Dean Riley

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Scholarly Contributions

Chapters

• Riley, D., & Hecker, C. (2013). Mineral mapping with airborne hyperspectral thermal infrared remote sensing at Cuprite, Nevada, USA. In Thermal Infrared Remote Sensing: Sensors, Methods, Applications(pp 495-514). Springer. doi:10.1007/978-94-007-6639-6_24
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  This is a case example of mineral mapping of unaltered and altered rocks at the Cuprite mining district, southwestern Nevada using the Spatially Enhanced Broadband Array Spectrograph System (SEBASS), a thermal infrared hyperspectral sensor that collects radiance measurements in the mid-wave infrared and thermal infrared portions of the electromagnetic spectrum. Cuprite, Nevada has been a test bed for a variety of multispectral and hyperspectral sensors that have predominantly covered the visible through short-wave infrared portion of the electromagnetic spectrum. In 2008, 20 SEBASS flight lines were collected at an average altitude of 4,735 m yielding an average 3.35 m ground sample distance (GSD). Rock forming and alteration minerals found in this mining district have reststrahlen features (emission minima due to fast changes in refractive index with wavelength) in the thermal infrared portion of the electromagnetic spectrum (7.5-13.5 µm). Mineral mapping with hyperspectral thermal infrared data provides unique and complementary information to visible-shortwave (0.4-2.5 µm) hyperspectral data. Mineral maps were produced using a spectral feature fitting algorithm with publicly available mineral spectral libraries containing signatures. These mineral maps were compared to the geological and alteration maps along with mineral maps generated by previous studies of visible-shortwave infrared hyperspectral sensors to assess some of the difference in mineral mapping with a hyperspectral thermal infrared sensor. This study shows that hyperspectral thermal infrared data can spectrally map rock forming minerals associated with unaltered rocks and alteration minerals associated with different phases of alteration in altered rocks at Cuprite, Nevada.

Journals/Publications

• Wellman, E. C., Riley, D., Hughes, A., Risso, N., Momayez, M., & Kemeny, J. (2025). A proposed concept for classifying uniaxial compressive strength (UCS) from SWIR hyperspectral data. Engineering Geology, 356(Issue). doi:10.1016/j.enggeo.2025.108300
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  With the development of lower-cost and portable spectral imagers and spectral radiometers, the question arises: Can hyperspectral image data be used to estimate the Unconfined Compressive Strength (UCS) of rock? Reflectance, emissivity, absorption, and transmission are fundamental properties of rock and minerals. This study focuses on correlating data from non-destructive hyperspectral images and destructive test methods. Hyperspectral images of 32 altered granite samples were acquired in the Shortwave Infrared (SWIR). The reflectance from the 1000 to 2500 nm range of core samples was analyzed. The primary objective of this study is to identify key spectral features that correlate with rock strength and classify samples into ISRM strength categories for weak, moderately strong, and strong rock. The methodology encompasses data preprocessing, feature extraction based on deviations from the mean spectral response, and statistical analysis to identify significant spectral components. The k-Nearest Neighbor (kNN) classifier demonstrated reliable performance for moderately strong and strong rock categories, achieving an overall accuracy of 90 %. This paper outlines the experimental procedure, machine learning analysis methods, and a recommended path forward for further developing this technique. The ultimate goal is to develop additional methods for quantifying UCS from hyperspectral images of both surface and drill core data, utilizing International Society of Rock Mechanics (ISRM) classification guidelines.
• He, J., Riley, D., & Barton, I. (2024). Is Endmember Extraction a Critical Step in the Analysis of Hyperspectral Images in Mining Environments?. Remote Sensing, 16(12). doi:10.3390/rs16122137
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  Hyperspectral imaging systems (HSIs) are becoming widespread in the mining industry for mineral classification. The spectral features detectable from near infrared to long-wave infrared make HSIs a potentially efficient tool for exploration, clay mapping, and leach pad modeling. However, the redundancy of hyperspectral data makes the analysis of hyperspectral images complicated and slow. Many researchers have proposed different algorithms and strategies to speed up processing and increase accuracy. These procedures rely on endmember extraction as one of the critical steps. However, no one has tested whether endmember extraction actually improves accuracy under all circumstances. Eliminating endmember extraction, if possible, would speed up the analysis of hyperspectral data. This study tested whether endmember extraction improves the accuracy and efficiency of mapping materials at leach pads, which are among the most complicated situations in mining environments. We compared the accuracy of abundance maps produced with fully constrained least squares (FCLS) (a) with endmember extraction by N-FINDR and (b) without endmember extraction, using a spectral library instead. The results from endmember extraction showed lower accuracy than the results from using a spectral library, probably because the spectral data were noisy and the scanned materials were mixtures. The application of FCLS to hyperspectral images provides useful information for metallurgists. The abundance maps showed that kaolinite, muscovite, and precipitation (hexahydrite and pickeringite) were the dominant minerals on the leach pad. The abundance maps of pipes and precipitation can be used to monitor leaching conditions. Lixiviant ponds mapped out in the abundance map of water can indicate saturation. This technique can also detect organic leakage and agglomeration effectiveness, but it will need different wavelength ranges and more future study. This paper also suggests best practices for using hyperspectral imaging systems to map leach pads.
• Salati, S., Barton, M., Neilson, J., Richardson, C., Orent, E., & Riley, D. (2024). Abandoned mine lands inventory: Progress, collaboration and challenges in Arizona. Mining Engineering, 76(4).
• Hecker, C., van Ruitenbeek, F., Bakker, W., Fagbohun, B., Riley, D., van der Werff, H., & van der Meer, F. (2019). Mapping the wavelength position of mineral features in hyperspectral thermal infrared data. International Journal of Applied Earth Observation and Geoinformation, 79. doi:10.1016/j.jag.2019.02.013
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  The Wavelength Mapper is an algorithm that searches for the deepest absorption feature in each pixel of a hyperspectral image. On a per pixel basis, it extracts the wavelength position, which serves as a proxy of the mineralogy and the feature depth as a proxy for the relative abundance. This algorithm has been used with near and shortwave infrared data, but has not yet been tested on hyperspectral thermal infrared images. It is unclear what results are expected when the Wavelength Mapper algorithm is applied to hyperspectral thermal infrared data since reststrahlen features characteristically overlap in emissivity spectra. In this paper, the Wavelength Mapper is tested on a multi-flightline airborne hyperspectral TIR dataset acquired over the Yerington Batholith, Nevada. Observations were made in the 8.05–11.65 μm wavelength range to include thermal spectral features of major rock-forming minerals, and a new color ramp is created to separate quartz-rich rocks from plagioclase-rich rocks. Our results indicate that the Wavelength Mapper creates coherent spatial patterns across flightlines. The results displayed represent different types of igneous and sedimentary rocks, as well as the products of hydrothermal alteration via different colors, mainly based on the relative abundance of quartz, feldspar and garnet, as well as mica and epidote. Comparison with published maps indicate that the Wavelength Mapper represents for each pixel a parameter value that can be linked to the spectrally dominate rock-forming mineral of that area, as mapped with traditional fieldwork methods. In conclusion, the Wavelength Mapper can be applied to airborne hyperspectral TIR data to achieve a simple, repeatable, per-pixel overview map of the dominating rock-forming mineral occurrences.
• Aslett, Z., Taranik, J., & Riley, D. (2018). Mapping rock forming minerals at Boundary Canyon, Death Valey National Park, California, using aerial SEBASS thermal infrared hyperspectral image data. International Journal of Applied Earth Observation and Geoinformation, 64(Issue). doi:10.1016/j.jag.2017.08.001
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  Aerial spatially enhanced broadband array spectrograph system (SEBASS) long-wave infrared (LWIR) hyperspectral image data were used to map the distribution of rock-forming minerals indicative of sedimentary and meta-sedimentary lithologies around Boundary Canyon, Death Valley, California, USA. Collection of data over the Boundary Canyon detachment fault (BCDF) facilitated measurement of numerous lithologies representing a contact between the relatively unmetamorphosed Grapevine Mountains allochthon and the metamorphosed core complex of the Funeral Mountains autochthon. These included quartz-rich sandstone, quartzite, conglomerate, and alluvium; muscovite-rich schist, siltstone, and slate; and carbonate-rich dolomite, limestone, and marble, ranging in age from late Precambrian to Quaternary. Hyperspectral data were reduced in dimensionality and processed to statistically identify and map unique emissivity spectra endmembers. Some minerals (e.g., quartz and muscovite) dominate multiple lithologies, resulting in a limited ability to differentiate them. Abrupt variations in image data emissivity amongst pelitic schists corresponded to amphibolite; these rocks represent gradation from greenschist- to amphibolite-metamorphic facies lithologies. Although the full potential of LWIR hyperspectral image data may not be fully utilized within this study area due to lack of measurable spectral distinction between rocks of similar bulk mineralogy, the high spectral resolution of the image data was useful in characterizing silicate- and carbonate-based sedimentary and meta-sedimentary rocks in proximity to fault contacts, as well as for interpreting some mineral mixtures.
• Scafutto, R., de Souza Filho, C., Riley, D., & de Oliveira, W. (2018). Evaluation of thermal infrared hyperspectral imagery for the detection of onshore methane plumes: Significance for hydrocarbon exploration and monitoring. International Journal of Applied Earth Observation and Geoinformation, 64. doi:10.1016/j.jag.2017.07.002
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  Methane (CH4) is the main constituent of natural gas. Fugitive CH4 emissions partially stem from geological reservoirs (seepages) and leaks in pipelines and petroleum production plants. Airborne hyperspectral sensors with enough spectral and spatial resolution and high signal-to-noise ratio can potentially detect these emissions. Here, a field experiment performed with controlled release CH4 sources was conducted in the Rocky Mountain Oilfield Testing Center (RMOTC), Casper, WY (USA). These sources were configured to deliver diverse emission types (surface and subsurface) and rates (20–1450 scf/hr), simulating natural (seepages) and anthropogenic (pipeline) CH4 leaks. The Aerospace Corporation's SEBASS (Spatially-Enhanced Broadband Array Spectrograph System) sensor acquired hyperspectral thermal infrared data over the experimental site with 128 bands spanning the 7.6 μm–13.5 μm range. The data was acquired with a spatial resolution of 0.5 m at 1500 ft and 0.84 m at 2500 ft above ground level. Radiance images were pre-processed with an adaptation of the In-Scene Atmospheric Compensation algorithm and converted to emissivity through the Emissivity Normalization algorithm. The data was processed with a Matched Filter. Results allowed the separation between endmembers related to the spectral signature of CH4 from the background. Pixels containing CH4 signatures (absorption bands at 7.69 μm and 7.88 μm) were highlighted and the gas plumes mapped with high definition in the imagery. The dispersion of the mapped plumes is consistent with the wind direction measured independently during the experiment. Variations in the dimension of mapped gas plumes were proportional to the emission rate of each CH4 source. Spectral analysis of the signatures within the plumes shows that CH4 spectral absorption features are sharper and deeper in pixels located near the emitting source, revealing regions with higher gas density and assisting in locating CH4 sources in the field accurately. These results indicate that thermal infrared hyperspectral imaging can support the oil industry profusely, by revealing new petroleum plays through direct detection of gaseous hydrocarbon seepages, serving as tools to monitor leaks along pipelines and oil processing plants, while simultaneously refining estimates of CH4 emissions.
• Hecker, C., Riley, D., Van Der Meijde, M., & Van Der Meer, F. (2016). Noise simulation and correction in synthetic airborne TIR data for mineral quantification. IEEE Transactions on Geoscience and Remote Sensing, 54(3). doi:10.1109/tgrs.2015.2482386
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  Rock-forming minerals (such as feldspar and quartz) can be identified and quantified from thermal infrared (TIR) laboratory spectroscopy using spectral models. This paper uses synthetic airborne TIR spectra to test whether the hyperspectral Spatially Enhanced Broadband Array Spectrograph System (SEBASS) would theoretically be able to detect quartz and feldspar minerals and quantitatively predict mineral modes in felsic igneous rocks. Data from a previous laboratory study were used to simulate TIR spectra with band locations and noise levels of the SEBASS sensor. The quantitative partial least squares regression (PLSR) models from that study were applied to newly created synthetic SEBASS data, and results were compared with the predictions from the previous study. Predicted compositions based on SEBASS band positions are nearly identical $(\rho = 0.995)$ to those based on laboratory resolution. Results are still reliable [prediction errors within 0.4% (absolute)] to the original laboratory PLSR predictions when adding up to 1% noise (about five times the SEBASS noise level) to the synthetic data. Prediction errors rapidly increase when noise levels beyond 1% are used. These results show that SEBASS' spectral resolution, spectral coverage, and signal-to-noise levels are sufficient to quantitatively predict quartz and feldspar amounts, and feldspar compositions with models based on PLSR. Spectral distortions, such as reduced spectral contrast, tilts, and vertical shifts, must be compensated for before these quantitative models are applied. A mean and standard deviation (MASD) normalization is proposed using a set of ground data for compensating systematic errors that are common to all image pixels.

Proceedings Publications

• Riley, D. N., & Barton, I. F. (2024). Integration of Hyperspectral Imaging and Geometallurgy. In 31st IMPC-International Mineral Processing Congress, IMPC 2024.
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  This talk covers the integration of hyperspectral imaging in geometallurgy. Imaging spectroscopy (aka hyperspectral imaging) is an important and powerful tool for mineral identification. Effective mineral identification using imaging spectroscopy is dependent on many factors. A mineral’s absorption features wavelengths, grain size, and the scale of imaging spectroscopy measurements are important considerations along with environmental constraints. In addition to scale, data availability, cost, spectral resolution, signal-to-noise ratio (SNR) impact the data selection process. These considerations lead to image processing, analysis, and interpretation focused on mineral identification, followed by integration and modeling with other geological, geochemical, metallurgical, and mineral processing data to satisfy the geometallurgical objective.
• Tenorio, V. O., Riley, D., Anani, A., Akbulut, N., Heath, G., Werner, J., & Risso, N. (2024). Outlining a Roadmap for the Deployment of a Digital Twin System for the San Xavier Mine Laboratory. In MINEXCHANGE 2024 SME Annual Conference and Expo.
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  Implementing a Digital Twin at the San Xavier Mine Laboratory (Sahuarita, AZ), requires a network redesign with a robust architecture. The goal is to create an ecosystem in where all personnel and equipment can be monitored in real-time from the University of Arizona campus, visualizing the site in a digital terrain model. A wireless mesh will help to test robots with autonomous features. Expected outcomes include data retrieval and analytics, the evolution of communications and safety protocols, tele-operation, and an innovated approach for managing the site with new supervision challenges. A timeline with expected commissioning benchmarks is also included.
• Tenorio Gutierrez, V. O., Anani, A., Riley, D., Risso, M. ., Heath, G., Akbulut, N. B., & Werner, J. D. (2024, Spring).

  Preprint 24-069 Outlining a Roadmap for the Deployment of a Digital Twin System for the San Xavier Mine Laboratory

  . In 2024 SME MineXchange. SME Annual Meeting, Feb. 25 – Feb 28, 2024, Phoenix, AZ, 4.
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  Implementing a Digital Twin at the San Xavier Mine Laboratory (Sahuarita, AZ), requires a network redesign with a robust architecture. The goal is to create an ecosystem in where all personnel and equipment can be monitored in real-time from the University of Arizona campus, visualizing the site in a digital terrain model. A wireless mesh will help to test robots with autonomous features. Expected outcomes include data retrieval and analytics, the evolution of communications and safety protocols, tele-operation, and an innovated approach for managing the site with new supervision challenges. A timeline with expected commissioning benchmarks is also included. Keywords: Autonomous Equipment, Data Collection, Digital Twin, Internet of Things, Network-based, Systems Integration, Wireless Mesh
• Aslett, Z., Taranik, J., & Riley, D. (2008). Mapping rock-forming minerals at daylight pass, death valley national park, california, using sebass thermal-infrared hyperspectral image data. In 2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings, 3.
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  Rock-forming minerals comprise the bulk of the rocks found at the surface of the Earth. These include, amongst others, quartz, feldspars, pyroxenes, micas and carbonates, all of which possess diagnostic emission features in the thermal-infrared portion of the electromagnetic spectrum. Using established emission spectra libraries we sought to map the distribution of these minerals using aerial remotely-sensed data centered on Daylight Pass, an alluvial wash dissecting the Grapevine Mountains to the northwest and the Funeral Mountains to the southeast, both of which effectively form the northeast perimeter of Death Valley. An abundance of Late Proterozoic and Cambrian sedimentary beds of dolomite, siltstone and sandstones, in addition to low- to moderate-grade metamorphic rocks form the bulk geology of the area of study, which is largely devoid of vegetation. Thermal-infrared spatially enhanced broadband array spectrograph system (SEBASS) hyperspectral image data was collected at Daylight Pass in mid-July of 2007. Standard reflectance hyperspectral processing techniques were implemented to reduce data dimensionality and, by referencing the emission spectra of both library and laboratory-measured ground specimens, we were able to successfully map the distribution of dominant rock-forming minerals in the form of outcrops and weathering products with a high degree of confidence. © 2008 IEEE.