Bispectra and time-frequency spectra of wind waves in the coastal zone

2006 ◽  
pp. 1005-1014 ◽  
Author(s):  
C Guedes Soares ◽  
Z Cherneva
Keyword(s):  
Coastal Zone ◽  
Wind Waves ◽  
Frequency Spectra ◽  
Time Frequency

scholarly journals Freak waves of different types in the coastal zone of the Baltic Sea

2010 ◽  
Vol 10 (9) ◽  
pp. 2021-2029 ◽  
Author(s):  
I. Didenkulova ◽  
C. Anderson
Keyword(s):  
Baltic Sea ◽  
Coastal Zone ◽  
Wave Height ◽  
Significant Wave Height ◽  
The Baltic Sea ◽  
Frequency Spectra ◽  
Freak Waves ◽  
Time Frequency ◽  
Significant Wave ◽  
The Baltic

Abstract. We present a statistical analysis of freak waves1 measured during the 203 h of observation on sea surface elevation at a location in the coastal zone of the Baltic Sea (2.7 m depth) during June–July 2008. The dataset contains 97 freak waves occurring in both calm and stormy weather conditions. All of the freak waves are solitary waves, 63% of them having positive shape, 17.5% negative shape and 19.5% sign-variable shape. It is suggested that the freak waves can be divided into two groups. Those of the first group, which includes 92% of the freak waves, have an amplification factor (ratio of freak wave height to significant wave height) which does not vary from significant wave height and has values largely within the range of 2.0 to 2.4; while for the second group, which contain the most extreme freak waves, amplification factors depend strongly on significant wave height and can reach 3.1. Analysis based on the Generalised Pareto distribution is used to describe the waves of the first group and lends weight to the identification of the two groups. It is suggested that the probable mechanism of the generation of freak waves in the second group is dispersive focussing. The time-frequency spectra of the freak waves are studied and dispersive tracks, which can be interpreted as dispersive focussing, are demonstrated. 1 taken to be waves whose height is 2 or more times greater than the significant wave height

scholarly journals Deep Learning Approach for Vibration Signals Applications

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3929
Author(s):  
Han-Yun Chen ◽  
Ching-Hung Lee
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Parameters Optimization ◽  
Frequency Spectra ◽  
Vibration Signals ◽  
Bearing Faults ◽  
Time Frequency ◽  
And Performance ◽  
The One ◽  
Short Time

This study discusses convolutional neural networks (CNNs) for vibration signals analysis, including applications in machining surface roughness estimation, bearing faults diagnosis, and tool wear detection. The one-dimensional CNNs (1DCNN) and two-dimensional CNNs (2DCNN) are applied for regression and classification applications using different types of inputs, e.g., raw signals, and time-frequency spectra images by short time Fourier transform. In the application of regression and the estimation of machining surface roughness, the 1DCNN is utilized and the corresponding CNN structure (hyper parameters) optimization is proposed by using uniform experimental design (UED), neural network, multiple regression, and particle swarm optimization. It demonstrates the effectiveness of the proposed approach to obtain a structure with better performance. In applications of classification, bearing faults and tool wear classification are carried out by vibration signals analysis and CNN. Finally, the experimental results are shown to demonstrate the effectiveness and performance of our approach.

Novel Method for Detecting Weak AE Signals Based on the Similarity of Time-Frequency Spectra

Geophysics ◽  
2021 ◽  
pp. 1-62
Author(s):  
Wencheng Yang ◽  
Xiao Li ◽  
Yibo Wang ◽  
Yue Zheng ◽  
Peng Guo
Keyword(s):  
Mathematical Morphology ◽  
Signal To Noise Ratio ◽  
Critical Role ◽  
Real Data ◽  
Monitoring Method ◽  
Frequency Spectra ◽  
Arrival Times ◽  
Time Frequency ◽  
Fracturing Process ◽  
Ae Signals

As a key monitoring method, the acoustic emission (AE) technique has played a critical role in characterizing the fracturing process of laboratory rock mechanics experiments. However, this method is limited by low signal-to-noise ratio (SNR) because of a large amount of noise in the measurement and environment and inaccurate AE location. Furthermore, it is difficult to distinguish two or more hits because their arrival times are very close when AE signals are mixed with the strong background noise. Thus, we propose a new method for detecting weak AE signals using the mathematical morphology character correlation of the time-frequency spectrum. The character in all hits of an AE event can be extracted from time-frequency spectra based on the theory of mathematical morphology. Through synthetic and real data experiments, we determined that this method accurately identifies weak AE signals. Compared with conventional methods, the proposed approach can detect AE signals with a lower SNR.

Hydrodynamic Сonditions of the Bakalskaya Spit Degradation (Western Crimea) over the Past 30 Years

2021 ◽  
Vol 37 (3) ◽  
Author(s):  
B. V. Divinsky ◽  
R. D. Kosyan ◽  
◽  
Keyword(s):  
Discriminant Analysis ◽  
Coastal Zone ◽  
Satellite Images ◽  
Wind Waves ◽  
Total Duration ◽  
Wave Model ◽  
Statistical Characteristics ◽  
Wave Impact ◽  
Annual Variability ◽  
The Impact

Purpose. The paper is aimed at studying the morphodynamic features of the Bakalskaya Spit evolution being influenced by the sea wind waves and swell, namely assessment of inter-annual variations in the alluvial (erosion) areas of the Bakalskaya Spit coastline, analysis of inter-annual variability of the wind wave parameters, determination of the surface wave characteristics (or a combination of a few ones) responsible for the processes of the bottom material erosion or accumulation in the coastal zone. Methods and Results. Based on the analysis of satellite images for 1984–2016, the areas of the bottom material accumulation or erosion of the Bakalskaya Spit coastline were determined. Application of the spectral wave model permitted to obtain time series of the main parameters of wind waves and swell (significant wave heights and propagation directions) in the Bakalskaya Spit coastal zone with the 1 hr time resolution for the period from 1984 to 2016. The characteristics of surface waves responsible for the coastline deformation were revealed using the discriminant analysis. Conclusions. Analysis of satellite images of the spit made it possible to distinguish three periods in the history of the Bakalskaya Spit evolution: 1985–1997, 1998–2007 and 2007–2016. The first period was characterized by relative stability. The strongest erosion took place in 1998; after that the alluvial and erosion cases alternated for 10 years weakly tending to general erosion that constituted the second period. The third one that began in 2007 can be defined as the period of spit degradation accompanied by the irreversible loss of beach material. The basic parameters conditioning hydrodynamics of the Bakalskaya Spit water area are: total duration of storms; average and maximum values of significant heights of wind waves and swell. Statistical characteristics of the wind waves’ parameters are of a fairly strong inter-annual variability. According to the average and maximum indices, the wind waves directed close to the normal to the coastline (WSW and WNW) are the most developed. The applied discriminant analysis permitted to draw a statistically reliable conclusion that the direction of the final (average annual) wave impact on the coastal zone, conditioning the processes of sand accumulation or erosion was set by the waves directed to NNW, at that the swell contribution was dominant. The impact degree is conditioned by strong storms with the directions close to the normal to the coastline, namely, the WSW ones

scholarly journals Analysis of ship wake transformation in the coastal zone using time–frequency methods

2015 ◽  
Vol 64 (3) ◽  
pp. 379 ◽  
Author(s):  
T Torsvik ◽  
H Herrmann ◽  
I Didenkulova ◽  
A Rodin
Keyword(s):  
Coastal Zone ◽  
Ship Wake ◽  
Time Frequency

Sparse spectral attributes to detect hydrocarbons: A case study from deep dolomite reservoirs

2019 ◽  
Vol 7 (3) ◽  
pp. T701-T711
Author(s):  
Jianhu Gao ◽  
Bingyang Liu ◽  
Shengjun Li ◽  
Hongqiu Wang
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Spectral Amplitude ◽  
Data Set ◽  
Frequency Spectra ◽  
Hydrocarbon Production ◽  
Hydrocarbon Detection ◽  
Time Frequency ◽  
Gas Bearing

Hydrocarbon detection is always one of the most critical sections in geophysical exploration, which plays an important role in subsequent hydrocarbon production. However, due to the low signal-to-noise ratio and weak reflection amplitude of deep seismic data, some conventional methods do not always provide favorable hydrocarbon prediction results. The interesting dolomite reservoirs in Central Sichuan are buried over an average depth of 4500 m, and the dolomite rocks have a low porosity below approximately 4%, which is measured by well-logging data. Furthermore, the dominant system of pores and fractures as well as strong heterogeneity along the lateral and vertical directions lead to some difficulties in describing the reservoir distribution. Spectral decomposition (SD) has become successful in illuminating subsurface features and can also be used to identify potential hydrocarbon reservoirs by detecting low-frequency shadows. However, the current applications for hydrocarbon detection always suffer from low resolution for thin reservoirs, probably due to the influence of the window function and without a prior constraint. To address this issue, we developed sparse inverse SD (SISD) based on the wavelet transform, which involves a sparse constraint of time-frequency spectra. We focus on investigating the applications of sparse spectral attributes derived from SISD to deep marine dolomite hydrocarbon detection from a 3D real seismic data set with an area of approximately [Formula: see text]. We predict and evaluate gas-bearing zones in two target reservoir segments by analyzing and comparing the spectral amplitude responses of relatively high- and low-frequency components. The predicted results indicate that most favorable gas-bearing areas are located near the northeast fault zone in the upper reservoir segment and at the relatively high structural positions in the lower reservoir segment, which are in good agreement with the gas-testing results of three wells in the study area.

Wavelet Analysis and Envelope Detection For Rolling Element Bearing Fault Diagnosis—Their Effectiveness and Flexibilities

2001 ◽  
Vol 123 (3) ◽  
pp. 303-310 ◽  
Author(s):  
Peter W. Tse ◽  
Y. H. Peng ◽  
Richard Yam
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Analysis ◽  
Rolling Element Bearing ◽  
Frequency Spectra ◽  
Bearing Faults ◽  
Time Frequency ◽  
Envelope Detection ◽  
Rolling Element ◽  
Element Bearing ◽  
Bearing Characteristic

The components which often fail in a rolling element bearing are the outer-race, the inner-race, the rollers, and the cage. Such failures generate a series of impact vibrations in short time intervals, which occur at Bearing Characteristic Frequencies (BCF). Since BCF contain very little energy, and are usually overwhelmed by noise and higher levels of macro-structural vibrations, they are difficult to find in their frequency spectra when using the common technique of Fast Fourier Transforms (FFT). Therefore, Envelope Detection (ED) is always used with FFT to identify faults occurring at the BCF. However, the computation of ED is complicated, and requires expensive equipment and experienced operators to process. This, coupled with the incapacity of FFT to detect nonstationary signals, makes wavelet analysis a popular alternative for machine fault diagnosis. Wavelet analysis provides multi-resolution in time-frequency distribution for easier detection of abnormal vibration signals. From the results of extensive experiments performed in a series of motor-pump driven systems, the methods of wavelet analysis and FFT with ED are proven to be efficient in detecting some types of bearing faults. Since wavelet analysis can detect both periodic and nonperiodic signals, it allows the machine operator to more easily detect the remaining types of bearing faults which are impossible by the method of FFT with ED. Hence, wavelet analysis is a better fault diagnostic tool for the practice in maintenance.

scholarly journals Common Mode Component and Its Potential Effect on GPS-Inferred Three-Dimensional Crustal Deformations in the Eastern Tibetan Plateau

2019 ◽  
Vol 11 (17) ◽  
pp. 1975 ◽  
Author(s):  
Yuanjin Pan ◽  
Ruizhi Chen ◽  
Hao Ding ◽  
Xinyu Xu ◽  
Gang Zheng ◽  
...  
Keyword(s):  
Time Series ◽  
Tibetan Plateau ◽  
Crustal Deformation ◽  
Three Dimensional ◽  
Vertical Component ◽  
Eastern Tibetan Plateau ◽  
Frequency Spectra ◽  
Common Mode ◽  
Time Frequency ◽  
Gps Time Series

Surface and deep potential geophysical signals respond to the spatial redistribution of global mass variations, which may be monitored by geodetic observations. In this study, we analyze dense Global Positioning System (GPS) time series in the Eastern Tibetan Plateau using principal component analysis (PCA) and wavelet time-frequency spectra. The oscillations of interannual and residual signals are clearly identified in the common mode component (CMC) decomposed from the dense GPS time series from 2000 to 2018. The newly developed spherical harmonic coefficients of the Gravity Recovery and Climate Experiment Release-06 (GRACE RL06) are adopted to estimate the seasonal and interannual patterns in this region, revealing hydrologic and atmospheric/nontidal ocean loads. We stack the averaged elastic GRACE-derived loading displacements to identify the potential physical significance of the CMC in the GPS time series. Interannual nonlinear signals with a period of ~3 to ~4 years in the CMC (the scaled principal components from PC1 to PC3) are found to be predominantly related to hydrologic loading displacements, which respond to signals (El Niño/La Niña) of global climate change. We find an obvious signal with a period of ~6 yr on the vertical component that could be caused by mantle-inner core gravity coupling. Moreover, we evaluate the CMC’s effect on the GPS-derived velocities and confirm that removing the CMC can improve the recognition of nontectonic crustal deformation, especially on the vertical component. Furthermore, the effects of the CMC on the three-dimensional velocity and uncertainty are presented to reveal the significant crustal deformation and dynamic processes of the Eastern Tibetan Plateau.

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