j. lapazaran a. martín-español j. otero f. navarro international symposium on radioglaciology 9-13...
TRANSCRIPT
J. Lapazaran
A. Martín-Español
J. Otero
F. Navarro
International Symposium on Radioglaciology9-13 September 2013, Lawrence, Kansas, USA
On the errors involved in the estimate of glacier ice volume from ice thickness
data
Photo: J. Lapazaran
1
Objectives
Analyze the error sources & transmit them to the volume estimate.
• Which are the sources?• Evaluate each error value.• Combining errors.
DATA: georadar ice thickness
DEM of glacier ice thickness
Glacier ice volume estimate
Involved processes Steps on error estimation
• Step1Thickness error in georadar data.
• Step2Thickness error in DEM.
• Step3Error in volume.
2
Step1: Thickness error in georadar data
Data error ԐHdata can be split in 2 independent errors
being GPR or other georadar type
Error in thickness measurement, ԐHGPR
Error in thickness positioning, ԐHGPS
being positioned by GPS or other positioning system
2 2
i i iHdata HGPR HGPS
3
Step1: Thickness error in georadar dataԐHGPR : Error in thickness measurement
Hypothesis:• Zero offset profiling: (Dyn. corr.
).• Migrated radargram.• Only picking where bed is clearly identified.
ԐHGPR can be split in 2 independent errorsError in RWV, Ԑc Error in TWTT, ԐƬ
/ 2H c 22 /ad c
2 2 2 212i i iHGPR c c
4
Step1: Thickness error in georadar dataԐHGPR : Error in thickness measurement
Ԑc : Error in RWV
RWV is measured (CMP) or estimated by experience.
We look for the mean RWV of the profile.• Bias: Error in the mean value of RWV chosen for the profile.• Rnd. error (Ԑc ): Variability around the mean RWV along the
profile.
Bias:• Unknown sign.• 2% in CMP (Barret et al, 2007) 2% of 168 = 3.36 m/µs• , so ±2% of c means ± 2% of H.• It must be considered separately.
Rnd. Error (Ԑc ):• About another 2%
164.6 m/µs171.4 m/µs/ 2H c
2 2 20.022i iHGPR
c
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Step1: Thickness error in georadar dataԐHGPR : Error in thickness measurement
ԐƬ: Error in TWTT
• Frequency of the radar• Threshold for vertical resolution
• Widess (1973) ʎ / 8 → 1 / 4f in TWTT (in absence of noise).• Yilmaz (2001) ʎ / 4 → 1 / 2f in TWTT.• Reynolds (1997) ʎ / 4 (theoretical) not realistic in real media.• Barret et al (2007) an error of ʎ is not impossible → 2 / f in TWTT.
• We conservatively take ʎ / 2 → ԐƬ = 1 / f in TWTT.
• Resolution of the recording• Sampling resolution. Much smaller than 1 / f. NEGLIGIBLE
• Migration• Profile must be migrated.• CAUTION with profiles close to lateral walls (or 3D migration).
• Moran et al (2000) found 15% of error in a small sample of 100x340 m.
• Picking error• DO NOT PICK if not sure where the bed is (scattering,
clutter).
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Step1: Thickness error in georadar dataԐHGPS : Error in thickness due to bad positioning
• Grows with the steepness of the thickness field.• Negligible in DGPS.
• GPS in autonomous → ԐXY = 5 m.We build the thickness DEM and evaluate its steepness in n directions around each measuring point:
• Odometer7
2
1
11i i
n
HGPS k ik
d dn
( )
𝑑𝑖 → mean value of the n differences of thickness between the n surrounding points (k) and the evaluated point (i)
Using the same method but we must estimate D.
• Is there any GPS track?• Who have done the profile?• 5-20% of the length, at the centre of the
profile.
D
2 2H k k Hinterp k HGPRx x x ( ) ( ) ( )
Data errorstransmitted to grid points (xk)
Thickness in DEM grid points (xk)
Interpolation errors in grid points (xk)
Step2: Thickness error in DEM
Errors in DEM construction
ԐHdata i
can be considered
independent 8
Transmission toDEM grid points.
I N T E R P O L A T I O N
Georadar thicknessdata (xi)
k Hinterpx( ) k HGPRx( )
Step2: Thickness error in DEM
Ԑ(xk)HGPR : Data errors transmitted to grid points
We have interpolated the measured data H(xi) in the grid points xk:
Now, data error are propagated into the grid using the same interpolation weighting:
9
1
n
k i ii
H x H x
( ) ( )
1
n
k HGPR i i HGPRi
x x
( ) ( )
points with georadar measurement
grid points
Step2: Thickness error in DEM
Ԑ(xk)Hinterp : Thickness interpolation error
Georadar data:• High concentration of data in several lines.• Huge spaces without data.
Evaluation of the interpolation error:• Cross-validation evaluates the error in data-
concentrated zones but not in data-free zones.Useless for georadar data interpolating.
• Kriging variance (if interp. with kriging) has been criticized (Rotschky et al, 2007; Journel, 1986; Chainey and Stuart, 1998) as "been ineffective and poor substitute for a true error", "the kriging variance, depending only on the geometrical arrangement of the sample data points, simply states that accuracy decreases with growing distance from input data". 10
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Step2: Thickness error in DEM
Ԑ(xk)Hinterp : Thickness interpolation error
Distance-Error & Distance-Bias Functions (DEF & DBF)
• Take (e.g.) 10 values of distance, between 0 and the maximum distance between grid point and measured point.
• For each distance value, center a blanking circumference of this radio on each data point and interpolate with remaining data -one at a time-.
• Mean discrepancies (biases) and their standard deviations (errors) are calculated for each distance.
R1R2
R3R4
-300
-200
-100
0
100
200
0 200 400 600 800 1000 1200 1400
Discrepancy (m) vs Distance (m)
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Step2: Thickness error in DEM
Ԑ(xk)Hinterp : Thickness interpolation error
Distance-Error & Distance-Bias Functions (DEF & DBF)
DBF & DEF are the mean squared adjusted curves.
DBF shows how the bias has negative values that grows with increasing the distance to the nearest measurement.
DEF shows how the error grows with increasing the distance to the nearest measurement.
y = 3E-08x3 - 7E-05x2 + 0,0027x - 5,4091R² = 0,9998
-70
-60
-50
-40
-30
-20
-10
00 500 1000
DBF
y = -6E-09x3 - 1E-05x2 + 0,0586x + 17,584R² = 0,9998
0
10
20
30
40
50
60
70
0 500 1000
DEF
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Distance (m)
Bia
s (
m)
Fre
qu
en
cy
- A bias value is applied to every cell in the grid, modifying the kriging prediction.
- Every cell in the grid receives an error value from the DEF.
Step2: Thickness error in DEM
Ԑ(xk)Hinterp : Thickness interpolation error
Distance-Error & Distance-Bias Functions (DEF & DBF)
A bias value and an error value are extracted from DBF and DEF and assigned to each node in the DEM grid, depending on its distance to the nearest measurement.
Step3: Error in volume
Volume error ԐV can be split in 2 independent errors
Error in volume due to error in thickness, ԐVH
Error in volume due to boundary error, ԐVB
2 2V VH VB
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1
N
k kk
V A H
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Step3: Error in volume
ԐVH : Error in volume due to error in thickness
• Can thickness errors be considered independent?• Are they linearly dependent?
There is a spatial dependency among ice thickness
measurements due to the surface continuity and thus
their errors are correlated too
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Step3: Error in volume
ԐVH : Error in volume due to error in thickness
Error correlation
The Range is the greatest distance to consider correlation.
Semivariogram relates the spatial correlation between pairs of points and the distance separating them.
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NR: Number of independent values =Number of points separated the independence distance (Range)
2
AreaRangeRN
Step3: Error in volume
ԐVH : Error in volume due to error in thicknessWe consider the glacier to have an independency degree derived from the number of range-size subsets.
2 2
1i
N
VH c HiR
NA
N
18
Step3: Error in volume
ԐVB : Error in volume due to boundary error
HA12 = 24 m !!
19
Step3: Error in volume
ԐVB : Error in volume due to boundary error
• Glacier covered by moraines.• Rocks covered by snow. fA (%)
glacier
bedrock20
Step3: Error in volume
ԐVB : Error in volume due to boundary error
glacier
bedrock
debris
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Step3: Error in volume
ԐVB : Error in volume due to boundary error
glacier
bedrock
HGPR
debris
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Step3: Error in volume
ԐVB : Error in volume due to boundary error
glacier
bedrock
HGPR
debris
error
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Step3: Error in volume
ԐVB : Error in volume due to boundary error
H m
de
vol er r or L e H = / 2m
H m = V / A
H m
L
earea error, f A
L e = f A
vol er r or f V = / 2
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Step3: Error in volume
ԐVB : Error in volume due to boundary error
What about the pixelation errors?Related to the software used to mask the ice thickness map.ArcGis 9.3:
- Inner cells are error free.- Frontier cells:
2 2
12
bc
A c
N
Vpix bc A
A
H
NEGLIGIBLECan be considered included in the boundary uncertainty error.
At each boundary cell, it can be approximated by the standard deviation of an uniform random variable between plus and minus half the cell area times the mean boundary-cell thickness (being zero the boundary thickness).
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Step3: Error in volume
ԐVB : Error in volume due to boundary error
Uncertainty inDEM boundary
Error involume
Step3
NR
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Summary
DEM construction: Transmission to grid
Thickness data
Interpolation
• Thickness DEM (grid)• Data error Interpolation error = = (grid)Thickness error in DEM
Step2
HGPR
Hdata
HGPS
c RWV ( )
TWTT ( )
Step1
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Results
Weren. 1 Weren. 2
Werenskioldbreen
G lac ie r Vol. inte rp . E rro r Error as in d ep en d . Kriging Varian ce Real Vo l.
(km 3 ) (km 3 ) (km 3 ) (km 3 ) (km 3 )
Weren. 1 2.80 0.10 0.001 0.21 2.88
Weren. 2 2.76 0.13 0.001 0.27 2.88
Aldg. 1 0.46 0.02 0.0001 0.06 0.45
Aldg. 2 0.44 0.02 0.0001 0.05 0.45
Aldg. 3 0.43 0.03 0.0001 0.08 0.45
Paiel. 1 1.24 0.09 0.0001 0.12 1.28
Paiel. 2 1.30 0.04 0.001 0.13 1.27
Hans. 1 0.991 0.05 0.001 0.10 1.04
Hans. 2 0.993 0.05 0.001 0.16 1.04
Hans. 3 1.02 0.05 0.0001 0.08 1.04
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Results
On the errors involved in the estimate of glacier ice volume from ice thickness
data
Thank you !
29Photo: J. Lapazaran
30
for your attention...Photo: J. Lapazaran
Thank you !
On the errors involved in the estimate of glacier ice volume from ice thickness
data