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Enhanced crustal and intermediate seismicity in the Hindu Kush-Pamir region revealed by attentive deep learning model

The Hindu Kush-Pamir region (HKPR) is characterized by complex ongoing deformation, unique slab geometry, and intermediate seismic activity. The availability of extensive seismological data in recent decades has prompted the use of deep learning algorithms to extract valuable insights. In this study, we present a fully automated approach for augmenting earthquake catalogue within the HKPR. Our method leverages an attention mechanism-based deep learning architecture to simultaneously detect events, perform phase picking, and estimate magnitudes. We applied this model to a ten-month dataset (January 2013–October 2013) from 83 stations in the region. Utilizing a robust criterion to evaluate the model's probabilities, we associated phases at different stations and pinpointed earthquake locations in the region. Our results demonstrate a significant enhancement, revealing nearly four and a half times more earthquakes than previously documented in the International Seismological Center (ISC) catalogue. A notable portion of these newly detected events falls within the category of very low-magnitude earthquakes (

Ketersediaan
299551Perpustakaan BIG (Eksternal Harddisk)Tersedia
Informasi Detail
Judul Seri
Artificial Intelligence in Geosciences
No. Panggil
551
Penerbit
Beijing : KeAi Communications Co. Ltd..,
Deskripsi Fisik
14 hlm PDF, 15.177 KB
Bahasa
Inggris
ISBN/ISSN
2666-5441
Klasifikasi
551
Tipe Isi
text
Tipe Media
-
Tipe Pembawa
-
Edisi
Vol.4, December 2023
Subjek
Deep-learning
Earthquake detection
STEAD
Hindu kush-pamir
Seismicity
Info Detail Spesifik
-
Pernyataan Tanggungjawab
Versi lain/terkait

Tidak tersedia versi lain

Lampiran Berkas
  • Enhanced crustal and intermediate seismicity in the Hindu Kush-Pamir region revealed by attentive deep learning model
    The Hindu Kush-Pamir region (HKPR) is characterized by complex ongoing deformation, unique slab geometry, and intermediate seismic activity. The availability of extensive seismological data in recent decades has prompted the use of deep learning algorithms to extract valuable insights. In this study, we present a fully automated approach for augmenting earthquake catalogue within the HKPR. Our method leverages an attention mechanism-based deep learning architecture to simultaneously detect events, perform phase picking, and estimate magnitudes. We applied this model to a ten-month dataset (January 2013–October 2013) from 83 stations in the region. Utilizing a robust criterion to evaluate the model's probabilities, we associated phases at different stations and pinpointed earthquake locations in the region. Our results demonstrate a significant enhancement, revealing nearly four and a half times more earthquakes than previously documented in the International Seismological Center (ISC) catalogue. A notable portion of these newly detected events falls within the category of very low-magnitude earthquakes (
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