Quantifying total sulfide content of cores and cut-rock surfaces using thermal infrared reflectance

Geophysics ◽  
2006 ◽  
Vol 71 (3) ◽  
pp. M1-M9 ◽  
Author(s):  
Jilu Feng ◽  
Benoit Rivard ◽  
E. Ann Gallie ◽  
Arturo Sanchez
Keyword(s):  
Quantitative Estimation ◽  
Thermal Infrared ◽  
Calibration Data ◽  
Infrared Reflectance ◽  
Data Set ◽  
Thin Sections ◽  
Core Logging ◽  
Total Sulfide ◽  
Sulfide Content ◽  
Text Model

In this study, core samples of sulfide-rich zones were delineated from their host rock and a quantitative estimation of sulfide content on cut-rock faces was made using thermal infrared reflectance (TIR; [Formula: see text] region) spectroscopy. Core sections and rocks were collected from mines in the Sudbury basin, Ontario, Canada. The TSC% (areal percentage of total sulfide content) of each sample was then estimated by summing the modal abundance of all sulfide minerals (chalcopyrite, pyrrhotite, and pentlandite). TIR at [Formula: see text] [Formula: see text] is linearly related to TSC%. The average of the spectral ratios [Formula: see text] and [Formula: see text] [Formula: see text] is related exponentially to TSC%. The coefficients of determination [Formula: see text] based on a calibration data set are 0.90 and 0.87, respectively. Two models were tested, the [Formula: see text] model and the [Formula: see text] model; the latter combines [Formula: see text] and [Formula: see text]. The value for [Formula: see text] for predicted TSC% versus observed TSC% (from thin sections) is 0.98 and 0.97, respectively, with little or no bias. The standard deviation of the residuals is 3.1 and 3.5 TSC%, respectively. The [Formula: see text] model is preferred over the [Formula: see text] model because it is linear and therefore does not have the problem with insensitivity at low TSC% that the exponential [Formula: see text] model has. In addition, from the viewpoint of developing an instrument for automated core logging, the [Formula: see text] model requires measurements at only one wavelength rather than three and hence requires a less expensive instrument.

Ore detection and grade estimation in the Sudbury mines using thermal infrared reflectance spectroscopy

Geophysics ◽  
2001 ◽  
Vol 66 (6) ◽  
pp. 1691-1698 ◽  
Author(s):  
Benoit Rivard ◽  
Jilu Feng ◽  
E. Ann Gallie ◽  
Helen Francis
Keyword(s):  
Thermal Infrared ◽  
Coefficient Of Determination ◽  
Sulfide Concentration ◽  
Infrared Reflectance ◽  
Diagnostic Features ◽  
Point Counts ◽  
Grade Estimation ◽  
Total Sulfide ◽  
Average Reflectance ◽  
Ore Grade

This pilot study investigated the usefulness of thermal infrared reflectance (TIR) spectroscopy to estimate ore grade in an underground environment and to separate ore‐bearing samples from their host rocks. Work was carried out under laboratory conditions to test the initial concept; all samples had naturally broken faces to mimic the situation in a freshly blasted underground opening. A total of 26 samples, including massive and disseminated ores, were collected from eight mines around the Sudbury basin in Ontario. Rock surfaces were measured wet and dry to address environmental conditions encountered underground. To separate barren rocks from ores and for ore‐grade estimation, an important finding of this research is that, in the region of [Formula: see text], most known silicate minerals converge to a common reflectance minima (<1.5%), but massive and disseminated sulfides have distinctly higher reflectance. Individual sulfide minerals (chalcopyrite, pyrrhotite, pentlandite), however, do not reveal diagnostic features in this spectral region. When sulfides are disseminated in the host rock, the average reflectance of the rock increases but the correlation with abundance is not systematic. However, sulfide concentration as a function of continuum‐removed reflectance (CRR) is systematic. The empirical correlation between CRR at [Formula: see text] versus the total sulfide concentration, estimated via thin‐section point counts, gives a coefficient of determination value [Formula: see text] of 0.93 for measurement of dry and wet surfaces when averaged. Similar results are observed when dry and wet locations are analyzed separately. The relationship demonstrates the feasibility to estimate total sulfide concentration from TIR reflectance data even when samples are wet.

scholarly journals Cariaco Basin Calibration Update: Revisions to Calendar and 14C Chronologies for Core Pl07-58Pc

Radiocarbon ◽  
2004 ◽  
Vol 46 (3) ◽  
pp. 1161-1187 ◽  
Author(s):  
Konrad A Hughen ◽  
John R Southon ◽  
Chanda J H Bertrand ◽  
Brian Frantz ◽  
Paula Zermeño
Keyword(s):  
Tree Ring ◽  
Cariaco Basin ◽  
Calibration Data ◽  
Data Set ◽  
Varve Chronology ◽  
Pine Tree ◽  
Tree Ring Data ◽  
Calibration Data Set ◽  
Recent Version ◽  
Radiocarbon Calibration

This paper describes the methods used to develop the Cariaco Basin PL07-58PC marine radiocarbon calibration data set. Background measurements are provided for the period when Cariaco samples were run, as well as revisions leading to the most recent version of the floating varve chronology. The floating Cariaco chronology has been anchored to an updated and expanded Preboreal pine tree-ring data set, with better estimates of uncertainty in the wiggle-match. Pending any further changes to the dendrochronology, these results represent the final Cariaco 58PC calibration data set.

Developing a modern pollen-climate calibration data set for Norway

Boreas ◽  
2010 ◽  
Vol 39 (4) ◽  
pp. 674-688 ◽  
Author(s):  
ANNE E. BJUNE ◽  
H. JOHN B. BIRKS ◽  
SYLVIA M. PEGLAR ◽  
ARVID ODLAND
Keyword(s):  
Calibration Data ◽  
Data Set ◽  
Modern Pollen ◽  
Calibration Data Set

scholarly journals A New 14C Calibration Data Set for the Last Deglaciation Based on Marine Varves

Radiocarbon ◽  
1997 ◽  
Vol 40 (1) ◽  
pp. 483-494 ◽  
Author(s):  
Konrad A. Hughen ◽  
Jonathan T. Overpeck ◽  
Scott J. Lehman ◽  
Michaele Kashgarian ◽  
John R. Southon ◽  
...  
Keyword(s):  
Global Climate ◽  
Ice Core ◽  
Younger Dryas ◽  
Core Layer ◽  
Cariaco Basin ◽  
Calibration Data ◽  
Last Deglaciation ◽  
Data Set ◽  
The Last Deglaciation ◽  
Calibration Data Set

Varved sediments of the tropical Cariaco Basin provide a new 14C calibration data set for the period of deglaciation (10,000 to 14,500 years before present: 10–14.5 cal ka bp). Independent evaluations of the Cariaco Basin calendar and 14C chronologies were based on the agreement of varve ages with the GISP2 ice core layer chronology for similar high-resolution paleoclimate records, in addition to 14C age agreement with terrestrial 14C dates, even during large climatic changes. These assessments indicate that the Cariaco Basin 14C reservoir age remained stable throughout the Younger Dryas and late Allerød climatic events and that the varve and 14C chronologies provide an accurate alternative to existing calibrations based on coral U/Th dates. The Cariaco Basin calibration generally agrees with coral-derived calibrations but is more continuous and resolves century-scale details of 14C change not seen in the coral records. 14C plateaus can be identified at 9.6, 11.4, and 11.7 14C ka bp, in addition to a large, sloping “plateau” during the Younger Dryas (∼10 to 11 14C ka bp). Accounting for features such as these is crucial to determining the relative timing and rates of change during abrupt global climate changes of the last deglaciation.

scholarly journals From pole to pole: 33 years of physical oceanography onboard R/V <i>Polarstern</i>

2017 ◽  
Vol 9 (1) ◽  
pp. 211-220 ◽  
Author(s):  
Amelie Driemel ◽  
Eberhard Fahrbach ◽  
Gerd Rohardt ◽  
Agnieszka Beszczynska-Möller ◽  
Antje Boetius ◽  
...  
Keyword(s):  
Data Collection ◽  
Water Cycle ◽  
Heat Budget ◽  
Ocean Dynamics ◽  
The Arctic ◽  
Sensor Calibration ◽  
Data Sets ◽  
Calibration Data ◽  
Data Set ◽  
Different Characteristics

Abstract. Measuring temperature and salinity profiles in the world's oceans is crucial to understanding ocean dynamics and its influence on the heat budget, the water cycle, the marine environment and on our climate. Since 1983 the German research vessel and icebreaker Polarstern has been the platform of numerous CTD (conductivity, temperature, depth instrument) deployments in the Arctic and the Antarctic. We report on a unique data collection spanning 33 years of polar CTD data. In total 131 data sets (1 data set per cruise leg) containing data from 10 063 CTD casts are now freely available at doi:10.1594/PANGAEA.860066. During this long period five CTD types with different characteristics and accuracies have been used. Therefore the instruments and processing procedures (sensor calibration, data validation, etc.) are described in detail. This compilation is special not only with regard to the quantity but also the quality of the data – the latter indicated for each data set using defined quality codes. The complete data collection includes a number of repeated sections for which the quality code can be used to investigate and evaluate long-term changes. Beginning with 2010, the salinity measurements presented here are of the highest quality possible in this field owing to the introduction of the OPTIMARE Precision Salinometer.

scholarly journals A Measuring and Evaluation Program for the Application of the Ring-Core or Hole-Drilling Method

2014 ◽  
Vol 996 ◽  
pp. 313-318 ◽  
Author(s):  
Andreas Nau ◽  
Berthold Scholtes
Keyword(s):  
Residual Stress ◽  
Thick Plate ◽  
Parametric Design ◽  
Practical Importance ◽  
Element Model ◽  
Hole Drilling ◽  
Calibration Data ◽  
Data Set ◽  
Mechanical Methods ◽  
Ring Core

There is a strong need for reliable residual stress measurements. On the one hand, residual stresses can be beneficial, when they are adapted to external loads. On the other hand, they can be detrimental, when they are unknown. Thus, their occurrence can lead to an uneconomical oversizing of components or in their failure, as well. Apart from diffraction methods, mechanical methods are well recognized in order to determine unknown residual stress states. Depending on the applied method, specific boundary conditions have to be taken into account. In the case of mechanical methods like the hole-drilling and the ring-core method, the characteristics of the geometry of the component should be in accordance with an ideal and thick plate. The reason behind is the need for a calibration data set to transform strains into stresses. The calibration is usually carried out numerically. For the sake of simplicity, the geometry of the component is an ideal thick plate and the hole is introduced in its center. However, in most cases, this is not identical with the geometry of the component under investigation. Hence, an application tool was designed that enables the parametric design of a Finite Element Model, the determination of calibration coefficients, the evaluation of the experiment and the visualization of the results for geometries of practical importance. So far, the application tool can represent plates variable in their geometries and in positioning of the point of measurement. The option for other geometries are also possible e.g. a turbine blade.

Ability of Vis-PIR spectroscopy to monitor changes in organic carbon of loamy soils at two depths

2021 ◽  
Author(s):  
Hayfa Zayani ◽  
Youssef Fouad ◽  
Didier Michot ◽  
Zeineb Kassouk ◽  
Zohra Lili-Chabaane ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Near Infrared ◽  
Full Range ◽  
Nir Spectroscopy ◽  
Soil Samples ◽  
Calibration Data ◽  
Least Squares Regression ◽  
Data Set ◽  
Soil Carbon Stocks ◽  
The Mean

&lt;p&gt;Visible-Near Infrared (Vis-NIR) spectroscopy has proven its efficiency in predicting several soil properties such as soil organic carbon (SOC) content. In this preliminary study, we explored the ability of Vis-NIR to assess the temporal evolution of SOC content. Soil samples were collected in a watershed (ORE AgrHys), located in Brittany (Western France). Two sampling campaigns were carried out 5 years apart: in 2013, 198 soil samples were collected respectively at two depths (0-15 and 15-25 cm) over an area of 1200 ha including different land use and land cover; in 2018, 111 sampling points out of 198 of 2013 were selected and soil samples were collected from the same two depths. Whole samples were analyzed for their SOC content and were scanned for their reflectance spectrum. Spectral information was acquired from samples sieved at 2 mm fraction and oven dried at 40&amp;#176;C, 24h prior to spectra acquisition, with a full range Vis-NIR spectroradiometer ASD Fieldspec&amp;#174;3. Data set of 2013 was used to calibrate the SOC content prediction model by the mean of Partial Least Squares Regression (PLSR). Data set of 2018 was therefore used as test set. Our results showed that the variation &amp;#8710;SOC&lt;sub&gt;obs&lt;/sub&gt;&lt;sub&gt;&lt;/sub&gt;obtained from observed values in 2013 and 2018 (&amp;#8710;SOC&lt;sub&gt;obs&lt;/sub&gt; = Observed SOC (2018) - Observed SOC (2013)) is ranging from 0.1 to 25.9 g/kg. Moreover, our results showed that the prediction performance of the calibrated model was improved by including 11 spectra of 2018 in the 2013 calibration data set (R&amp;#178;= 0.87, RMSE = 5.1 g/kg and RPD = 1.92). Furthermore, the comparison of predicted and observed &amp;#8710;SOC between 2018 and 2013 showed that 69% of the variations were of the same sign, either positive or negative. For the remaining 31%, the variations were of opposite signs but concerned mainly samples for which &amp;#8710;SOCobs is less than 1,5 g/kg. These results reveal that Vis-NIR spectroscopy was potentially appropriate to detect variations of SOC content and are encouraging to further explore Vis-NIR spectroscopy to detect changes in soil carbon stocks.&lt;/p&gt;

Automation of Carbonate Rock Thin Section Description Using Cognitive Image Recognition

2021 ◽  
Author(s):  
Hesham Talaat Shebl ◽  
Mohamed Ali Al Tamimi ◽  
Douglas Alexander Boyd ◽  
Hani Abdulla Nehaid
Keyword(s):  
Thin Section ◽  
Image Recognition ◽  
Oil Recovery ◽  
Reservoir Rock ◽  
Supervised Machine Learning ◽  
Neural Net ◽  
Data Set ◽  
Thin Sections ◽  
Rock Quality ◽  
Pore Types

Abstract Simulation Engineers and Geomodelers rely on reservoir rock geological descriptions to help identify baffles, barriers and pathways to fluid flow critical to accurate reservoir performance predictions. Part of the reservoir modelling process involves Petrographers laboriously describing rock thin sections to interpret the depositional environment and diagenetic processes controlling rock quality, which along with pressure differences, controls fluid movement and influences ultimate oil recovery. Supervised Machine Learning and a rock fabric labelled data set was used to train a neural net to recognize Modified Durham classification reservoir rock thin section images and their individual components (fossils and pore types) plus predict rock quality. The image recognition program's accuracy was tested on an unseen thin section image database.

scholarly journals Inferring late-Holocene climate in the Ecuadorian Andes using a chironomid-based temperature inference model

2016 ◽  
Vol 12 (5) ◽  
pp. 1263-1280 ◽  
Author(s):  
Frazer Matthews-Bird ◽  
Stephen J. Brooks ◽  
Philip B. Holden ◽  
Encarni Montoya ◽  
William D. Gosling
Keyword(s):  
Bayesian Method ◽  
Mean Squared Error ◽  
Weighted Averaging ◽  
Mean Annual Temperature ◽  
Calibration Data ◽  
Tropical Andes ◽  
Data Set ◽  
Inference Models ◽  
Calibration Data Set ◽  
Study Sites

Abstract. Presented here is the first chironomid calibration data set for tropical South America. Surface sediments were collected from 59 lakes across Bolivia (15 lakes), Peru (32 lakes), and Ecuador (12 lakes) between 2004 and 2013 over an altitudinal gradient from 150 m above sea level (a.s.l) to 4655 m a.s.l, between 0–17° S and 64–78° W. The study sites cover a mean annual temperature (MAT) gradient of 25 °C. In total, 55 chironomid taxa were identified in the 59 calibration data set lakes. When used as a single explanatory variable, MAT explains 12.9 % of the variance (λ1/λ2 =  1.431). Two inference models were developed using weighted averaging (WA) and Bayesian methods. The best-performing model using conventional statistical methods was a WA (inverse) model (R2jack =  0.890; RMSEPjack =  2.404 °C, RMSEP – root mean squared error of prediction; mean biasjack =  −0.017 °C; max biasjack =  4.665 °C). The Bayesian method produced a model with R2jack =  0.909, RMSEPjack =  2.373 °C, mean biasjack =  0.598 °C, and max biasjack =  3.158 °C. Both models were used to infer past temperatures from a ca. 3000-year record from the tropical Andes of Ecuador, Laguna Pindo. Inferred temperatures fluctuated around modern-day conditions but showed significant departures at certain intervals (ca. 1600 cal yr BP; ca. 3000–2500 cal yr BP). Both methods (WA and Bayesian) showed similar patterns of temperature variability; however, the magnitude of fluctuations differed. In general the WA method was more variable and often underestimated Holocene temperatures (by ca. −7 ± 2.5 °C relative to the modern period). The Bayesian method provided temperature anomaly estimates for cool periods that lay within the expected range of the Holocene (ca. −3 ± 3.4 °C). The error associated with both reconstructions is consistent with a constant temperature of 20 °C for the past 3000 years. We would caution, however, against an over-interpretation at this stage. The reconstruction can only currently be deemed qualitative and requires more research before quantitative estimates can be generated with confidence. Increasing the number, and spread, of lakes in the calibration data set would enable the detection of smaller climate signals.

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