Seismic signal processing is an important task in geophysics sounding and represents a permanent challenge in petroleum exploration. Although seismograms could in principle give us a picture of a geological structure, they are very contaminated by spurious signals and the ground roll noise is a strongly undesired signal present in the seismograms – it does not carry physical information about t...

The discrete wavelet transform (DWT) approximately decorrelates a fractionally di erenced (FD) process, allowing for simple maximum likelihood estimation of the FD process parameters using the wavelet coeÆcients. In previous work we have established limit theorems for the parameters based on a model where scales are uncorrelated and two simple models for withinscale correlation, namely, white n...

In this paper we consider the Easy Path Wavelet Transform (EPWT) on spherical triangulations. The EPWT has been introduced in [7] in order to obtain sparse image representations. It is a locally adaptive transform that works along pathways through the array of function values and exploits the local correlations of the data in a simple appropriate manner. In our approach the usual one-dimensiona...

In this paper, the use of wavelet analysis and artificial neural networks for generating artificial earthquake accelerograms from target spectrum is suggested as a new method. This procedure uses the learning capabilities of neural network to expand the knowledge of the inverse mapping from response spectrum to earthquake accelerogram. In the first step, wavelet analysis is used to decompose ea...

Seismologists working on the reflection of seismic data have been dealing with noise and trying to attenuate noise as much as possible without damaging a particular signal. many methods have been used to remove noise types, each being partially effective. None of the transforms have been ideal and, even at the point of transform, noise becomes added to data. The double density dual tree (DDDT) ...

This paper introduces a new method to separate PD from other disturbing signals present on the high voltage generators and motors. The method is based on combination of a pattern classifier, the Discrete Wavelet Transform (DWT), to de-noise PD and Time-Of-Arrival method to separate PD sources. Furthermore, it will be shown that it can recognize PD sources including rotating machine’s internal a...

The wavelet transform is one of the most important method used in signal processing. In this study, we have introduced frequency-energy characteristics of local earthquakes using discrete wavelet transform. Frequency-energy characteristic was analyzed depend on difference between P and S wave arrival time and noise within records. We have found that local earthquakes have similar characteristic...

There are standard modifications of certain compactly supported wavelets that yield orthonormal bases on a bounded interval. We extend one such construction to those wavelets, such as ‘coiflets’, that may have fewer vanishing moments than had to be assumed previously. Our motivation lies in function estimation in statistics. We use these boundary-modified coiflets to show that the discrete wave...