SEISMIC NOISE ESTIMATION USING HORIZONTAL COMPONENTS

Geophysics ◽  
1967 ◽  
Vol 32 (4) ◽  
pp. 617-632
Author(s):  
Thomas F. Potter ◽  
Robert B. Roden
Keyword(s):  
Signal To Noise Ratio ◽  
Single Point ◽  
Vertical Component ◽  
Horizontal Component ◽  
Noise Component ◽  
Physical Separation ◽  
Noise Rejection ◽  
Particle Orbit ◽  
Circular Rings ◽  
Noise Models

The use of seismometer arrays containing both horizontal‐ and vertical‐component instruments for attenuation of surface‐wave noise has been studied theoretically. If a process can be defined to estimate the vertical noise component by operating on the outputs of one or more horizontal‐component seismometers, the estimate may be subtracted from the vertical‐component record to improve signal‐to‐noise ratio. The exact waveforms of vertically‐incident signals must be preserved in an operation of this kind. Formulas are developed to describe the response of a system employing three components measured at a single point. This system is found to be useful only in cases where the noise is strongly directional. A physical separation between the vertical‐ and horizontal‐component instruments is necessary to resolve the difficulties caused by uncertainties in the sense of the propagation velocity vector and particle orbit vector. Formulas, derived for systems consisting of circular rings of radially‐oriented horizontals and a central vertical show, that useful noise rejection can be obtained even in the most unfavorable case of uniform azimuthal noise distribution. The performance of arrays of this kind is not affected very much by uncorrelated noise or Love‐wave noise. Comparisons with similar arrays containing only vertical‐component seismometers indicate that, for some of the noise models studied, the multicomponent array should provide useful noise rejection over a greater bandwidth and at longer wavelengths than an all‐vertical array with the same dimensions.

Calibrating the 2016 IRIS Wavefields Experiment Nodal Sensors for Amplitude Statics and Orientation Errors

2021 ◽  
Author(s):  
Oluwaseyi J. Bolarinwa ◽  
Charles A. Langston
Keyword(s):  
Signal To Noise Ratio ◽  
Vertical Component ◽  
Calibration Method ◽  
Horizontal Component ◽  
High Signal ◽  
Correction Factors ◽  
Small Aperture ◽  
S Waves ◽  
Amplitude Correction ◽  
Array Calibration

ABSTRACT We used teleseismic P and S waves recorded in the course of the 2016 Incorporated Research Institutions for Seismology (IRIS) community-planned experiment in northern Oklahoma, to estimate amplitude correction factors (ACFs) and orientation correction factors (OCFs) for the gradiometer’s three-component Fairfield nodal sensors and two other gradiometer-styled subarray nodal sensors. These subarrays were embedded in the 13 km aperture nodal array that was also fielded during the 2016 IRIS experiment. The array calibration method we used in this study is based on the premise that a common wavefield should be recorded over a small-aperture array using teleseismic observation. In situ estimates of ACF for the gradiometer vary by 2.3% (standard deviation) for the vertical components and, typically, variability is less than 4.3% for the horizontal components; associated OCFs generally dispersed by 3°. For the two subarrays, the vertical-component ACF usually vary up to 2.4%; their horizontal-component ACFs largely spread up to 3.6%. OCFs for the subarrays generally disperse by 6.5°. ACF and OCF estimates for the gradiometer are seen to be stable across frequency bands having high signal coherence and/or signal-to-noise ratio. Gradiometry analyses of calibrated and uncalibrated gradiometer records from a local event revealed notable improvements in accuracy of attributes obtained from analyzing the calibrated horizontal-component waveforms in the light of catalog epicenter-derived azimuth. The improved waveform relative amplitudes after calibration, coupled with the enhanced wave attribute accuracy, suggests that instrument calibration for amplitude statics and orientation errors should be encouraged prior to doing gradiometry analysis in future studies.

Galilean Relativity

2018 ◽  
Author(s):  
David M. Wittman
Keyword(s):  
Inertial Frame ◽  
Vertical Component ◽  
Horizontal Component ◽  
The Other ◽  
One Dimension ◽  
Everyday Experience ◽  
Low Speed ◽  
Principle Of Relativity ◽  
Galilean Relativity ◽  
Laws Of Motion

Galilean relativity is a useful description of nature at low speed. Galileo found that the vertical component of a projectile’s velocity evolves independently of its horizontal component. In a frame that moves horizontally along with the projectile, for example, the projectile appears to go straight up and down exactly as if it had been launched vertically. The laws of motion in one dimension are independent of any motion in the other dimensions. This leads to the idea that the laws of motion (and all other laws of physics) are equally valid in any inertial frame: the principle of relativity. This principle implies that no inertial frame can be considered “really stationary” or “really moving.” There is no absolute standard of velocity (contrast this with acceleration where Newton’s first law provides an absolute standard). We discuss some apparent counterexamples in everyday experience, and show how everyday experience can be misleading.

Peripheral representation of antennal orientation by the scapal hair plate of the cockroach Periplaneta americana

2001 ◽  
Vol 204 (24) ◽  
pp. 4301-4309 ◽  
Author(s):  
J. Okada ◽  
Y. Toh
Keyword(s):  
Single Unit ◽  
Periplaneta Americana ◽  
Vertical Component ◽  
Horizontal Component ◽  
Vertical Position ◽  
Horizontal Position ◽  
Joint Movement ◽  
Basal Segment ◽  
Dynamic Phase ◽  
Hair Sensilla

SUMMARY Arthropods have hair plates that are clusters of mechanosensitive hairs, usually positioned close to joints, which function as proprioceptors for joint movement. We investigated how angular movements of the antenna of the cockroach (Periplaneta americana) are coded by antennal hair plates. A particular hair plate on the basal segment of the antenna, the scapal hair plate, can be divided into three subgroups: dorsal, lateral and medial. The dorsal group is adapted to encode the vertical component of antennal direction, while the lateral and medial groups are specialized for encoding the horizontal component. Of the three subgroups of hair sensilla, those of the lateral scapal hair plate may provide the most reliable information about the horizontal position of the antenna, irrespective of its vertical position. Extracellular recordings from representative sensilla of each scapal hair plate subgroup revealed the form of the single-unit impulses in response to hair deflection. The mechanoreceptors were characterized as typically phasic-tonic. The tonic discharge was sustained indefinitely (>20 min) as long as the hair was kept deflected. The spike frequency in the transient (dynamic) phase was both velocity- and displacement-dependent, while that in the sustained (steady) phase was displacement-dependent.

scholarly journals Comparison between sprinkler irrigation and natural rainfall based on droplet diameter

2016 ◽  
Vol 14 (1) ◽  
pp. e1201 ◽  
Author(s):  
MaoSheng Ge ◽  
Pute Wu ◽  
Delan Zhu ◽  
Daniel P. Ames
Keyword(s):  
Soil Erosion ◽  
Kinetic Energy ◽  
Droplet Diameter ◽  
Vertical Component ◽  
Radial Distance ◽  
Sprinkler Irrigation ◽  
Application Rate ◽  
Horizontal Component ◽  
Natural Rainfall ◽  
Actual Field

<p>An indoor experiment was conducted to analyze the movement characteristics of different sized droplets and their influence on water application rate distribution and kinetic energy distribution. Radial droplets emitted from a Nelson D3000 sprinkler nozzle under 66.3, 84.8, and 103.3 kPa were measured in terms of droplet velocity, landing angle, and droplet kinetic energy and results were compared to natural rainfall characteristics. Results indicate that sprinkler irrigation droplet landing velocity for all sizes of droplets is not related to nozzle pressure and the values of landing velocity are very close to that of natural rainfall. The velocity horizontal component increases with radial distance while the velocity vertical component decreases with radial distance. Additionally, landing angle of all droplet sizes decreases with radial distance. The kinetic energy is decomposed into vertical component and horizontal component due to the oblique angles of droplet impact on the surface soil, and this may aggravate soil erosion. Therefore the actual oblique angle of impact should be considered in actual field conditions and measures should be taken for remediation of soil erosion if necessary.</p>

Single-station SVD-based polarization filtering of ground roll: Perfection and investigation of limitations and pitfalls

Geophysics ◽  
2012 ◽  
Vol 77 (2) ◽  
pp. V41-V59 ◽  
Author(s):  
Olena Tiapkina ◽  
Martin Landrø ◽  
Yuriy Tyapkin ◽  
Brian Link
Keyword(s):  
Time Window ◽  
Synthetic Data ◽  
Vertical Component ◽  
Horizontal Component ◽  
Frequency Bandwidth ◽  
Polarization Filter ◽  
Data Set ◽  
Single Station ◽  
Ground Roll ◽  
Polarization Filtering

The advent of single receiver point, multi-component geophones has necessitated that ground roll be removed in the processing flow rather than through acquisition design. A wide class of processing methods for ground-roll elimination is polarization filtering. A number of these methods use singular value decomposition (SVD) or some related transformations. We focus on a single-station SVD-based polarization filter that we consider to be one of the best in the industry. The method is comprised of two stages: (1) ground-roll detection and (2) ground-roll estimation and filtering. To detect the ground roll, a special attribute dependent on the singular values of a three-column matrix formed by a sliding time window is used. The ground roll is approximated and subtracted using the first two eigenimages of this matrix. To limit the possible damage to the signal, the filter operates within the record intervals where the ground roll is detected and within the ground-roll frequency bandwidth only. We improve the ground-roll detector to make it theoretically insensitive to ambient noise and more sensitive to the presence of ground roll. The advantage of the new detector is demonstrated on synthetic and field data sets. We estimate theoretically and with synthetic data the attenuation of the underlying reflections that can be caused by the polarization filter. We show that the underlying signal always loses almost all the energy on the vertical component and on the horizontal component in the ground-roll propagation plane and within the ground-roll frequency bandwidth. The only signal component, if it exists, that can retain a significant part of its energy is the horizontal component orthogonal to the above plane. When 2D 3C field operations are conducted, the signal particle motion can deviate from the ground-roll propagation plane and can therefore retain some of its energy due to a set of offline reflections. In the case of 3D 3C seismic surveys, the reflected signal always deviates from the ground-roll propagation plane on the receiver lines that do not contain the source. This is confirmed with a 2.5D 3C synthetic data set. We discuss when the ability of the filter to effectively subtract the ground roll may, or may not, allow us to ignore the inevitable harm that is done to the underlying reflected waves.

scholarly journals Improving Speech Quality for Hearing Aid Applications Based on Wiener Filter and Composite of Deep Denoising Autoencoders

Signals ◽  
2020 ◽  
Vol 1 (2) ◽  
pp. 138-156
Author(s):  
Raghad Yaseen Lazim ◽  
Zhu Yun ◽  
Xiaojun Wu
Keyword(s):  
Hearing Loss ◽  
Signal To Noise Ratio ◽  
Hearing Aid ◽  
Contextual Information ◽  
Hybrid Approach ◽  
Wiener Filter ◽  
Speech Quality ◽  
Denoising Autoencoder ◽  
Noise Component ◽  
Perceptual Evaluation

In hearing aid devices, speech enhancement techniques are a critical component to enable users with hearing loss to attain improved speech quality under noisy conditions. Recently, the deep denoising autoencoder (DDAE) was adopted successfully for recovering the desired speech from noisy observations. However, a single DDAE cannot extract contextual information sufficiently due to the poor generalization in an unknown signal-to-noise ratio (SNR), the local minima, and the fact that the enhanced output shows some residual noise and some level of discontinuity. In this paper, we propose a hybrid approach for hearing aid applications based on two stages: (1) the Wiener filter, which attenuates the noise component and generates a clean speech signal; (2) a composite of three DDAEs with different window lengths, each of which is specialized for a specific enhancement task. Two typical high-frequency hearing loss audiograms were used to test the performance of the approach: Audiogram 1 = (0, 0, 0, 60, 80, 90) and Audiogram 2 = (0, 15, 30, 60, 80, 85). The hearing-aid speech perception index, the hearing-aid speech quality index, and the perceptual evaluation of speech quality were used to evaluate the performance. The experimental results show that the proposed method achieved significantly better results compared with the Wiener filter or a single deep denoising autoencoder alone.

scholarly journals Infrared Stripe Correction Algorithm Based on Wavelet Analysis and Gradient Equalization

2019 ◽  
Vol 9 (10) ◽  
pp. 1993 ◽  
Author(s):  
Ende Wang ◽  
Ping Jiang ◽  
Xukui Hou ◽  
Yalong Zhu ◽  
Liangyu Peng
Keyword(s):  
Wavelet Analysis ◽  
Infrared Imaging ◽  
Signal To Noise Ratio ◽  
Vertical Component ◽  
Infrared Image ◽  
Structural Similarity ◽  
Correction Algorithm ◽  
Uncooled Infrared Imaging ◽  
Correction Result ◽  
Better Than

In the uncooled infrared imaging systems, owing to the non-uniformity of the amplifier in the readout circuit, the infrared image has obvious stripe noise, which greatly affects its quality. In this study, the generation mechanism of stripe noise is analyzed, and a new stripe correction algorithm based on wavelet analysis and gradient equalization is proposed, according to the single-direction distribution of the fixed image noise of infrared focal plane array. The raw infrared image is transformed by a wavelet transform, and the cumulative histogram of the vertical component is convolved by a Gaussian operator with a one-dimensional matrix, in order to achieve gradient equalization in the horizontal direction. In addition, the stripe noise is further separated from the edge texture by a guided filter. The algorithm is verified by simulating noised image and real infrared image, and the comparison experiment and qualitative and quantitative analysis with the current advanced algorithm show that the correction result of the algorithm in this paper is not only mild in visual effect, but also that the structural similarity (SSIM) and peak signal-to-noise ratio (PSNR) indexes can get the best result. It is shown that this algorithm can effectively remove stripe noise without losing details, and the correction performance of this method is better than the most advanced method.

scholarly journals Improving electrocorticograms of awake and anaesthetized mice using wavelet denoising

2018 ◽  
Vol 4 (1) ◽  
pp. 469-472 ◽  
Author(s):  
Michael Schweigmann ◽  
Klaus Peter Koch ◽  
Fabian Auler ◽  
Frank Kirchhoff
Keyword(s):  
Signal To Noise Ratio ◽  
Wavelet Denoising ◽  
Functional Evaluation ◽  
Electrode Impedance ◽  
Electrode Arrays ◽  
Brain Surface ◽  
Micro Electrode Arrays ◽  
Cellular Circuits ◽  
Noise Models

AbstractThe quality of bioelectrical signals is essential for functional evaluation of cellular circuits. The electrical activity recorded from the cortical brain surface represents the average of many individual synaptic processes. By downsizing micro-electrode arrays, the spatial resolution of electrocortico-grams (ECoGs) can be increased. But, upon increasing electrode impedance, recorded noise from the electrode-tissue interface and the surroundings will become more prominent. Frequently, signal interpretation is improved by post-processing using filtering or pattern recognition. For a variety of applications, wavelet denoising has become an accepted tool. Here, we present how wavelet denoising affects the signal-to-noise ratio of ECoGs. The recording qualities from awake and anesthetized mice was artificially reduced by adding two noise models prior to filtering. Raw and filtered signals were compared by calculating the linear correlation coefficient.

Effect of Ge-Nanoislands on the Low-Frequency Noise in Si/SiOx/Ge Structures

2013 ◽  
Vol 854 ◽  
pp. 21-27 ◽  
Author(s):  
N.P. Garbar ◽  
Valeriya N. Kudina ◽  
V.S. Lysenko ◽  
S.V. Kondratenko ◽  
Yu.N. Kozyrev
Keyword(s):  
High Surface ◽  
Low Frequency ◽  
Noise Model ◽  
Frequency Noise ◽  
Low Frequency Noise ◽  
Physical Processes ◽  
Noise Sources ◽  
Noise Component ◽  
First Time ◽  
Noise Models

Low-frequency noise of the structures with Ge-nanoclusters of rather high surface density grown on the oxidized silicon surface is investigated for the first time. It was revealed that the 1/f γ noise, where γ is close to unity, is the typical noise component. Nevertheless, the 1/f γ noise sources were found to be distributed nonuniformly upon the oxidized silicon structure with Ge-nanoclusters. The noise features revealed were analyzed in the framework of widely used noise models. However, the models used appeared to be unsuitable to explain the noise behavior of the structures studied. The physical processes that should be allowed for to develop the appropriate noise model are discussed.

scholarly journals ULF magnetic emissions connected with under sea bottom earthquakes

2001 ◽  
Vol 1 (1/2) ◽  
pp. 23-31 ◽  
Author(s):  
V. S. Ismaguilov ◽  
Yu. A. Kopytenko ◽  
K. Hattori ◽  
P. M. Voronov ◽  
O. A. Molchanov ◽  
...  
Keyword(s):  
Seismic Activity ◽  
Vertical Component ◽  
Horizontal Component ◽  
Field Variation ◽  
Frequency Range ◽  
Magnetic Gradient ◽  
Sea Bottom ◽  
Activity Center ◽  
G Ratio ◽  
Electromagnetic Disturbances

Abstract. Measurements of ULF electromagnetic disturbances were carried out in Japan before and during a seismic active period (1 February 2000 to 26 July 2000). A network consists of two groups of magnetic stations spaced apart at a distance of ≈140 km. Every group consists of three, 3-component high sensitive magnetic stations arranged in a triangle and spaced apart at a distance of 4–7 km. The results of the ULF magnetic field variation analysis in a frequency range of F = 0.002–0.5 Hz in connection with nearby earth-quakes are presented. Traditional Z/G ratios (Z is the vertical component, G is the total horizontal component), magnetic gradient vectors and phase velocities of ULF waves propagating along the Earth’s surface were constructed in several frequency bands. It was shown that variations of the R(F) = Z/G parameter have a different character in three frequency ranges: F1 = 0.1 ± 0.005, F2 = 0.01 ± 0.005 and F3 = 0.005 ± 0.003 Hz. Ratio R(F3)/R(F1) sharply increases 1–3 days before strong seismic shocks. Defined in a frequency range of F2 = 0.01 ± 0.005 Hz during nighttime intervals (00:00–06:00 LT), the amplitudes of Z and G component variations and the Z/G ratio started to increase ≈ 1.5 months before the period of the seismic activity. The ULF emissions of higher frequency ranges sharply increased just after the seismic activity start. The magnetic gradient vectors (∇ B ≈ 1 – 5 pT/km), determined using horizontal component data (G ≈ 0.03 – 0.06 nT) of the magnetic stations of every group in the frequency range F = 0.05 ± 0.005 Hz, started to point to the future center of the seismic activity just before the seismoactive period; furthermore they continued following space displacements of the seismic activity center. The phase velocity vectors (V ≈ 20 km/s for F = 0.0067 Hz), determined using horizontal component data, were directed from the seismic activity center. Gradient vectors of the vertical component pointed to the closest seashore (known as the "sea shore" effect). The location of the seismic activity centers by two gradient vectors, constructed at every group of magnetic stations, gives an ≈ 10 km error in this experiment.

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