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Moho interface modeling beneath the himalayas, tibet and central siberia using GOCO02S and DTM2006.0
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
2013 (English)In: Terrestrial, Atmospheric and Oceanic Science, ISSN 1017-0839, E-ISSN 2223-8964, Vol. 24, no 4 PART1, 581-590 p.Article in journal (Refereed) Published
Abstract [en]

We apply a newly developed method to estimate the Moho depths and density contrast beneath the Himalayas, Tibet and Central Siberia. This method utilizes the combined least-squares approach based on solving the inverse problem of isostasy and using the constraining information from the seismic global crustal model (CRUST2.0). The gravimetric forward modeling is applied to compute the isostatic gravity anomalies using the global geopotential model (GOCO02S) and the global topographic/ bathymetric model (DTM2006.0). The estimated Moho depths vary between 60 - 70 km beneath most of the Himalayas and Tibet and reach the maxima of ~79 km. The Moho depth under Central Siberia is typically 50 - 60 km. The Moho density contrast computed relative to the CRUST2.0 lower crustal densities has the maxima of ~300 kg m-3 under Central Tibet. It substantially decreases to 150 - 250 kg m-3 under Himalayas and north Tibet. The estimated Moho density contrast under central Siberia is within 100 - 200 kg m-3.

Place, publisher, year, edition, pages
2013. Vol. 24, no 4 PART1, 581-590 p.
Keyword [en]
Crust, Gravity, Himalaya, Isostasy, Moho interface, Tibet plateau
National Category
Other Earth and Related Environmental Sciences
Identifiers
URN: urn:nbn:se:kth:diva-124885ISI: 000329894700011Scopus ID: 2-s2.0-84880009086OAI: oai:DiVA.org:kth-124885DiVA: diva2:638700
Note

QC 20130801

Available from: 2013-08-01 Created: 2013-08-01 Last updated: 2017-04-28Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
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  • modern-language-association-8th-edition
  • vancouver
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  • de-DE
  • en-GB
  • en-US
  • fi-FI
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  • nn-NB
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Output format
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