Download Western Balkans Moho depth and crustal

WESTERN BALKANS MOHO DEPTH
AND CRUSTAL STRUCTURE
EXPLOITING GOCE DATA
D. SAMPIETRO(1), M. REGUZZONI(1,2)
(1) GReD s.r.l. – Geomatics Research &
(2) Department of Civil and
Development
Environmental Engineering – Politecnico
Spin-off del Politecnico di Milano
di Milano
http://www.g-red.eu
[email protected] GOCE user workshop– Paris, 26/11/2014
http:// http://www.dica.polimi.it/
5° International
THE WESTERN BALKAN AREA
While the crustal structure of the
Pannonian, Transilvanian, Adriatic, and
Carpathian basins is reasonably well know
thanks to the exploration of oil companies the
Dinarides and the surrounding areas suffer
from lack of measurements, which results in
high uncertainties in the estimations of the
Moho depths.
Well known provinces
Raykova, R., & Nikolova, S. (2007). Studia
Geophysica et Geodaetica, 51(1), 165-184.
In the present work we will study
the Moho depth of the Western
Balkan Region by exploiting GOCE
data.
5° International GOCE user workshop– Paris, 26/11/2014
THE INVERSION PROCEDURE
Sediment,
ocean, and
ice models
GOCE
data
Crystalline
crust model
Data
stripping
Reduced
data
Mantle
effect
DATA REDUCTION
Geological
provinces
class.
The Inversion procedure is
composed by two main steps
the data reduction and the
inversion of the residual field.
GOCE data are used in both
steps:
• In the former they are used
to classify the area in
geological
homogeneous
patches;
Seismic
combination
Linearizati
on point
INVERSION
Δρ
Inversion
operator
Density
functions
• In the latter they are used as
observation to apply the
inversion algorithm.
D
ρ
Convergence
?
yes
ρ
CRUSTAL
no
MODEL
D
5° International GOCE user workshop– Paris, 26/11/2014
STEP 1: DATA REDUCTION
Removed with geometry from
ETOPO1 and fixed density.
Removed with geometry from
ETOPO1 and fixed density.
Crystalline crust represents the
most important contribute. Its
density defines the gravitational
effect to be removed and the
density contrast at the Moho
discontinuity.
Unknown
geometry,
density from crystalline
crust and upper mantle
models.
Removed with geometry defined by the mean
Moho depth and density from GyPSuM model
(Simmons et al. Journal of Geophysical
Research: Solid Earth 2010)
Removed with geometry
and density from Laske &
Masters, EOS Trans. AGU,
78, F483, 1997).
5° International GOCE user workshop– Paris, 26/11/2014
THE IMPORTANCE OF GEOLOGICAL PROVINCES
FOR MOHO DEPTH DETERMINATION
𝜌=𝑎+
b
𝑉𝑝
Christensen and Mooney, J GEOPHYS RES, 100(B7), PP.
9768, JUNE 10, 1995.
Note that the definition of the
geological provinces is crucial to
correctly reduce the data and invert
the residual signal.
5° International GOCE user workshop– Paris, 26/11/2014
THE IMPORTANCE OF GEOLOGICAL PROVINCES
FOR MOHO DEPTH DETERMINATION
[KM]
GEMMA1.0[1]
[1] M. Reguzzoni, D. Sampietro. GEMMA: An Earth crustal model based on GOCE satellite data,
Int J Appl Earth Obs, doi:10.1016/j.jag.2014.04.002
5° International GOCE user workshop– Paris, 26/11/2014
BAYESIAN ESTIMATION OF
GEOLOGICAL PROVINCES BOUNDARIES
We suppose to have a rough geological
provinces model and that the geological
province Gi of a pixel i can be either the apriori one or the ones of its neighborhood
Δi.
THE PRIOR probability of Gi is computed by
defining a weight-matrix W, e.g. the
probability that the geological province Gi is
G1 is equal to:
P  Gi  G1  

j i ,i 
Geologic Province and Thermo-Tectonic
Age Maps (Exxon, Technical Report, 1995) .
 1jW j
where:
  1j  1
G j  G1
if
 1
1


0
G

G
j
 j
e.g.
P  Gi1   2   
P  Gi2     2   3
5° International GOCE user workshop– Paris, 26/11/2014
BAYESIAN ESTIMATION OF
GEOLOGICAL PROVINCES BOUNDARIES
THE LIKELIHOOD is defined by supposing an isostatic Moho depth (Airy model)
di  hi c ,i  m,i  c ,i 
1
in planar approximation (with hi topography) and considering at each pixel the gravitational
effect of a Bouguer plate of thickness ti  hi  di , so that:
 gi  2c ,iti .
Under these assumptions an approximated relation between ρc,i and δgi holds:
𝜌𝑐,𝑖 = 𝑓 𝛿𝑔𝑖
The likelihood is supposed to be normally distributed with a mean given by 𝝁𝜌𝑐 𝑮𝓵
and a standard deviation 𝝈𝜌𝑐 𝑮𝓵
Where 𝜇𝜌𝑐 𝐺 𝓁 is the mean value of 𝜌𝑐,𝑖 𝐺 𝓁 computed for the geological province 𝐺 𝓁
and 𝜎𝜌𝑐 𝐺 𝓁 is its standard deviation.
5° International GOCE user workshop– Paris, 26/11/2014
BAYESIAN ESTIMATION OF
GEOLOGICAL PROVINCES BOUNDARIES
𝜌𝑐,𝑖
[kg/m3]
GOCE gravity disturbances
𝛿𝑔
Approximated density variation used for
the Bayesian classification
5° International GOCE user workshop– Paris, 26/11/2014
[m/s2]
BAYESIAN ESTIMATION OF
GEOLOGICAL PROVINCES BOUNDARIES
THE POSTERIOR distribution of Gi is computed by applying the well known Bayes theorem.
Finally the geological province of the pixel is chosen by maximizing the posterior
distribution (MAP).
INITIAL MODEL
ADJUSTED MODEL
MAX OF THE POSTERIOR PROBABILITY
Bada et al. (1998). Geophysical Journal
International, 134(1), 87-101.
The algorithm allows
to compute also the
probability that a
pixel belongs to a
geological province
5° International GOCE user workshop– Paris, 26/11/2014
STEP 2: INVERSION
Basically the solution is based on the same procedure developed to
compute the GEMMA1.0 global model but the global inversion in
terms of spherical harmonic is here substituted by the regional
inversion in terms of Wiener deconvolution.
The Inversion procedure allows:
- to estimate the mean Moho depth even once the normal field is
removed;
- to take into account the crustal density variation in the radial
direction;
- to correct the a-priori density for scale factors;
5° International GOCE user workshop– Paris, 26/11/2014
STEP 2: INVERSION
Reduced
GOCE data
GEMMA1.0
model
Local seismic
model
Signal Power
spectrum
Average Moho
depth for each
geological
province
Linearization
𝛿𝑔 𝑥 =
𝑘 𝑥 − 𝜉 𝛿𝐷 𝜉 Δ𝜌 𝜉 𝑑𝜉
𝑘=𝑘 𝐷
Crustal
model
Fast Fourier Transform
Wiener Filter
no
Δρ, δD
Seismic combination
Convergence
?
Δρ
yes
Final
model
5° International GOCE user workshop– Paris, 26/11/2014
RESULTS
LOCAL MOHO MODEL FROM SEISMIC OBSERVATIONS
CRUST 1.0
LOCAL SOLUTION
ESC MOHO
GEMMA1.0
5° International GOCE user workshop– Paris, 26/11/2014
[KM]
RESULTS
LOCAL MOHO MODEL FROM SEISMIC OBSERVATIONS
CRUST 1.0
LOCAL SOLUTION
Difference
Mean
[km]
Std
[km]
Crust 1.0
-2.3
2.6
ESC
-5.3
2.9
GEMMA 1.0
-0.1
2.5
Local Solution
-0.7
0.9
ESC MOHO
GEMMA1.0
5° International GOCE user workshop– Paris, 26/11/2014
[KM]
CONCLUSIONS
In the present work an algorithm to refine the shape of the main geological
provinces driven by GOCE data in a Bayesian scheme has been studied and
implemented.
First tests (on real data) seem to prove the reliability of this Bayesian method thus
encouraging possible applications of GOCE observations in this field.
An improved procedure to estimate Moho depth from GOCE data at local scale
has been studied and implemented.
Results in terms of geological provinces modelling and Moho depth estimation
shown the reliability of the method giving results comparable with those
obtained from seismic profiles.
The use of gravity gradients can improve the geological provinces modelling. This
should be investigated in future studies
5° International GOCE user workshop– Paris, 26/11/2014
THANKS
FOR
YOUR ATTENTION
GReD s.r.l. – Geomatics Research &
Department of Civil and
Development
Environmental Engineering – Politecnico
Spin-off del Politecnico di Milano
di Milano
http://www.g-red.eu
[email protected] GOCE user workshop– Paris, 26/11/2014
http:// http://www.dica.polimi.it/
5° International