Mineral texture identification using local binary patterns equipped with a Classification and Recognition Updating System (CARUS)

In this paper, a rotation-invariant local binary pattern operator equipped with a local contrast measure (riLBPc) is employed to characterize the type of mineral twinning by inspecting the texture properties of crystals. The proposed method uses photomicrographs of minerals and produces LBP histograms, which might be compared with those included in a predefined database using the Kullback–Leibler divergence-based metric. The paper proposes a new LBP-based scheme for concurrent classification and recognition tasks, followed by a novel online updating routine to enhance the locally developed mineral LBP database. The discriminatory power of the proposed Classification and Recognition Updating System (CARUS) for texture identification scheme is verified for plagioclase, orthoclase, microcline, and quartz minerals with sensitivity (TPR) near 99.9%, 87%, 99.9%, and 96%, and accuracy (ACC) equal to about 99%, 97%, 99%, and 99%, respectively. According to the results, the introduced CARUS system is a promising approach that can be applied in a variety of different fields dealing with classification and feature recognition tasks. © 2022 by the authors

Hemispheric contrasts of ice formation in stratiform supercooled liquid clouds: Long-term observations with the ground-based remote-sensing supersite LACROS

Die vorliegende Arbeit untersucht hemisphärische Unterschiede der heterogenen Eisbildung in unterkühlten Schichtwolken auf Basis von drei Datensätzen, die mit der mobilen bodengebundenen Fernerkundungsplattform LACROS (Leipzig Aerosol and Cloud Remote Observations System; Leipziger Aerosol- und Wolken- Fernerkundungssystem) erhoben wurden. Für die Nordhemisphäre wurden zwischen 2014 und 2018 gesammelte LACROS-Datensätze aus Leipzig (Deutschland, 51,4°N, 12,4°E) und Limassol (Zypern, 34,7°N, 33,0°E) verwendet. Ein zentraler Bestandteil dieser Arbeit war die Umsetzung des mehr als zwei Jahre umfassenden Einsatzes von LACROS im Rahmen der Kampagne DACAPO-PESO (Dynamics Aerosol Clouds And Precipitation Observation in the Pristine Environment of the Southern Ocean; Beobachtung von Dynamik, Aerosol, Wolken und Niederschlag in der unverschmutzen Umgebung des Südozeans) in Punta Arenas, Chile (53,1°S, 70,9°W). Dieser Datensatz stellt die ersten mehrjährigen bodengebundenen Fernerkundungsbeobachtungen in der westlichen Hälfte des Südozeans dar. Durch die Kombination aus Radar- und Lidarinstrumenten, einschließlich der Fähigkeit Vertikalbewegungen zu beobachten, ist es möglich, mit LACROS Aerosol-Wolken-Dynamik-Wechselwirkungen detailliert zu untersuchen. Von großer Bedeutung für die Umsetzung der Arbeit war die durchgeführte Entwicklung und Integration eines automatisierten Datenanalyseschemas. Besonders hervorzuheben sind die kontinuierliche Charakterisierung der Luftmassenherkunft, die Auswertung von multiplen Maxima im Wolkenradar-Dopplerspektrum, eine Methode zur Erkennung von durch Schwerewellen beeinflussten Wolken mit Doppler Lidar und die Integration aller Datenquellen in die verteilte LACROS-Forschungsdatenanwendung. Wichtigste Ergebnisse dieser Arbeit sind, dass atmosphärische Schwerewellen die Bildung und Detektierbarkeit der Eisphase erschweren und dass eine Kopplung von Wolken mit der planetaren Grenzschicht die Häufigkeit der Eisbildung erhöht. Wenn diese beiden Effekte berücksichtigt werden, tritt Eisbildung in Schichtwolken über Punta Arenas etwas weniger häufig auf als über Limassol und Leipzig. Dieser Unterschied kann auf eine geringere Verfügbarkeit von Eiskeimen in der freien Troposphäre über Punta Arenas zurückgeführt werden.:1 Introduction 2 Heterogeneous ice formation in shallow mixed-phase clouds 3 The mobile ground-based remote-sensing facility LACROS 3.1 LACROS instruments 3.1.1 MIRA-35 cloud radar 3.1.2 PollyXT multi-wavelength lidar 3.1.3 StreamLine XR Doppler lidar 3.1.4 Additional instruments and auxillary datasets 3.2 Campaigns under study 3.2.1 CyCARE field campaign 3.2.2 DACAPO-PESO field campaign 3.2.3 Observations at Leipzig 4 Methods and advancements in data processing 4.1 LACROS Research Data Application 4.2 Aerosol statistics based on the PollyNET processing chain 4.3 Estimating moments from radar Doppler spectra 4.4 Synergistic retrieval Cloudnet 4.5 Automated cloud identification 4.6 Gravity-wave detection 4.7 Continuous airmass source attribution 4.8 Transforming the Doppler spectrum into a tree structure 5 Contrasts in temperature, cloud and aerosol profiles 5.1 Occurrence of heterogeneous freezing regime 5.2 Cloud frequency 5.3 Profiles of airmass source 5.4 Aerosol optical properties 5.5 Lidar-based estimate of INP profiles 6 Properties of supercooled stratiform clouds 6.1 Overview on observed clouds 6.2 Case studies 6.2.1 Punta Arenas, 4/5 September 2019: stratiform cloud with variable ice formation 6.2.2 Punta Arenas, 27 September 2019: Wave cloud 6.2.3 Punta Arenas, 12 June 2019: Surface coupling 6.3 Phase occurrence frequency 6.4 Context to lidar-only observations 6.5 Effect of boundary-layer aerosol load on phase occurrence 6.6 Gravity-wave influence on phase occurrence at low temperatures 6.7 Ice-formation frequency of free-tropospheric and fully turbulent clouds 6.8 Contrasts of radar reflectivity factor in the ice virga 7 Multi-peak occurrence statistics of deeper clouds 8 Summary, Conclusions, and Outlook 8.1 Summary and conclusions 8.2 Outlook A Further equations Publication record List of Abbreviations and Acronyms List of Symbols BibliographyThis work investigates hemispheric contrasts of ice formation in stratiform supercooled liquid clouds using observations of three long-term campaigns of the mobile ground-based remote-sensing supersite LACROS (Leipzig Aerosol and Cloud Remote Observations System). For the northern hemisphere, LACROS datasets collected at Leipzig (Germany, 51.4°N, 12.4°E) and Limassol (Cyprus, 34.7°N, 33.0°E) between 2014 and 2018 were used. A key component of this work was the implementation of the more than two-year-long deployment of LACROS as part of the Dynamics Aerosol Clouds And Precipitation Observation in the Pristine Environment of the Southern Ocean (DACAPO-PESO) field campaign at Punta Arenas (Chile, 53.1°S, 70.9°W). The dataset assembled during this campaign resembles the first comprehensive multi-year ground-based remote-sensing dataset in the western part of the Southern Ocean. The synergistic combination of radar and lidar, including the capability to observe vertical velocities, allows detailed investigation of aerosol-cloud-dynamics interaction. One major part of this work was the development and integration of an automated data analysis scheme. Highlights are a continuous time-height-resolved airmass source characterization, a multi-peak analysis algorithm for radar Doppler spectra, a gravity-wave identification method based on Doppler lidar-vertical velocity observation, and the integration of the data sources into the distributed LACROS Research Data Application. The most important results of this work were that atmospheric gravity waves impede the formation and detectability of the ice phase, whereas the coupling of clouds with the planetary boundary layer increases the frequency of ice formation. When these two effects are taken into account, ice formation in stratiform clouds over Punta Arenas occurs slightly less frequent than over Limassol and Leipzig. This difference can be attributed to a lower availability of ice nuclei in the free troposphere over Punta Arenas.:1 Introduction 2 Heterogeneous ice formation in shallow mixed-phase clouds 3 The mobile ground-based remote-sensing facility LACROS 3.1 LACROS instruments 3.1.1 MIRA-35 cloud radar 3.1.2 PollyXT multi-wavelength lidar 3.1.3 StreamLine XR Doppler lidar 3.1.4 Additional instruments and auxillary datasets 3.2 Campaigns under study 3.2.1 CyCARE field campaign 3.2.2 DACAPO-PESO field campaign 3.2.3 Observations at Leipzig 4 Methods and advancements in data processing 4.1 LACROS Research Data Application 4.2 Aerosol statistics based on the PollyNET processing chain 4.3 Estimating moments from radar Doppler spectra 4.4 Synergistic retrieval Cloudnet 4.5 Automated cloud identification 4.6 Gravity-wave detection 4.7 Continuous airmass source attribution 4.8 Transforming the Doppler spectrum into a tree structure 5 Contrasts in temperature, cloud and aerosol profiles 5.1 Occurrence of heterogeneous freezing regime 5.2 Cloud frequency 5.3 Profiles of airmass source 5.4 Aerosol optical properties 5.5 Lidar-based estimate of INP profiles 6 Properties of supercooled stratiform clouds 6.1 Overview on observed clouds 6.2 Case studies 6.2.1 Punta Arenas, 4/5 September 2019: stratiform cloud with variable ice formation 6.2.2 Punta Arenas, 27 September 2019: Wave cloud 6.2.3 Punta Arenas, 12 June 2019: Surface coupling 6.3 Phase occurrence frequency 6.4 Context to lidar-only observations 6.5 Effect of boundary-layer aerosol load on phase occurrence 6.6 Gravity-wave influence on phase occurrence at low temperatures 6.7 Ice-formation frequency of free-tropospheric and fully turbulent clouds 6.8 Contrasts of radar reflectivity factor in the ice virga 7 Multi-peak occurrence statistics of deeper clouds 8 Summary, Conclusions, and Outlook 8.1 Summary and conclusions 8.2 Outlook A Further equations Publication record List of Abbreviations and Acronyms List of Symbols Bibliograph

Target categorization of aerosol and clouds by continuous multiwavelength-polarization lidar measurements

Absolute calibrated signals at 532 and 1064 nm and the depolarization ratio from a multiwavelength lidar are used to categorize primary aerosol but also clouds in high temporal and spatial resolution. Automatically derived particle backscatter coefficient profiles in low temporal resolution (30 min) are applied to calibrate the lidar signals. From these calibrated lidar signals, new atmospheric parameters in temporally high resolution (quasi-particle-backscatter coefficients) are derived. By using thresholds obtained from multiyear, multisite EARLINET (European Aerosol Research Lidar Network) measurements, four aerosol classes (small; large, spherical; large, non-spherical; mixed, partly nonspherical) and several cloud classes (liquid, ice) are defined. Thus, particles are classified by their physical features (shape and size) instead of by source. The methodology is applied to 2 months of continuous observations (24 h a day, 7 days a week) with the multiwavelength-Raman-polarization lidar PollyXT during the High-Definition Clouds and Precipitation for advancing Climate Prediction (HD(CP)2) Observational Prototype Experiment (HOPE) in spring 2013. Cloudnet equipment was operated continuously directly next to the lidar and is used for comparison. By discussing three 24 h case studies, it is shown that the aerosol discrimination is very feasible and informative and gives a good complement to the Cloudnet target categorization. Performing the categorization for the 2-month data set of the entire HOPE campaign, almost 1 million pixel (5 min×30 m) could be analysed with the newly developed tool. We find that the majority of the aerosol trapped in the planetary boundary layer (PBL) was composed of small particles as expected for a heavily populated and industrialized area. Large, spherical aerosol was observed mostly at the top of the PBL and close to the identified cloud bases, indicating the importance of hygroscopic growth of the particles at high relative humidity. Interestingly, it is found that on several days non-spherical particles were dispersed from the ground into the atmosphere

Automated Segmentation of Olivine Phenocrysts in a Volcanic Rock Thin Section Using a Fully Convolutional Neural Network

An example of automated characterization and interpretation of the textural and compositional characteristics of solids phases in thin sections using machine learning (ML) is presented. In our study, we focus on the characterization of olivine in volcanic rocks, which is a phase that is often chemically zoned with variable Mg/(Mg + Fe) ratios, so-called magnesian number or mg#. As the olivine crystals represent only less than 10 vol% of the volcanic rock, a pre-processing step is necessary to automatically detect the phases of interest in the images on a pixel level, which is achieved using Deep Learning. A major contribution of the presented approach is to use backscattered electron (BSE) images to: 1) automatically segment all olivine crystals present in the thin section; 2) determine quantitatively their mg#; and 3) identify different populations depending on zoning type (e.g., normal vs reversal zoning) and textural characteristics (e.g., microlites vs phenocrysts). The segmentation of the olivine crystals is implemented with a pretrained fully convolutional neural network model with DeepLabV3 architecture. The model is trained to identify olivine crystals in backscattered electron images using automatically generated training data. The training data are generated automatically from images which can easily be created from X-Ray element maps. Once the olivines are identified in the BSE images, the relationship between BSE intensity value and mg# is determined using a simple regression based on a set of microprobe measurements. This learned functional relationship can then be applied to all olivine pixels of the thin section. If the highest possible map resolution (1 micron per 1 pixel) is selected for the data acquisition, the full processing time of an entire thin section of (Formula presented.) containing more than 1,500 phenocrysts and 20.000 microliths required 140 h of data acquisition (BSE + X-Ray element maps), 8 h of training and 16 h of segmentation and classification. Our further tests demonstrated that the 140 h of data acquisition can be reduced at least by a factor of 4 since only a part of the thin section area (25% or even less) needs to be used for training. The characterization of each additional thin section would only require the BSE data acquisition time (less than 48 h for a whole thin section), without an additional training step. The paper describes the training and processing in detail, shows analytical results and outlines the potential of this Deep Learning approach for petrological applications, resulting in the automatic characterization and interpretation of mineral textures and compositions with an unprecedented high resolution. Copyright © 2022 Leichter, Almeev, Wittich, Beckmann, Rottensteiner, Holtz and Sester

Single-scattering properties of ellipsoidal dust aerosols constrained by measured dust shape distributions

Most global aerosol models approximate dust as spherical particles, whereas most remote sensing retrieval algorithms approximate dust as spheroidal particles with a shape distribution that conflicts with measurements. These inconsistent and inaccurate shape assumptions generate biases in dust single-scattering properties. Here, we obtain dust single-scattering properties by approximating dust as tri-axial ellipsoidal particles with observationally constrained shape distributions. We find that, relative to the ellipsoidal dust optics obtained here, the spherical dust optics used in most aerosol models underestimate dust single-scattering albedo, mass extinction efficiency, and asymmetry parameter for almost all dust sizes in both the shortwave and longwave spectra. We further find that the ellipsoidal dust optics are in substantially better agreement with observations of the scattering matrix and linear depolarization ratio than the spheroidal dust optics used in most retrieval algorithms. However, relative to observations, the ellipsoidal dust optics overestimate the lidar ratio by underestimating the backscattering intensity by a factor of ~2. This occurs largely because the computational method used to simulate ellipsoidal dust optics (i.e., the improved geometric optics method) underestimates the backscattering intensity by a factor of ~2 relative to other computational methods (e.g., the physical geometric optics method). We conclude that the ellipsoidal dust optics with observationally constrained shape distributions can help improve global aerosol models and possibly remote sensing retrieval algorithms that do not use the backscattering signal.</p

Small business innovation research. Abstracts of 1988 phase 1 awards

Non-proprietary proposal abstracts of Phase 1 Small Business Innovation Research (SBIR) projects supported by NASA are presented. Projects in the fields of aeronautical propulsion, aerodynamics, acoustics, aircraft systems, materials and structures, teleoperators and robots, computer sciences, information systems, data processing, spacecraft propulsion, bioastronautics, satellite communication, and space processing are covered

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

Spatially detailed analysis of drill core samples with Laser-Induced Breakdown Spectroscopy: Detection, classification, and quantification of rare earth elements and lithium

In the transformation towards climate neutral consumption, electric alternatives rise in favour of fossil energy sources in a variety of different fields. Lithium and several elements from the group of Rare Earth Elements (REEs) are of particular importance for modern battery production and the supply of green energy, and therefore play a crucial role for this transformation. Their demand has increased constantly over the last years and an ongoing trend is expected for the future. New instruments and analytical methods for the geochemical investigation of drill cores can support mineral exploration and active mining and thereby help to cope with the growing demand. Laser-Induced Breakdown Spectroscopy (LIBS) is an analytical technique with many advantages for the analysis of drill core material. It has a high measurement speed, no sample preparation is needed, and major, minor as well as trace elements can be detected in a single spectrum under atmospheric conditions. Nevertheless, physical and chemical matrix effects prevent a straightforward analysis of heterogeneous material, which is especially relevant for spatially resolved investigations of drill core samples. This work displays novel methods that enable the analysis of LIBS mappings of large REE- and Li-bearing drill core samples by overcoming the problematic matrix effects with different un- semi- and supervised machine learning algorithms. In the first application, drill core samples of brecciated carbonatites were spatially investigated with LIBS to establish an intensity limit for La using the k-means clustering algorithm. Based on this intensity limit, REE enrichments were detected in the investigated sample. Afterwards, the REE content of the sample was estimated with mass balance calculations. For the second application, different Li-bearing drill core samples were mapped in high resolution with LIBS and a new classification model was developed. It combines Linear Discriminant Analysis (LDA) and One-Class Support Vector Machines (OC-SVM) to enable the classification of minerals that were covered by a train set, while also identifying LIBS matrices that are unknown to the model. The third application combined Laser Ablation – Inductively Coupled Plasma – Time of Flight Mass Spectrometry (LA-ICP-TOFMS) with LIBS measurements of the same sample. After image registration, this reference sample was used to create a Least-Square Support Vector Machine (LS-SVM) quantification model, which can be employed to convert LIBS intensities of similar material into element concentrations. The model allows a pixel-specific, spatially resolved quantification of multiple minerals with a single model. Each application displays possible solutions to minimize the influence of physical and chemical matrix effects on the spatial analysis of LIBS mappings of large drill core samples, which enables different kinds of analysis. Thereby, the great potential but also the challenges of LIBS as an analytical tool in geology and mining are highlighted