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          <full_title>Seismological Research Letters</full_title>
          <issn media_type="print">0895-0695</issn>
          <issn media_type="electronic">1938-2057</issn>
        </journal_metadata>
        <journal_issue>
          <publication_date media_type="online">
            <month>12</month>
            <day>19</day>
            <year>2018</year>
          </publication_date>
          <publication_date media_type="print">
            <month>03</month>
            <year>2019</year>
          </publication_date>
          <journal_volume>
            <volume>90</volume>
          </journal_volume>
          <issue>2A</issue>
        </journal_issue>
        <journal_article publication_type="full_text">
          <titles>
            <title>A Deep Convolutional Neural Network for Localization of Clustered Earthquakes Based on Multistation Full Waveforms</title>
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            <person_name sequence="first" contributor_role="author">
              <given_name>Marius</given_name>
              <surname>Kriegerowski</surname>
              <affiliation>University of Potsdam, Institut für Erd‐ und Umweltwissenschaften, Karl‐Liebknecht‐Str. 24‐25, 14476 Potsdam, Germany, kriegero@uni-potsdam.de, hvasbath@uni-potsdam.de, mao@geo.uni-potsdam.de</affiliation>
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            <person_name sequence="additional" contributor_role="author">
              <given_name>Gesa M.</given_name>
              <surname>Petersen</surname>
              <affiliation>Helmholtz Centre Potsdam German Research Centre for Geosciences ‐ GFZ, Telegrafenberg, 14473 Potsdam, Germany, gesap@gfz-potsdam.de</affiliation>
              <affiliation>Also at University of Potsdam, Institut für Erd‐ und Umweltwissenschaften, Karl‐Liebknecht‐Str. 24‐25, 14476 Potsdam, Germany.</affiliation>
            </person_name>
            <person_name sequence="additional" contributor_role="author">
              <given_name>Hannes</given_name>
              <surname>Vasyura‐Bathke</surname>
              <affiliation>University of Potsdam, Institut für Erd‐ und Umweltwissenschaften, Karl‐Liebknecht‐Str. 24‐25, 14476 Potsdam, Germany, kriegero@uni-potsdam.de, hvasbath@uni-potsdam.de, mao@geo.uni-potsdam.de</affiliation>
            </person_name>
            <person_name sequence="additional" contributor_role="author">
              <given_name>Matthias</given_name>
              <surname>Ohrnberger</surname>
              <affiliation>University of Potsdam, Institut für Erd‐ und Umweltwissenschaften, Karl‐Liebknecht‐Str. 24‐25, 14476 Potsdam, Germany, kriegero@uni-potsdam.de, hvasbath@uni-potsdam.de, mao@geo.uni-potsdam.de</affiliation>
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            <month>12</month>
            <day>19</day>
            <year>2018</year>
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            <month>03</month>
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          <pages>
            <first_page>510</first_page>
            <last_page>516</last_page>
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          <doi_data>
            <doi>10.1785/0220180320</doi>
            <resource>https://pubs.geoscienceworld.org/ssa/srl/article/90/2A/510/567690/A-Deep-Convolutional-Neural-Network-for</resource>
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