Value of Mineralogical Monitoring for the Mining and Minerals Industry In memory of Prof. Dr. Herbert Pöllmann

This Special Issue, focusing on the value of mineralogical monitoring for the mining and minerals industry, should include detailed investigations and characterizations of minerals and ores of the following fields for ore and process control: Lithium ores—determination of lithium contents by XRD methods; Copper ores and their different mineralogy; Nickel lateritic ores; Iron ores and sinter; Bauxite and bauxite overburden; Heavy mineral sands. The value of quantitative mineralogical analysis, mainly by XRD methods, combined with other techniques for the evaluation of typical metal ores and other important minerals, will be shown and demonstrated for different minerals. The different steps of mineral processing and metal contents bound to different minerals will be included. Additionally, some processing steps, mineral enrichments, and optimization of mineral determinations using XRD will be demonstrated. Statistical methods for the treatment of a large set of XRD patterns of ores and mineral concentrates, as well as their value for the characterization of mineral concentrates and ores, will be demonstrated. Determinations of metal concentrations in minerals by different methods will be included, as well as the direct prediction of process parameters from raw XRD data

Summaries of the Sixth Annual JPL Airborne Earth Science Workshop

This publication contains the summaries for the Sixth Annual JPL Airborne Earth Science Workshop, held in Pasadena, California, on March 4-8, 1996. The main workshop is divided into two smaller workshops as follows: (1) The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) workshop, on March 4-6. The summaries for this workshop appear in Volume 1; (2) The Airborne Synthetic Aperture Radar (AIRSAR) workshop, on March 6-8. The summaries for this workshop appear in Volume 2

Review of soil salinity assessment for agriculture across multiple scales using proximal and/or remote sensors

Mapping and monitoring soil spatial variability is particularly problematic for temporally and spatially dynamic properties such as soil salinity. The tools necessary to address this classic problem only reached maturity within the past 2 decades to enable field- to regional-scale salinity assessment of the root zone, including GPS, GIS, geophysical techniques involving proximal and remote sensors, and a greater understanding of apparent soil electrical conductivity (ECa) and multi- and hyperspectral imagery. The concurrent development and application of these tools have made it possible to map soil salinity across multiple scales, which back in the 1980s was prohibitively expensive and impractical even at field scale. The combination of ECa-directed soil sampling and remote imagery has played a key role in mapping and monitoring soil salinity at large spatial extents with accuracy sufficient for applications ranging from field-scale site-specific management to statewide water allocation management to control salinity within irrigation districts. The objective of this paper is: (i) to present a review of the geophysical and remote imagery techniques used to assess soil salinity variability within the root zone from field to regional scales; (ii) to elucidate gaps in our knowledge and understanding of mapping soil salinity; and (iii) to synthesize existing knowledge to give new insight into the direction soil salinity mapping is heading to benefit policy makers, land resource managers, producers, agriculture consultants, extension specialists, and resource conservation field staff. The review covers the need and justification for mapping and monitoring salinity, basic concepts of soil salinity and its measurement, past geophysical and remote imagery research critical to salinity assessment, current approaches for mapping salinity at different scales, milestones in multi-scale salinity assessment, and future direction of field- to regional-scale salinity assessment

Matched filter stochastic background characterization for hyperspectral target detection

Algorithms exploiting hyperspectral imagery for target detection have continually evolved to provide improved detection results. Adaptive matched filters, which may be derived in many different scientific fields, can be used to locate spectral targets by modeling scene background as either structured geometric) with a set of endmembers (basis vectors) or as unstructured stochastic) with a covariance matrix. In unstructured background research, various methods of calculating the background covariance matrix have been developed, each involving either the removal of target signatures from the background model or the segmenting of image data into spatial or spectral subsets. The objective of these methods is to derive a background which matches the source of mixture interference for the detection of sub pixel targets, or matches the source of false alarms in the scene for the detection of fully resolved targets. In addition, these techniques increase the multivariate normality of the data from which the background is characterized, thus increasing adherence to the normality assumption inherent in the matched filter and ultimately improving target detection results. Such techniques for improved background characterization are widely practiced but not well documented or compared. This thesis will establish a strong theoretical foundation, describing the necessary preprocessing of hyperspectral imagery, deriving the spectral matched filter, and capturing current methods of unstructured background characterization. The extensive experimentation will allow for a comparative evaluation of several current unstructured background characterization methods as well as some new methods which improve stochastic modeling of the background. The results will show that consistent improvements over the scene-wide statistics can be achieved through spatial or spectral subsetting, and analysis of the results provides insight into the tradespaces of matching the interference, background multivariate normality and target exclusion for these techniques

APPLICATIONS OF INNOVATIVE BUILDING MATERIAL AND COMPUTER VISION METHODS IN GEOTECHNICAL ENGINEERING

Ph.D

Applications of Artificial Intelligence (AI) Techniques on Remote Sensing Data for Ground Failure Detection, Mobility Assessment, and Infrastructure Monitoring

This dissertation incorporates artificial intelligence (AI) techniques on remote-sensing data for ground failure detection, mobility assessment, and infrastructure monitoring. First, the use of AI for landslide detection is investigated. Although an increasing body of work is observed on this topic, a systematic investigation of the factors (input and algorithms) that affect the accuracy of the machine-learning co-seismic landslide detection model has not been attempted. This study leverages the state-of-the-art detailed 3D inventory of more than 700 landslides triggered by the Mw 6.5 Lefkada earthquake on November 17, 2015. The result highlights that feature selection is the most essential factor for successful landslide detection, but the number of features needed is not particularly high. The geospatial distribution and size of the training sample are also important. Input data resolution and machine learning algorithms are the secondary factors that influence detection accuracy. Geospatial distribution affects the training sample size needed to create an accurate landslide detection model, and a wider geospatial distribution of training samples generates a more precise landslide detection model. The work is expanded to consider the generality of the above results for two additional co-seismic landslide events, namely the 2016 Kaikōura earthquake and the 2021 Nippes earthquake, with the goal to identify the commonalities and differences in the success of the machine learning-based landslide detection model. It is found that although feature selection is the most vital factor in the landslide detection model, both topographic and spectral features are useful, with spectral features being most significant in two of the study areas due to their geologic and climatic setting. The input data resolution and training sample size similarly influence the model performance for the three earthquake events, but the importance of segmentation and machine learning algorithms varies across events.Next, a simple mechanistic-model that is based on the Voellmy friction law as incorporated in Rapid Mass Movement Simulation Debris Flow (RAMMS-DF) is tested against statistically significant observations of landslide runout for hundreds of mapped rock avalanches triggered by the Mw 6.5 Lefkada earthquake on November 17, 2015. It is found that the dry-Coulomb friction (μ) controls the simulation's performance, whereas the simulation is less sensitive to viscous-turbulent friction (ξ), especially for large values of ξ. The simulation's accuracy positively correlates with landslide source area, height, and 3D travel distance. The model does not match very well landslides with small source areas (<4,000 m2), but in these cases, it systematically overestimates landslide runout, i.e., it is inherently conservative. The fourth part of this dissertation leverages lessons learned from the damage observed along Highway 1 during the January 2021 atmospheric river event. A remote sensing-based methodology is developed for system-level monitoring and assessment following natural disasters. It is shown that remote sensing indicators of vegetation loss can detect the occurrence of debris flows and ground failure and indicate the severity of highway damage. Damage severity is correlated to increasingly broader distribution and a lower minimum value of the vegetation loss curve. The last part of this dissertation aims to develop a methodology for fully autonomous remote-sensing-based monitoring of mines. Specifically, the detection of mining instability using high-resolution satellite imagery for eight recent failure cases is considered: the 2022 Jagersfontein tailings dam failure, the 2022 Pau Branco iron ore mine landslide, the 2020 Carmen copper mine landslide, the 2020 Singrauli fly ash dam breach, the 2019 Córrego De Feijão tailings dam failure, the 2018 Cadia gold mine tailings dam failure, the 2014 Mount Polley mine tailing dam failure, and the 2013 Bingham Canyon copper mine landslide. The results show that remote sensing indexes can successfully detect mining failure. In summary, this dissertation demonstrates that new approaches that leverage Artificial Intelligence (AI) and remote-sensing data can be valuable for ground instability detection following natural hazards and can set the stage for fully-autonomous infrastructure monitoring in an expedited and efficient manner