The New Madrid seismic zone of the central Mississippi River valley has been interpreted to be a right-lateral strike-slip fault zone with a left stepover restraining bend (Reelfoot reverse fault). This model is overly simplistic because New Madrid seismicity continues 30 km southeast of the stepover. In this study we have analyzed 1704 earthquake hypocenters obtained between 1995 and 2006 in three-dimensional (3-D) space to more accurately map fault geometry in the New Madrid seismic zone. Most of the earthquakes appear to align along fault planes. The faults identifi ed include the New Madrid North (29°, 72° SE), Risco (92°, 82° N), Axial (46°, 90°), Reelfoot North (167°, 30° SW), and Reelfoot South (150°, 44° SW) faults. A diffuse zone of earthquakes exists where the Axial fault divides the Reelfoot fault into the Reelfoot North and Reelfoot South faults. Regional mapping of the top of the Precambrian crystalline basement indicates that the Reelfoot North fault has an average of 500 m and the Reelfoot South fault 1200 m of down-to-the-southwest normal displacement. Since previously published seismic refl ection profi les reveal reverse displacement on top of the Paleozoic and younger strata, the Reelfoot North and South faults are herein interpreted to be inverted basement normal faults. The Reelfoot North and Reelfoot South faults differ in strike, dip, depth, and displacement, and only the Reelfoot North fault has a surface scarp (monocline). Thus, the Reelfoot fault is actually composed of two left-stepping restraining bends and two faults that together extend across the entire width of the Reelfoot rift.
Geophysical and drill-hole data within the Reelfoot rift of Arkansas, Tennessee, Missouri, and Kentucky, USA, were integrated to create a structure contour map and threedimensional computer model of the top of the Precambrian crystalline basement. The basement map and model clearly defi ne the northeast-trending Cambrian Reelfoot rift, which is crosscut by southeast-trending basement faults. The Reelfoot rift consists of two major basins, separated by an intrarift uplift, that are further subdivided into eight subbasins bound by northeast-and southeast-striking rift faults. The rift is bound to the south by the White River fault zone and to the north by the Reelfoot normal fault. The modern Reelfoot thrust fault, responsible for most of the New Madrid seismic zone earthquakes, is interpreted as an inverted basement normal fault. Geologic interpretation of 5077 shallow borings in the central Mississippi River valley enabled the construction of a structure contour map of the Pliocene-Pleistocene unconformity (top of the Eocene-base of Mississippi River alluvium) that overlies most of the Reelfoot rift. This map reveals both river erosion and tectonic deformation.Deformation of the Pliocene-Pleistocene unconformity appears to be controlled by the northeast-and southeast-trending basement faults. The northeast-trending rift faults have undergone and continue to undergo Quaternary dextral transpression. This has resulted in displacement of two major rift blocks and formation of the Lake County uplift, Joiner ridge, and the southern half of Crowley's Ridge as compressional stepover zones that appear to have originated above basement fault intersections. The Lake County uplift has been tectonically active over the past ~2400 yr and corresponds with a major segment of the New Madrid seismic zone. The aseismic Joiner ridge and the southern portion of Crowley's Ridge may refl ect earlier uplift, thus indicating Quaternary strain migration within the Reelfoot rift.
This study examined the utility of a high resolution ground-based (mobile and terrestrial) Light Detection and Ranging (LiDAR) dataset (0.2 m point-spacing) supplemented with a coarser resolution airborne LiDAR dataset (5 m point-spacing) for use in a flood inundation analysis. The techniques for combining multi-platform LiDAR data into a composite dataset in the form of a triangulated irregular network (TIN) are described, and quantitative comparisons were made to a TIN generated solely from the airborne LiDAR dataset. For example, a maximum land surface elevation difference of 1.677 m and a mean difference of 0.178 m were calculated between the datasets based on sample points. Utilizing the composite and airborne LiDAR-derived TINs, a flood inundation comparison was completed using a one-dimensional steady flow hydraulic modeling analysis. Quantitative comparisons of the water surface profiles and depth grids indicated an underestimation of flooding extent, volume, and maximum flood height using the airborne LiDAR data alone. A 35% increase in maximum flood height was observed using the composite LiDAR dataset. In addition, the extents of the water surface profiles generated from the two datasets were found to be statistically significantly different. The urban and mountainous characteristics of the study area as well as the density (file size) of the high resolution ground based LiDAR data presented both opportunities and challenges for flood modeling analyses. OPEN ACCESSWater 2013, 5 1534
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