Therefore one of the most common problems encountered in gravity data studies is how to separate each anomaly. The wavelet transform operator has recently been introduced in the domain of potential fields both as a filtering and as a source-analysis tool. Here we study the ability of improving resolution gravity anomaly based on wavelet analysis and power spectrum analysis.
Therefore one of the most common problems encountered in gravity data studies is how to separate each anomaly. The wavelet transform operator has recently been introduced in the domain of potential fields both as a filtering and as a source-analysis tool. Here we study the ability of improving resolution gravity anomaly based on wavelet analysis and power spectrum analysis.
By applying the method of wavelet transformation and the annual torrent runoff time series of 51 years (1951~2001) records of Longtan hydrologic station,the torrent runoff variation along the upper reaches of the Panlong River is studied. The periods of runoff and points of abrupt change at different time scales are also analyzed.
Wavelets analysis has the characteristics of multi-resolution analysis and ability of expressing local features of signal in both time and frequency domains, it is a local analysis method, the window size of which is fixed and unchanged, the window shape of which can be changed, in both time and frequency domains.
In this article, starting from the introduction of brief developing histoiy of wavelets analysis, the basic principle of wavelets analysis is briefly described, analyzing and summarizing the new application fruits of wavelets analysis are emphasized in geophysical exploration and signal and image processing.
In this paper, radial basis function networks based on the wavelet analysis method is introduced to forecast hydrologic time series. Firstly the hydrologic time series is decomposed to different frequency components with wavelet analysis. Then the artificial neural network is used in multi-scale forecasting of these coefficients.
Often, the Dyadic Wavelet Transform is performed and implemented with the Daubechies wavelets, the Battle-Lemarie wavelets, or the splines wavelets, whereas in continuous-time wavelet decomposition a much larger variety of mother wavelets is used.
Our work builds on a uniqueness result for reconstructing an L2 signal from irregular sampling of its wavelet transform of Grochenig and the related work of Benedetto, Heller, Mallat, and Zhong.