In this study, wavelet transforms and neural networks are used to analyze the formation and evolution of steepened magnetosonic waves (shocklets) generated in hybrid (fluid electrons, particle ions) simulations. These waves model the shocklets observed upstream of planetary bow shocks and at comets. Specifically, the usefulness of wavelet transforms and neural networks for understanding the nature of the steepening process, and the further evolution of shocklets into less coherent structures is investigated. Previous studies had suggested that the nonlinear steepening process is associated with the excitation of higher frequency waves within the original wave. This hypothesis, however, could not be directly substantiated using Fourier transforms. In order to continue with the analysis of shocklets, it has become necessary to implement new techniques using wavelet transforms and neural networks. Wavelet transforms are tailored to the analysis of localized structures such as shocklets, while neural networks have the ability to model nonlinear dynamical systems. Application of wavelet transforms has verified the presence of higher frequency waves within the steepening shocklet and has identified them as the forward propagating and the backward propagating magnetosonic waves as well as the backward propagating Alfv¿n ion-cyclotron mode.

The wavelet transform has also located the source of the whistler wave packet attached to the shocklet to be the region of steep magnetic field gradient. In order to understand the further evolution of shocklets, neural networks have been applied in the analysis. Used in conjunction with a translation invariant transform, neural networks have been successfully trained to identify shocklets. This allows for the scanning of large data sets as well as the development of a classification system for shocklets. A multinetwork classification system using various techniques, including wavelet preprocessing, has been developed to analyze the further evolution of shocklets and their components. Identification and classification of neural networks have increased our understanding of shocklet evolution. The techniques involving wavelet transforms and neural networks that have been employed in this study show considerable potential for the study of not only shocklets, but also other wave phenomena in space plasmas. ¿ American Geophysical Union 1995