Application of frequency-dependent multichannel Wiener filters to detect events in 2D three-component seismometer arrays

Geophysics ◽  
2009 ◽  
Vol 74 (6) ◽  
pp. V133-V141 ◽  
Author(s):  
J. Wang ◽  
F. Tilmann ◽  
R. S. White ◽  
P. Bordoni
Keyword(s):  
Least Squares Method ◽  
Signal To Noise Ratio ◽  
Noise Levels ◽  
Signal To Noise ◽  
Coherent Noise ◽  
Gas Field ◽  
Frequency Dependent ◽  
Wiener Filters ◽  
Noise Ratio

Hydraulic fracture-induced microseismic events in producing oil and gas fields are usually small, and noise levels are high at the surface as a result of the heavy equipment in use. Similarly, in nonhydrocarbon settings, arrays for detecting local earthquakes will benefit from reduced noise levels and the ability to detect smaller events will be increased. We propose a frequency-dependent multichannel Wiener filtering technique with linear constraints that uses an adaptive least-squares method to remove coherent noise in seismic array data. The noise records on several reference channels are used to predict the noise on a primary channel and then can be subtracted from the observed data. On a test with an unconstrained version of this filter, maximal noise suppression leads to signal distortion. Two methods of im-posing constraints then achieve signal preservation. In one case study, synthetic signals are added to noise from a pilot deployment of a hexagonal array (nine three-component seismometers, approximately [Formula: see text]) above a gas field; noise levels are suppressed by up to [Formula: see text] (at [Formula: see text]). In a second case study, natural seismicity recorded at a dense array ([Formula: see text] spacing) in Italy is used, where the application of the filter improves the signal-to-noise ratio (S/N) more than [Formula: see text] (at [Formula: see text]) using 35 stations. In both cases, the performance of the multichannel Wiener filters is significantly better than stacking, espe-cially at lower frequencies where stacking does not help to suppress the coherent noise. The unconstrained version of the filter yields the best improvement in signal-to-noise ratio, but the constrained filter is useful when waveform distortion is unacceptable.

Does Motion Perception Follow Weber's Law?

Perception ◽  
1995 ◽  
Vol 24 (4) ◽  
pp. 363-372 ◽  
Author(s):  
Johannes M Zanker
Keyword(s):  
Motion Perception ◽  
Stimulus Intensity ◽  
Signal To Noise Ratio ◽  
Weber’S Law ◽  
Noise Levels ◽  
Signal To Noise ◽  
Weber's Law ◽  
The Absolute ◽  
Noise Ratio ◽  
Random Dot Patterns

The subjective strength of a percept often depends on the stimulus intensity in a nonlinear way. Such coding is often reflected by the observation that the just-noticeable difference between two stimulus intensities (JND) is proportional to the absolute stimulus intensity. This behaviour, which is usually referred to as Weber's Law, can be interpreted as a compressive nonlinearity extending the operating range of a sensory system. When the noise superimposed on a motion stimulus is increased along a logarithmic scale (in order to provide linear steps in subjective difference) in motion-coherency measurements, observers often report that the subjective differences between the various noise levels increase together with the absolute level. This observation could indicate a deviation from Weber's Law for variation of motion strength as obtained by changing the signal-to-noise ratio in random-dot kinematograms. Thus JNDs were measured for the superposition of uncorrelated random-dot patterns on static random-dot patterns and three types of motion stimuli realised as random-dot kinematograms, namely large-field and object ‘Fourier’ motion (all or a group of dots move coherently), ‘drift-balanced’ motion (a travelling region of static dots), and paradoxical ‘theta’ motion (the dots on the surface of an object move in opposite direction to the object itself). For all classes of stimuli, the JNDs when expressed as differences in signal-to-noise ratio turned out to increase with the signal-to-noise ratio, whereas the JNDs given as percentage of superimposed noise appear to be similar for all tested noise levels. Thus motion perception is in accordance with Weber's Law when the signal-to-noise ratio is regarded as stimulus intensity, which in turn appears to be coded in a nonlinear fashion. In general the Weber fractions are very large, indicating a poor differential sensitivity in signal-to-noise measurements.

scholarly journals Collecting Microplastics in Gardens: Case Study (i) of Soil

2021 ◽  
Vol 9 ◽  
Author(s):  
Zahra Sobhani ◽  
Yunlong Luo ◽  
Christopher T. Gibson ◽  
Youhong Tang ◽  
Ravi Naidu ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Point Of View ◽  
Signal To Noise ◽  
Statistical Point ◽  
Image Mapping ◽  
Noise Ratio ◽  
In The Beginning ◽  
Raman Image ◽  
Selection Of

As an emerging contaminant, microplastic is receiving increasing attention. However, the contamination source is not fully known, and new sources are still being identified. Herewith, we report that microplastics can be found in our gardens, either due to the wrongdoing of leaving plastic bubble wraps to be mixed with mulches or due to the use of plastic landscape fabrics in the mulch bed. In the beginning, they were of large sizes, such as > 5 mm. However, after 7 years in the garden, owing to natural degradation, weathering, or abrasion, microplastics are released. We categorize the plastic fragments into different groups, 5 mm–0.75 mm, 0.75 mm–100 μm, and 100–0.8 μm, using filters such as kitchenware, meaning we can collect microplastics in our gardens by ourselves. We then characterized the plastics using Raman image mapping and a logic-based algorithm to increase the signal-to-noise ratio and the image certainty. This is because the signal-to-noise ratio from a single Raman spectrum, or even from an individual peak, is significantly less than that from a spectrum matrix of Raman mapping (such as 1 vs. 50 × 50) that contains 2,500 spectra, from the statistical point of view. From the 10 g soil we sampled, we could detect the microplastics, including large (5 mm–100 μm) fragments and small (<100 μm) ones, suggesting the degradation fate of plastics in the gardens. Overall, these results warn us that we must be careful when we do gardening, including selection of plastic items for gardens.

ROBUST SVD FILTERING FOR LOW SIGNAL-TO-NOISE RATIO SEISMIC DATA

Geophysics ◽  
2021 ◽  
pp. 1-51
Author(s):  
Chao Wang ◽  
Yun Wang
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Singular Values ◽  
New Method ◽  
Signal To Noise ◽  
Coherent Noise ◽  
Edge Preserving ◽  
Singular Vectors ◽  
Reduced Rank ◽  
Noise Ratio

Reduced-rank filtering is a common method for attenuating noise in seismic data. As conventional reduced-rank filtering distinguishes signals from noises only according to singular values, it performs poorly when the signal-to-noise ratio is very low, or when data contain high levels of isolate or coherent noise. Therefore, we developed a novel and robust reduced-rank filtering based on the singular value decomposition in the time-space domain. In this method, noise is recognized and attenuated according to the characteristics of both singular values and singular vectors. The left and right singular vectors corresponding to large singular values are selected firstly. Then, the right singular vectors are classified into different categories according to their curve characteristics, such as jump, pulse, and smooth. Each kind of right singular vector is related to a type of noise or seismic event, and is corrected by using a different filtering technology, such as mean filtering, edge-preserving smoothing or edge-preserving median filtering. The left singular vectors are also corrected by using the filtering methods based on frequency attributes like main-frequency and frequency bandwidth. To process seismic data containing a variety of events, local data are extracted along the local dip of event. The optimal local dip is identified according to the singular values and singular vectors of the data matrices that are extracted along different trial directions. This new filtering method has been applied to synthetic and field seismic data, and its performance is compared with that of several conventional filtering methods. The results indicate that the new method is more robust for data with a low signal-to-noise ratio, strong isolate noise, or coherent noise. The new method also overcomes the difficulties associated with selecting an optimal rank.

scholarly journals Performance Evaluation of Frame based Adaptive Compressed Sensing and Non-Adaptive Compressed Sensing based on Average Frame Signal to Noise Ratio

2018 ◽  
Vol 7 (4.10) ◽  
pp. 202
Author(s):  
T. S. Arulananth ◽  
R. Satheesh ◽  
P. Bhaskara Reddy
Keyword(s):  
Performance Evaluation ◽  
Compressed Sensing ◽  
Signal To Noise Ratio ◽  
Projection Matrix ◽  
Noise Levels ◽  
Signal To Noise ◽  
Normal Frame ◽  
Noise Ratio

The primary inspiration of our work is to discovering upgrades in the current Compressed Sensing procedure that utilizations Non Adaptive Projection Matrix rule. Normal Frame Signal-to-Noise Ratio (AFSNR) is intended to evaluate the show of the Frame-Based Adaptive Compressed Sensing with the Non-Adaptive Compressed Sensing (CS). It is a developing sign securing strategy and straight gathers the signs in a compacted shape on the off chance that they are meager on some specific premise. Proposed approach utilizes Adaptive Projection Matrix in light of edge examination which gives fundamentally enhanced discourse recreation quality and decreases the noise levels.

Long-period seafloor seismology and deformation under ocean waves

1999 ◽  
Vol 89 (6) ◽  
pp. 1535-1542 ◽  
Author(s):  
Spahr C. Webb ◽  
Wayne C. Crawford
Keyword(s):  
Shallow Water ◽  
Signal To Noise Ratio ◽  
Wave Spectrum ◽  
Vertical Component ◽  
Ocean Waves ◽  
Vertical Acceleration ◽  
Noise Levels ◽  
Signal To Noise ◽  
Long Period ◽  
Noise Ratio

Abstract The deformation of the seafloor under loading by long-period ocean waves raises vertical component noise levels at the deep seafloor by 20 to 30 dB above noise levels at good continental sites in the band from 0.001 to 0.04 Hz. This noise substantially limits the detection threshold and signal-to-noise ratio for long-period phases of earthquakes observed by seafloor seismometers. Borehole installation significantly improves the signal-to-noise ratio only if the sensor is installed at more than 1 km below the seafloor because the deformation signal decays slowly with depth. However, the vertical-component deformation signal can be predicted and suppressed using seafloor measurements of pressure fluctuations observed by differential pressure gauges. The pressure observations of ocean waves are combined with measurements of the transfer function between vertical acceleration and pressure to predict the vertical component deformation signal. Subtracting the predicted deformation signal from pressure observations can reduce vertical component noise levels near 0.01 Hz by more than 25 dB, significantly improving signal-to-noise ratios for long-period phases. There is also a horizontal-component deformation signal but it is smaller than the vertical-component signal and only significant in shallow water (<1-km deep). The amplitude of the deformation signal depends both on the long-period ocean-wave spectrum and the elastic-wave velocities in the oceanic crust. It is largest at sedimented sites and in shallow water.

A Signal-to-Noise Ratio Model for the Speech-Reception Threshold of the Hearing Impaired

1986 ◽  
Vol 29 (2) ◽  
pp. 146-154 ◽  
Author(s):  
Reinier Plomp
Keyword(s):  
Hearing Aids ◽  
Noise Level ◽  
Signal To Noise Ratio ◽  
Hearing Impaired ◽  
Noise Levels ◽  
Threshold Elevation ◽  
Signal To Noise ◽  
Speech Reception ◽  
Noise Ratio ◽  
Two Parameters

This paper reviews the results of a series of investigations inspired by a model of the speech-reception threshold (SRT) of hearing-impaired listeners. The model contains two parameters accounting for the SRT of normal-hearing listeners (SRT in quiet and signal-to-noise ratio corresponding to the threshold at high noise levels), two parameters describing the hearing loss (attenuation and threshold elevation in terms of signal-to-noise ratio), and three parameters describing the hearing aid (acoustic gain, threshold elevation expressed in signal-to-noise ratio, and equivalent internal noise level). Experimental data are reported for three different types of hearing impairment: presbycusis, hearing losses with a pathological origin, and noise-induced losses. The model gives an excellent description of the data. It demonstrates that for many hearing-impaired persons speech intelligibility at noise levels beyond 50 to 60 dB(A) is their main problem, whereas hearing aids are most effective below that noise level.

scholarly journals The Effect of Signal-to-Noise Ratio on the Study of Sea Level Trends

2011 ◽  
Vol 24 (5) ◽  
pp. 1396-1408 ◽  
Author(s):  
B. D. Hamlington ◽  
R. R. Leben ◽  
R. S. Nerem ◽  
K.-Y. Kim
Keyword(s):  
Least Squares ◽  
Sea Level ◽  
Least Squares Method ◽  
Secular Trend ◽  
Signal To Noise Ratio ◽  
Empirical Orthogonal Functions ◽  
Signal To Noise ◽  
Orthogonal Functions ◽  
Geophysical Processes ◽  
Noise Ratio

Abstract Extracting secular sea level trends from the background ocean variability is limited by how well one can correct for the time-varying and oscillating signals in the record. Many geophysical processes contribute time-dependent signals to the data, making the sea level trend difficult to detect. In this paper, cyclostationary empirical orthogonal functions (CSEOFs) are used to quantify and improve the signal-to-noise ratio (SNR) between the secular trend and the background variability, obscuring this trend in the altimetric sea level record by identifying and removing signals that are physically interpretable. Over the 16-yr altimetric record the SNR arising from the traditional least squares method for estimating trends can be improved from 4.0% of the ocean having an SNR greater than one to 9.9% when using a more sophisticated statistical method based on CSEOFs. From a standpoint of signal detection, this implies that the secular trend in a greater portion of the ocean can be estimated with a higher degree of confidence. Furthermore, the CSEOF method improves the standard error on the least squares estimates of the secular trend in 97% of the ocean. The convergence of the SNR as the record length is increased is used to estimate the SNR of sea level trends in the near future as more measurements become available from near-global altimetric sampling.

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