based on the convolutional model, a seismic trace is the convolution of seismic source wavelet and reflection coefficient series of the earth. seismic source wavelet estimation is one of the most important stages in processing and interpretation of seismic data. accurate estimation of wavelet increases the efficiency of the deconvolution and temporal resolution of seismic data. on the other han...

For seismic resistant design of critical structures, a dynamic analysis, based on either response spectrum or time history is frequently required. Due to the lack of recorded data and randomness of earthquake ground motion that might be experienced by the structure under probable future earthquakes, it is usually difficult to obtain recorded data which fit the necessary parameters (e.g. soil ty...

The main objective of this paper is to use ant optimized neural networks to generate artificial earthquake records. In this regard, training accelerograms selected according to the site geology of recorder station and Wavelet Packet Transform (WPT) used to decompose these records. Then Artificial Neural Networks (ANN) optimized with Ant Colony Optimization and resilient Backpropagation algorith...

We consider the recent suggestion that spectrum estimation can be accomplished by applying wavelet denoising methodology to wavelet packet coefficients derived from the logarithm of a spectrum estimate. The particular algorithm we consider consists of computing the logarithm of the multitaper spectrum estimator, applying an orthonormal transform derived from a wavelet packet table to the log mu...

Endurance Time Acceleration Functions are specially predesigned intensifying excitation functions that their amplitude increases with time. On the other hand, wavelet transform is a mathematical tool that indicates time variations of frequency in a signal. In this paper, an approach is presented for generating endurance time acceleration functions (ETAFs) whose response spectrum is compatible w...

Algorithms for designing a mother wavelet ( ) such that it matches a signal of interest and such that the family of wavelets 2 ( 2) (2 ) forms an orthonormal Riesz basis of ( ) are developed. The algorithms are based on a closed form solution for finding the scaling function spectrum from the wavelet spectrum. Many applications of signal representation, adaptive coding and pattern recognition r...

The paper derives a 2-Dimensional spectrum estimator from some recent results on the statistical properties of wavelet packet coefficients of random processes. It provides an analysis of the bias of this estimator with respect to the wavelet order. The paper also discusses the performance of this wavelet based estimator, in comparison with the conventional 2-D Fourier-based spectrum estimator o...

Orthonormal wavelet expansions are applied to atmospheric surface layer velocity measurements. The effect of intermittent events on the energy spectrum of the inertial subrange is investigated through analysis of wavelet coefficients. The local nature of the orthonormal wavelet transform in physical space makes it possible to identify a relationship between the inertial subrange slope of the lo...

This study proposes and justifies a Bayesian approach to modeling wavelet coefficients and finding statistically significant features in wavelet power spectra. The approach utilizes ideas elaborated in scale-space smoothing methods and wavelet data analysis. We treat each scale of the discrete wavelet decomposition as a sequence of independent random variables and then apply Bayes’ rule for con...