Imaging spectrometry and the Ronda peridotites

In Chapter 2, the results of detailed geologic mapping of sedimentary strata of the Subbeticum of Ronda are presented including a stratigraphic description of several sections near Ronda, El Burgo, Ardales, Teba, and Arriate in the Ronda-Málaga area. These are used to develop time-facies profiles on basis of which the paleogeography is reconstructed with emphasis on Miocene turning points. Timing of rift-related Mesozoic tectonic events, Early Miocene compressional tectonic events, and Late Miocene strike-slip tectonic events are derived from the result of a backstripping analysis using a synthetic composite stratigraphic section as input data. The final results are presented within the framework of Western Mediterranean plate tectonics yielding a number of deformation events characterizing the tectono-sedimentary evolution of the area.A review of recent work on field and microstructures within the Ronda peridotite body is given in Chapter 3. Paleostress analysis of fault-slip data from the peridotites are discussed. These lead to three stress tensors: an extensional, a compressional, and a strike-slip related tensor. Similar work conducted on Cretaceous and Tertiary carbonates of the Subbeticum give identical stress tensors. For the carbonates, tectonic stylolites and extension veins allow to relatively date these stress tensors. This dating together with the timing of tectonic phases from the backstripping analysis is used to derive an alternative model for the emplacement of the Ronda peridotites.Visible and near-infrared reflectance spectra of carbonate minerals, carbonate mineral mixtures, and un-weathered rock samples are discussed in Chapter 4. First, bidirectional reflectance theory is addressed forming the basis for a semi-empirical model to simulate reflectance of mineral mixtures from their single-component reflectance spectra. The following topics are discussed in the remainder of this Chapter: carbonate absorption band position, effects of particle size, effects of sorting, calcite-dolomite mixtures and the shifting 2.3μm absorption band, and the effects of impurities such as aqueous fluid inclusions, transition metal ions, organic matter. Finally, spectra of rock samples are discussed in relation to the mineral spectra.Reflectance spectra of ultrarnafic rocks and minerals forming these rocks are discussed in Chapter 5. After a review of some earlier work, mineral spectra of olivines, orthopyroxenes, and clinopyroxenes are presented. Un-weathered rock spectra of several samples of dunites, lherzolites, harzburgites, and pyroxenites are discussed. The remainder of the Chapter is devoted to the effect of serpentinization on the reflectance spectra of ultrarnafic rocks. Its is demonstrated that the degree of serpentinization can be quantified by using the depth of OH -features occurring near 1.4μm and 2.3μm and the brightness of a sample measured as the reflectivity at 1.6μm. From a study of synthetic mixtures of olivine, serpentine, and magnetite prepared in the laboratory it is derived that the spectral quenching effect of magnetite affects the spectral properties of serpentines with 55 % serpentine minerals or less. Thus highly serpentinized areas can be mapped using the above mentioned characteristics regardless the amount of magnetite.In Chapter 6, reflectance spectra of weathered peridotite and carbonate rock samples, soils, and vegetation, derived from the Ronda-Málaga area, are discussed. Part of this Chapter is based on a case study developed for the Los Pedroches batholith near Almadén (southCentral Spain). A total of 13 soil samples were taken over the contact aureole of the granitic intrusion and analyzed spectrally as well as chemically. The reflectance spectra of relevant minerals are discussed and subsequently the soil spectra. From these spectra, the change of albedo, Fe, AlOH absorption, H 2 O, and OH -absorption over the contact zone are quantified using the spectra. Field spectra using a TM simulator spectrometer are used to link the laboratory spectra and the TM image. Finally, image processing techniques using ratio images are used to map changes in iron content etc of soils over the area. This case study demonstrates the possibility of linking laboratory and pixel spectra when calibrating the data carefully using field spectrometry.In Chapter 7, the preprocessing of the raw imaging spectrometer data is discussed. First, a technical description of the sensors used is given. These are the Airborne Visible and Infrared Imaging spectrometer (AVIRIS) and the GER imaging spectrometer. Next quantification of signal-to- noise ratios is discussed using two alternative methods. The main topic is the atmospheric calibration of imaging spectrometer data. Several methods are used: Flat-field calibration, Internal Average Reflectance method, Empirical line method, Logresidual method, and a atmospheric simulation model. The performance of these methods is compared using a number well-characterized standard field targets in the Cuprite mining district AVIRIS data set and the GERIS data set from southern Spain. Comparison of the results directly reflects the difference between atmospheric calibration in presence or absence of heavy vegetation.If the imaging spectrometer data are properly calibrated, the next logical step would be to use the data to map surface mineralogy. Mineral mapping techniques are treated in Chapter 8. Some techniques have been proposed for this purpose which are not always very successful, many studies use conventional classification techniques. Specific imaging spectrometer techniques are introduced: inverse mixing modelling, spectral angle mapping, and band-depth analysis. The main body of the text deals with a new mineral mapping technique based on indicator kriging. This technique uses automatic zonation techniques to identify absorption features and non-parametric geostatistics to calculate the probability that a pixel belongs to a certain mineralogy. The performance of this technique is discussed in the framework of conventional image classification techniques.In last final Chapter 9, several techniques and methodologies discussed in previous Chapters are applied to the GER 63-channel imaging spectrometer data set flown over the Ronda peridotites and adjacent carbonates which was calibrated using techniques discussed in Chapter 7. Two Case Studies are presented to emphasize the use of imaging spectrometry for geologic mineral exploration purposes. The first Case Study deals with the problem of dolomitization which is an important parameter in oil exploration. Calcite-dolomite mixtures and dolomitization patterns are mapped using the semi-linear model for the position of the carbonate absorption band derived in Chapter 4. Conditional simulation techniques and indicator kriging are used to estimate the calcite content of pixels. The second Case Study addresses the degree of serpentinization in the peridotites; an indication for possible asbestos deposits. The approach is based on the theoretical results derived in Chapter 5. Results of mineral mapping using techniques discussed in Chapter 8, show that several minerals could be mapped using the GER data set which give information on metamorphic facies. These metamorphic minerals bear important conclusions on the conditions of metamorphism related to the emplacement of the peridotites discussed in Chapters 2 and 3

The influence of cap rock composition on hydrocarbon seep type in the Zagros oil fields, a study using Advanced Spaceborne Thermal Emission and Reflection Radiometer mineral map

The persistent natural hydrocarbon seepage in onshore basins challenges observation and exploration technologies, which are required to document and assess these valuable indications of the presence of oil and gas in the subsurface. This paper aims at demonstrating the relationship between the compositional variation of an evaporite cap rock and the types of seeps occurring at the surface. For this purpose, the multispectral Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data was utilized for mapping mineral variations of a petroleum system in the Zagros oil fields. Relative absorption-band depth (RBD), band rationing and the boosted regression trees (BRT) were applied to enhance and classify the mineral composition of evaporite, sandstone, and marly limestone formations. The gas seeps were associated with the areas of gypsum-bearing evaporite cap rock while oil seeps were mostly associated with calcite and clay zones within the cap rock, which was more prone to fracturing during the tectonic activities of the basin. It is suggested that the application of remote sensing in the oil and gas industry could be widened by detection of sleep-induced alteration to assess the efficiency of cap rock and to evaluate the productivity of reservoirs at a regional scale

A back-propagation neural network for mineralogical mapping from AVIRIS data

Imaging spectrometers have the potential to identify surface mineralogy based on the unique absorption features in pixel spectra. A back-propagation neural network (BPN) is introduced to classify Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) of the Cuprite mining district (Nevada) data into mineral maps. The results are compared with the traditional acquired surface mineralogy maps from spectral angle mapping (SAM). There is no misclassification for the training set in the case of BPN; however 17 percent misclassification occurs in SAM. The validation accuracy of the SAM is 69 percent, whereas BPN results in 86 per cent accuracy. The calibration accuracy of the BPN is higher than that of the SAM, suggesting that the training process of BPN is better than that of the SAM. The high classification accuracy obtained with the BPN can be explained by: (1)its ability to deal with complex relationships (e.g., 40 dimensions) and (2) the nature of the dataset, the minerals are highly concentrated and they are mostly represented by pure pixels. This paper demonstrates that BPN has superior classification ability when applied to imaging spectrometer data.Remote SensingImaging Science & Photographic TechnologySCI(E)EI29ARTICLE197-1102