Imaging spectroscopy of jarosite cement in the Jurassic Navajo Sandstone

Bell, Julianne H.; Bowen, Brenda B.; Martini, Brigette A.

doi:10.1016/j.rse.2010.05.002

Cited by 16 publications

(13 citation statements)

References 32 publications

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“…A case in point is the suite of Fe-sulfate and oxide minerals successfully mapped using AVIRIS data over the Dutton Basin Anticline (Staskowski et al, 2004). In a very arid environment, however, jarosite has reported to endure weathering and constitute a potential target over several microseepage-related facies (Bell et al, 2010;Elwood Madden et al, 2004;Everett et al, 2002;Perry and Kruse, 2010). Ultimately, secondary hematite cement formed due to severe and prolonged oxidation of initial ferrous minerals have been observed in several oil and gas fields (Donovan et al, 1979(Donovan et al, -1982Kirkland et al, 1995;Segal et al, 1986).…”

Section: The Inadequacy Of the Microseepage Modelmentioning

confidence: 99%

Spectral remote sensing for onshore seepage characterization: A critical overview

Asadzadeh

Filho

2017

Earth-Science Reviews

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In this article, we overview the application of spectral remote sensing data collected by multi-, and hyperspectral instruments in the visible-near infrared (VNIR), short-wave infrared (SWIR), and longwave infrared (LWIR) wavelengths for characterization of seepage systems as an exploration indicator of subsurface hydrocarbon (HC) accumulations. Two seepage systems namely macro-, and microseepage are recognized. A macroseepage is defined as visible indications of oil and gas on the surface and in the air detectable directly by a remote sensing approach. A microseepage is defined as invisible traces of light HCs in soils and sediments that are detectable by its secondary footprints in the strata, hence an indirect remote sensing target. Based on these broad categories, firstly, a comprehensive set of well-described and reliable remote sensing case studies available in the literature are thoroughly reviewed and then systematically assessed as regards the methodological shortcomings and scantiness in data gathering, processing, and interpretation. The work subsequently attempts to go through seminal papers published on microseepage concept and interrelated geochemical and geophysical techniques, exhumed HC reservoirs, lab-based spectroscopic analysis of petroleum and other related disciplines from a remote sensing standpoint. The aim is to enrich the discussion and highlight the still unexplored capabilities of this technique in accomplishing exploration objectives using the concept of seepage system. Aspects of seepage phenomenon in environmental pollution and uncertainties associated with their role in global warming are also underlined.This work benefits from illustrative products generated over two study areas located in the Ventura Basin, State of California, USA and the Tucano Basin, State of Bahia, Brazil known to host distinctive macro-, and microseepage systems, respectively. In conclusion, we recommend further research over a diverse range of seepage systems and advocate for a mature conceptual model for microseepage phenomenon. ), whereas microseeps, which are argued to occur in a near vertical fashion over an accumulation, are employed as a targeting tool for petroleum exploration.Recent investigations have also revealed that seeps are a potent source of methane (with ethane and propane) greenhouse gasses to the atmosphere. It has been estimated that in the natural methane budget, seeps are the second most important source of emissions after wetlands. The estimates also reveal that onshore seepages are a more significant emitter of CH 4 than their offshore counterparts (Etiope, 2015;Etiope and Ciccioli, 2009;Etiope and Klusman, 2010;Etiope et al., 2008).Over the years, a diverse range of techniques, including remote sensing, has been employed for seepage detection. The remote sensing approach holds a great promise for this aim because it is a fast and costeffective tool applicable to different operational scales for both direct and indirect seepage mapping. In the marine environment, this technology alre...

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Section: The Inadequacy Of the Microseepage Modelmentioning

confidence: 99%

Spectral remote sensing for onshore seepage characterization: A critical overview

Asadzadeh

Filho

2017

Earth-Science Reviews

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“…The classification itself is based on a user-defined threshold of α, while α itself can be interpreted as a value quantifying the similarity between endmember and target spectra (Bell, Breitler Bowen, & Martini, 2010). The smaller the angle α, the more similar are the two compared spectra (Exelis Visual Information Solutions, 2013).…”

Section: Glacier Surface Classification and Estimation Of Pixel Abundmentioning

confidence: 99%

“…Bell et al, 2010;Petropoulos, Vadrevu, Xanthopoulos, Karantounias, & Scholze, 2010). The high spatial and spectral resolution of the APEX sensor enables a clear distinction between surface materials that have subtle spectral contrasts, e.g.…”

Section: Mapping Of Glacier Surface Materialsmentioning

confidence: 99%

Imaging spectroscopy to assess the composition of ice surface materials and their impact on glacier mass balance

Naegeli

Damm

Huss

et al. 2015

Remote Sensing of Environment

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Glacier surfaces are not only composed of ice or snow but are heterogeneous mixtures of different materials. The occurrence and dynamics of light-absorbing impurities affect ice surface characteristics and strongly influence glacier melt processes. However, our understanding of the spatial distribution of impurities and their impact on ice surface characteristics and the glacier's energy budget is still limited. We use imaging spectroscopy in combination with in-situ experiments to assess the composition of ice surface materials and their respective impact on surface albedo and glacier melt rates. Spectroscopy data were acquired in August 2013 using the Airborne Prism EXperiment (APEX) imaging spectrometer and were used to map the abundances of six predominant surface materials on Glacier de la Plaine Morte, Swiss Alps. A pixel-based classification revealed that about 10% of the ice surface is covered with snow, water or debris. The remaining 90% of the surface can be divided into three types of glacier ice, namely~7% dirty ice,~43% pure ice and~39% bright ice. Spatially distributed spectral albedo derived from APEX reflectance data in combination with in-situ multi-angular spectroscopic measurements was used to analyse albedo patterns present on the glacier surface. About 85% of all pixels exhibit a low albedo between 0.1 and 0.4 (mean albedo 0.29 ± 0.12), indicating that Glacier de la Plaine Morte is covered with a significant amount of light-absorbing impurities, resulting in a strong ice-albedo feedback during the ablation season. Using a pixel-based albedo map instead of a constant albedo for ice (0.34) as input for a mass balance model revealed that the glacier-wide total ablation remained similar (10% difference). However, the large local variations in mass balance can only be reproduced using the pixel-based albedo derived from APEX, emphasizing the need to quantify spatial albedo differences as an important input for glacier mass balance models.

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“…The errors with which wavelength position estimated by each method could estimate the proportion of goethite in samples were determined by converting the estimated wavelength position to values representing the proportion of goethite. To do this, linear regressions of the proportion of goethite (derived from laboratory XRD analyses) on wavelength position (determined directly from ASD spectra) were done separately for each surface (1)(2)(3)(4). Coefficients from these regressions were then applied to the wavelength positions estimated by GP and MLR to derive the proportion of goethite.…”

Section: F Data Analyses 1) Data For Deriving the Mlr Coefficients Amentioning

confidence: 99%

Gaussian Processes for Estimating Wavelength Position of the Ferric Iron Crystal Field Feature at $\sim$900 nm From Hyperspectral Imagery Acquired in the Short-Wave Infrared (1002–1355 nm)

Murphy

Chlingaryan

Melkumyan

2015

IEEE Trans. Geosci. Remote Sensing

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Many economically important minerals have absorption features in the short-wave infrared (SWIR; 2000-2500 nm). Sensors which measure this part of the spectrum cannot detect the wavelength minimum of a feature at ∼900 nm (F 900 ), indicative of ferric iron mineralogy. A method based on Gaussian processes (GPs) was developed and compared with multiple linear regression (MLR) to estimate the wavelength position of F 900 from SWIR data (1002-1355 nm). SWIR data with different signal-to-noise ratios were acquired from crushed rock samples by a nonimaging spectrometer and an imaging spectrometer. GP estimates of wavelength position were converted to the proportion of goethite using coefficients from a regression of the proportion of goethite determined from X-ray diffraction (XRD) on wavelength position measured directly from spectra. GP-estimated wavelength positions were within the 2-nm and ∼4-nm root-mean-square error of measurements made directly from spectra for nonimaging and imaging spectrometer data, respectively. Proportions of goethite derived from these estimates were respectively within 4% and 6% of the values measured by XRD. MLR performed poorly compared to GPs when applied to data with no added noise and failed when applied to data with added noise or to imaging spectrometer data. These findings indicate that the wavelength position of F 900 -an indicator of ferric iron mineralogy-can be estimated from data acquired at SWIR wavelengths (1002-1355 nm). This opens up possibilities for using a single (SWIR) sensor to acquire information on ferric iron mineralogy (using F 900 ) and other minerals with diagnostic absorptions between 1000 and 2500 nm.

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Imaging spectroscopy of jarosite cement in the Jurassic Navajo Sandstone

Cited by 16 publications

References 32 publications

Spectral remote sensing for onshore seepage characterization: A critical overview

Spectral remote sensing for onshore seepage characterization: A critical overview

Imaging spectroscopy to assess the composition of ice surface materials and their impact on glacier mass balance

Gaussian Processes for Estimating Wavelength Position of the Ferric Iron Crystal Field Feature at $\sim$900 nm From Hyperspectral Imagery Acquired in the Short-Wave Infrared (1002–1355 nm)

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