998 resultados para Deposit type


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The cores and dredges described in this report were taken on Cruise 16 of the R.R.S. "Discovery" from January until May 1967 by the National Institute of Oceanography, Wormley, United Kingdom. A total of 73 cores and dredges were recovered and are available through the British Oceanographic Data Centre for sampling and study.

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The author is summarising the marine deposits of the south-west Pacific from a total of 773 samples taken during cruises of hms Challenger in 1874; USS Tuscarora and USS Enterprise in 1875 and 1876; HMS Egeria in 1888, 1889, and 1890; HMS Penguin in 1894, 1895, and 1896; and HMS Waterwitch in 1895.

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In late June and July, 1967, the Deep Submergence Research Vehicle (DSRV) ALVIN, aboard its mother ship, LULU, proceeded from the spring base of operations, Nassau, to its home port of Woods Hole. During this trip, from July 2 to July 14, a series of five dives were made by ALVIN on the Blake Plateau off Georgia and South Carolina, and on the continental slope north of Cape Hatteras. One of the objectives of the dive was to investigate the manganese and phosphate deposits of the Blake Plateau.

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The cores and dredges described in this report were taken on the TETHYS Expedition from June 1960 until July 1960 by the Scripps Institution of Oceanography from the R/V Spencer F. Baird. A total of 124 cores and dredges were recovered and are available at Scripps for sampling and study.

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The cores described in this report were taken on the WAHINE Expedition in February to March 1965 by the Scripps Institution of Oceanography from the R/V Spencer F. Baird. A total of 54 cores and dredges were recovered and are available at Scripps for sampling and study.

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The paper presents an outline of the first Austrian expedition to the Northern part of the Red Sea with the R/V Pola.

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New surveys were completed and data from the field sheets were kindly furnished by the U. S. Coast and Geodetic Survey to the Woods Hole Oceanographic Institution for use in dredging and coring operations. This field work, first reported in 1936, was continued from time to time until 1941 as new soundings became available. Rock dredging and coring has been carried out in every major canyon on the slope from Corsair Canyon at the tip of Georges Bank to Norfolk Canyon off the entrance to the Chesapeake. Numerous cores have also been taken from the areas in between; and while the whole slope from Georges to the Chesapeake has not been covered, it is believed that no significant areas have been missed. In the following report the tows and cores will be described by areas from Georges Bank southwards, as the same region was revisited in successive years. The various samples, however, will be referred to by number followed by the year in which they were taken. The material is in storage in the Woods Hole Oceanographic Institution and in the Museum of Comparative Zoology at Harvard University.

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Chemical composition of manganese nodules and crust collected from the Hakurei Maru Cruise GH74-5, September-October, 1974, in the Eastern Central Pacific Basin by the Geological Survey of Japan according to: Analyses and sample manganese deposits data from Cruise GH74-5 (private communication) (unpublished), Scripps Institution of Oceanography. http://www.ngdc.noaa.gov/mgg_mmbib/showref.jsp?mmbib=MIZUAA7702.

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The cores and dredges described in this report were taken during the Robert Conrad Cruise 8 from November 1963 until August 1964 by the Lamont Geological Observatory, Columbia University from the R/V Robert Conrad. A total of 140 cores and dredges were recovered and are available at Lamont-Doherty Earth Observatory for sampling and study.

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The cores and dredges described in this report were taken on the GH74-5 Expedition in September-October, 1974 by the Geological Survey of Japan from the R/V Hakurei Maru. A total of 36 cores, dredges and submarine camera sites have been visited. The survey conducted an investigation of the manganese deposits in the Eastern Pacific Basin and the East of the Okinawa islands

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A test of the ability of a probabilistic neural network to classify deposits into types on the basis of deposit tonnage and average Cu, Mo, Ag, Au, Zn, and Pb grades is conducted. The purpose is to examine whether this type of system might serve as a basis for integrating geoscience information available in large mineral databases to classify sites by deposit type. Benefits of proper classification of many sites in large regions are relatively rapid identification of terranes permissive for deposit types and recognition of specific sites perhaps worthy of exploring further. Total tonnages and average grades of 1,137 well-explored deposits identified in published grade and tonnage models representing 13 deposit types were used to train and test the network. Tonnages were transformed by logarithms and grades by square roots to reduce effects of skewness. All values were scaled by subtracting the variable's mean and dividing by its standard deviation. Half of the deposits were selected randomly to be used in training the probabilistic neural network and the other half were used for independent testing. Tests were performed with a probabilistic neural network employing a Gaussian kernel and separate sigma weights for each class (type) and each variable (grade or tonnage). Deposit types were selected to challenge the neural network. For many types, tonnages or average grades are significantly different from other types, but individual deposits may plot in the grade and tonnage space of more than one type. Porphyry Cu, porphyry Cu-Au, and porphyry Cu-Mo types have similar tonnages and relatively small differences in grades. Redbed Cu deposits typically have tonnages that could be confused with porphyry Cu deposits, also contain Cu and, in some situations, Ag. Cyprus and kuroko massive sulfide types have about the same tonnages. Cu, Zn, Ag, and Au grades. Polymetallic vein, sedimentary exhalative Zn-Pb, and Zn-Pb skarn types contain many of the same metals. Sediment-hosted Au, Comstock Au-Ag, and low-sulfide Au-quartz vein types are principally Au deposits with differing amounts of Ag. Given the intent to test the neural network under the most difficult conditions, an overall 75% agreement between the experts and the neural network is considered excellent. Among the largestclassification errors are skarn Zn-Pb and Cyprus massive sulfide deposits classed by the neuralnetwork as kuroko massive sulfides—24 and 63% error respectively. Other large errors are the classification of 92% of porphyry Cu-Mo as porphyry Cu deposits. Most of the larger classification errors involve 25 or fewer training deposits, suggesting that some errors might be the result of small sample size. About 91% of the gold deposit types were classed properly and 98% of porphyry Cu deposits were classes as some type of porphyry Cu deposit. An experienced economic geologist would not make many of the classification errors that were made by the neural network because the geologic settings of deposits would be used to reduce errors. In a separate test, the probabilistic neural network correctly classed 93% of 336 deposits in eight deposit types when trained with presence or absence of 58 minerals and six generalized rock types. The overall success rate of the probabilistic neural network when trained on tonnage and average grades would probably be more than 90% with additional information on the presence of a few rock types.

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In Alzheimer's disease (AD) and Down's syndrome (DS), the size frequency distribution of the beta-amyloid (Abeta) deposits can be described by a log-normal model and may indictae the growth of the deposits. This study determined the size frequency distribution of the Abeta deposits in the temporal lobe in 8 casaes of dementia with Lewy bodies (DLB) with associated AD pathology (DLB/AD. The size distributions of Abeta deposits were unimodal and positively skewed; the mean size of deposi and the degree of skew varying with deposit type and brain region. Size distributions of the primitive deposits had lower means and were less skewed compared with the diffuse and classic deposits. In addition, size distributions in the hippocampus and parahippocampal gyrus (PHG) had larger means and a greater degree of skew compared with other cortical gyri. All size distributions deviated significantly from a log-normal model. There were more Abeta deposits than expected in the smaller size classes and fewer than expected near the mean and in the larger size classes. The data suggest thatthe pattern of growth of the Abeta deposits in DLB/AD depends both on deposit morphology and brain area. In addition, Abeta deposits in DLB appear to grow to within a more restricted size range than predicted and hence, to have less potential for growth compared with cases of 'pure' AD and DS.

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Deep sea manganese nodules are considered as important natural resources for the future because of their Ni, Cu and Co contents. Their different shapes cannot be correlated clearly with their chemical composition. Surface constitution, however, can be associated with the metal contents. A classification of the nodules is suggested on the basis of these results. The iron content of the nodules strikingly shows relations to the physical properties (e.g. density and porosity). The method of density-measurement is the reason for this covariance. The investigation of freeze-dried nodular substance does not give this result. The Fe-rich nodules lose more hydration water than the Fe-poor ones during heat drying. The reason for this effect is the different crystallinity, respectively the particle size. The mean particle size is calculated on the basis of geometrical models. The X-ray-diffraction analysis proves the variation of crystallinity in connection with the Fe-content, too. The internal nodular textures also show characteristic distinctions.

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Deep sea manganese nodules from the Central Pacific Basin are mainly composed of 10Å manganite and d-MnO2 Two zones equivalent to the minerals are evidently distinguishable according to their optical properties. Microscopic and microprobe analyses revealed quite different chemical compositions and textnral characteristics of the two zones. These different feature of the two zones of nodules suggest the different conditions under which they were formed. Concentrations of 11 metal elements in the zones and inter-element relationships show that the 10Å manganite zone is a monomineralic oxide phase containing a large amount of manganese and minor amounts of useful metals, and that the d-MnO2 zone which is apparently homogeneous under the microscope is a mixture of three or more different minerals. The chemical characteristics of the two zones can explain the variation of bulk composition of deep sea manganese nodules and inter-element relationships previously reported, suggesting that the bulk compositions are attributable to the mixing of the 10Å manganite and d-MnO2 zones in various ratios. Characteristic morphology and surface structure of some types of nodules and their relationships to chemistry are also attribut able to the textural and chemical features of the above mentioned two phases. Synthesis of hydrated manganese oxides was carried out in terms of the formation of manganese minerals in the ocean. The primary product which is an equivalent to d-MnO2 was precipitated from Mn 2+ -bearing alkaline solution under oxigenated condition by air bubbling at one atmospheric pressure and room temperature. The primary product was converted to a l0Å manganite equivalent by contact with Ni 2+, Cu 2++ or CO2+ chloride solutions. This reaction caused the decrease of Ni2+, Cu2+ or CO2+ concentrations and the increase of Na+ concentration in the solution. The reaction also proceeded even in diluted solutions of nickel chloride and resulted in a complete removal of Ni2+ from the solution. Reaction products were exclusively 10Å manganite equivalents and their chemical compositions were very similar to those of 10Å manganite in manganese nodules. The maximum value of(Cu+Ni+Co)/Mn ratio of 10Å manganite zones in manganese nodules is 0.16, and the Ni/Mn ratio of synthetic 10Å manganite ranges from 0.15 to 0.18 with the average of 0.167.

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Data comes from the NGDC database, https://www.ngdc.noaa.gov/.