A temporal and spatial analysis of anthropogenic noise sources affecting SNMR

Dalgaard, Esben; Christiansen, P. A.; Larsen, Jakob Juul; Auken, Esben

doi:10.1016/j.jappgeo.2014.08.009

Cited by 18 publications

(13 citation statements)

References 11 publications

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“…Analysis of noise in surface NMR measurements have been reported by many authors, and the consensus is that surface NMR measurements are primarily affected by two noise sources (Plata and Rubio, 2002;Legchenko and Valla, 2003;Costabel and Müller Petke, 2014;Dalgaard et al, 2014;Larsen et al, 2013;Müller Petke and Costabel, 2014). The first major noise source is harmonics from powerlines; i.e., sinusoidal signals with frequency mf 0 , where f 0 is the fundamental powerline frequency and m is an integer.…”

Section: Noise In Surface Nmr Datamentioning

confidence: 99%

“…An investigation of noise affecting surface NMR data is carried out by Dalgaard et al (2014). Using a two-coil data acquisition device and decomposition of the recorded signals into noise categories, comprehensive noise maps are presented showing the spatial variation of powerline harmonic noise, spike noise, and random noise.…”

Section: Impulsive Noisementioning

confidence: 99%

“…To validate the hypothesis that spikes from electric fences being due to (near) impulsive excitations of the surface NMR instrument with a subsequent temporal shape governed by the instrument band-pass filter, several spike measurements were undertaken with a noise recording instrument (Dalgaard et al, 2014). Briefly, the instrument consists of two seven-turn, 10 × 10 m coils with amplifiers attached.…”

Section: High Bandwidth Spike Measurementsmentioning

confidence: 99%

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Model-based subtraction of spikes from surface nuclear magnetic resonance data

Larsen

2016

GEOPHYSICS

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(2016). "Model-based subtraction of spikes from surface nuclear magnetic resonance data." GEOPHYSICS, 81(4), WB1-WB8. https://doi.org/10.1190/geo2015-0442.1 Publication metadata Title:Model-based subtraction of spikes from surface nuclear magnetic resonance data. Author(s):Jakob Model-based subtraction of spikes from surface nuclear magnetic resonance data Jakob Juul Larsen 1 ABSTRACT Surface nuclear magnetic resonance (surface NMR) has progressed significantly in recent years due to advances in instrumentation. In particular, the introduction of multichannel surface NMR instruments has been effective in improving the signal-to-noise ratio. The current methodology for noise reduction with multichannel instruments is, however, inadequate in complex noise environments, and there is a need for improved signal processing. We have evaluated a study of impulsive noise (spikes) in surface NMR data acquired with a Numis Poly instrument. We have determined how the spectral content can be used to classify spikes as originating from electric fences or sferics. Measurements of spikes from two electric fences were evaluated. The spikes were highly deterministic and can be modeled as impulsive excitations of the band-pass filter in the surface NMR receiver system. We investigated the feasibility of a model-based approach for subtraction of electric fence spikes. Model-based subtraction was shown to be possible, but it is limited by accidental fitting of the NMR signal in its current embodiment. We evaluated an example of a surface NMR data set in which subtraction of powerline harmonic noise and electric fence spike noise removed all coherence in the multichannel data, and the consequences for further noise reduction using multichannel methods were developed.

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Section: Noise In Surface Nmr Datamentioning

confidence: 99%

Section: Impulsive Noisementioning

confidence: 99%

Section: High Bandwidth Spike Measurementsmentioning

confidence: 99%

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Model-based subtraction of spikes from surface nuclear magnetic resonance data

Larsen

2016

GEOPHYSICS

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“…Hence, random noise suppression becomes more and more significant in MRS data processing. For the sake of analyzing the distribution of random noise, Dalgaard et al developed a noise collector [20]. In order to suppress random noise, a stacking procedure and statistical stacking were used to improve the signal to noise ratio of the MRS signal [16].…”

Section: Introductionmentioning

confidence: 99%

Random Noise Suppression of Magnetic Resonance Sounding Data with Intensive Sampling Sparse Reconstruction and Kernel Regression Estimation

et al. 2019

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The magnetic resonance sounding (MRS) method is a non-invasive, efficient and advanced geophysical method for groundwater detection. However, the MRS signal received by the coil sensor is extremely susceptible to electromagnetic noise interference. In MRS data processing, random noise suppression of noisy MRS data is an important research aspect. We propose an approach for intensive sampling sparse reconstruction (ISSR) and kernel regression estimation (KRE) to suppress random noise. The approach is based on variable frequency sampling, numerical integration and statistical signal processing combined with kernel regression estimation. In order to realize the approach, we proposed three specific sparse reconstructions, namely rectangular sparse reconstruction, trapezoidal sparse reconstruction and Simpson sparse reconstruction. To solve the distortion of peaks and valleys after sparse reconstruction, we introduced the KRE to deal with the processed data by the ISSR. Further, the simulation and field experiments demonstrate that the ISSR-KRE approach is a feasible and effective way to suppress random noise. Besides, we find that rectangular sparse reconstruction and trapezoidal sparse reconstruction are superior to Simpson sparse reconstruction in terms of noise suppression effect, and sampling frequency is positively correlated with signal-to-noise improvement ratio (SNIR). In one case of field experiment, the standard deviation of noisy MRS data was reduced from 1200.80 nV to 570.01 nV by the ISSR-KRE approach. The proposed approach provides theoretical support for random noise suppression and contributes to the development of MRS instrument with low power consumption and high efficiency. In the future, we will integrate the approach into MRS instrument and attempt to utilize them to eliminate harmonic noise from power line.

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“…Typically, the noise field contains components from several sources, and the noise level from each source is often nonstationary . Analysis and discussions of the noise sources affecting surface-NMR measurements have been done by several authors (Walsh, 2008;Dlugosch et al, 2011;Dalgaard et al, 2012Dalgaard et al, , 2014Costabel and Müller-Petke, 2014;Larsen et al, 2014). The general conclusion is that surface NMR is mainly affected by two types of noise: harmonics of the fundamental powerline frequency from electrical installations and impulsive noise, known as spikes, typically originating from electric fences or thunderstorms.…”

Section: Introductionmentioning

confidence: 99%

Processing of surface-nuclear magnetic resonance data from sites with high noise levels

Larsen

Behroozmand

2016

GEOPHYSICS

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The applicability of surface nuclear magnetic resonance (NMR) in investigations of groundwater is often limited by high noise levels in many areas of interest. We have evaluated measurements from a high-noise-level area in Ristrup, Denmark, recorded with a Numis Poly instrument. Standard multichannel filtering techniques for noise reduction are inadequate for several data sets acquired in this area, and surface-NMR signals cannot be resolved from the acquired data. Based on a careful assessment of the frequency content of the data, we have determined how a model-based noise reduction approach can be generalized and used to subtract two harmonic noise components from the data. Reliable surface-NMR data can be extracted from the noise-reduced data. Moreover, we have determined the impact of the proposed processing approaches on our inversion results, and we have also developed an example in which the proposed methodology allowed us to reveal and avoid an otherwise overlooked contamination of the reference coil signals with a surface-NMR signal. No borehole data were available for the investigated sites, and the validity of the noise reduction approach was instead verified using a synthetic five-layer model embedded in noise-only records from Ristrup. Our results have determined that a careful processing of the recorded data made it possible to extract surface-NMR data in more places of interest.

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A temporal and spatial analysis of anthropogenic noise sources affecting SNMR

Cited by 18 publications

References 11 publications

Model-based subtraction of spikes from surface nuclear magnetic resonance data

Model-based subtraction of spikes from surface nuclear magnetic resonance data

Random Noise Suppression of Magnetic Resonance Sounding Data with Intensive Sampling Sparse Reconstruction and Kernel Regression Estimation

Processing of surface-nuclear magnetic resonance data from sites with high noise levels

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