Transcript Engineering Economy: Applying Theory to Practice, 2nd

AMARILLO BY MORNING:
DATA VISUALIZATION IN
GEOSTATISTICS
William V. Harper, Otterbein College, USA
[email protected]
Isobel Clark, Geostokos, Scotland
Environmental Statistics, Session 6A2
ICOTS8, Ljubljana, Slovenia
11 – 16 July 2010
Amarillo by Morning – a
Haunting Country Song
Amarillo by morning, up from San Antone.
Everything that I’ve got is just what I’ve got on.
When that sun is high in that Texas sky
I’ll be bucking it to county fair.
Amarillo by morning, Amarillo I’ll be there.
They took my saddle in Houston, broke my leg in Santa Fe.
Lost my wife and a girlfriend somewhere along the way.
Well I’ll be looking for eight when they pull that gate,
And I’m hoping that judge ain’t blind.
Amarillo by morning, Amarillo’s on my mind.
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Amarillo, Wolfcamp Aquifer,
and Nuclear Waste Repository
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In the United States in 1987, the possible
nuclear waste sites were reduced to:
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Salt bed in Texas
Basalt formation in Washington state
Tuff formation near Las Vegas, Nevada
Wolfcamp Aquifer underlies salt site
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Briny (salty) slow moving water
Modeled as 2-D plane using geostatistics
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Geostatistics
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Geostatistics
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Spatial statistics used for continuous data
Each data value has a location in space
Roots in Mining, not Statistics
Observations close have similar values
Goals of Geostatistics
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Estimate spatial correlation structure
Predict values at un-sampled locations
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Wolfcamp Potentiometric
Data: 85 values
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Wolfcamp Initial Assessment
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Higher values in Southwest, Lower in
Northeast
Travel path from Deaf Smith county toward
Amarillo in lower Potter County
If a breach, flow is toward Amarillo
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Kriging, Universal Kriging
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Universal Kriging (combines trend, kriging)
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Many possible iterative steps to produce
minimum variance linear unbiased estimates
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Distribution Analysis, Data Transformation
Trend, Isotropy/Anisotropy analysis
Semi-variogram modeling of spatial variability
Cross-Validation to partially validate model
Kriged expected value map at un-sampled locations
Kriged standard error map
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Will you be my Neighbor?
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Nearest Neighbor Analysis
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Empirical Semi-variogram,
Semi-variogram Cloud
Empirical Semi-Variogram
 ( h) 
*
1
2Nh
 x  x 
i
2
j
h
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Directional Semi-variograms
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Directional Shaded Plot Semivariograms
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Directional Semi-variograms
on Regression Residuals
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Omni-directional Semivariogram on Residuals
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Universal Kriging
Potentiometric Surface
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Universal Kriging Standard
Error Map
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