Automatic Slowness Vector Measurements of Seismic Arrivals with Uncertainty Estimates using Bootstrap Sampling, Array Methods and Unsupervised Learning

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1093/gji/ggab196. This is version 2 of this Preprint.

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Authors

James Ward, michael Thorne , Andy Nowacki , Sebastian Rost

Abstract

Horizontal slowness vector measurements using array techniques have been used to analyse many Earth phenomena from lower mantle heterogeneity to meteorological event location. While providing observations essential for studying much of the Earth, slowness vector analysis is limited by the necessary and subjective visual inspection of observations. Furthermore, it is challenging to determine the uncertainties caused by limitations of array processing such as array geometry, local structure, noise and their effect on slowness vector measurements. To address these issues, we present a method to automatically identify seismic arrivals and measure their slowness vector properties with uncertainty bounds. We do this by bootstrap sampling waveforms, therefore also creating random sub arrays, then use linear beamforming to measure the coherent power at a range of slowness vectors. For each bootstrap sample, we take the top $N$ peaks from each power distribution as the slowness vectors of possible arrivals. The slowness vectors of all bootstrap samples are gathered and the clustering algorithm DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is used to identify arrivals as clusters of slowness vectors. The mean of each cluster gives the slowness vector measurement for that arrival and the distribution of slowness vectors in each cluster gives the uncertainty estimate. We tuned the parameters of DBSCAN using a dataset of 2489 SKS and SKKS observations at a range of frequency bands from 0.1 Hz to 1 Hz. We then present examples at higher frequencies (0.5 to 2.0 Hz) than the example dataset, identifying PKP precursors, and lower frequency by identifying multipathing in surface waves (0.04 to 0.06 Hz). While we use a linear beamforming process, this method can be implemented with any beamforming process such as cross-correlation beamforming or phase weighted stacking. This method allows for much larger datasets to be analysed without visual inspection of data. Phenomena such as multipathing, reflections or scattering can be identified automatically in body or surface waves and their properties analysed with uncertainties.

DOI

https://doi.org/10.31223/X5W89N

Subjects

Physical Sciences and Mathematics

Keywords

Structure of the Earth, Surface waves and free oscillations

Dates

Published: 2020-11-25 09:29

Last Updated: 2021-05-26 13:32

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License

CC BY Attribution 4.0 International

Additional Metadata

Conflict of interest statement:
None

Data Availability (Reason not available):
Code to perform the analysis is available at: https://github.com/eejwa/Array_Seis_Circle. Data used for tuning and the examples is available to download from: https://figshare.com/ s/fbcb167ad15d581cfd4e. Seismic arrays used were the Kaapvaal array (James et al., 2001), the Grafenberg array (Federal Institute For Geosciences And Natural Resources (BGR), 1976) and the Southern California Seismic Network (California Institute of Technology and United States Geo- logical Survey Pasadena, 1926).

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