Siamese unsupervised clustering for removing uncertainty in microseismic signal labelling
Murray, David and Stankovic, Lina and Stankovic, Vladimir; (2024) Siamese unsupervised clustering for removing uncertainty in microseismic signal labelling. In: 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE International Symposium on Geoscience and Remote Sensing (IGARSS) . IEEE, GRC. ISBN 979-8-3503-6031-8 (https://doi.org/10.1109/IGARSS53475.2024.10641807)
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Abstract
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.
ORCID iDs
Murray, David ORCID: https://orcid.org/0000-0002-5040-9862, Stankovic, Lina ORCID: https://orcid.org/0000-0002-8112-1976 and Stankovic, Vladimir ORCID: https://orcid.org/0000-0002-1075-2420;-
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Item type: Book Section ID code: 90122 Dates: DateEvent5 September 2024Published15 March 2024AcceptedSubjects: Technology > Electrical engineering. Electronics Nuclear engineering Department: Faculty of Engineering > Electronic and Electrical Engineering Depositing user: Pure Administrator Date deposited: 05 Aug 2024 11:14 Last modified: 04 Oct 2024 00:13 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/90122