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The use of weighted self-organizing maps to interrogate large seismic data sets


Meyer, SG and Reading, AM and Bassom, AP, The use of weighted self-organizing maps to interrogate large seismic data sets, Geophysical Journal International, 231, (3) pp. 2156-2172. ISSN 0956-540X (2022) [Refereed Article]

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Copyright Statement

The Author(s) 2022. Published by Oxford University Press on behalf of The Royal Astronomical Society. This article is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License (, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

DOI: doi:10.1093/gji/ggac322


Modern microseismic monitoring systems can generate extremely large data sets with signals originating from a variety of natural and anthropogenic sources. These data sets may contain multiple signal types that require classification, analysis and interpretation: a considerable task if done manually. Machine learning techniques may be applied to these data sets to expedite and improve such analysis. In this study, we apply an unsupervised technique, the Self-Organizing Map (SOM), to high-volume data recorded by an in-mine microseismic network. This represents a good example of a large seismic data set that contains a wide range of signals, owing to the diversity of source processes occurring within the mine. The signals are quantified by extracting a number of features (temporal and spectral) from the waveforms which are provided as input data for the SOM. We develop and implement a weighted variant of the SOM in which the contributions of various different features to the training of the map are allowed to evolve. The standard and weighted SOMs are applied to the data, and the output maps compared. Both variants are able to separate source types based on the waveform characteristics, allowing for rapid, automatic classification of signals and the ability to find sources with similar waveforms. Fast classification of such signals provides practical benefit by automatically discarding waveforms associated with anthropogenic sources within the mine while seismic signals originating from genuine microseismic events, which constitute a small fraction of all signals, can be prioritized for subsequent processing and analysis. The weighted variant provides an exploratory tool through quantification of the contribution of different features to the clustering process. This helps to optimize the performance of the SOM through the identification of redundant features. Furthermore, those features that are assigned large weights are considered to be more representative of the source generation processes as they contribute more to the cluster separation process. We apply weighted SOMs to data from a mine recorded during two different time periods, corresponding to different stages of the mine development. Changes in feature importance and in the observed distribution of feature values indicate evolving source generation processes and may be used to support investigatory analysis. The weighted SOM therefore represents an effective tool to help manage and investigate large seismic data sets, providing both practical benefit and insight into underlying event mechanisms.

Item Details

Item Type:Refereed Article
Keywords:earthquake source observations, induced seismicity, seismic noise, machine learning
Research Division:Earth Sciences
Research Group:Geophysics
Research Field:Seismology and seismic exploration
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in the earth sciences
UTAS Author:Meyer, SG (Mr Stephen Meyer)
UTAS Author:Reading, AM (Professor Anya Reading)
UTAS Author:Bassom, AP (Professor Andrew Bassom)
ID Code:153803
Year Published:2022
Deposited By:Physics
Deposited On:2022-10-07
Last Modified:2023-01-09
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