Marta Pienkowska, Stuart E. J. Nippress, David Bowers, Tarje Nissen-Meyer

Over the last decade there has been an international effort to find methods to recover and digitize recordings from historical earthquakes and explosions that occurred during the 1950’s through to the 1980’s. Making these recordings accessible in digital format offers opportunities to study what signatures are encoded in the data, and to apply state-of-the-art techniques and methods to historical data. In this study we employ unsupervised machine learning to cluster historical teleseismic waveforms from nuclear explosions conducted at the former USSR Degelen test site, in Kazakhstan, recorded at seismic arrays in the UK (EKA), Canada (YKA), Australia (WRA) and India (GBA). In particular, we use two unsupervised algorithms to cluster waveforms using shape-based clustering: kernel k-means and k-Shape. The algorithms clearly split waveforms into distinct clusters that are spatially related, even when waveform differences are subtle, and we show with local and teleseismic numerical simulations that the clusters are related to the topography. The topography at the Degelen test site has characteristic wavelengths of 2-4 km and local simulations highlight that the seismic wavefield is trapped in reverberating mountain peaks. The location of the explosion is crucial in determining which section of the mountain range reverberates, influencing the outgoing wavefield. Teleseismic waveform simulations confirm that it is this superposition of energy leaving the reverberating peaks that results in the observed teleseismic waveform differences we observe.