Characterizing Kimberlite Dilution by Crustal Rocks at the Snap Lake Diamond Mine (Northwest Territories, Canada) using SWIR (1.90–2.36<i>μ</i>m) and LWIR (8.1–11.1<i>μ</i>m) Hyperspectral Imagery Collected from Drill Core

Tappert, Michelle C.; Rivard, Benoît; Fülöp, Alexandrina; Rogge, Derek; Feng, Jiang; Tappert, Ralf; Stalder, Roland

doi:10.2113/econgeo.110.6.1375

Cited by 18 publications

(9 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Each band in both of these wavelength ranges was used in the model development. Each spectrum was processed using CWT (e.g., Rivard et al 2008;Tappert et al 2015;Scafutto et al 2016) using the wmtsa package in R (Percival et al 2016). The CWT outputs were calculated for each spectral range using an eight-scale second-order Gaussian transform.…”

Section: Statistical Analysis Data Processingmentioning

confidence: 99%

Monitoring organic carbon, total nitrogen, and pH for reclaimed soils using field reflectance spectroscopy

Sorenson

Small

Tappert³

et al. 2017

Can. J. Soil. Sci.

View full text Add to dashboard Cite

Assessing the success of soil reclamation programs can be costly and time-consuming due to the cost of traditional soil analytical techniques. One cost-effective tool that has been successfully used to efficiently analyze a range of soil parameters is reflectance spectroscopy. We used reflectance data to analyze natural and reclaimed soils in the field, examining three key soil parameters: soil organic carbon (SOC), total nitrogen (TN), and soil pH. Continuous wavelet transforms combined with machine learning models were used to predict these parameters. Based on the root mean square error (RMSE), R 2 value, and the ratio of performance to deviation (RPD), the Cubist model produced the most accurate models for SOC, TN, and pH. The RMSE, R 2 , and RPD values for SOC were 0.60%, 0.80, and 2.2, respectively. The TN model results were 0.05%, 0.81 and 2.5, and pH model results were 0.44, 0.69 and 1.8. Overall, the optimal model can be used to predict SOC and TN accurately, and improvements in the pH model are needed as pH values less than 6.5 were consistently overpredicted.Key words: reflectance spectroscopy, reclamation, carbon, nitrogen.Résumé : Le coût des techniques classiques d'analyse du sol rend parfois l'efficacité des programmes de restauration du sol aussi onéreuse que laborieuse à évaluer. La spectroscopie par réflectance est une technique rentable dont on s'est servi pour analyser efficacement un éventail de paramètres du sol. Les auteurs ont recouru à la réflectance pour analyser des sols naturels et restaurés sur le terrain, notamment les trois grands paramètres que sont la teneur en carbone organique, la concentration d'azote total et le pH. Ils ont prévu ces paramètres en combinant les transformées d'ondelettes continues et des modèles d'apprentissage machine. D'après l'écart-type, le coefficient R 2 et le ratio performance/écart (RPD), le modèle cubiste est celui qui donne les résultats les plus précis pour le carbone organique du sol, la concentration totale d'azote et le pH. L'écart-type, le coefficient R 2 et le RPD du carbone organique du sol s'établissent respectivement à 0,60 %, 0,80 et 2,2. Les résultats du modèle de l'azote total se chiffrent à 0,05 %, 0,81 et 2,5, tandis que ceux du modèle du pH sont de 0,44, 0,69 et 1,8. En règle générale, on peut se servir du modèle optimal pour prévoir avec précision la concentration de carbone organique et celle d'azote total dans le sol; il faudrait améliorer le modèle du pH, car il y a toujours surestimation des prévisions quand le pH est inférieur à 6,5. [Traduit par la Rédaction]

show abstract

Section: Statistical Analysis Data Processingmentioning

confidence: 99%

Monitoring organic carbon, total nitrogen, and pH for reclaimed soils using field reflectance spectroscopy

Sorenson

Small

Tappert³

et al. 2017

Can. J. Soil. Sci.

View full text Add to dashboard Cite

show abstract

“…The push broom AisaOWL uses a cooled mercury cadmium telluride (MCT) detector and is integrated as part of the Specim SisuROCK drill-core scanner setup [20]. The field of view (FOV) of the OWL is identical to other available SisuROCK sensors, which allows for a straight-forward co-registration of RGB, VNIR, SWIR, and LWIR data [21,22], however, only at a rather coarse spatial resolution (~1.6 mm in the SisuROCK setup). The Telops Hyper-Cam provides a promising alternative: Designed as a Fourier-transform infrared spectrometry (FTIR) frame imager, it allows for a distinctly finer spatial and spectral resolution at a comparable spectral sensitivity.…”

Section: Introductionmentioning

confidence: 99%

Multi-Sensor Spectral Imaging of Geological Samples: A Data Fusion Approach Using Spatio-Spectral Feature Extraction

Lorenz

Seidel

Ghamisi

et al. 2019

Sensors

View full text Add to dashboard Cite

Rapid, efficient and reproducible drillcore logging is fundamental in mineral exploration. Drillcore mapping has evolved rapidly in the recent decade, especially with the advances in hyperspectral spectral imaging. A wide range of imaging sensors is now available, providing rapidly increasing spectral as well as spatial resolution and coverage. However, the fusion of data acquired with multiple sensors is challenging and usually not conducted operationally. We propose an innovative solution based on the recent developments made in machine learning to integrate such multi-sensor datasets. Image feature extraction using orthogonal total variation component analysis enables a strong reduction in dimensionality and memory size of each input dataset, while maintaining the majority of its spatial and spectral information. This is in particular advantageous for sensors with very high spatial and/or spectral resolution, which are otherwise difficult to jointly process due to their large data memory requirements during classification. The extracted features are not only bound to absorption features but recognize specific and relevant spatial or spectral patterns. We exemplify the workflow with data acquired with five commercially available hyperspectral sensors and a pair of RGB cameras. The robust and efficient spectral-spatial procedure is evaluated on a representative set of geological samples. We validate the process with independent and detailed mineralogical and spectral data. The suggested workflow provides a versatile solution for the integration of multi-source hyperspectral data in a diversity of geological applications. In this study, we show a straight-forward integration of visible/near-infrared (VNIR), short-wave infrared (SWIR) and long-wave infrared (LWIR) data for sensors with highly different spatial and spectral resolution that greatly improves drillcore mapping.

show abstract

“…There are various conventional analytical methods ( Table 1.1) that have been developed and employed to determine and estimate the mineral chemistry of carbonate rocks, as well as to characterize and control the chemical compositions of the cement raw materials and products. Infrared spectroscopy and laboratory-based hyperspectral imaging (imaging spectroscopy) techniques have been found suitable for determining mineral components of rocks or geologic materials (Baissa et al, 2011;Clark et al, 1990;Haest et al, 2012a;Hunt & Salisbury, 1971;Mathieu et al, 2017;Murphy et al, 2014;Oh et al, 2017;Schodlok et al, 2016;Tappert et al, 2011;Tappert et al, 2015;. However, these spectroscopic approaches have not been fully investigated in quantifying mineral chemistry of carbonate rocks.…”

Section: Problem Statementmentioning

confidence: 99%

“…The developments of field and laboratory-based imaging spectrometry or spectroscopy sensors such as HyLogging TM (Haest et al, 2012a(Haest et al, , 2012b, HyLogger-3 Schodlok et al, 2016), HySpex (Baissa et al, 2011;Kurz et al, 2012;Mathieu et al, 2017), and SisuROCK and SisuCHEMA of Spectral Imaging Ltd. (SPECIM), Finland (Murphy et al, 2014;Murphy et al, 2016;Specim, 2007;Tappert et al, 2015; hyperspectral imagers, which integrated the digital imaging of airborne hyperspectral sensor technologies with the high spectroscopic resolution of field and laboratory spectrometers, have generated a new type of hyperspectral imagery that can be used for identifying geologic mixtures of surface mineralogy (Baissa et al, 2011;Haest et al, 2012aHaest et al, , 2012bKurz et al, 2012;Mathieu et al, 2017;Murphy et al, 2014;Murphy et al, 2016;Schodlok et al, 2016;Specim, 2007;Tappert et al, 2015;. The spectrometers can acquire hyperspectral images of various geologic samples or sites from the ideal condition in the laboratory to the close range application in the field.…”

Section: Hyperspectral Imagerymentioning

confidence: 99%

Infrared carbonate rock chemistry characterization

Zaini¹

View full text Add to dashboard Cite

INFRARED CARBONATE ROCK CHEMISTRY CHARACTERIZATION DISSERTATION to obtain the degree of doctor at the University of Twente, on the authority of the rector magnificus, prof.dr. T.T.M. Palstra, on account of the decision of the graduation committee, to be publicly defended on Thursday October 4, 2018 at 14.45 hrs First and foremost, I must praise and thank Almighty Allah, the Lord of the worlds, the Most Gracious and the Most Merciful, who gave me the infinite blessing, guidance and ability to successfully complete this PhD study. The life stories of the Prophet Muhammad, peace and blessing of Allah be upon him, has become a major inspiration and encouragement for attaining any circumstances and each step of my PhD journey in the University of Twente, Enschede, the Netherlands. I am deeply indebted to my parents, my dear father (Ayah) Almarhum Zaini Usman and my beloved mother (Nyak) Cut Nilawati Ibrahim for their love, care and prayers throughout my life. I could never have attained this stage of life without your unwavering support, hardship, suffering and encouragement. O my Lord, bestow on them Thy mercy and grace as they nurtured and cherished me when I was a child. I would like to take this opportunity to express my acknowledgement to all people and organizations who have contributed to accomplish this work. I am heartily grateful to my promoter and supervisor, Prof. Freek van der Meer. This dissertation might not be completed without your endless support, motivation and encouragement. Freek, from the beginning of the PhD research I am uncertain how to accomplish this challenging work due to my insufficient scientific knowledge, especially in geology, mineralogy and hyperspectral remote sensing, but with your futuristic scientific vision, immense knowledge, trust, enthusiasm and patience, its became possible. You have also educated me a lot on scientific guidance and constructive comments how to improve the quality of my research and to revise a manuscript after a nasty review by journal's reviewers. Thank you very much for always been caring, understanding and supporting me during my PhD research, difficult moments and scientific writing. I am particularly grateful to my second supervisor, Dr. Harald van der Werff. Harald, your contributions and scientific assistance and experience are the valuable basis of the results in this dissertation. Many thanks for your immense knowledge, support and constructive comments. I would like to extend my hearty gratitude to my third supervisor, Dr. Frank van Ruitenbeek. Frank, your inspiring discussions and contributions to the thesis have been invaluable for me. Thank you for your immense support and scientific advises. My most sincerely gratitude goes to the Government of the Province of Aceh and Human Resources Development Commission (HRDC), Banda Aceh, Indonesia for awarding me a PhD scholarship. Thanks for their financial support and trust. I am thankful to Head of

show abstract

Characterizing Kimberlite Dilution by Crustal Rocks at the Snap Lake Diamond Mine (Northwest Territories, Canada) using SWIR (1.90–2.36μm) and LWIR (8.1–11.1μm) Hyperspectral Imagery Collected from Drill Core

Cited by 18 publications

References 34 publications

Monitoring organic carbon, total nitrogen, and pH for reclaimed soils using field reflectance spectroscopy

Monitoring organic carbon, total nitrogen, and pH for reclaimed soils using field reflectance spectroscopy

Multi-Sensor Spectral Imaging of Geological Samples: A Data Fusion Approach Using Spatio-Spectral Feature Extraction

Infrared carbonate rock chemistry characterization

Contact Info

Product

Resources

About