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Adaptive learning gravity inversion for 3D salt body imaging. Fernando J. S. Silva Dias Valéria C. F. Barbosa National Observatory. João B. C. Silva Federal University of Pará. Content. Introduction and Objective. Methodology. Synthetic Data Inversion Result.
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Adaptive learning gravity inversion for 3D salt body imaging
Fernando J. S. Silva Dias
Valéria C. F. Barbosa
National Observatory
João B. C. Silva
Federal University of Pará
Content
Introduction
Seismic and gravity data are combined to interpret salt bodies
Brazilian sedimentary basin
Introduction
It is much harder to “see” what lies beneath salt bodies.
Where is the base of the salt body ?
Top of the salt body
Objective
Methods that reconstruct 3D (or 2D) salt bodies from gravity data
Interactive gravity forward modeling:
Starich et al. (1994)
Yarger et al. (2001)
Oezsen (2004)
Huston et al. (2004)
Gravity inversion methods
Jorgensen and Kisabeth (2000)
Bear et al. (1995)
Moraes and Hansen (2001)
Routh et al. (2001)
Krahenbuhl and Li (2006)
We adapted the 3D gravity inversion through an adaptive learning procedure (Silva Dias et al., 2007) to estimate the shape of salt bodies.
Methodology
Forward modeling of gravity anomalies
Gravity anomaly
Source Region
x
y
x
y
Depth
3D salt body
z
Forward modeling of gravity anomalies
Source Region
The source region is divided into an mx× my× mzgrid of M 3D vertical juxtaposed prisms
dy
dz
x
dx
y
Depth
z
Forward modeling of gravity anomalies
Observed gravity anomaly
Source Region
To estimate the 3D densitycontrast distribution
x
x
y
y
Depth
z
Methodology

z
'
z
òòò
=
g
r
i
r
(
r
'
)
dv
'
i
3

r
'
r
i

z
'
z
òòò
=
g
i
A
(
r
)
dv
'
ij
i
3

V
r
'
r
j
i
The vertical component of the gravity field produced by the densitycontrast distribution r (r’):
g
(
)
V
The discrete forward modeling operator for the gravity anomaly can be expressed by:
g = A p
(N x 1)
(NxM)
(M x 1)
where
o
g
Methodology
The unconstrained Inverse Problem
The linear inverse problem can be formulated by minimizing
2
1
g
p

A
=
f
N
illposed problem
Methodology
Source Region
Concentrationof salt mass aboutspecifiedgeometric elements (axes and points)
x
y
Depth
3D salt body
z
r =  0.3 g/cm3
homogeneous sediments
Methodology
Homogeneous salt body embedded in homogeneous sediments
Firstguess skeletal outline of the salt body
Only one target density contrast
3D salt body
Depth
z
Methodology
Homogeneous salt body embedded in a heterogeneous sedimentary pack
A reversal 3D densitycontrast distribution
3D salt body
Depth
Heterogeneous sedimentary pack
z
r = + 0.3 g/cm3
r = + 0.2 g/cm3
r =  0.1 g/cm3
r =  0.2 g/cm3
Methodology
Heterogeneous salt body embedded in homogeneous sediments
Firstguess skeletal outline of a particular homogeneous section of the salt body
A reversal 3D densitycontrast distribution
Heterogeneous salt body
Depth
Homogeneous sediments
z
Methodology
x
y
x
pjtarget =  0.3 g/cm3
z
x
y
y
z
z
Iterative inversion method consists of two nested iterative loops:
The outer loop: adaptive learning procedure
The inner loop: Iterative inversion method
zero
or a nonnull value.
Methodology
2
k
k
k
1/2
(
(
(
)
)
)
Δp
W
p
2
o
= d
(po+
Δp )

g
1
A
N
1/2
Prior reference vector
3
d
1/2
k
k
k
(
(
(
)
)
)
w
j
Wp
{
}
+
=
(
k
)
(
k
1
)
(
k
)
=
+
ˆ
ˆ
≡
p
p
Δ
p
jj
o
(
)
k1
+
e
ˆ
p
j
The inversion method of the inner loopestimates iteratively the constrained parameter correction Δp by
Minimizing
Subject to
and updates the densitycontrast estimates by
Methodology
d
l
j
x
d
j
y
d
j
l
xe
ye
ze
)
)
,
,
l
l
l
z
2
2
2
[
]
1
/
2

+

+

=
=
=
d
x
xe
)
(
y
ye
)
(
z
ze
)
1
,
,
N
,
j
1
,
,
M
(
l
L
L
j
j
j
E
l
l
l
j
l
Inner loop
=
}
d
{
min
j
£
£
1
N
l
E
The method defines dj as the distance from the center of the j th prism to the
closest geometric element
closest geometric element
Adaptive Learning Procedure
Outer Loop
Adaptive Learning Procedure
INNER LOOP:
First densitycontrast distribution estimate
static geologic reference model
First interpretation model
firstguess geometric elements and associated
New interpretation model
New geometric elements (points) and associated target density contrasts
target density contrasts
x
OUTER LOOP:
Second Iteration
OUTER LOOP:
First Iteration
y
Dynamic geologic reference model
z
Each 3D prism is divided
Inversion of Synthetic Data
9
8
7
0.5
6
)
m
0.3
k
5
(
x
4
0.1
3
0.1
mGal
2
1
1
0
1
2
3
4
5
6
7
y (km)
Synthetic example with a variable density contrast
Noisecorrupted gravity anomaly
Synthetic example with a variable density contrast
Homogeneous salt dome with density of 2.2 g/cm3 embedded in five sedimentary layers
with density varying with depth from 1.95 to 2.39 g/cm3.
1.95 g/cm3
1.5 km
Nil zone
2.39 g/cm3
Depth
3D salt body
Synthetic example with a variable density contrast
The true reversal 3D densitycontrast distribution
Depth (km)
below
above
Density contrast (g/cm3)
Synthetic example with a variable density contrast
The blue axes are the firstguess skeletal outlines: static geologic reference model
Synthetic example with a variable density contrast
Interpretation model at the fourth iteration: 80×72×40 grid of 3D prisms.
True Salt Body
Estimated Salt Body
Synthetic example with a variable density contrast
Estimated Salt Body
Fitted anomaly
9
8
7
6
)
m
k
5
(
x
4
3
2
1
1
0
1
2
3
4
5
6
7
y (km)
Real Gravity Data
Galveston Island salt dome Texas
Localization of Galveston Island salt dome
Study area
Localization of Galveston Island salt dome
Study area
Location map of the study area (after Fueg, 1995; Moraes and Hansen, 2001)
Galveston Island salt dome
N
N
3152
3150
3148
3146
3144
3142
3140
3138
3136
3134
km E
314
320
326
332
(UTM15)
km E
(UTM15)
mGal
Fueg’s (1995) density models
2.2
1
0.2
1.4
Bouguer anomaly maps
Galveston Island salt dome
0.08
0.08
0.00 (g/cm3)
0.00 (g/cm3)
0.15
0.15
0.20 (g/cm3)
0.20 (g/cm3)
0.5
0.5
0.10 (g/cm3)
0.10 (g/cm3)
0.8
0.8
0.06 (g/cm3)
0.06 (g/cm3)
Depth (km)
1.2
1.2
Depth (km)
0.02 (g/cm3)
0.02 (g/cm3)
1.5
1.5
 0.04 (g/cm3)
 0.04 (g/cm3)
2.0
2.0
 0.08 (g/cm3)
 0.08 (g/cm3)
2.6
 0.13 (g/cm3)
3.2
3.4
 0.18 (g/cm3)
 0.13 (g/cm3)
3.8
 0.23 (g/cm3)
3.9
The first geologic hypothesis about the salt dome
First static geologic reference model based on Fueg’s (1995) density models
Galveston Island salt dome
The first estimated reversal 3D densitycontrast distribution
Galveston Island salt dome
N
3152
3150
3148
3146
3144
3142
3140
3138
3136
3134
314
320
326
332
km E
(UTM15)
mGal
2.2
1
0.2
1.4
The second geologic hypothesis about the salt dome
0.04
0.00 (g/cm3)
0.31
0.19 (g/cm3)
0.35
0.08 (g/cm3)
1.2
Depth (km)
 0.04 (g/cm3)
2.0
 0.13 (g/cm3)
2.2
Galveston Island salt dome
Density contrast (g/cm3)
0.13
0.042
0.045
0.22
0.13
The second estimated reversal 3D densitycontrast distribution
Overhang
Conclusions
Adaptive learning gravity inversion for 3D salt body imaging
Thank You
We thank Dr. Roberto A. V. Moraes and Dr. Richard O. Hansen for providing the real gravity data
1 CPU ATHLON with one core and
2.4 GHertz and 1 MB of cache L22GB of DDR1 memory
Large source surrounding a small source
The red dots are the firstguess skeletal outlines: static geologic reference model
Large source surrounding a small source
Fifth iteration
interpretation model: 48×48×24 grid of 3D prisms.
Multiple buried sources at different depths
0.4 g/cm3
0.15 g/cm3
0.3g/cm3
density contrast (g/cm3)
The points are the firstguess skeletal outlines:
static geologic reference model
Third iteration
Interpretation model: 28×48×24 grid of 3D prisms.
Methodology
+
(
k
)
(
k
1
)
ˆ
p
p
o
k
(
)
( k )
p
target
p
(
(
(
(
)
)
)
)
k
k
k
k
ˆ
ˆ
ˆ
ˆ
p
p
p
p
o
j
j
target
target
p
p
j
j
j
j
j
j
=
1/2
10+8
wp
=
jj
(
k
)
=
+
ˆ
Δ
p
Penalization Algorithm:
0 (g/cm3)
0 (g/cm3)
or
0 (g/cm3)
Methodology
k
(
)
( k )
( k )
p
target
p
p
(
(
(
(
(
(
)
)
)
)
)
)
k
k
k
k
k
k
ˆ
ˆ
ˆ
ˆ
ˆ
ˆ
3
p
p
p
p
p
p
d
wp
o
o
p
j
target
j
j
j
=
target
target
p
p
j
j
j
j
j
j
j
2
jj
(
)
k1
+
e
ˆ
j
j
p
j
=
=
p
target
j
2
1/2
+
0 (g/cm3)
(
k
)
(
k
1
)
(
k
)
=
+
ˆ
ˆ
p
p
Δ
p
o
Penalization Algorithm:
0 (g/cm3)
0 (g/cm3)