Global‐ and Local‐Scale High‐Resolution Event Catalogs for Algorithm Testing

Author:

Linville Lisa1,Brogan Ronald Chip2,Young Christopher1,Aur Katherine Anderson1

Affiliation:

1. Geophysics Department, Sandia National Laboratories, MS 0758, P.O. Box 5800, Albuquerque, New Mexico 87185‐0758 U.S.A., llinvil@sandia.gov

2. ENSCO, 4849 N. Wickham Road, Melbourne, Florida 32940 U.S.A.

Abstract

ABSTRACT During the development of new seismic data processing methods, the verification of potential events and associated signals can present a nontrivial obstacle to the assessment of algorithm performance, especially as detection thresholds are lowered, resulting in the inclusion of significantly more anthropogenic signals. Here, we present two 14 day seismic event catalogs, a local‐scale catalog developed using data from the University of Utah Seismograph Stations network, and a global‐scale catalog developed using data from the International Monitoring System. Each catalog was built manually to comprehensively identify events from all sources that were locatable using phase arrival timing and directional information from seismic network stations, resulting in significant increases compared to existing catalogs. The new catalogs additionally contain challenging event sequences (prolific aftershocks and small events at the detection and location threshold) and novel event types and sources (e.g., infrasound only events and long‐wall mining events) that make them useful for algorithm testing and development, as well as valuable for the unique tectonic and anthropogenic event sequences they contain.

Publisher

Seismological Society of America (SSA)

Subject

Geophysics

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Application of Neural Network Technologies in Signal Detection Tasks;2023 IEEE International Conference on Information and Telecommunication Technologies and Radio Electronics (UkrMiCo);2023-11-13

2. Microseismic Event Classification With Time-, Frequency-, and Wavelet-Domain Convolutional Neural Networks;IEEE Transactions on Geoscience and Remote Sensing;2023

3. Domain Knowledge Informed Multitask Learning for Landslide-Induced Seismic Classification;IEEE Geoscience and Remote Sensing Letters;2023

4. Comparing Traditional and Deep Learning Signal Features for Event Detection in the Utah Region;Bulletin of the Seismological Society of America;2022-07-05

5. Classification of Local Seismic Events in the Utah Region: A Comparison of Amplitude Ratio Methods with a Spectrogram‐Based Machine Learning Approach;Bulletin of the Seismological Society of America;2019-10-22

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