414 research outputs found
Bolt Detection Signal Analysis Method Based on ICEEMD
The construction quality of the bolt is directly related to the safety of the
project, and as such, it must be tested. In this paper, the improved complete
ensemble empirical mode decomposition (ICEEMD) method is introduced to the bolt
detection signal analysis. The ICEEMD is used in order to decompose the anchor
detection signal according to the approximate entropy of each intrinsic mode
function (IMF). The noise of the IMFs is eliminated by the wavelet soft
threshold de-noising technique. Based on the approximate entropy, and the
wavelet de-noising principle, the ICEEMD-De anchor signal analysis method is
proposed. From the analysis of the vibration analog signal, as well as the bolt
detection signal, the result shows that the ICEEMD-De method is capable of
correctly separating the different IMFs under noisy conditions, and also that
the IMF can effectively identify the reflection signal of the end of the bolt
Blind Curvelet based Denoising of Seismic Surveys in Coherent and Incoherent Noise Environments
The localized nature of curvelet functions, together with their frequency and
dip characteristics, makes the curvelet transform an excellent choice for
processing seismic data. In this work, a denoising method is proposed based on
a combination of the curvelet transform and a whitening filter along with
procedure for noise variance estimation. The whitening filter is added to get
the best performance of the curvelet transform under coherent and incoherent
correlated noise cases, and furthermore, it simplifies the noise estimation
method and makes it easy to use the standard threshold methodology without
digging into the curvelet domain. The proposed method is tested on
pseudo-synthetic data by adding noise to real noise-less data set of the
Netherlands offshore F3 block and on the field data set from east Texas, USA,
containing ground roll noise. Our experimental results show that the proposed
algorithm can achieve the best results under all types of noises (incoherent or
uncorrelated or random, and coherent noise)
Bayesian wavelet de-noising with the caravan prior
According to both domain expert knowledge and empirical evidence, wavelet
coefficients of real signals tend to exhibit clustering patterns, in that they
contain connected regions of coefficients of similar magnitude (large or
small). A wavelet de-noising approach that takes into account such a feature of
the signal may in practice outperform other, more vanilla methods, both in
terms of the estimation error and visual appearance of the estimates. Motivated
by this observation, we present a Bayesian approach to wavelet de-noising,
where dependencies between neighbouring wavelet coefficients are a priori
modelled via a Markov chain-based prior, that we term the caravan prior.
Posterior computations in our method are performed via the Gibbs sampler. Using
representative synthetic and real data examples, we conduct a detailed
comparison of our approach with a benchmark empirical Bayes de-noising method
(due to Johnstone and Silverman). We show that the caravan prior fares well and
is therefore a useful addition to the wavelet de-noising toolbox.Comment: 32 pages, 15 figures, 4 table
Extraction of coherent structures in a rotating turbulent flow experiment
The discrete wavelet packet transform (DWPT) and discrete wavelet transform
(DWT) are used to extract and study the dynamics of coherent structures in a
turbulent rotating fluid. Three-dimensional (3D) turbulence is generated by
strong pumping through tubes at the bottom of a rotating tank (48.4 cm high,
39.4 cm diameter). This flow evolves toward two-dimensional (2D) turbulence
with increasing height in the tank. Particle Image Velocimetry (PIV)
measurements on the quasi-2D flow reveal many long-lived coherent vortices with
a wide range of sizes. The vorticity fields exhibit vortex birth, merger,
scattering, and destruction. We separate the flow into a low-entropy
``coherent'' and a high-entropy ``incoherent'' component by thresholding the
coefficients of the DWPT and DWT of the vorticity fields. Similar thresholdings
using the Fourier transform and JPEG compression together with the Okubo-Weiss
criterion are also tested for comparison. We find that the DWPT and DWT yield
similar results and are much more efficient at representing the total flow than
a Fourier-based method. Only about 3% of the large-amplitude coefficients of
the DWPT and DWT are necessary to represent the coherent component and preserve
the vorticity probability density function, transport properties, and spatial
and temporal correlations. The remaining small amplitude coefficients represent
the incoherent component, which has near Gaussian vorticity PDF, contains no
coherent structures, rapidly loses correlation in time, and does not contribute
significantly to the transport properties of the flow. This suggests that one
can describe and simulate such turbulent flow using a relatively small number
of wavelet or wavelet packet modes.Comment: experimental work aprox 17 pages, 11 figures, accepted to appear in
PRE, last few figures appear at the end. clarifications, added references,
fixed typo
Application of Local Wave Decomposition in Seismic Signal Processing
Local wave decomposition (LWD) method plays an important role in seismic signal processing for its superiority in significantly revealing the frequency content of a seismic signal changes with time variation. The LWD method is an effective way to decompose a seismic signal into several individual components. Each component represents a harmonic signal localized in time, with slowly varying amplitudes and frequencies, potentially highlighting different geologic and stratigraphic information. Empirical mode decomposition (EMD), the synchrosqueezing transform (SST), and variational mode decomposition (VMD) are three typical LWD methods. We mainly study the application of the LWD method especially EMD, SST, and VMD in seismic signal processing including seismic signal deânoising, edge detection of seismic images, and recovery of the target reflection near coal seams
On the energy leakage of discrete wavelet transform
The energy leakage is an inherent deficiency of discrete wavelet transform (DWT) which is often ignored by researchers and practitioners. In this paper, a systematic investigation into the energy leakage is reported. The DWT is briefly introduced first, and then the energy leakage phenomenon is described using a numerical example as an illustration and its effect on the DWT results is discussed. Focusing on the Daubechies wavelet functions, the band overlap between the quadrature mirror analysis filters was studied and the results reveal that there is an unavoidable tradeoff between the band overlap degree and the time resolution for the DWT. The dependency of the energy leakage to the wavelet function order was studied by using a criterion defined to evaluate the severity of the energy leakage. In addition, a method based on resampling technique was proposed to relieve the effects of the energy leakage. The effectiveness of the proposed method has been validated by numerical simulation study and experimental study
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