NOISE AND TARGET SIGNALS SIMULATION IN PASSIVE SEISMIC LOCATION SYSTEMS

The paper proposes the model of a person seismic signal with noise for the investigation of passive seismic location system characteristics. The known models based on Gabor and Berlage pulses have been analyzed. These models are not able wholly to consider statistical properties of seismic signals. The proposed model is based on almost cyclic character of seismic signals, Gauss character of fluctuations inside a pulse, random amplitude change from pulse to pulse and relatively small fluctuation of separate pulses positions. The simulation procedure consists of passing the white noise through a linear generating filter with characteristics formed by real steps of a person, and the primary pulse sequence modulation by Gauss functions. The model permits to control the signal-to-noise ratio after its reduction to unity and to vary pulse shifts with respect to person steps irregularity. It has been shown that the model of a person seismic signal with noise agrees with experimental data.

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Passive seismic imaging using microearthquakes

Geophysics ◽

10.1190/1.1443846 ◽

1995 ◽

Vol 60 (4) ◽

pp. 1178-1186 ◽

Cited By ~ 37

Author(s):

M. Reza Daneshvar ◽

Clarence S. Clay ◽

Martha K. Savage

Keyword(s):

Wave Velocity ◽

Signal To Noise Ratio ◽

Velocity Model ◽

P Wave ◽

Seismic Signals ◽

Subsurface Structure ◽

Near Surface ◽

P Wave Velocity ◽

Recording Station ◽

Passive Seismic

We have developed a method of processing seismic signals generated by microearthquakes to image local subsurface structure beneath a recording station. This technique uses the autocorrelation of the vertically traveling earthquake signals to generate pseudoreflection seismograms that can be interpreted for subsurface structure. Processed pseudoreflection data, from microearthquakes recorded in the island of Hawaii, show consistent reflectivity patterns that are interpreted as near‐surface horizontal features. Forward modeling of the pseudoreflection data results in a P‐wave velocity model that shows reasonable agreement with the velocity model derived from a refraction study in the region. Usable signal‐to‐noise ratio is obtained down to 2 s. A shear‐wave velocity model was also generated by applying this technique to horizontal component data.

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Local seismic attributes

Geophysics ◽

10.1190/1.2437573 ◽

2007 ◽

Vol 72 (3) ◽

pp. A29-A33 ◽

Cited By ~ 218

Author(s):

Sergey Fomel

Keyword(s):

Image Registration ◽

Seismic Signal ◽

Seismic Attributes ◽

Data Sets ◽

Local Similarity ◽

Seismic Signals ◽

Practical Applications ◽

Regularized Inversion ◽

Signal Characteristics ◽

Land Survey

Local seismic attributes measure seismic signal characteristics not instantaneously, at each signal point, and not globally, across a data window, but locally in the neighborhood of each point. I define local attributes with the help of regularized inversion and demonstrate their usefulness for measuring local frequencies of seismic signals and local similarity between different data sets. I use shaping regularization for controlling the locality and smoothness of local attributes. A multicomponent-image-registration example from a nine-component land survey illustrates practical applications of local attributes for measuring differences between registered images.

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Bayesian feature learning for seismic compressive sensing and denoising

Geophysics ◽

10.1190/geo2016-0373.1 ◽

2017 ◽

Vol 82 (6) ◽

pp. O91-O104 ◽

Cited By ~ 7

Author(s):

Georgios Pilikos ◽

A. C. Faul

Keyword(s):

Compressive Sensing ◽

State Of The Art ◽

Feature Learning ◽

Seismic Signal ◽

Basis Functions ◽

Sparse Signals ◽

Seismic Signals ◽

Process Factor ◽

Learned Features ◽

Fixed Basis

Extracting the maximum possible information from the available measurements is a challenging task but is required when sensing seismic signals in inaccessible locations. Compressive sensing (CS) is a framework that allows reconstruction of sparse signals from fewer measurements than conventional sampling rates. In seismic CS, the use of sparse transforms has some success; however, defining fixed basis functions is not trivial given the plethora of possibilities. Furthermore, the assumption that every instance of a seismic signal is sparse in any acquisition domain under the same transformation is limiting. We use beta process factor analysis (BPFA) to learn sparse transforms for seismic signals in the time slice and shot record domains from available data, and we use them as dictionaries for CS and denoising. Algorithms that use predefined basis functions are compared against BPFA, with BPFA obtaining state-of-the-art reconstructions, illustrating the importance of decomposing seismic signals into learned features.

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Twofold improved tumor-to-brain contrast using a novel T1 relaxation-enhanced steady-state (T1RESS) MRI technique

Science Advances ◽

10.1126/sciadv.abd1635 ◽

2020 ◽

Vol 6 (44) ◽

pp. eabd1635

Author(s):

R. Edelman ◽

N. Leloudas ◽

J. Pang ◽

J. Bailes ◽

R. Merrell ◽

...

Keyword(s):

Steady State ◽

Cancer Detection ◽

Pulse Sequence ◽

Signal To Noise Ratio ◽

Cancer Imaging ◽

Dark Blood ◽

T1 Relaxation ◽

Contrast Enhanced ◽

Magnetic Resonance Imaging Mri ◽

Standard Techniques

A technique that provides more accurate cancer detection would be of great value. Toward this end, we developed T1 relaxation-enhanced steady-state (T1RESS), a novel magnetic resonance imaging (MRI) pulse sequence that enables the flexible modulation of T1 weighting and provides the unique feature that intravascular signals can be toggled on and off in contrast-enhanced scans. T1RESS makes it possible to effectively use an MRI technique with improved signal-to-noise ratio efficiency for cancer imaging. In a proof-of-concept study, “dark blood” unbalanced T1RESS provided a twofold improvement in tumor-to-brain contrast compared with standard techniques, whereas balanced T1RESS greatly enhanced vascular detail. In conclusion, T1RESS represents a new MRI technique with substantial potential value for cancer imaging, along with a broad range of other clinical applications.

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NOISE STABILITY OF SIGNAL RECEPTION ALGORITHMS WITH MULTIPULSE PULSE-POSITION MODULATION

Computer Optics ◽

10.18287/2412-6179-2018-42-1-167-174 ◽

2018 ◽

Vol 42 (1) ◽

pp. 167-174 ◽

Cited By ~ 1

Author(s):

V. I. Parfenov ◽

D. Y. Golovanov

Keyword(s):

Communication Systems ◽

Pulse Sequence ◽

Signal To Noise Ratio ◽

Point Of View ◽

Estimation Accuracy ◽

Pulse Position Modulation ◽

Signal To Noise ◽

Pass Filter ◽

Low Pass Filter ◽

Noise Ratio

An algorithm for estimating time positions and amplitudes of a periodic pulse sequence from a small number of samples was proposed. The number of these samples was determined only by the number of pulses. The performance of this algorithm was considered on the assumption that the spectrum of the original signal is limited with an ideal low-pass filter or the Nyquist filter, and conditions for the conversion from one filter to the other were determined. The efficiency of the proposed algorithm was investigated through analyzing in which way the dispersion of estimates of time positions and amplitudes depends on the signal-to-noise ratio and on the number of pulses in the sequence. It was shown that, from this point of view, the efficiency of the algorithm decreases with increasing number of sequence pulses. Besides, the efficiency of the proposed algorithm decreases with decreasing signal-to-noise ratio.It was found that, unlike the classical maximum likelihood algorithm, the proposed algorithm does not require a search for the maximum of a multivariable function, meanwhile characteristics of the estimates are practically the same for both these methods. Also, it was shown that the estimation accuracy of the proposed algorithm can be increased by an insignificant increase in the number of signal samples.The results obtained may be used in the practical design of laser communication systems, in which the multipulse pulse-position modulation is used for message transmission.

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Agent Based Load Balancing in Grid Computing

10.5772/intechopen.94219 ◽

2020 ◽

Author(s):

Wided Ali ◽

Fatima Bouakkaz

Keyword(s):

Load Balancing ◽

Heterogeneous Systems ◽

Agent Based ◽

Selection Policy ◽

Development Framework ◽

Policy Selection ◽

Proposed Model ◽

Multi Agent ◽

Location Policy ◽

System Characteristics

Load-Balancing is an important problem in distributed heterogeneous systems. In this paper, an Agent-based load-balancing model is developed for implementation in a grid environment. Load balancing is realized via migration of worker agents from overloaded resources to underloaded ones. The proposed model purposes to take benefit of the multi-agent system characteristics to create an autonomous system. The Agent-based load balancing model is implemented using JADE (Java Agent Development Framework) and Alea 2 as a grid simulator. The use of MAS is discussed, concerning the solutions adopted for gathering information policy, location policy, selection policy, worker agents migration, and load balancing.

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Applicataion of ormsby wavelet for generation of synthetic seismic signals

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.7.10980 ◽

2018 ◽

Vol 7 (2.7) ◽

pp. 794

Author(s):

E Sai Sumanth ◽

V Joseph ◽

Dr K S Ramesh ◽

Dr S Koteswara Rao

Keyword(s):

Wavelet Transform ◽

Seismic Data ◽

Wavelet Transforms ◽

Seismic Signal ◽

Core Structure ◽

Seismic Signals ◽

Seismic Reflections ◽

Input Wave

Investigation of signals reflected from earth’s surface and its crust helps in understanding its core structure. Wavelet transforms is one of the sophisticated tools for analyzing the seismic reflections. In the present work a synthetic seismic signal contaminated with noise is synthesized and analyzed using Ormsby wavelet[1]. The wavelet transform has efficiently extracted the spectra of the synthetic seismic signal as it smoothens the noise present in the data and upgrades the flag quality of the seismic data due to termers. Ormsby wavelet gives the most redefined spectrum of the input wave so it could be used for the analysis of the seismic reflections.

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Seismic monitoring of torrential and fluvial processes

Earth Surface Dynamics ◽

10.5194/esurf-4-285-2016 ◽

2016 ◽

Vol 4 (2) ◽

pp. 285-307 ◽

Cited By ~ 32

Author(s):

Arnaud Burtin ◽

Niels Hovius ◽

Jens M. Turowski

Keyword(s):

Seismic Noise ◽

Seismic Waves ◽

Seismic Signal ◽

Seismic Monitoring ◽

Surface Processes ◽

Seismic Signals ◽

Seismic Arrays ◽

Time Frequency ◽

Continuous Survey ◽

Long Time

Abstract. In seismology, the signal is usually analysed for earthquake data, but earthquakes represent less than 1 % of continuous recording. The remaining data are considered as seismic noise and were for a long time ignored. Over the past decades, the analysis of seismic noise has constantly increased in popularity, and this has led to the development of new approaches and applications in geophysics. The study of continuous seismic records is now open to other disciplines, like geomorphology. The motion of mass at the Earth's surface generates seismic waves that are recorded by nearby seismometers and can be used to monitor mass transfer throughout the landscape. Surface processes vary in nature, mechanism, magnitude, space and time, and this variability can be observed in the seismic signals. This contribution gives an overview of the development and current opportunities for the seismic monitoring of geomorphic processes. We first describe the common principles of seismic signal monitoring and introduce time–frequency analysis for the purpose of identification and differentiation of surface processes. Second, we present techniques to detect, locate and quantify geomorphic events. Third, we review the diverse layout of seismic arrays and highlight their advantages and limitations for specific processes, like slope or channel activity. Finally, we illustrate all these characteristics with the analysis of seismic data acquired in a small debris-flow catchment where geomorphic events show interactions and feedbacks. Further developments must aim to fully understand the richness of the continuous seismic signals, to better quantify the geomorphic activity and to improve the performance of warning systems. Seismic monitoring may ultimately allow the continuous survey of erosion and transfer of sediments in the landscape on the scales of external forcing.

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Theoretical analysis on the adaptive vibration attenuation of a fixed-fixed beam realized by a piezo-shape memory alloy ferrule

Journal of Intelligent Material Systems and Structures ◽

10.1177/1045389x19853640 ◽

2019 ◽

Vol 30 (14) ◽

pp. 2079-2090 ◽

Cited By ~ 1

Author(s):

Longfei Wang ◽

Ying Wu ◽

Zishun Liu

Keyword(s):

Shape Memory Alloy ◽

Shape Memory ◽

Self Regulation ◽

Nonlinear Dynamic Response ◽

Adaptive Property ◽

Proposed Model ◽

Vibration Attenuation ◽

The Stability ◽

System Characteristics ◽

Fixed Beam

In this article, the vibration attenuation of a fixed-fixed beam with a piezo-shape memory alloy ferrule is theoretically investigated. First, a dynamic model of the beam with a piezo-shape memory alloy ferrule is established, and the nonlinear dynamic response of the model is numerically analysed. The results show that the stability of the beam structure can be improved adaptively through self-regulation of the stiffness of the piezo-shape memory alloy ferrule undergoing external excitations. The effects of some internal system characteristics, such as the ferrule dimensions as well as the initial ferrule temperatures and boundary conditions, on the vibration attenuation of the beam are discussed. The stability of the proposed model under different external factors, including damping and external excitations, is also investigated. Compared with an aluminium ferrule, the present ferrule is better at the suppressing vibrations of the beam, and its adaptive property avoids the structural resonances for bigger ferrule sizes, making it more intelligent, efficient and convenient.

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