Deep Compressed Seismic Learning for fast location and moment tensor inferences with natural and induced seismicity

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1038/s41598-022-19421-z. This is version 1 of this Preprint.

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Authors

Ismael Adan Vera Rodriguez , Erik B. Myklebust

Abstract

Fast detection and characterization of seismic sources is crucial for decision-making and warning systems that monitor natural and induced seismicity. However, besides the laying out of ever denser monitoring networks of seismic instruments, the incorporation of new sensor technologies such as Distributed Acoustic Sensing (DAS) further challenges our processing capabilities to deliver short turnaround answers from seismic monitoring. In response, this work describes a methodology for the learning of the seismological parameters: location and moment tensor from compressed seismic records. In this method, data dimensionality is reduced by applying a general encoding protocol derived from the principles of compressive sensing. The data in compressed form is then fed directly to a convolutional neural network that outputs fast predictions of the seismic source parameters. Thus, the proposed methodology can not only expedite data transmission from the field to the processing center, but also remove the decompression overhead that would be required for the application of traditional processing methods. An autoencoder is also explored as an equivalent alternative to perform the same job. We observe that the CS-based compression requires only a fraction of the computing power, time, data and expertise required to design and train an autoencoder to perform the same task. Implementation of the CS-method with a continuous flow of data together with generalization of the principles to other applications such as classification are also discussed.

DOI

https://doi.org/10.31223/X5V34Q

Subjects

Earth Sciences, Geophysics and Seismology, Physical Sciences and Mathematics

Keywords

Seismology, moment tensor, machine learning, compressive sensing, autoencoder, event location, Seismic monitoring

Dates

Published: 2022-05-19 21:10

Last Updated: 2022-05-20 01:10

License

CC BY Attribution 4.0 International

Additional Metadata

Data Availability (Reason not available):
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.