Mode switching in volcanic seismicity: El Hierro 2011–2013
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- 作者：Nick S. Roberts ; Andrew F. Bell ; Ian G. Main
- 刊名：Geophysical Research Letters
- 出版年：2016
- 出版时间：16 May 2016
- 年：2016
- 卷：43
- 期：9
- 页码：4288-4296
- 全文大小：2.8MB
- ISSN：1944-8007
文摘
The Gutenberg-Richter b value is commonly used in volcanic eruption forecasting to infer material or mechanical properties from earthquake distributions. Such studies typically analyze discrete time windows or phases, but the choice of such windows is subjective and can introduce significant bias. Here we minimize this sample bias by iteratively sampling catalogs with randomly chosen windows and then stack the resulting probability density functions for the estimated lass="math-equation-construct">lass="math-equation-image">l="true" class="math-equation-mathml" style="display:none">l:math xmlns:mml="http://www.w3.org/1998/Math/MathML">lns:w="http://www.wiley.com/namespaces/wiley" xmlns:wiley="http://www.wiley.com/namespaces/wiley/wiley" xmlns:cr="urn://wiley-online-library/content/render" xmlns="http://www.w3.org/1998/Math/MathML" accent="true">b ˜ l:math> value to determine a net probability density function. We examine data from the El Hierro seismic catalog during a period of unrest in 2011–2013 and demonstrate clear multimodal behavior. Individual modes are relatively stable in time, but the most probable lass="math-equation-construct">lass="math-equation-image">l="true" class="math-equation-mathml" style="display:none">l:math xmlns:mml="http://www.w3.org/1998/Math/MathML">lns:w="http://www.wiley.com/namespaces/wiley" xmlns:wiley="http://www.wiley.com/namespaces/wiley/wiley" xmlns:cr="urn://wiley-online-library/content/render" xmlns="http://www.w3.org/1998/Math/MathML" accent="true">b ˜ l:math> value intermittently switches between modes, one of which is similar to that of tectonic seismicity. Multimodality is primarily associated with intermittent activation and cessation of activity in different parts of the volcanic system rather than with respect to any systematic inferred underlying process.