ADAPTIVE NEURAL NETS FOR GENERATION OF ARTIFICIAL EARTHQUAKE PRECURSORS

A novel methodology for generation of artificial earthquake precursors was tested on Southern California earthquake data in reverse and real time modes. When it was tried as a real time generator of earthquake precursors, it successfully predicted the June, 1992 Landers earthquake. The methodology is based on the use of Adaptive Neural Nets (ANN) that process a set of time-dependent attributes calculated in amoving time-window. The most important of them is a danger function. The structure of the neural net is defined by the properties of input data in the moving time window. Thus, the neural net continuously adapts its structure to the time variant properties of the input attributes. The main problem we encountered in training neural net on the earthquake data was the small size of the training set compared to the number of parameters that describe the structure of the ANN. To prevent instability and over-fitting in the training session, we used a technique similar to the damping method in least squares approximation.