<?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>Margaret Elizabeth Glasgow</dc:contributor>
  <dc:contributor>Elizabeth S. Cochran</dc:contributor>
  <dc:contributor>Zhigang Peng</dc:contributor>
  <dc:creator>Sean Maher</dc:creator>
  <dc:date>2025</dc:date>
  <dc:description>&lt;p&gt;As seismic data are increasingly used to investigate a diverse range of subsurface phenomena beyond regular fast-rupturing earthquakes (Peng and Gomberg, 2010; Beroza and Ide, 2011), it is important to acknowledge that human-generated ground vibrations may be mistaken for naturally generated subsurface processes (Larose et al., 2015; Li et al., 2018). Correct discrimination of natural processes from anthropogenic noise is especially pressing given the trend in seismic detection research toward automated algorithms and machine learning methods (Yoon et al., 2015; Kong et al., 2019;Mousavi and Beroza, 2022) and the growth in seismic data collection in new environments such as urban and industry settings (e.g., Díaz et al.,2017).&lt;/p&gt;</dc:description>
  <dc:format>application/pdf</dc:format>
  <dc:identifier>10.1785/0220240330</dc:identifier>
  <dc:language>en</dc:language>
  <dc:publisher>Seismological Society of America</dc:publisher>
  <dc:title>Distinguishing natural sources from anthropogenic events in seismic data</dc:title>
  <dc:type>article</dc:type>
</oai_dc:dc>