3d gravity inversion incorporating prior information through an adaptive learning procedure
DESCRIPTION
3D gravity inversion incorporating prior information through an adaptive learning procedure. Fernando J. S. Silva Dias Valéria C. F. Barbosa National Observatory João B. C. Silva Federal University of Pará. Content. Introduction and Objective. Methodology. - PowerPoint PPT PresentationTRANSCRIPT
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3D gravity inversion incorporating
prior information through an
adaptive learning procedure Fernando J. S. Silva Dias
Valéria C. F. Barbosa National Observatory
João B. C. SilvaFederal University of Pará
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Content• Introduction and Objective
• Methodology
• Real Data Inversion Result
• Conclusions
• Synthetic Data Inversion Results
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To estimate 3D source geometries that may
give rise to interfering gravity anomalies.
Introduction and Objective
Objective
Methods that estimate 3D density-contrast distributions:
• Bear et al. (1995)
• Li and Oldenburg (1998)
• Portniaguine and Zhdanov (1999)
• Zhdanov et al. (2004) density contrast (g/cm3)
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Methodology
• Forward modeling of gravity anomalies
• Inverse Problem
• Adaptive Learning Procedure
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Gravity anomaly
x
y
z
3D gravity sources
Source Region
Forward modeling of gravity anomalies
y
xD
epth
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y
z
Dep
th
x
Source Region
dy
dzdx
The source region is divided into an mx × my× mz grid
of M 3D vertical juxtaposed prisms
Forward modeling of gravity anomalies
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x
Observed gravity anomaly
y
z
Dep
th
Source Region
To estimate the 3D density-contrast distribution
y
x
Forward modeling of gravity anomalies
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The vertical component of the gravity field produced by the
density-contrast distribution (r’):
)(g ir )'(rV
i
''
'3
i dvzz
rr
Methodology
The discrete forward modeling operator for the gravity anomaly can be expressed by:
g A p
''
')( 3
jVi
iiij dv
zzA
rrr
where
Steiner (1978)
(N x 1) (M x 1)(NxM)
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Methodology
2 Ago 1
Ng
The unconstrained Inverse Problem
The linear inverse problem can be formulated by
minimizing
The problem of obtaining a vector of parameter
estimates, p , that minimizes this functional is an
ill-posed problem.
^
p
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x
y
z Source Region
Dep
thMethodology
Concentration of mass excess about NE specified
geometric elements (axes and points)
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MethodologyIterative inversion method that:
• The density-contrast distribution must assume just two known
density contrast values: zero
• The estimated nonnull density contrast must be concentrated
about a set of NE geometric elements (axes and points)
fits the gravity data
satisfies two constraints:
or a nonnull value.
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The method estimates iteratively the constrained
parameter correction Δp by
Minimizing
Subject to
Methodology
Δp2 )( k
W )( k1/2
p
and updates the density-contrast estimates by
2 Ago 1
NΔp )(po +
)( k )( k
)()()1( ˆˆ kkk pΔpp o
≡
)(
3
ˆ k-1j
jjj
p
dwWp
)( k1/2 )( k1/2
={ }Prior reference vector
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}{min1 N
jdE
d j
MjNzezyeyxd Ejjj ,,1,,,1)()((2/1222
xe )j
Methodology
z
y
x
xe
)
ye, , ze)
jd
The method defines dj as the
distance from the center of the
j th prism to the
closest geometric elementclosest geometric element
d j
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Mjdptargetj ,...,1},{min arg, j
**
En1
}{min1
jN
j ddE
z
y
x
xe
)
ye, , ze )
MethodologyThe method assigns to the jth prism the target density contrast
of the geometric element closest to the jth prism
d j
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Methodology
z
x
axisaxis
point
jy
jd
g/cm3 g/cm3
g/cm3
At the first iteration:
• Initial interpretation model
• First-guess geometric elements• The corresponding target density
contrasts
Static Geologic Reference Model
pj targetg/cm3
jd andj
p targetThe method assigns to each prism a pair of:
.
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Methodology
Penalization Algorithm:
)(ˆ kjp
jp target
0 (g/cm3)
jp target 0 (g/cm3)
• For positive target density contrast
• For negative target density contrast
)(ˆ kjp
)(ˆ kjp )(ˆ k
jp
jjwp
)( k1/2
=
target
jp or 0 (g/cm3)
)(ˆ kpΔ)(kp o )1(ˆ kp
( k )
op
j
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Methodology
Penalization Algorithm:
jp target
0 (g/cm3)
jp target
0 (g/cm3)
• For positive target density contrast
• For negative target density contrast
)(ˆ kjp
)()()1( ˆˆ kkk pΔpp o
pjtarget
2
pjtarget
2
)(ˆ kjp)(ˆ k
jp
)(ˆ kjp
target
jp( k )
op
j
( k )
op
j
0 (g/cm3)
)(
3
ˆ k-1
j
j
jj p
dwp
)( k1/2
=)(ˆ k
jp
)(ˆ kjp
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The choice of the interpretation model
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noise-corrupted gravity anomaly
geometric element
The choice of the interpretation model
True source
target = 0.4 g/cm3. = 0.4 g/cm3.
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x(km)-6 -4 -2 0 2 4 6 8 10 12
-6
-4
-2
0
2
4
6
8
10
12
y(k
m)
True source
Fitted anomaly
Rough interpretation model: 4×4×4 grid of 3D prisms
First
-6 -4 -2 0 2 4 6 8 10 12-6
-4
-2
0
2
4
6
8
10
12
x(km)
y(k
m)
True source
0.200.00 0.10 0.30 0.40density contrast (g/cm3)
density contrast (g/cm3)0.200.00 0.10 0.30 0.40
Rough interpretation model: 5×5×5 grid of 3D prisms
Fitted anomaly
Second
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True source
Refined interpretation model: 24×24×24 grid of 3D prisms
Fourth
0.200.00 0.10 0.30 0.40density contrast (g/cm3)
Fitted anomaly
True source
Refined interpretation model: 12×12×12 grid of 3D prisms
Third
Fitted anomaly
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Adaptive Learning Procedure
• New interpretation model
• New geometric elements
• Associated target density contrasts
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Adaptive Learning Procedure x
y
zSource Region
First Iteration
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Adaptive Learning Procedure
x
y
z Each 3D prism is divided
Second Iteration
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Adaptive Learning Procedure
x
y
z
Iteration
Iteration
Iteration
Iteration
New interpretation model
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static geologic
reference model
x
y
z
First Iteration
Second Iteration
New geometric elements (points) and associated target density contrasts
Dynamic geologic reference model
Adaptive Learning Procedure First interpretation model and the static geologic reference model
First density-contrast distribution estimate
New interpretation model
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Adaptive Learning Procedure
Static geologic reference model
target = 0.4 g/cm3.
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Adaptive Learning Procedure Fourth iteration
0.200.00 0.10 0.30density contrast (g/cm3)
0.40
Without using the adaptive learning procedure
Both interpretation models consist of 24×24×24 grid of 3D prisms
0.200.00 0.10 0.30density contrast (g/cm3)
0.40
True source
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Inversions of Synthetic Data
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Large source surrounding a small source
-2 -1 0 1 2 3 4
y (km)
-2
-1
1
2
3
4
0
x (
km)
density contrast (g/cm3)density contrast (g/cm3)
Anorthosite ( 0.4 g/m3 )
Granite ( 0.2 g/cm3 )
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Large source surrounding a small sourceThe red dots are the first-guess skeletal outlines:
static geologic reference model
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Large source surrounding a small sourceFirst iteration
Interpretation model: 4×3×3 grid of 3D prisms.
density contrast (g/cm3)
-2 -1 0 1 2 3 4
Y (km)
-2
-1
0
1
2
3
4
X (
km)
Fitted anomaly
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Large source surrounding a small sourceFourth iteration
interpretation model: 32×24×24 grid of 3D prisms.
-2 -1 0 1 2 3 4
Y (km)
-2
-1
0
1
2
3
4
X (
km)
Fitted anomaly density contrast (g/cm3)
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density contrast (g/cm3)
Multiple buried sources at different depths
0.15 g/cm3
0.3g/cm3
0.4 g/cm3
The axes are the first-guess skeletal outlines: static geologic reference model
density contrast (g/cm3)
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Multiple buried sources at different depths
Third iteration Interpretation model: 28×48×24 grid of 3D prisms.
density contrast (g/cm3)Fitted
anomaly
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Real Gravity DataRedenção Granite
(Brazil)
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The Amazon Craton in northern Brazil, within the Archean Greenstone unit,
comprising a part of the Carajás metallogenic province.
Localization and Geological Setting
Oliveira et al. (2007)
Brazil
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Geologic Map of the Redenção Area
SRTM / Gamma Thorium
Oliveira et al. (2007)
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The red dots are the first-guess skeletal outlinesstatic reference model
Redenção Granite
The associated target density contrasts are: -0.09 g/cm3 or -0.12 g/cm3
-0.09 g/cm3
-0.12 g/cm3
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Redenção Granite
density contrast (g/cm3)
Fitted anomaly
Fourth iteration Interpretation model: 64×72×32 grid of 3D prisms.
Dynamic Geometric Elements
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Conclusions
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3D gravity inversion incorporating prior information through an adaptive learning procedure
Estimates 3D source geometries that may give rise to an interfering gravity anomaly
Concentrates the largest density contrast estimates about first-guess skeletal outlines
Creates new skeletal outlines and a new refined interpretation model
Makes it possible to reconstruct a sharp image of multiple and closely located gravity sources.
The proposed method:
density contrast (g/cm3)
first-guess skeletal outlines
density contrast (g/cm3)
density contrast (g/cm3)
New skeletal
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Thank You I cordially invite
you to attend the upcoming
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Extra Figures
1 CPU ATHLON with one core and 2.4 GHertz and 1 MB of cache L22GB of DDR1 memory
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= 0.4 g/cm3.
Isolated gravity
anomalies
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density contrast (g/cm3)
Li and Oldenburg (1998)
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density contrast (g/cm3)
Portniaguine and Zhdanov (2002)
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density contrast (g/cm3)
Our gravity inversion
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Interfering gravity
anomalies
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density contrast (g/cm3)
Li and Oldenburg (1998)
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density contrast (g/cm3)
Portniaguine and Zhdanov (2002)
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density contrast (g/cm3)
Our gravity inversion
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MethodologyThe Iterative Constrained Inverse Problem
Starting from the minimum-norm solution
ogIAAAp 1o ) (ˆ TT
we look, at the kth iteration, for a constrained parameter correction
ˆˆˆ )(
o pΔpp ) 1( k k )( k
and update the density-contrast distribution estimate by
)(11)( ˆ)(ˆ k
o
T
)(k
T
)(k
kpAgIAA WAWpΔ
o 1
,