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Yin, An, Xie, Zhoumin, Meng, Lingsen (2018) A viscoplastic shear-zone model for deep (15–50 km) slow-slip events at plate convergent margins. Earth and Planetary Science Letters, 491. 81-94 doi:10.1016/j.epsl.2018.02.042

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Reference TypeJournal (article/letter/editorial)
TitleA viscoplastic shear-zone model for deep (15–50 km) slow-slip events at plate convergent margins
JournalEarth and Planetary Science Letters
AuthorsYin, AnAuthor
Xie, ZhouminAuthor
Meng, LingsenAuthor
Year2018 (June)Volume491
PublisherElsevier BV
DOIdoi:10.1016/j.epsl.2018.02.042Search in ResearchGate
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Mindat Ref. ID391982Long-form Identifiermindat:1:5:391982:2
GUID0
Full ReferenceYin, An, Xie, Zhoumin, Meng, Lingsen (2018) A viscoplastic shear-zone model for deep (15–50 km) slow-slip events at plate convergent margins. Earth and Planetary Science Letters, 491. 81-94 doi:10.1016/j.epsl.2018.02.042
Plain TextYin, An, Xie, Zhoumin, Meng, Lingsen (2018) A viscoplastic shear-zone model for deep (15–50 km) slow-slip events at plate convergent margins. Earth and Planetary Science Letters, 491. 81-94 doi:10.1016/j.epsl.2018.02.042
In(2018) Earth and Planetary Science Letters Vol. 491. Elsevier BV

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Ochi, Tadafumi (2015) Temporal change in plate coupling and long-term slow slip events in southwestern Japan. Earth and Planetary Science Letters, 431. 8-14 doi:10.1016/j.epsl.2015.09.012
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Guo, Shun, Hermann, Joerg, Tang, Pan, Chu, Xu, Chen, Yi, Su, Bin (2022) Formation of carbon-bearing silicate melts by melt-metacarbonate interaction at convergent plate margins. Earth and Planetary Science Letters, 597. 117816 doi:10.1016/j.epsl.2022.117816
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Meng, Lingsen, Huang, Hui, Bürgmann, Roland, Ampuero, Jean Paul, Strader, Anne (2015) Dual megathrust slip behaviors of the 2014 Iquique earthquake sequence. Earth and Planetary Science Letters, 411. 177-187 doi:10.1016/j.epsl.2014.11.041
He, Bing, Wei, XiaoZhuo, Wei, Meng, Shen, Yang, Alvarez, Marco, Schwartz, Susan Y. (2023) A shallow slow slip event in 2018 in the Semidi segment of the Alaska subduction zone detected by machine learning. Earth and Planetary Science Letters, 612. 118154 doi:10.1016/j.epsl.2023.118154
 
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