global distribution of crustal material inferred by seismology
DESCRIPTION
Global Distribution of Crustal Material Inferred by Seismology. Nozomu Takeuchi (ERI, Univ of Tokyo). Importance of Directional Measurements from geophysicists’ point of view. (2) Improvements of Neutrino Flux Modeling in the seismological aspects. - PowerPoint PPT PresentationTRANSCRIPT
Global Distribution of Crustal MaterialInferred by Seismology
Nozomu Takeuchi(ERI, Univ of Tokyo)
(1) Importance of Directional Measurements from geophysicists’ point of view
(2) Improvements of Neutrino Flux Modeling in the seismological aspects
Parameters Required for Geo-neutrino Simulation = Parameters Resolved by Geo-neutrino Observation
• Earth’s Composition
• Earth’s Structure
(compositions of crust & mantle)
(distributions of crustal materials)
Approach for Retrieving Earth’s Structure
• “Geophysical Decomposition” as a tool for interpretation of the observed data
Importance of directional measurements
Prediction by High Pressure Experiments
Ringwood & Irifune (1988)
Density measurements in theupper mantle conditions
Oceanic crusts can be trapped around the 660, but finally entrained into the lower mantle.
Fate of the Oceanic Crusts (1)
Suggestion by Mantle Convection Simulation
Nakagawa & Tackley (2005)
Oceanic crusts can sink into the lowermost mantle, and accumulate at the bottom of upwelling regions.
Fate of the Oceanic Crusts (2)
Fate of the Oceanic Crusts (3)Indirect Evidence by Seismic Tomography
S velocity Bulk-sound velocity
Masters et al. (2000)
Chemical heterogeneities are suggestedat the bottom of upwelling regions.
possible accumulation of oceanic crusts
Example Classification of Geo-Neutrino Source
continental crust oceanic crust
(1) Surface Crust (2) Ambient Mantle
(3) Crust in and around Subducting Slabs
(4) Crust at the bottom of upwelling regions (LLSVPs)
detector
Can we decompose the observed flux into the above four components?
We can utilize differences in incoming directions (directivities).
dΦ (Eν ,𝐫 ′ )d Eν
=Adn (Eν )d E ν
∫ d3𝐫a (𝐫 ′ ) ρ (𝐫 ′) P (Eν ,|𝐫−𝐫 ′|)
4 π|𝐫−𝐫 ′|2
neutrino fluxat the detector (r’) decay rate= x intensity factor determined by
source distributions
(U at Eν=1.2 MeV )
Formulation by Enomoto et al. (2007)
Expected Directivity by the Surface Crust (1)
Intensity Factor from j-th Directional Bin
ΔI j=∫ΔV j
d3𝐫a (𝐫 ′ ) ρ (𝐫 ′ ) P (E ν ,|𝐫−𝐫′|)
4 π|𝐫−𝐫 ′|2
ΔV j
V
Expected Directivity by the Surface Crust (2)
N
S
EW
distance from the center bottoming radius
azimuthdirection from the center
painted color log ΔI j
Difference in Expected Directivities
+2% +1%
N
S
EW
240-290 km depth
550-630 km depth
Obayashi et al. (2009)
“Geophysical Decomposition” As an Interpretation Tool
+a3Ψ (θ ,ϕ )slab+a4Ψ (θ ,ϕ )LLSVPΨ (θ ,ϕ )obs=a1Ψ (θ ,ϕ )crust+a2Ψ (θ ,ϕ )mantle
θ ϕ: incident angle : incident azimuth
Coefficients can be determined by solving an inverse problem.
a1=a2=𝑎3=𝑎4=1reference model :
a1>1
a2>1
larger mass fraction of depleted mantle?
anomalies in bulk composition of the Earth?
a3>1entrainments of continental crust?megalith on the 660?
a4>1 enriched elements in the lowermost mantle?
(short period data)
(broadband data)
Appropriate Choice of the Tomography Models
Fukao et al. (2001)
broadband sensorshort period (high sensitivity) sensor
Type of Seismic Data
0.01-0.05 Hz
0.05-0.1 Hz
0.1-0.5 Hz
0.5-1 Hz
1-5 Hz
5-10 Hz
Usefulness of Broadband Waveforms
all frequencies
broa
dban
d da
taShort period data
Comparison of Station Coverage
200 stations 20,000 stations
short period dataBroadband data
homogeneous heterogeneous
500 km depthMasters et al. (2000)
Data Type and Obtained Tomography Models
Bijwaard et al. (1998)500 km depth
broadband data Short period data
Models Obtained by Using
: overall structures, structures beneath oceansbroadband data
short period data : detailed structures in subduction zones
Difficulties to Obtain Data-Based Crustal Models
• Too thin to resolve the global map.
• Sensitive frequency band is very “noisy”.
Recent Progresses in Seismology
• Dense broadband arrays with sufficient resolving power.
• Use of “noise” to reveal crustal structures.
• Current global model (CRUST 2.0) is not fully data-based.
Improvements in Crust Models (1)
Zheng et al. (2011)
Dense broadband arrays are beginningto reveal crustal maps
Mapping by Broadband Data
Improvements in Crust Models (2)Future Challenge
Broadband networks installed by ERI
• Use of broadband OBS data
• Data based crustal map in wide areas around Japan
Challenge to Detection of Crusts in the Mantle (1)
Station 1
Station 2
Station 3
coherentphase incoherent phase
(scattered waves)
coherent phase: sensitive to larger-scale structures
incoherent phase: sensitive to smaller-scale structures
Conventionaltomography
This Study
Challenge to Detection of Crusts in the Mantle (2)
Required Resolution Current Resolution
Use of incoherent phases may fill the gapbetween supply and demand.
Summary of The Talk
• “Geophysical Decomposition”
Importance of Directional Measurements
• Data Based Seismological Earth Models
Use of “noise” in our broadband OBS
Use of “incoherence” in seismic waveforms