Magneto-telluric (MT) data processing

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dc.contributor KenGen is
dc.contributor Jarðhitaskóli Háskóla Sameinuðu þjóðanna is
dc.contributor United Nations University is
dc.contributor United Nations University, Geothermal Training Programme is
dc.contributor.author Mwakirani, Raymond M. is
dc.date.accessioned 2020-06-30T17:54:29Z
dc.date.available 2020-06-30T17:54:29Z
dc.date.issued 2018
dc.identifier.issn 1670-794x
dc.identifier.uri http://hdl.handle.net/10802/23825
dc.description Presented at SDG Short Course III on Exploration and Development of Geothermal Resources, organized by UNU-GTP and KenGen, at Lake Bogoria and Lake Naivasha, Kenya, Nov. 7-27, 2018. is
dc.description.abstract One of the most successful ways to improve the quality of MT transfer functions estimations is to follow the principles of robust statistics. Adaptation of robust statistics to the MT data processing problem has been discussed by Egbert and Booker (1986), Chave et al. (1987) and Chave and Thomson (2003). This paper is intended to guide on the Magneto-telluric data processing. It introduces softwares applied in data processing. It details procedures used in the MT data processing with emphasis on actual steps that are followed to realise good results. is
dc.format.extent 1 rafrænt gagn (7 bls.). is
dc.language.iso en
dc.publisher United Nations University is
dc.relation.ispartof 001578070
dc.relation.ispartofseries United Nations University., UNU Geothermal Training Programme, Iceland. Short Course ; SC-27
dc.relation.uri https://orkustofnun.is/gogn/unu-gtp-sc/UNU-GTP-SC-27-0402.pdf
dc.subject Jarðfræði is
dc.subject Jarðeðlisfræði is
dc.subject Jarðhitaleit is
dc.title Magneto-telluric (MT) data processing en
dc.type Bók is
dc.identifier.gegnir 001578303


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