Faults in. Houston. University. of Houston Geosciences

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1 DEM Programs for Active Surface Faults in Houston Richard Engelkemeir Schlumberger Information Sol lutions University of Houston Geosciences

2 Geologic Overview Salt deposited during extension prior to sea floor spreading Deposition prograding into bas sin Faulting initiating along shelf margin Depositional and salt role in fau ulting Faults apparently more active in Holocene than Pleistocene Contributions from ground wate er withdrawal subsidence Glacial Isostatic Adjustments Ongoing deposition, especially in South Louisiana

3 Northwest Gulf of Mexico Region

4 Houston Observations : Long Point Fault on Morehead.

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6 LIDAR Fault Interpretation Fault Mapping Hillshading shows elev ation contrast Other methods tried, but not as sucesful DEM refinement - Raw Data (30 M points/ quarter quadrangle) - Use coarse DEM for weighted grid interpolation ti - Ground cover may provide additional controls Line shapefiles used for fault interpretation Field Confirmation

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8 Scarp Scarp clearly visible on 15 ft (5 m) Hillshade, Scarp not detectable on 1 ft (0.3 m) orthophoto

9 Grid Ref finement 15 ft Bare Earth DEM (filte ering) 30 million raw points per quarter quadrangle Point density permits 5 ft cell size Honor filtering and use add ditional points. Trapezoidal filter to weight differences Bilinear interpolation ti of weighted points to output t grid Bilinear interpolation of input where necessary Optional smoothing program after examination Hillshade resultant image

10 Refinement Weighting if dz < dmin W = 1 else if dz > dmax Else W = 0 W = 1 (dz dmin)/(dmax dz = absolute value of point and interpolated input grid value dmin = minimum threshold (2 ft) dmax = maximum threshold (5 W = resultant point weight dmin) ft)

11 Grid Refinement Comparison

12 Fault Displace ement Studies GPS examine subsidence rates and correlate with fault and salt dome locations INSAR look for displacement along fault, see if uplift of salt domes. DEM differences e Compute throw along faults Compare different generations of DEMs (2001, 1996) Throw methods Profiles perpendicular to fault Polygon pairs on opposite sides of fault Operate on differences to avoid differences in DEM processing

13 Profile based Thr row Computation Automatically ti generate profiles perpendicular to fault User control over spacing, length and sampling Remove linear trend to highlight top and base of scarp Snap fault trace to steepes slope Programmatic evaluation of results Opposite throw sense ( likely l DEM anomaly) Never reach minimum and maximum (remains monotonic) Throw larger than specified threshold (likely DEM anomaly)

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15 Profiles and Computed Throw

16 Profiles and Throw Differences

17 Polygon based Th hrow Computation Assume mean elevation reasonable measure Define polygon file with attributes for computations Digitize up down pairs to compute throw Keep polygon grid statistic cs for QC Minimum and maximumm Standard d deviation Compute polygon values for grids (mean diff. = throw) Subtract throw from 2 different generations of DEMs

18 Sample polygons

19 Polygon pairs with throw values

20 Throw Difference polygon pairs

21 Seismic and GPR Data Seismic reflection and refraction lines GPR lines at same locations as seismic Long Point Fault chosen as study area GPR shows indications of stru ucture across fault GPR over possible LIDAR fault suggests that some other feature, not a fault Longer lines and greater offsets needed. DEM used to extract elevations along gprofiles

22 Seismic and GPR Field Locations Line 1 Line 2 Possible Fault Li ne 2

23 GPR at Line 1 location f3

24 Looking N along Hollister towards Battlewood

25 Summary With LIDAR able to better map surface faults. InSAR and GPS will enable measuring deformation (fault slip and subsidence) DEM differences provide another measure Seismic and GPR data provide near surface geometry for faults Together, these studies will provide for better hazard delineation and provide more insights i into local tectonics.

26 Bowl limits