<?xml version='1.0' encoding='utf-8'?>
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:contributor>Michael W. Asten</dc:contributor>
  <dc:contributor>William J. Stephenson</dc:contributor>
  <dc:contributor>Cecile Cornou</dc:contributor>
  <dc:contributor>Manuel Hobiger</dc:contributor>
  <dc:contributor>Marco Pilz</dc:contributor>
  <dc:contributor>Hiroaki Yamanaka</dc:contributor>
  <dc:creator>Koichi Hayashi</dc:creator>
  <dc:date>2022</dc:date>
  <dc:description>Microtremor array measurements (MAM) and passive surface wave methods in general, have been increasingly used to non-invasively estimate shear-wave velocity structures (Vs) for various purposes. The methods estimate dispersion curves and invert them for retrieving S-wave velocity profiles. This paper summarizes principles, limitations, data collection and processing methods. It intends to enable students and practitioners to understand the principles needed to plan a microtremor array investigation, record and process the data, and evaluate the quality of investigation result. The paper focuses on the spatial autocorrelation (SPAC) processing method among microtremor array processing methods because of its relatively simple calculation and stable applicability.</dc:description>
  <dc:format>application/pdf</dc:format>
  <dc:identifier>10.1007/s10950-021-10051-y</dc:identifier>
  <dc:language>en</dc:language>
  <dc:publisher>Springer</dc:publisher>
  <dc:title>Microtremor array method using spatial autocorrelation  analysis of Rayleigh‑wave data</dc:title>
  <dc:type>article</dc:type>
</oai_dc:dc>