved UKF Algorithm for Extracting Weak Signals Based on RBF Neural Network

It is difficult to extract effective weak signals from complex noise environment in many engineering applications. To address the problem, an improved unscented Kalman filter (UKF) algorithm based on a radial basis function (RBF)-neural network (NN) is proposed in this paper. This algorithm achieves effective extraction of weak signals under the condition of low signal-to-noise ratio (SNR). It uses RBF-NN to establish a distributed system of state and observation models to replace the traditional UKF modeling method. Then, the RBF-NN is applied to build a constant coefficient model to modify the UKF filtering deviation value, and thus, the accuracy of the algorithm is improved. Finally, simulation experiments are conducted at different SNRs of surface nuclear magnetic resonance signals. Results show that effective weak signals can still be obtained when SNR = -10 dB, verifying the effectiveness of the algorithm.

Feasibility study on prepolarized surface nuclear magnetic resonance for soil moisture measurements

AbstractIn the past few years, small‐scale (2 m) prepolarized surface nuclear magnetic resonance (SNMR) has gained increasing interest in the research community. As recent studies demonstrated, the application of a strong prepolarization field enhances the SNMR signal of coils with a footprint <1 m2 up to a level that even enables investigations in urban areas. In particular, it is expected that this noninvasive method provides the soil moisture distribution in the upper 2 m of the subsurface in the near future. However, until now all field experiments have been carried out on water reservoirs only, in an approach to test and implement this rather new technique into the field of SNMR applications. We present the first prepolarized SNMR measurement on a real soil and demonstrate the general feasibility of this technique to qualitatively and quantitatively detect soil moisture in the upper first 0.5 m. Our soil moisture measurements are validated by independent time domain reflectometry data. To complement the field experiments with numerical simulations, we adapted the underlying SNMR spin dynamics simulations and account for prepolarization switch‐off effects in the forward modeling of the SNMR excitation.

Surface Nuclear Magnetic Resonance Monitoring Reveals Karst Unsaturated Zone Recharge Dynamics during a Rain Event

Understanding karst unsaturated zone (UZ) recharge dynamics is crucial for achieving sustainable management of karst hydrosystems. In this paper, we provide the first report of the application of surface nuclear magnetic resonance (SNMR) monitoring of a karst UZ during a typical Mediterranean rain event. This 79 days’ SNMR monitoring is a part of a more than 2 years of SNMR monitoring at the Low Noise Underground Laboratory (LSBB) experimental site located within the Fontaine de Vaucluse karst hydrosystem (southeastern France). We present eight SNMR soundings conducted before and after the rain event that accumulated 168 mm in 5 days. The obtained results demonstrate the applicability and the efficiency of SNMR for investigating infiltration dynamics in karst UZs at the time scale of a few days. We present the SNMR amplitudes that highlight strong signal variations related to water dynamics in the karst UZ. Infiltrated water cause increased SNMR signal during 5 days after the rain event. A significant draining process of the medium starts 15 days after the main event. Finally, after 42 days, the SNMR signal returns close to the initial state.

Improved technology using transmitting currents with short shutdown times for surface nuclear magnetic resonance.

Surface nuclear magnetic resonance (SNMR) technology is widely used in the detection of groundwater. However, the dead time arising from the coupling effect of the transmitting circuit on the receiving coil results in partial or complete loss of the SNMR signal. This situation is especially unfavorable for the detection of short relaxation time targets. To solve this problem, we analyzed the shortcomings of the traditional SNMR launch system, and we propose a new transmission method based on an untuned constant voltage-clamped technology to overcome the problems of high resonance voltage, an uncontrollable shutdown process, and long shutdown times. Untuned transmission topology without a matching capacitor, pulse width modulation, and a constant voltage-clamped technique were applied to guide the current rise and shutdown of the system in a controllable way using an integer-period transmission pulse. A simulation experiment comparing the traditional method of transmission and this new method was conducted. The results showed that not only can the new method control the transmission current shutdown process but it can also avoid the delay in response. When the transmitting current drops from 10 A to 0.12 µA, the traditional method requires 2.29 ms and the new method requires only 4 µs. The new transmission system that we have developed based on an untuned constant voltage-clamped technology can improve the level of the transmitting current effectively and shorten the shutdown time.

A lock-in amplifier modeling recovery method to extract the surface nuclear magnetic resonance signal from residual noise.

Surface nuclear magnetic resonance (SNMR) could provide direct insights for hydrological investigations but is often limited because of its low signal-to-noise ratio. Many types of residual noise remain after denoising procedures, including despiking, power harmonic noise cancellation, and random noise attenuation. This residual noise prevents the detection of valid signals, especially in strong noise environments, such as cities and industrial areas. In this work, a lock-in amplifier modeling recovery (LIAMR) method is proposed for extracting SNMR signals from high-level residual noise after denoising. The desired SNMR signals can be extracted directly by establishing a model of the SNMR signal passing through the lock-in amplifier and then transforming mathematically the output of the amplifier. The performance of the proposed method is tested on synthetic SNMR signals under varied average relaxation times, simulation noise at different levels, and field noise. Experiment results show that LIAMR can obtain good estimations of SNMR signal parameters with residual noise. Moreover, the proposed method can provide more precise parameters compared with traditional signal extraction methods. LIAMR provides theoretical support for the application of SNMR technology in strong noise environments.

First Measurements of Surface Nuclear Magnetic Resonance Signals in a Grounded Bipole

AbstractSurface nuclear magnetic resonance (surface NMR) soundings are geophysical techniques that offer direct detection of groundwater. Ordinary surface NMR soundings are achieved with a wire loop that acts as both transmitter and receiver. We extend the capability of the technique by using a grounded electrical bipole as the measurement sensor. We provide the first successful measurements of surface NMR signals taken with a grounded electrode pair on a beach outside Perth, Western Australia. Simple changes to existing equations are sufficient to provide forward models for the changes in measurement technique, and the resulting groundwater models are consistent with coincident loop soundings. Our result opens the field for novel sounding techniques of surface NMR signals that could have broad impact on near‐surface groundwater investigations.

Study on Shortening the Dead Time of Surface Nuclear Magnetic Resonance Instrument Using Bipolar Phase Pulses

Surface nuclear magnetic resonance (NMR) is a geophysical technique for direct and quantitative detection of underground water sources. In recent years, with the application of lightweight instruments in tunnels and mines, coils with small dimensions and multiturn have been used for surface NMR. Increasing the coil turns increases the inductance and causes problems such as severe ringing, long dead time, and significant loss of early stage surface NMR signal. To address these problems, the cause of ringing in surface NMR instruments and the effect of ringing on the dead time are analyzed in this paper. The bipolar phase pulse (BPP) method is introduced to reduce the ringing based on the positive and negative superposition principle and therefore shorten the dead time. A surface NMR signal simulation test and a free-radical solution test are performed to verify the effectiveness of the BPP method. A comparison with technologies such as Q-SWITCH shows that the dead time of the surface NMR instrument decreases from 35 to 5 ms and that the signal-to-noise ratio (SNR) of the collected signal improves by 5 dB.

Characterizing the Critical Zone Using Borehole and Surface Nuclear Magnetic Resonance

Core Ideas Unsaturated saprolite, saturated saprolite, and fractured rock have unique NMR responses. Surface NMR signals will be dominated by water in saturated saprolite. The heterogeneous nature of fractured rock results in low‐amplitude surface NMR signals. Understanding critical zone (CZ) structure below the first few meters of Earth's surface is challenging and yet important to understand hydrologic and surface processes that influence life on Earth. Nuclear magnetic resonance (NMR) is an emerging geophysical tool that can quantify the volume of groundwater and pore‐scale properties. Nuclear magnetic resonance has potential to aid in CZ studies, but it can be difficult to collect high‐quality NMR data in weathered and fractured rock. We present data from seven surface NMR soundings and six borehole NMR profiles collected on a weathered and fractured granite in the Laramie Range, Wyoming. First, we show that it is possible to collect high‐quality surface NMR data in a fractured rock. Second, we use the NMR data to delineate the weathering profile into three distinct zones—unsaturated saprolite, saturated saprolite, and fractured rock—and show that the surface NMR signal is dominated by saturated saprolite. Third, we show that lateral heterogeneity significantly reduces the surface NMR signal magnitude, which suggests that the boundary dividing saprolite and fractured rock is laterally heterogeneous. The NMR measurements, when combined with previously collected seismic refraction data, provide a unique opportunity to define the lateral heterogeneity of the boundary dividing saprolite and weathered bedrock in an eroding landscape underlain by crystalline rock.

Surface NMR Responses of Typical 3-D Water-Bearing Structures Evaluated by a Vector Finite-Element Method

Surface nuclear magnetic resonance (SNMR) technique has been widely applied to noninvasive groundwater exploration and aquifer quantitative characterization. Furthermore, it has the potential for studying and detecting hydrocarbon contaminants and for water resource management. To increase the tomography resolution of groundwater and expand the application range of SNMR, we first implemented its 3-D forward modeling using a vector finite-element (FE) method based on the total electric field. The key of SNMR forward modeling is the calculation of excitation magnetic field, and the total-field algorithm is an alternative scheme to numerically simulate the electromagnetic field. In this paper, we viewed the circular loop source as the combination of a certain number of horizontal electric dipoles. The unstructured tetrahedral mesh and local refinement technology were combined to precisely delineate the distribution of circular loop source, which effectively reduces the adverse effects of field source singularity. Then, the vector FE solver based on the total electric field was used to calculate the magnetic field distribution. The accuracy of the calculated results was validated by the analytical solutions for a circular loop laid on the surface of a homogeneous half-space. After calculating the excitation field, SNMR responses of three typical synthetic 3-D groundwater models were obtained with the basic signal response formula. Finally, we studied the effects of some important factors which have significant influences on SNMR signal responses and have practical importance in groundwater issues.

Removal of Co-Frequency Powerline Harmonics From Multichannel Surface NMR Data

Powerline harmonics are often the primary noise source in surface nuclear magnetic resonance (NMR) measurements. State-of-the-art techniques, such as notch filtering, Wiener filtering, and model-based subtraction, have been demonstrated to greatly mitigate powerline harmonic noise, but these approaches break down when one of the powerline harmonics has a frequency close to or coincident with the Larmor frequency $f_{L}$ , referred to as a co-frequency harmonic. We propose a hybrid scheme where model-based subtraction of powerline harmonics is coupled with data from a synchronous reference coil to specifically subtract the co-frequency harmonic component. In standard model-based subtraction of powerline harmonics, a sinusoidal model of all harmonic components is fit to the data and subtracted. In the new approach, the amplitude and phase of the co-frequency harmonic are determined by a sinusoidal model fit to the synchronous noise-only data recorded in a reference coil. From the reference coil co-frequency model, the co-frequency harmonic in the primary coil is estimated using relationships between the amplitude and phase of the co-frequency harmonic in the two coils established during noise-only segments. By utilizing data from the reference coil to model the co-frequency harmonic, accidental fitting of the surface NMR signal is avoided. We investigate the efficiency of the method using a synthetic surface NMR signal embedded in noise-only data recorded in Denmark. Our results demonstrate that the co-frequency powerline harmonic can be removed efficiently without distorting the surface NMR signal and the new method performs better than standard methods.

Enabling surface nuclear magnetic resonance at high-noise environments using a pre-polarization pulse

Summary The technique of surface nuclear magnetic resonance (SNMR) has been widely used for hydrological investigations in recent years. Unfortunately, the detected SNMR signals are limited to tens of nanovolts and are thus susceptible to environmental noise. While pre-polarization pulses to enhance the detected signal amplitudes are common in laboratory applications, SNMR field testing has only utilized excitation pulses until now. In conducting measurements in China, we demonstrate that adding a pre-polarization field to the SNMR pulse sequence is feasible and allows for the reliable detection of SNMR signals in noisy scenarios that otherwise prohibit signal detection. We introduce a forward modelling for pre-polarization using SNMR and present a three-layer model obtained from inverse modelling that satisfies the observed data from the field experiment. We expect this development to open up new applications for SNMR technology, especially in high-noise level places, such as active mines.

Anti-saturation system for surface nuclear magnetic resonance in efficient groundwater detection.

Compared to other geophysical techniques, the surface nuclear magnetic resonance (SNMR) method could provide unique insights into the hydrologic properties of groundwater in the subsurface. However, the SNMR signal is in the order of nanovolts (10-9 V), and the complex environmental noise, i.e., the spike and the harmony noise (10-4 V), can reach up to 105 times the signal amplitude. Saturation of the amplifier is therefore a serious problem in current SNMR systems. In this study, we propose an anti-saturation method based on an instantaneous floating-point amplifier. The gain of a programmable amplifier is controlled by the value of the input signal. A regulating speed of 50 kS/s is thus achieved to satisfy the self-adaptive adjustment of the real-time SNMR system, which replaces the original man-made setting gain. A large dynamic range of 192.65 dB with a 24-bit high speed analog-digital converter module is then implemented. Compared to traditional SNMR instruments, whose magnification factor is fixed during the experiment, our system can effectively inhibit the distortion of the SNMR signal in both laboratory and field settings. Furthermore, an improved SNR, which is realized by the real-time SNMR system, enables the accurate inversion of the aquifer. Our study broadens the applicability of SNMR systems to use in and around developed areas.

Surface nuclear magnetic resonance signals recovery by integration of a non‐linear decomposition method with statistical analysis

ABSTRACTPresence of noise in the acquisition of surface nuclear magnetic resonance data is inevitable. There are various types of noise, including Gaussian noise, spiky events, and harmonic noise that affect the signal quality of surface nuclear magnetic resonance measurements. In this paper, we describe an application of a two‐step noise suppression approach based on a non‐linear adaptive decomposition technique called complete ensemble empirical mode decomposition in conjunction with a statistical optimization process for enhancing the signal‐to‐noise ratio of the surface nuclear magnetic resonance signal. The filtering procedure starts with applying the complete ensemble empirical mode decomposition method to decompose the noisy surface nuclear magnetic resonance signal into a finite number of intrinsic mode functions. Afterwards, a threshold region based on de‐trended fluctuation analysis is defined to identify the noisy intrinsic mode functions, and then the no‐noise intrinsic mode functions are used to recover the partially de‐noised signal. In the second stage, we applied a statistical method based on the variance criterion to the signal obtained from the initial phase to mitigate the remaining noise. To demonstrate the functionality of the proposed strategy, the method was evaluated on an added‐noise synthetic surface nuclear magnetic resonance signal and on field data. The results show that the proposed procedure allows us to improve the signal‐to‐noise ratio significantly and, consequently, extract the signal parameters (i.e., and V0) from noisy surface nuclear magnetic resonance data efficiently.

First evidence of detecting surface nuclear magnetic resonance signals using a compact B‐field sensor

AbstractThe noninvasive detection and characterization of subsurface aquifer structures demands geophysical techniques. Surface nuclear magnetic resonance (SNMR) is the only technique that is directly sensitive to hydrogen protons and, therefore, allows for unambiguous detection of subsurface water. Traditionally, SNMR utilizes large surface coils for both transmitting excitation pulses and recording the groundwater response. Recorded data are thus a voltage induced by the time derivative of the secondary magnetic field. For the first time, we demonstrate that the secondary magnetic field in a SNMR experiment can be directly detected using a superconducting quantum interference device magnetometer. Conducting measurements at a test site in Germany, we demonstrate not only the ability to detect SNMR signals on the order of femtoTesla but also we are able to satisfy the observed data by inverse modeling. This is expected to open up completely new applications for this exciting technology.

Monte Carlo inversion of 3-D magnetic resonance measurements

The surface nuclear magnetic resonance (SNMR) method is a geophysical method designed for non-invasive groundwater investigations. Inversion of experimental data provides the spatial distribution of the water content in the subsurface. However, SNMR inversion is ill-posed and admits many solutions because of the imaging equation properties that are compounded by experimental error. SNMR data sets are conveniently presented as complex numbers, thus possessing phase and amplitude components. Subsurface electroconductive formations and fluctuations of the Earth's magnetic field cause non-negligible phase shifts. Consequently, the forward modelling of the SNMR signal generated by 3-D water saturated formations is achieved in the complex domain. Nevertheless, in many cases, phase measurements are less reliable than amplitude measurements and water content rendering cannot be carried out using the complex SNMR signal. This problem is resolved by performing inversion using complex forward modelling whose resulting signal amplitude is used for comparison with the data. Along with water content boundaries ranging from 0 to 1, this property turns the linear initial value inversion problem into a non-linear one. In such a situation, the comprehensive analysis of inversion uncertainties is achieved by performing a solution space exploration based on a Monte Carlo approach. An adapted Metropolis-Hastings algorithm has been used on SNMR 3-D data sets to perform such an exploration. Computing time depends on the problem dimensions. With a standard laptop computer about 10 hr were necessary for the inversion of our field data set. The resulting model collection is used to calculate the probability density functions of the water content. From there, it is possible to estimate the uncertainty of the water content imagery. Using both synthetic and experimental data, we show that our routine provides robust estimates of the spatial distribution of the water content for the SNMR 3-D initial amplitude inversion.