Strong-Earthquake-Prone Areas Recognition Based on an Algorithm with a Single Pure Training Class: I. Altai–Sayan–Baikal Region, М ≥ 6.0


如何引用文章

全文:

开放存取 开放存取
受限制的访问 ##reader.subscriptionAccessGranted##
受限制的访问 订阅存取

详细

A new version of the Barrier algorithm is proposed for recognition of strong-earthquake prone regions based on training over a single reliable training class. The modification of the algorithm consists in creating blocks that reveal the geological–geophysical features (attributes) characteristic of the recognized highly seismic objects and provide their quantitative estimates. The recognition of the areas prone to earthquakes with M ≥ 6.0 is carried out for the Altai–Sayan–Baikal region. The results of the recognition are used for assessing the effect of the remote earthquakes that occurred in the Altai–Sayan orogenic region on the stability of structural-tectonic crustal blocks in the contact zone of the West Siberian platform and the Siberian plate.

作者简介

B. Dzeboev

Geophysical Center, Russian Academy of Sciences; Geophysical Institute, Vladikavkaz Scientific Center, Russian Academy of Sciences

编辑信件的主要联系方式.
Email: b.dzeboev@gcras.ru
俄罗斯联邦, Moscow, 119296; Vladikavkaz, 362002

A. Gvishiani

Geophysical Center, Russian Academy of Sciences; Schmidt Institute of Physics of the Earth, Russian Academy of Sciences

Email: b.dzeboev@gcras.ru
俄罗斯联邦, Moscow, 119296; Moscow, 123242

I. Belov

Geophysical Center, Russian Academy of Sciences

Email: b.dzeboev@gcras.ru
俄罗斯联邦, Moscow, 119296

S. Agayan

Geophysical Center, Russian Academy of Sciences

Email: b.dzeboev@gcras.ru
俄罗斯联邦, Moscow, 119296

V. Tatarinov

Geophysical Center, Russian Academy of Sciences; Schmidt Institute of Physics of the Earth, Russian Academy of Sciences

Email: b.dzeboev@gcras.ru
俄罗斯联邦, Moscow, 119296; Moscow, 123242

Yu. Barykina

Geophysical Center, Russian Academy of Sciences

Email: b.dzeboev@gcras.ru
俄罗斯联邦, Moscow, 119296

补充文件

附件文件
动作
1. JATS XML

版权所有 © Pleiades Publishing, Ltd., 2019