The Miocene northeast Honshu magmatic arc, Japan, formed at a terrestrial continental margin via a stage of spreading in a back‐arc basin (23–17 Ma) followed by multiple stages of submarine rifting (19–13 Ma). The Kuroko deposits formed during this period, with most forming during the youngest rifting stage. The mode of magma eruption changed from submarine basalt lava flows during back‐arc basin spreading to submarine bimodal basalt lava flows and abundant rhyolitic effusive rocks during the rifting stage. The basalts produced during the stage of back‐arc basin spreading are geochemically similar to mid‐ocean ridge basalt, with a depleted Sr–Nd mantle source, whereas those produced during the rifting stage possess arc signatures with an enriched mantle source. The Nb/Zr ratios of the volcanic rocks show an increase over time, indicating a temporal increase in the fertility of the source. The Nb/Zr ratios are similar in basalts and rhyolites from a given rift zone, whereas the Nd isotopic compositions of the rhyolites are less radiogenic than those of the basalts. These data suggest that the rhyolites were derived from a basaltic magma via crystal fractionation and crustal assimilation. The rhyolites associated with the Kuroko deposits are aphyric and have higher concentrations of incompatible elements than do post‐Kuroko quartz‐phyric rhyolites. These observations suggest that the aphyric rhyolite magma was derived from a relatively deep magma chamber with strong fractional crystallization. Almost all of the Kuroko deposits formed in close temporal relation to the aphyric rhyolite indicating a genetic link between the Kuroko deposits and highly differentiated rhyolitic magma. 相似文献
The temperature distribution at depth is a key variable when assessing the potential of a supercritical geothermal resource as well as a conventional geothermal resource. Data-driven estimation by a machine-learning approach is a promising way to estimate temperature distributions at depth in geothermal fields. In this study, we developed two methodologies—one based on Bayesian estimation and the other on neural networks—to estimate temperature distributions in geothermal fields. These methodologies can be used to supplement existing temperature logs, by estimating temperature distributions in unexplored regions of the subsurface, based on electrical resistivity data, observed geological/mineralogical boundaries, and microseismic observations. We evaluated the accuracy and characteristics of these methodologies using a numerical model of the Kakkonda geothermal field, Japan, where a temperature above 500 °C was observed below a depth of about 3.7 km. When using geological and geophysical knowledge as prior information for the machine learning methods, the results demonstrate that the approaches can provide subsurface temperature estimates that are consistent with the temperature distribution given by the numerical model. Using a numerical model as a benchmark helps to understand the characteristics of the machine learning approaches and may help to identify ways of improving these methods.
Abstract: The Shin-Ohtoyo Cu–Au deposit is located in the Harukayama district, 20 km west of Sapporo, Hokkaido, Japan. Both acid-type disseminated and adularia–quartz–type vein Au mineralizations have been recognized within a small distance of less than 500 m in the district. Mineralogical characteristics of sulfide ores from the Shin-Ohtoyo deposit have been proved to be polymetallic. Ore minerals containing Sn, V, Bi and Te are recognized. Nine ore types are recognized in terms of characteristic mineral assemblage; (1) chalcedonic quartz veinlets in silicified zone around the deposit, (2) bismuthinite, emplectite, friedrichite and tetrahedrite, (3) an unnamed Cu–Sn–Fe–Zn sulfide, colusite-series minerals, stannoidite, emplectite and tetrahedrite, (4) bournonite, Se-bearing galena and tetrahedrite, (5) luzonite/famatinite and Ag-bearing tetrahedrite, (6) colusite-series minerals, emplectite, aikinite and tetrahedrite/goldfieldite, (7) luzonite/famatinite, colusite-series minerals, mawsonite and tetra–hedrite/goldfieldite, (8) enargite, luzonite/famatinite and tetrahedrite, and (9) colusite-series minerals and tetrahedrite. The first occurrence of friedrichite and stibiocolusite from Japan are reported. The chemical formula of the unnamed phase corresponds to Cu6(Cu, Fe, Zn)Sn3S10. Sulfur isotopic ratios (δ34S) of sulfides from the stockpile range from –0. 5% to +1. 9%, and those from drill cores recovered by Metal Mining Agency of Japan (MMAJ) vary from –2. 7% to +0. 8%. Sulfur isotopic ratio of barite in a cavity in the silicified tuff breccia collected from the stock pile yields +27. 1%, while that of barite collected from MMAJ core is +21. 7%. Sulfur isotopic thermometry applied for a pair of barite (+21. 7%) and associated pyrite (+1. 8%) indicates about 300°C. High–Te tetrahedrite composition from both the chalcedonic quartz vein in the silicified zone around the Shin-Ohtoyo deposit and the polymetallic sulfide ores from the adit of the deposit, suggests that the Au mineralization in the former is attributed to a hydrothermal system marginal to the polymetallic mineralization. 相似文献
The single prism approximation SPA of the cluster variation method has been used to model the antiferromagnetic-paramagnetic transition in hematite. This calculation yields insight into the accuracy of the SPA and other approximate methods for modeling order-disorder phenomena. Published values of the magnetic coupling constants were used to calculate the Néel temperature, sublattice magnetization, and magnetic specific heat. The calculated Néel temperature is found to be 1.21 times the observed value, as compared to 1.36 times observed for a mean field theory approximation, an improvement that reflects the superior treatment of configurational entropy in the SPA. Qualitative to semiquantitative agreement is obtained between observed and calculated values for sublattice magnetization and magnetic specific heat; however we find that previously published values for the magnetic specific heat are too large by a factor of two. 相似文献
Abstract. Recent discoveries of seafloor hydrothermal mineralization in submarine volcanic centers of felsic magma in western Pacific island arcs are regarded as modern analogues of Kuroko type deposits. Studies of these deposits and their surrounding geology raised question whether the exploration activity for the Kuroko deposits on land which peaked in the 1960's was adequate or not. However, such an evaluation is not easy because the exploration data are about to be lost as a result of the closure of all the Kuroko mines in the area since 1994. The Metal Mining Agency of Japan (MMAJ), therefore, decided to compile existing data on about 180 Kuroko deposits and related mineral occurrences in northeast Japan as a new Kuroko database. This study extends a concept called "exploration indices" which was developed based on a case study of the thoroughly surveyed Hokuroku district to draw a potential map of the Kuroko occurrences for the entire northeast Japan quantitatively with a Geographical Information System (GIS). Effective exploration indices include: 1) distribution of dacitic-rhy-olitic submarine volcanic rocks of the Nishikurosawa and Onnagawa stages, 2) distribution of intrusive rocks of pre- and post-Kuroko horizon, 3) low aeromagnetic anomaly caused by hydrothermal alteration of magnetite, 4) low gravity anomaly which suggests depressions in the basement rocks such as a tectonic basin and/or caldera, and 5) nearby existence of vein type deposits. It is concluded that about 33 % of known Kuroko deposits fall within the high potential zone (score=4 and 5) that occupies only 4 % of the entire northeast Japan arc. The Kuroko potential map is, therefore, useful for limiting the target area for Kuroko type deposits in an island arc setting. 相似文献
In order to determine whether it is desirable to quantify mineral-deposit models further, a test of the ability of a probabilistic
neural network to classify deposits into types based on mineralogy was conducted. Presence or absence of ore and alteration
mineralogy in well-typed deposits were used to train the network. To reduce the number of minerals considered, the analyzed
data were restricted to minerals present in at least 20% of at least one deposit type. An advantage of this restriction is
that single or rare occurrences of minerals did not dominate the results. Probabilistic neural networks can provide mathematically
sound confidence measures based on Bayes theorem and are relatively insensitive to outliers. Founded on Parzen density estimation,
they require no assumptions about distributions of random variables used for classification, even handling multimodal distributions.
They train quickly and work as well as, or better than, multiple-layer feedforward networks. Tests were performed with a probabilistic
neural network employing a Gaussian kernel and separate sigma weights for each class and each variable. The training set was
reduced to the presence or absence of 58 reported minerals in eight deposit types. The training set included: 49 Cyprus massive
sulfide deposits; 200 kuroko massive sulfide deposits; 59 Comstock epithermal vein gold districts; 17 quartzalunite epithermal
gold deposits; 25 Creede epithermal gold deposits; 28 sedimentary-exhalative zinc-lead deposits; 28 Sado epithermal vein gold
deposits; and 100 porphyry copper deposits. The most common training problem was the error of classifying about 27% of Cyprus-type
deposits in the training set as kuroko. In independent tests with deposits not used in the training set, 88% of 224 kuroko
massive sulfide deposits were classed correctly, 92% of 25 porphyry copper deposits, 78% of 9 Comstock epithermal gold-silver
districts, and 83% of six quartzalunite epithermal gold deposits were classed correctly. Across all deposit types, 88% of
deposits in the validation dataset were correctly classed. Misclassifications were most common if a deposit was characterized
by only a few minerals, e.g., pyrite, chalcopyrite,and sphalerite. The success rate jumped to 98% correctly classed deposits
when just two rock types were added. Such a high success rate of the probabilistic neural network suggests that not only should
this preliminary test be expanded to include other deposit types, but that other deposit features should be added 相似文献