  In this paper, we establish a new scheme for identification and
classification of high intensity events generated by the propagation of light
through a photorefractive SBN crystal. Among these events, which are the
inevitable consequence of the development of modulation instability, are
speckling and soliton-like patterns. The usual classifiers developed on
statistical measures, such as the significant intensity, often provide only a
partial characterization of these events. Here, we try to overcome this
deficiency by implementing the convolution neural network method to relate
experimental data of light intensity distribution and corresponding numerical
outputs with different high intensity regimes. The train and test sets are
formed of experimentally obtained intensity profiles at the crystal output
facet and corresponding numerical profiles. The accuracy of detection of
speckles reaches maximum value of 100%, while the accuracy of solitons and
caustic detection is above 97%. These performances are promising for the
creation of neural network based routines for prediction of extreme events in
wave media.
