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Modeling the Surficial Geology of Greater Boston

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Modeling the Surficial Geology of Greater Boston Alex Grant, Geo 104 Introduction. The objective of this project was to sample surficial geol- ogy data and topography of the Boston area using a uni- formly spaced grid that could then be used in earthquake hazard modeling. This effort is part of a larger research project of Prof. Baise, Civil and Environmental Engineer- ing, and her colleagues. The benefit of creating a discrete system of grid points containing relevant GIS data is that finite element methods can be applied to the discrete system to run analyses for various earthquake outcomes based on physical parameters categorized in this study. Additional parameters relevant to Prof. Baise’s research, such as soil velocities can easily be added to this new dis- crete system of grid points in excel or similar programs by assigning specific characteristics to different soil types. Methods. Surficial geology data for deposit categorization and thicknesses were provided by Prof. Baise and collected from MassGIS. A grid of null data points spaced evenly laterally and vertically at 200m was then created in excel to match the state coordinate system used in Massachu- setts and spatially joined to the continuous surficial geol- ogy map found in Figure F. At 200m spacing the grid points did not sufficiently capture the surficial geology of Greater Boston, which led to the creation of a 44,895- node grid with 100m spacing. As seen in Figure A. below, the region’s surficial geology is captured with a high degree of precision using this grid, particularly along the Mystic River and small marsh deposits. In a similar manner surficial geology thick- nesses (Figure D.) were applied to the data points via spatial joins. Thickness ranges were then averaged in excel and null points were assigned a value of 10ft in thickness for mod- eling purposes. Elevation data was assigned to data points by spatially joining weighted average of elevation data from the nearest topography lines (Figure B.) to each data point. After the preceding three spatial joins the data point grid was able to display all three layers of information as a set of discrete points. Figure A. Detail showing close correspondence between sampled grid nodes and mapped surficial geology. Legend Known Surficial Geology Artificial Fill Beach Deposits Drumlin (Glacial Till) Glaciofluvial deposits (Outwash) Ground Moraine (Till w/ bedrock) Marsh Deposits Legend Sampled Surficial Geology Artificial Fill Atlantic Beach Deposits Drumlin (Glacial Till) Glaciofluvial deposits (Outwash) Ground Moraine (Till w/ bedrock) Marsh Deposits Water Legend Sampled Surficial Geology Thickness of Surficial Geology 10 25 75 150 200 Legend Sampled Surficial Geology Elevation, m -9 - 6 7 - 15 16 - 24 25 - 33 34 - 45 46 - 57 58 - 69 70 - 81 82 - 93 94 - 114 Legend Thickness of Surficial Deposits RANGE (FT) NULL 0-50 50-100 100-200 200+ Figure B. Topography of Greater Boston Figure C. Sampled Topography of Greater Boston Figure D. Thickness ranges of surficial geology Figure E. Sampled thicknesses of surficial geology Figure F. Surficial geology Figure G. Sampled surficial geology Conclusion. The dataset created for this project will now hopefully serve as a backbone to further augmentation and research by Prof. Baise and her colleagues as they investigate wave propagation and earthquake hazard mapping in the Greater Boston region. By using finite element modeling techniques on this dataset, paired with soil velocity values, should provide a higher resolution model of how the surficial geology in the region will react to various earthquake events. It was an additional goal of this project to de- velop a three-dimensional model of the region’s surficial geology but challenges due to the discontinuities in the geology have prevented this stage of the project. This model, if created, would aid in the visualization of the region’s surficial geology and as borehole data is added could provide a basis for a full three-dimensional model of Greater Boston’s geology down to bedrock. Results. As seen in Figures C., E., and G., discrete point sampling from the available GIS data has created a model that captures the surficial geology data for Greater Boston with a high degree of precision. Similar coloring schemes have been used for each image pair for ease of comparison. One notable difference is that because sampled elevation is based on an average of neighboring points the peak elevations in the sampled data (114m) are much less than the true peak elevation (192m). This discrepancy appears in topographic data but not surficial geology or thickness data due to the nearest neighbor technique used for topography data rather than direct spatial joins for the other data sets. Improve- ments for topographic data may be possible with better averag- ing techniques or if the topographic map was first converted to a continuous surface and then sampled via spatial joins. All external data for this project courtesy of: Dr. Laurie Gaskins Baise, Tufts University MassGIS Region of Interest
Transcript
  • Modeling the Surcial Geology of Greater Boston Alex Grant, Geo 104 Introduction.The objective of this project was to sample surcial geol-ogy data and topography of the Boston area using a uni-formly spaced grid that could then be used in earthquake hazard modeling. This eort is part of a larger research project of Prof. Baise, Civil and Environmental Engineer-ing, and her colleagues. The benet of creating a discrete system of grid points containing relevant GIS data is that nite element methods can be applied to the discrete system to run analyses for various earthquake outcomes based on physical parameters categorized in this study. Additional parameters relevant to Prof. Baises research, such as soil velocities can easily be added to this new dis-crete system of grid points in excel or similar programs by assigning specic characteristics to dierent soil types.

    Methods.Surcial geology data for deposit categorization and thicknesses were provided by Prof. Baise and collected from MassGIS. A grid of null data points spaced evenly laterally and vertically at 200m was then created in excel to match the state coordinate system used in Massachu-setts and spatially joined to the continuous surcial geol-ogy map found in Figure F. At 200m spacing the grid points did not suciently capture the surcial geology of Greater Boston, which led to the creation of a 44,895-node grid with 100m spacing.As seen in Figure A. below, the regions surcial geology is captured with a high degree of precision using this grid, particularly along the Mystic River and small marsh deposits. In a similar manner surcial geology thick-nesses (Figure D.) were applied to the data points via spatial joins. Thickness ranges were then averaged in excel and null points were assigned a value of 10ft in thickness for mod-eling purposes. Elevation data was assigned to data points by spatially joining weighted average of elevation data from the nearest topography lines (Figure B.) to each data point. After the preceding three spatial joins the data point grid was able to display all three layers of information as a set of discrete points.

    Figure A. Detail showing close correspondence between sampled grid nodes and mapped surcial geology.

    Legend

    Known Surficial GeologyArtificial Fill

    Beach Deposits

    Drumlin (Glacial Till)

    Glaciofluvial deposits (Outwash)

    Ground Moraine (Till w/ bedrock)

    Marsh Deposits

    LegendSampled Surficial Geology

    Artificial Fill

    Atlantic

    Beach Deposits

    Drumlin (Glacial Till)

    Glaciofluvial deposits (Outwash)

    Ground Moraine (Till w/ bedrock)

    Marsh Deposits

    Water

    LegendSampled Surficial GeologyThickness of Surficial Geology

    10

    25

    75

    150

    200

    LegendSampled Surficial GeologyElevation, m

    -9 - 6

    7 - 15

    16 - 24

    25 - 33

    34 - 45

    46 - 57

    58 - 69

    70 - 81

    82 - 93

    94 - 114

    LegendThickness of Surficial DepositsRANGE (FT)

    NULL

    0-50

    50-100

    100-200

    200+

    Figure B. Topography of Greater Boston

    Figure C. Sampled Topography of Greater Boston

    Figure D. Thickness ranges of surcial geology

    Figure E. Sampled thicknesses of surcial geology

    Figure F. Surcial geology

    Figure G. Sampled surcial geology

    Conclusion.The dataset created for this project will now hopefully serve as a backbone to further augmentation and research by Prof. Baise and her colleagues as they investigate wave propagation and earthquake hazard mapping in the Greater Boston region. By using nite element modeling techniques on this dataset, paired with soil velocity values, should provide a higher resolution model of how the surcial geology in the region will react to various earthquake events. It was an additional goal of this project to de-velop a three-dimensional model of the regions surcial geology but challenges due to the discontinuities in the geology have prevented this stage of the project. This model, if created, would aid in the visualization of the regions surcial geology and as borehole data is added could provide a basis for a full three-dimensional model of Greater Bostons geology down to bedrock.

    Results.As seen in Figures C., E., and G., discrete point sampling from the available GIS data has created a model that captures the surcial geology data for Greater Boston with a high degree of precision. Similar coloring schemes have been used for each image pair for ease of comparison. One notable dierence is that because sampled elevation is based on an average of neighboring points the peak elevations in the sampled data (114m) are much less than the true peak elevation (192m). This discrepancy appears in topographic data but not surcial geology or thickness data due to the nearest neighbor technique used for topography data rather than direct spatial joins for the other data sets. Improve-ments for topographic data may be possible with better averag-ing techniques or if the topographic map was rst converted to a continuous surface and then sampled via spatial joins.

    All external data for this project courtesy of:Dr. Laurie Gaskins Baise, Tufts UniversityMassGIS Region of Interest


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