Michaela R. Johnson

Investigation of land subsidence in the Houston-Galveston region of Texas by using the Global Positioning System and interferometric synthetic aperture radar, 1993-2000

Bibliogov, United States, 2013. Paperback. Book Condition: New. 246 x 189 mm. Language: English . Brand New Book ***** Print on Demand *****.Since the early 1900s, groundwater has been the primary source of municipal, industrial, and agricultural water supplies for the Houston-Galveston region, Texas. The region s combination of hydrogeology and nearly century-long use of groundwater has resulted in one of the largest areas of subsidence in the United States; by 1979, as much as 3 meters (m) of subsidence...

Hyperspectral remote sensing data maps minerals in Afghanistan

Although Afghanistan has abundant mineral resources, including gold, silver, copper, rare earth elements, uranium, tin, iron ore, mercury, lead-zinc, bauxite, and industrial minerals, most have not been successfully developed or explored using modern methods. The U.S. Geological Survey (USGS) with cooperation from the Afghan Geological Survey (AGS) and support from the Department of Defenses Task Force for Business and Stability Operations (TFBSO) has used new imaging spectroscopy surface material maps to help refine the geologic signatures of known but poorly understood mineral deposits and identify previously unrecognized mineral occurrences. To help assess the potential mineral deposit types, the high-resolution hyperspectral data were analyzed to detect the presence of selected minerals that may be indicative of past mineralization processes. This legacy data set is providing tangible support for economic decisions by both the government of Afghanistan and other public and private sector parties interested in the development of the nations natural resources.

Mineral information at micron to kilometer scales: Laboratory, field, and remote sensing imaging spectrometer data from the orange hill porphyry copper deposit, Alaska, USA

Using imaging spectrometers at multiple scales, the USGS, in collaboration with the University of Alaska, is examining the application of hyperspectral data for identifying large-tonnage, base metal-rich deposits in Alaska. Recent studies have shown this technology can be applied to regional mineral mapping [1] and can be valuable for more local mineral exploration [2]. Passive optical remote sensing of high latitude regions faces many challenges, which include a short acquisition season and poor illumination due to low solar elevation [3]. Additional complications are encountered in the identification of surface minerals useful for mineral resource characterization because minerals of interest commonly are exposed on steep terrain, further challenging reflectance retrieval and detection of mineral signatures. Laboratory-based imaging spectrometer measurements of hand samples and field-based imaging spectrometer scans of outcrop are being analyzed to support and improve interpretations of remote sensing data collected by airborne imaging spectrometers and satellite multispectral sensors.

Geospatial compilation of results from field sample collection in support of mineral resource investigations, Western Alaska Range, Alaska, July 2013

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Base of principal aquifer for parts of the North Platte, South Platte, and Twin Platte Natural Resources Districts, western Nebraska

After initial processing by SkyTEM, the data were inverted using the Aarhus Geophysics Aps, (Aarhus, Denmark) program Workbench (Auken and others, 2009). To make the AEM data useful for geologic interpretation, numerical inversion converted measured data into a depth-dependent subsurface resistivity model, which was displayed as a resistivity profile. The inverted resistivity model (referred to hereafter as inverted AEM profiles), along with sensitivity analyses and test-hole information, were used to identify hydrogeologic features such as bedrock highs and paleochannels. A depth of investigation (DOI) calculation using the method given in Christiansen and Auken (2010) for each sounding is included in appendix 2 of U.S. Geological Survey Crustal Geophysics and Geochemistry Science Center (2014). The DOI can be defined as a critical depth below which the resistivity value is no longer constrained and interpretations of layer boundaries applied below DOI should be used with caution. Details on the data processing and inversion modeling can be located in Smith and others (2009, 2010). An interpretation of the location of the BOA was completed using a GIS that output x, y, and z coordinates. Before interpreting the inverted AEM profiles, several complementary datasets were included and graphically displayed in twoand three-dimensional GIS environments. Complementary data included test-hole lithology, test-hole geophysical logs (including natural-gamma and normal resistivity; University of Nebraska-Lincoln, Conservation and Survey Division, 2014; J.C. Cannia, U.S. Geological Survey, written commun., 2012; Hobza and Sibray, 2014; T.A. Kuntz, Adaptive Resources Inc., written commun., 2012), TDEM resistivity models (Abraham and others, 2012; M.A. Kass, U.S. Geological Survey, written commun., 2014), airborne measurements of the intensity of 60-hertz power-line interference, airborne measurements of the magnetic total-field intensity, aerial photographs (Esri, 2014), unpublished bedrock outcrop maps (R.F. Diffendal, Jr., University of Nebraska-Lincoln, Conservation and Survey Division, unpub. data, 2013; J.B. Swinehart, University of Nebraska-Lincoln, Conservation and Survey Division, unpub. data, 2013), and the 10-m digital elevation model (DEM; Nebraska Department of Natural Resources, 1998). The inverted AEM profiles were displayed as colored resistivity profiles within the GIS environment. To assist interpretation, the inverted AEM profiles were plotted using a consistent color scale, and all of the datasets for each NRD were placed in the same projected coordinate system. This allowed the data to be examined at varying spatial scales, and for data to be iteratively displayed or hidden to fully examine how the geophysical data correlated with complementary datasets. The overview of the process of creating BOA maps from inverted AEM profiles is described below with a more detailed discussion included in the subsequent subsections. Geologic interpretation involved manually picking locations (BOA elevations) on the displayed AEM profile by the project geophysicist, hydrologist, and geologist. These locations, or picks, of the BOA (herein referred to as BOA picks; typically the top of the Brule) were then stored in a georeferenced database. The BOA picks were made by comparing the inverted AEM profile along a flight line to the known lithology of the area based on lithologic descriptions and borehole geophysical logs from test holes. Using a GIS to view all available data at one time in a spatially georeferenced manner provides a high degree of confidence in the elevation values for the picks. The point dataset of the BOA picks’ elevation was the input to a surface-interpolation algorithm of the GIS. A contouring algorithm subsequently was used to construct contours of the BOA elevation. The generated contours then were manually adjusted based on the interpreted location of paleovalleys eroded into the BOA surface and associated bedrock highs. The interpreted BOA surface is the result of erosion and subsequent valley-filling fluvial deposition from eastward draining streams (Cannia and others, 2006), and therefore is not expected to contain enclosed depressions. These newly revised contours were compared with land-surface elevation as a consistency check. Where the interpolated BOA intersected land surface, the contours were reshaped manually to follow the 10-m DEM. This was done to correct areas in the final dataset where the BOA elevation exceeded the land surface. As another consistency check, the DOI information (appendix 2, U.S. Geological Survey Crustal Geophysics and Geochemistry Science Center, 2014) was compared to the BOA-pick depth. In nearly every case, the BOA picks were above the DOI depth. In cases where the BOA picks exceeded the DOI, the supported BOA contours are dashed to indicate the contour locations are inferred (fig. 2); however, the inferred BOA contours and BOA picks are included in the final GIS dataset because they are supported by test-hole and other complementary geophysical data.

Hyperspectral surface materials map of quadrangles 3360 and 3460, Kawir-e Naizar (413), Kohe-Mahmudo-Esmailjan (414), Kol-e Namaksar (407), and Ghoriyan (408) quadrangles, Afghanistan, showing iron-bearing minerals and other materials

HYPERSPECTRAL SURFACE MATERIALS MAP OF QUADRANGLES 3360 AND 3460, KAWIR-E NAIZAR (413), KOHE-MAHMUDO-ESMAILJAN (414), KOL-E NAMAKSAR (407), AND GHORIYAN (408) QUADRANGLES, AFGHANISTAN, SHOWING IRON-BEARING MINERALS AND OTHER MATERIALS By Trude V.V. King, Todd M. Hoefen, Raymond F. Kokaly, Keith E. Livo, Michaela R. Johnson, and Stuart A. Giles 2013 SCALE 1:250 000 5 5 0 10 15 20 25 30 35 40 KILOMETERS 10 5 0 5 15 20 MILES Cultural data from digital files from Afghanistan Information Management Service (http://www.aims.org.af) Projection: Universal Transverse Mercator, Zone 41, WGS 1984 Datum Figure 1.—Provinces and selected cities, towns, and villages in the map area. Topography is shown as shaded relief. EXPLANATION OF MATERIAL CLASSES USGS OPEN-FILE REPORT 2013–1203–B AGS OPEN-FILE REPORT (413/414/407/408) 2013–1203–B USGS Afghanistan Project Product No. 226 U.S. DEPARTMENT OF THE INTERIOR U.S. GEOLOGICAL SURVEY AFGHANISTAN MINISTRY OF MINES AFGHANISTAN GEOLOGICAL SURVEY Prepared in cooperation with the U.S. Geological Survey under the auspices of the U.S. Department of Defense Task Force for Business and Stability Operations

Hyperspectral surface materials map of quadrangles 3664 and 3764, Char Shengo (123), Shibirghan (124), Jalajin (117), and Kham-Ab (118) quadrangles, Afghanistan, showing iron-bearing minerals and other materials

124 123 3664 118 117 3764 REFERENCES CITED Clark, R.N., Swayze, G.A., Wise, R.A, Livo, K.E., Hoefen, T.M., Kokaly, R.F., and Sutley, S.J., 2007, USGS digital spectral library splib06a: U.S. Geological Survey Data Series 231. King, T.V.V., Kokaly, R.F., Hoefen, T.M., Dudek, K.B., and Livo, K.E., 2011, Surface materials map of Afghanistan; iron-bearing minerals and other materials: U.S. Geological Survey Scientific Investigations Map 3152–B, one sheet, scale 1:1,100,000. Kokaly, R.F., King, T.V.V., and Hoefen, T.M., 2013, Surface mineral maps of Afghanistan derived from HyMapTM imaging spectrometer data, version 2: U.S. Geological Survey Data Series 787. Kokaly, R.F., King, T.V.V., and Livo, K.E., 2008, Airborne hyperspectral survey of Afghanistan 2007; flight line planning and HyMapTM data collection: U.S. Geological Survey Open-File Report 2008–1235, 14 p. DATA SUMMARY This map shows the spatial distribution of selected iron-bearing minerals and other materials derived from analysis of airborne HyMapTM imaging spectrometer (hyperspectral) data of Afghanistan collected in late 2007 (Kokaly and others, 2008). This map is one in a series of U.S. Geological Survey/Afghanistan Geological Survey quadrangle maps covering Afghanistan and is a subset of the version 2 map of the entire country showing iron-bearing minerals and other materials (Kokaly and others, 2013). This version 2 map improved mineral mapping from the previously published version (King and others, 2011) by refining the classification procedures, especially in areas having wet soils. The version 2 map more accurately represents the mineral distributions and contains an additional mineral classification (FeFe type 3). Flown at an altitude of 50,000 feet (15,240 meters (m)), the HyMapTM imaging spectrometer measured reflected sunlight in 128 channels, covering wavelengths between 0.4 and 2.5 μm. The data were georeferenced, atmospherically corrected and converted to apparent surface reflectance, empirically adjusted using ground-based reflectance measurements, and combined into a mosaic with 23-m pixel spacing. Variations in water vapor and dust content of the atmosphere, in solar angle, and in surface elevation complicated correction; therefore, some classification differences may be present between adjacent flight lines. The reflectance spectrum of each pixel of HyMapTM imaging spectrometer data was compared to the reference materials in a spectral library of minerals, vegetation, water, and other materials (Clark and others, 2007). Minerals occurring abundantly at the surface and those having unique spectral features were easily detected and discriminated. Minerals having slightly different compositions but similar spectral features were less easily discriminated; thus, some map classes consist of several minerals having similar spectra, such as “Goethite and jarosite.” A designation of “Not classified” was assigned to the pixel when there was no match with reference spectra. Further information regarding the processing procedures is presented in King and others (2011) and Kokaly and others (2013). International boundary City, town, or village Peak; elevation in meters 3725

Hyperspectral surface materials map of quadrangles 3664 and 3764, Char Shengo (123), Shibirghan (124), Jalajin (117), and Kham-Ab (118) quadrangles, Afghanistan, showing carbonates, phyllosilicates, sulfates, altered minerals, and other materials

124 123 3664 118 117 3764 DATA SUMMARY This map shows the spatial distribution of selected carbonates, phyllosilicates, sulfates, altered minerals, and other materials derived from analysis of airborne HyMapTM imaging spectrometer (hyperspectral) data of Afghanistan collected in late 2007 (Kokaly and others, 2008). This map is one in a series of U.S. Geological Survey/Afghanistan Geological Survey quadrangle maps covering Afghanistan and is a subset of the version 2 map of the entire country showing carbonates, phyllosilicates, sulfates, altered minerals, and other materials (Kokaly and others, 2013). This version 2 map improved mineral mapping from the previously published version (Kokaly and others, 2011) by refining the classification procedures, especially in areas having wet soils. The version 2 map more accurately represents the mineral distributions and contains modifications to the material class names, as well as an additional mineral classification (Carbonate and clay/muscovite). Flown at an altitude of 50,000 feet (15,240 meters (m)), the HyMapTM imaging spectrometer measured reflected sunlight in 128 channels, covering wavelengths between 0.4 and 2.5 μm. The data were georeferenced, atmospherically corrected and converted to apparent surface reflectance, empirically adjusted using ground-based reflectance measurements, and combined into a mosaic with 23-m pixel spacing. Variations in water vapor and dust content of the atmosphere, in solar angle, and in surface elevation complicated correction; therefore, some classification differences may be present between adjacent flight lines. The reflectance spectrum of each pixel of HyMapTM imaging spectrometer data was compared to the reference materials in a spectral library of minerals, vegetation, water, and other materials (Clark and others, 2007). Minerals occurring abundantly at the surface and those having unique spectral features were easily detected and discriminated. Minerals having slightly different compositions but similar spectral features were less easily discriminated; thus, some map classes consist of several minerals having similar spectra, such as “Epidote or chlorite.” A designation of “Not classified” was assigned to the pixel when there was no match with reference spectra. Further information regarding the processing procedures is presented in Kokaly and others (2011, 2013).