Morphological Interpretation of

Seamounts in Tutuila, American Samoa:

Inferring Probable Genesis Through Shape and Distribution

Analysis

Morphological Interpretation of

Seamounts in Tutuila, American Samoa:

Inferring Probable Genesis Through Shape and Distribution

AnalysisJed RobertsGEO 580 Project

6/7/2006

Jed RobertsGEO 580 Project

6/7/2006

Image produced by the National Park Service

Study AreaStudy Area

Study AreaStudy Area

Tutuila, American Samoa

Image produced by the National Park Service

Multibeam Bathymetry Grid

Multibeam Bathymetry Grid

• 200 meter resolution• Depth range from sea level to 5300 meters

below sea level• Collected in 2002 by Revelle Research Vessel• Numerous volcanic seamounts

Define SeamountsDefine Seamounts

• 300 meters or more above average baseline• Identify characteristic cross-sections• Avoid bathymetric influence of nearby islands

– Cross-sections typically parallel to bathymetric gradient

• Avoid incomplete cross-sections due to data gaps

Shape StatisticsShape Statistics

Diagrams from Smith 1988

• Basal Diameter• Summit Diameter• Height

• Average Slope• Flatness (ratio of

summit to base)

Shape Statistic Associations

Shape Statistic Associations

Basal Diameter as control of HeightBasal Diameter as control of Height

Shape Statistic Associations

Shape Statistic Associations

Height as control of Average SlopeHeight as control of Average Slope

Shape Statistic Associations

Shape Statistic Associations

Height as control of FlatnessHeight as control of Flatness

Create Point Feature Class

Create Point Feature Class

• 12 seamounts met criteria• Height range: 380 to 680 meters• Populate feature class with shape

statistics• Export .CSV file for distribution analysis

Geographically Weighted Regression

Geographically Weighted Regression

• Creates weighted regression model for each point and averages intercepts

• Accounts for spatial variability

GWR ResultsGWR Results

Basal Diameter as control of HeightBasal Diameter as control of Height

GWR ResultsGWR Results

Height as control of Average SlopeHeight as control of Average Slope

GWR ResultsGWR Results

Height as control of FlatnessHeight as control of Flatness

Identifying Spatial Variability

Identifying Spatial Variability

• Comparison of GWR model to global regression model reveals that spatial variability is a factor in this “association”

• No apparent correlation• Too few observations for statistical significance

Morphological InterpretationMorphological Interpretation

• Basal diameter and height association coincides with observations by Smith (1988) of larger seamounts (500 to 3800 meters)

• Height as control of slope (Smith 1988) not as strong with smaller seamounts

• Isolated seamounts more characteristic of point source magma intrusions

• Cause of asymmetrical elongation in seamounts unclear– Too few observations of elongation– Lithospheric fractures?– Point source magma flowing down island slope?

UncertaintyUncertainty

One seamount, or two?

Conclusions and Improvements

Conclusions and Improvements• Need more observations!

– Reduce arbitrary height cut-off– Expand study area (thesis!)

• Use multiple cross-sections for each seamount– Help to account for elongation

• GWR reveals spatial variability, but not significantly with any correlatable variables

• Additional shape statistics– Volume– Maximum slope

ReferencesReferences

Fotheringham, A. S., Charlton, M. E., and Brunsdon C. 1998. Geographically weighted regression: a natural evolution of the expansion method for spatial data analysis. Environment and Planning A. 30: 1905-27.

Smith, D. K. 1988. Shape analysis of Pacific seamounts. Earth and Planetary Science Letters. 90: 457-66.