PolarCAP – A deep learning approach for first motion polarity classification of earthquake waveforms

Chakraborty, Megha; Cartaya, Claudia Quinteros; Li, Wei; Faber, Johannes; Rümpker, Georg; Stoecker, Horst; Srivastava, Nishtha

doi:10.1016/j.aiig.2022.08.001

Cited by 5 publications

(1 citation statement)

References 17 publications

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“…Thus, the use of deep learning has been widely embraced in first-motion polarity identification of earthquake waveforms (Chakraborty et al, 2022a), seismic event detection (Perol et al, 2018;Mousavi et al, 2019b;Fenner et al, 2022;Li et al, 2022b), earthquake magnitude classification and estimation (Chakraborty et al, 2021(Chakraborty et al, , 2022b, and seismic phase picking (Ross et al, 2018;Zhu and Beroza, 2019;Mousavi et al, 2020;Li et al, 2021aLi et al, , 2022a. Stepnov et al (2021) stated that seismic phase picking approaches can be roughly divided into two main streams: continuous seismic waveform-based and small window-format-based methods.…”

Section: Introductionmentioning

confidence: 99%

Earthquake monitoring using deep learning with a case study of the Kahramanmaras Turkey earthquake aftershock sequence

Li,

Chakraborty,

Köhler

et al. 2024

Solid Earth

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Abstract. Seismic phase picking and magnitude estimation are fundamental aspects of earthquake monitoring and seismic event analysis. Accurate phase picking allows for precise characterization of seismic wave arrivals, contributing to a better understanding of earthquake events. Likewise, accurate magnitude estimation provides crucial information about an earthquake's size and potential impact. Together, these components enhance our ability to monitor seismic activity effectively. In this study, we explore the application of deep-learning techniques for earthquake detection and magnitude estimation using continuous seismic recordings. Our approach introduces DynaPicker, which leverages dynamic convolutional neural networks to detect seismic body-wave phases in continuous seismic data. We demonstrate the effectiveness of DynaPicker using various open-source seismic datasets, including both window-format and continuous recordings. We evaluate its performance in seismic phase identification and arrival-time picking, as well as its robustness in classifying seismic phases using low-magnitude seismic data in the presence of noise. Furthermore, we integrate the phase arrival-time information into a previously published deep-learning model for magnitude estimation. We apply this workflow to continuous recordings of aftershock sequences following the Turkey earthquake. The results of this case study showcase the reliability of our approach in earthquake detection, phase picking, and magnitude estimation, contributing valuable insights to seismic event analysis.

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