Siamese unsupervised clustering for removing uncertainty in microseismic signal labelling

The labelling of large seismic datasets is a challenging problem. Currently the methods most favoured by geoscientists are based on well known geophysical properties with STA/LTA ratio pickers remaining highly trusted to generate results which can be quickly attributed due to their ability to pick relatively high Signal to Noise Ratio (SNR) events with high speed and accuracy.
We aim to improve on the ability of deep learning methods by the unsupervised clustering of events which can help to visually identify results as belonging to a certain cluster with high confidence without the need for event by event processing.
From our previous work we use a Siamese model trained with known labels from an open source dataset we show performance as a classifier and then expand on the method by showing clustering of events, where an expert can have high confidence that certain events are correctly identified, or require further evaluation.