Elemental geochemistry to complement stable isotope data of fossil travertine: Importance of digestion method and statistics

Biogenic calcretes associated with a regional Cretaceous to Paleogene subaerial unconformity and an intraformational composite (polygenic) surface in Upper Cretaceous intra-platform peritidal successions in central Dalmatia and eastern Istria, Croatia (Adriatic-Dinaridic Carbonate Platform), were analyzed for their δ13C and δ18O signatures in order to provide insight into the conditions of subaerial exposure and calcrete development. The distinctly negative δ13C signatures of biogenic calcretes marking the regional subaerial unconformity differ considerably from the δ13C values of the host marine limestones. This indicates carbon isotope exchange of primary marine CaCO3 with CO2 released by root and rhizomicrobial respiration and subsequent precipitation of pedogenic calcrete. The range of δ13C (from −13.1 to −8.2 ‰ Vienna PeeDee Belemnite standard, VPDB) and δ18O (from −10.1 to −6.1 ‰ VPDB) values of calcretes are similar to those reported from calcretes elsewhere, and the δ13C values of biogenic calcretes with typical Microcodium aggregates (−13.1 to −12.3 ‰ VPDB) at the Sibenik locality are very close to, or at the lower limit of, values for soil carbonates formed in isotopic equilibrium with soil CO2. These values are expected for authigenic pedogenic carbonates formed under the influence of C3 plant communities, without influence from heavier carbon from pre-existing carbonate and lack of input of atmospheric CO2. Such low δ13C values support the interpretation of Microcodium aggregates as being precipitated under a direct biological control within the soil, although the relationship between formation mechanisms and stable isotope signatures of Microcodium needs further investigation. The δ13C values (−4.4 to −3.6 ‰ VPDB) of rhizogenic calcretes formed inside firmground Thalassinoides burrows of the composite surface at the Sibenik locality are more negative than the δ13C values of the host marine limestones, which confirms that the composite surface went through a phase of meteoric pedo(dia)genesis. However, the overall δ13C values of calcretes are less negative than expected, which might reflect contamination from associated primary marine carbonate. This study represents the first detailed stable isotope investigation of calcretes from carbonate successions of the External Dinarides, and the results may be applied to discontinuities present in other shallow-water carbonate rock successions.

Simulation experiments have been conducted to examine the potential usefulness of R-mode and Q-mode factor methods in the analysis and interpretation of geochemical data. The R-mode factor analysis experiment consisted of constructing a factor model, using the model to generate a correlation matrix, and attempting to recover the model by R-mode techniques. The techniques were successful in determining the number of factors in the model, but the factor loadings could not be estimated even approximately on the basis of mathematical procedures alone. Q-mode factor methods were successful in recovering all of the properties of a model used to generate hypothetical chemicaldata on olivinesamples, but it was necessary to use a correction previously regarded as unimportant.

Investigation by spectral gamma-ray (SGR) and inductively coupled plasma-mass spectrometry (ICP-MS) geochemical analyses of the Upper Jurassic Arab-D reservoir analogue (central Saudi Arabia) revealed a strong correlation between the SGR response of the outcrop lithofacies and their elemental content. The two units of the reservoir (the Upper Jubaila Member of the Jubaila Formation and the Arab-D Member of the Arab Formation) showed distinctive SGR log profiles controlled mainly by their lithofacies associations. The geochemical analysis revealed four groups of chemical associations. Group 1 includes SiO2, Al3O2, Fe2O3, K2O, TiO2, Zr and Zn. This group has a strong relationship with the radioactive elements U, K and Th (Group 4). The reservoir facies exhibit high concentrations of chemicals from these two clusters. Group 2 includes CaO and Sr. A high concentration of chemicals from this group indicates a tendency towards pure carbonate facies and fewer siliciclastic impurities. A high concentration of MgO, the only chemical included in Group 3, marks dolomitic zones. Group 4 contains the radioactive elements. The boundary between the Upper Jubaila Member and the Arab-D Member is clearly defined from vertical SGR log profiles, vertical geochemical data logging and cross-plots of Group 1 chemicals with the radioactive elements in Group 4. The geochemical data for the Upper Jubaila Member show a very low concentration of U, K and Th. Consequently, the SGR response of the lithofacies was very low. All of the reservoir lithofacies showed high concentration of Group 1 and Group 4 components compared with the non-reservoir lithofacies. The Th/U ratio indicates a general shoaling upwards following the same trend of the outcrop lithofacies. A high Th/U ratio characterizes reservoir lithofacies, whereas a low ratio characterizes non-reservoir lithofacies. The lithofacies and the SGR log motifs were related in the measured sections. This study provides a method for predicting lithofacies from SGR log motifs within a high-resolution stratigraphic framework integrated with the geochemical data analysis. Here, we introduce an exploration guide for subsurface reservoir zonation and the identification of formation tops.

Advantages of robust procedures over ordinary least-squares procedures in geochemical data analysis is demonstrated using NURE data from the Hot Springs Quadrangle, South Dakota, U.S.A. Robust principal components analysis with 5% multivariate trimming successfully guarded the analysis against perturbations by outliers and increased the number of interpretable factors. Regression with SINE estimates significantly increased the goodness-of-fit of the regression and improved the correspondence of delineated anomalies with known uranium prospects. Because of the ubiquitous existence of outliers in geochemical data, robust statistical procedures are suggested as routine procedures to replace ordinary least-squares procedures.

In the context of increasing droughts related to climate change, our results showed that heavy thinning and/or very low initial planting density can increase CO 2 assimilation rate in needles, and may be used as a short-term management strategy for loblolly pine plantation across sites prone to drought. The dry summer of 2013 provided us an opportunity to understand the CO2 assimilation rate and stomatal conductance after density manipulation treatments using the dual isotope (δ13C and δ18O) signatures in needles of planted loblolly pine (Pinus taeda L.) trees in the southeastern USA. To our knowledge, this is the first study using the dual isotope approach to examine the physiological response of loblolly pine trees, one of the most widely planted tree species in the world, to stand density manipulation treatments (i.e., thinning intensity and planting density). In 2001–2003, trees were planted with five different planting densities, 494, 1111, 1729, 2346, and 2964 trees ha−1 at three sites. In 2009–2011, two thinning treatments (none and moderate thinning) were applied in the 1111 trees ha−1 plots, whereas three treatments (none, light and heavy thinning) were applied in the 1729 trees ha−1 plots. Response variables (specific leaf area (SLA), foliar N, δ13C and δ18O) were measured in February 2014. SLA was lower, while δ18O was higher in the 494 trees ha−1 plots than the 2964 trees ha−1 plots without thinning. In plots planted to 1729 trees ha−1 SLA was lower, while δ13C and δ18O were higher following heavy thinning than in the unthinned control. These responses plus increased crown length, DBH, and height following heavy thinning may reflect an increased tree-level CO2 assimilation rate. Our results showed that heavy thinning and/or very low initial planting density can be used as a short-term management strategy for loblolly pine plantation across sites prone to drought.

Geochemical data are frequently collected from mineral exploration drill-hole samples to more accurately define and characterise the geological units intersected by the drill hole. However, large multi-element data sets are slow and challenging to interpret without using some form of automated analysis, such as mathematical, statistical or machine learning techniques. Automated analysis techniques also have the advantage in that they are repeatable and can provide consistent results, even for very large data sets. In this paper, an automated litho-geochemical interpretation workflow is demonstrated, which includes data exploration and data preparation using appropriate compositional data-analysis techniques. Multiscale analysis using a modified wavelet tessellation has been applied to the data to provide coherent geological domains. Unsupervised machine learning (clustering) has been used to provide a first-pass classification. The results are compared with the detailed geologist’s logs. The comparison shows how the integration of automated analysis of geochemical data can be used to enhance traditional geological logging and demonstrates the identification of new geological units from the automated litho-geochemical logging that were not apparent from visual logging but are geochemically distinct. KEY POINTS To reduce computational complexity and facilitate interpretation, a subset of geochemical elements is selected, and then a centred log-ratio transform is applied. The wavelet tessellation method is used to domain the drill holes into rock units at a range of scales. Several clustering methods were tested to identify distinct rock units in the samples and multiscale domains for classification. Results are compared with geologist’s logs to assess how geochemical data analysis can inform and improve traditional geology logs.

Effects of sample pretreatment and external contamination on bivalve shell and Carrara marble δ18O and δ13C signatures

Essential (copper, nickel and zinc) and nonessential (cadmium, lead and tin) elements were determined in canned and fresh food samples employing the technique of Flame Atomic Absorption Spectrophotometry. Food samples were purchased from the shelves of local markets of Rawalpindi, Islamabad and Wah Cantt, Pakistan. Canned food samples were analyzed to study the effect of temperature, time and pH on concentration of elements. To determine the magnitude of variation in concentration of elements in canned food, elemental analysis was performed on samples (food and medium) upon initial opening, and samples upon 4 weeks after opening. The physical observation of the canned food samples was also made to determine the internal coating (lacquer), headspace level and pH of the medium upon opening the can. The result showed that concentration of elements in canned food samples was greater than fresh food, and it increases with increasing time and temperature but decreases with increasing pH. A low concentration of elements was found related to fully lacquered cans, high pH of medium, low nitrate concentration and small headspace. The concentration of elements in each sample was compared statistically. PRACTICAL APPLICATIONS Food quality surveillance is a very important tool to develop a perspective of human health risks. The study is significant in evaluating the concentration of different elements in the food and the effect of their toxicities. This research has a very significant application at the end user level as to give information about the benefits of the fresh food over the canned food. The food industry can benefit from the study as it provides the details over the canned food about the usage and storage. It also provides background knowledge about the deadlines of storage with respect to different essential metal levels. Taking it as a precautionary measure, many of the digestive diseases and abdominal problems can be avoided in masses. Industry can benefit by it to reduce these elements to the minimum possible levels, and can search techniques to decrease the concentration of these elements. It will assist the industries to develop procedures to conserve the food in good optimal manner for best possible, healthier levels. It will stress the desirability of the establishment of tolerances for metal in all foods as a public health measure. It will create awareness in Pakistan of food science and technology. It will also aid the health and food inspectors in improving food quality and developing strategies to reduce the level of metal contamination in the food and to make it according to the WHO guidelines for food quality. This work has opened the doors to investigate the food quality in details with special reference to public health.

ABSTRACTUntil recently, no physical rock samples from the Palaeozoic to Mesozoic eras had been recovered from the South Yellow Sea (SYS), leaving the palaeoclimatic significance of this period largely unexplored. This study analyzes data from the Lower Silurian to Lower Triassic strata of the CSDP‐2 Well, including magnetic susceptibility (MS) data from 251 samples and geochemical element data for 345 samples, to determine environmental changes and their implications. We successfully establishes vertically continuous and complete MS and geochemical curves within the SYS, and two depth segments with anomalous values and four climatic shifts are identified. Through the coupled analysis of MS and geochemical data, along with the synthesis of previously published data, one anomalous segment from 1690‐2006. 9 meters below sea floor (mbsf) could represent the Late Devonian‐Early Permian Ice Age, which lasted approximately 80 Ma. Another segment from 820‐930 mbsf represents Emeishan and Siberian volcanism, with an interval between the two events spanning approximately 2.7 Ma. And 15 variation cycles are identified by analyzing changes of MS values, we found that geological event cycles spanned 3‐9 Ma during the Early Silurian to Permian, but the interval shorten to approximately 28.6 Kyr in the Early Triassic. In summary, the CSDP‐2 Well records several significant geological events and the SYS underwent an exceptionally complex evolution of geological environments during the Early Mesozoic. Additionally, MS data from CSDP‐2 Well reveal unique variation patterns not seen in adjacent areas or other blocks, indicating that while the SYS was influenced by global climate and tectonic activities at block‐level, it also experienced distinct regional geological environmental changes.

Isotopic characterization of three groundwater recharge sources and inferences for selected aquifers in the upper Klamath Basin of Oregon and California, USA

Petrology and geochemistry of volcanic rocks from the island of Panarea: implications for mantle evolution beneath the Aeolian island arc (southern Tyrrhenian sea)

Interest in biomass has increased due to current environmental issues, and biomass analysis is usually performed using element and proximate analyses to ascertain its fuel characteristics. Mainly, element component prediction models have been developed based on proximate analysis, yet few studies have predicted proximate components based on element analysis. Hence, this study developed a proximate component prediction model following the calorific value calculation. Analysis of Pearson’s correlation coefficient showed that volatile matter (VM) and fixed carbon (FC) were positively correlated with hydrogen and oxygen, and with carbon, respectively. Thus, the model correlation was developed using a combination of the “stepwise” and “enter” methods along with linear or nonlinear regressions. The optimal models were developed for VM and ash content (Ash). The VM optimal model values were: R2 = 0.9402, root-mean-square error (RMSE) = 7.0063, average absolute error (AAE) = 14.8170%, and average bias error (ABE) = −11.7862%. For Ash, the values were: R2 = 0.9249, RMSE = 2.9614, AAE = 168.9028%, and ABE = 167.2849%, and for FC, the values were: R2 = 9505, RMSE = 6.3214, AAE = 18.3199%, and ABE = 15.0094%. This study provides a model to predict the proximate component by element analysis. Contrary to existing method, proximate analysis can be predicted based on elemental analysis, and shows that consume samples can be performed at once.

1. Integration of Data for Resource Evaluation.- Application of recent developments of regression analysis in regional mineral resource evaluation.- Poisson regression analysis and its application.- Measurement, sampling and interpretation.- Levels of information and probability mapping.- Review of geomathematical applications for mineral resources evaluation in China.- 2. Statistical Analysis of Geochemical Data.- New approaches to the recognition of anomalies in exploration geochemistry.- Univariate patterns in the design of multivariate analysis techniques for geochemical data evaluation.- Application of robust statistics in the analysis of geochemical data.- 3. Statistical Mehtods for Oil and Gas Resource Evaluation.- U.S. Geological Survey assessment methodology for estimation of undiscovered petroleum resources in play analysis of the Artic National Wildlife Refuge.- Statistical evaluation of petroleum deposits before discovery.- On assessing dry probabilities in offshore oil and gas exploration: an application of Bayes's theorem.- Statistical analysis of oil and gas discovery data.- 4. Image Processing in Earth Sciences.- Automated integration of mineral resource data by image processing and artificial intelligence.- Principles of mathematical morphology.- The use of geological image analysis in remote sensing and seismic attribute integration for resource assessment.- Statistical image processing.- 5. Statistical Analysis of Spatial Data.- Spatial analysis of patterns of land-based and ocean-floor ore deposits.- Point processes for the earth sciences.- 6. Geostatistics.- An unconventional approach to geostatistical estimation.- Case studies on modelling complex sulphide orebodies for ore reserve estimation using geostatistical methods.- A Bayesian approach to surface estimation.- Conditional simulation of indicator data. Case study of a multi-seam coal deposit.- Factorial kriging as a method to include spatial structure into classification. A case study on a sulphide orebody.- Geostatistical techniques for interpreting multivariate spatial information.- 7. Other Statisitcal Methodologies for Geoscience Data.- The shape of Lloydminster oil and gas deposit attribute data.- Confidence bands for the distribution and quantile functions for truncated and randomly censored data.- Geostatistical crustal abundance resource models.- 8. Artificial Intelligence in Earth Sciences.- The usage of Artificial intelligence in remote sensing: a review of applications and current research.- GEOVALUATOR, an expert system for resource appraisal: a demonstration prototype for kaolin in Georgia, U.S.A.- GEMS: a microcomputer-based expert system for digital image data.- On the understanding of geological cross-sections by expert systems.- 9. Other Contributed Papers.- Examples of spatial analysis and management in the geographical and conformal projection domains.- A classification procedure for exploitable reserves.- Drainage and divide networks derived from high-fidelity digital terrain models.- An algebraic model for data structure and manipulation as a design basis for integrated geoscience processing systems.- Case study on application of qualitative data analysis techniques to an uranium mineralization.- A shell for microcomputer exploration software used in developing countries.- Optimal exploration strategies: the synthesis of resource assessments and operations research.- Advantages and limitations of discovery process modeling: the case of the Northern West Siberian gas plays.- Some aspects of multivariate analysis.- Workshop Reports.- Current problems and future developments in multivariate analysis.- Current problems and future developments in oil and gas resource modeling and forecasting.- Current problems and future developments in spatial statistics and image processing.- Current problems and future developments in mineral and energy resource expert system development.- Current problems and future developments in the use of microcomputers.

Origin, transport and discharge of CO 2 in central Italy

Mineral matter and elemental concentrations in selected western canadian coals