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SUMMARY:Crust modeling with quantitative and objective uncertainty estimat
ion
DTSTART;VALUE=DATE-TIME:20191021T130000Z
DTEND;VALUE=DATE-TIME:20191021T132000Z
DTSTAMP;VALUE=DATE-TIME:20210618T205200Z
UID:indico-contribution-3547724@indico.cern.ch
DESCRIPTION:Speakers: Sanshiro Enomoto (University of Washington)\, Tsuyos
hi Iizuka (University of Tokyo)\, Kenta Ueki (JAMSTEC)\, Nozomu Takeuchi (
University of Tokyo)\nGeoneutrino observations\, first achieved by KamLAND
in 2005 and followed by Borexino in 2010\, have accumulated statistics an
d improved sensitivity for more than ten years. The uncertainty of the geo
neutrino flux at the surface is now reduced to a level small enough to set
useful constraints on U and Th abundances in the bulk silicate earth (BSE
). However\, in order to make inferences on earth’s compositional model\
, the contributions from the local crust need to be understood within a si
milar uncertainty. Here we develop a new method to construct a stochastic
crustal composition model utilizing Bayesian inference. While the methodol
ogy has general applicability\, it incorporates all the local uniqueness i
n its probabilistic framework.\n\nIn our method\, we consistently use expl
icit PDFs for all relevant quantities. We utilize Bayesian inference techn
iques to model the 3-D lithology map by combining seismological data as
“observation” with a prior model constructed from local exposure. By u
sing seismological tomography\, we avoid the difficulty of dealing with th
e upper / middle / lower crust classification and boundary definition. For
rock composition\, we adopt a gamma distribution model\, which does not b
ias the mean value estimation (unlike the log-normal model) and fits consi
stently well to both highly-skewed and close-to-normal distributions (for
which neither log-normal nor normal distributions apply). Convolving the o
btained PDFs of lithology distribution map and rock composition\, we const
ruct 3-D PDFs of U and Th concentrations.\n\nAt the time of the presentati
on\, after showing the flow chart of our modeling method\, we will discuss
the key features of our lithology map inference. Our lithology model repr
esents a probabilistic distribution map and allows quantitative studies wi
th error estimations\, making it fundamentally different from previous mod
els. Note that the probabilistic representation allows us to construct pro
bability density functions and thus errors of various physical quantities
(such as abundance of radioactive elements\, total mass of the crust\, and
geoneutrino flux) evaluated from the lithology distribution model\, while
the deterministic statements only allow estimation of central values. We
also discuss the plausibility of our prior and the obtained posterior toge
ther with future topics for further improvements.\nhttps://indico.cern.ch/
event/825708/contributions/3547724/
LOCATION:Room 319 (3rd floor) (Conference center of the Czech Association
of Scientific and Technical Societies)
RELATED-TO:indico-event-825708@indico.cern.ch
URL:https://indico.cern.ch/event/825708/contributions/3547724/
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