A neural network based approach to classify VLF signals as rock rupture precursors

The advent of novel technologies revealed that other geophysical signals than those directly related to fault motion could be used to probe the state of deformation of the Earth's crust. Electromagnetic signals belonging to this category have been increasingly investigated in the last decade in association to natural earthquakes and laboratory rock fractures. These studies are hampered by the lack of continuous recordings and a systematic mathematical processing of large data sets. Indeed, electromagnetic signals exhibit characteristic patterns on a specific frequency band (the very low frequency, VLF) that correlate uniquely with the paroxistic rupture of rocks specimens under uniaxial laboratory tests and were also detected in the atmosphere, in association to moderate magnitude earthquakes. The similarity of laboratory and atmospheric VLF offers an unique opportunity to study the relation between VLF and rock deformation on at least two different scales and to enlarge the dataset by combining laboratory and atmospheric data. In this paper we show that the enlarged VLF dataset can be successfully used, with a neural network approach based on LSTM neural networks to investigate the potential of the VLF spectrum in classifying rock rupture precursors both in nature and in the laboratory. The proposed approach lays foundation to the automatic detection of interesting VLF patterns for monitoring deformations in the seismically active Earth's crust.