Chemistry and Sulfur Isotopes in a Chert-dominant Sequence around the Stratiform Manganese Deposit of the Noda-Tamagawa Mine, Northern Kitakami Terrane, Northeast Japan: Implication for Paleoceanographic Environmental Setting

Abstract. Chemistry and sulfur isotopes are analyzed for a series of rocks in the chert‐dominant sequence around the stratiform manganese ore deposit of the Noda‐Tamagawa mine in the northern Kitakami Terrane, northeast Japan. The sequence is litholog‐ically classified into six units in ascending order: lower bedded chert, lower black shale, massive chert, manganese ore, upper black shale, and upper bedded chert. The rocks around the manganese ore deposit exhibit anomalous enrichment in Ni (max. 337 ppm), Zn (102) and U (30) in the upper part of lower bedded chert, Mo (122), Tl (79) and Pb (33) in the lower black shale, MnO, Cu (786) and Co (62) in the manganese ore, and As (247) and Sb (17) in the upper black shale. The aluminum‐normalized profiles reveal zonal enrichment of redox‐sensitive elements around the manganese bed: Zn‐Ni‐Fe‐Mo‐U(‐Co), Tl‐Pb(‐Mo), Mn‐Fe‐Cu‐V‐Cr‐Co(‐Zn) and As‐Sb in ascending order. The uppermost part of the lower bedded chert and black shale exhibit negative Ce/Ce* values, whereas the massive chert, manganese ore and lower part of the upper bedded chert display positive values. The isotopic δ³⁴S values are 0±6 % in the lower part of the lower bedded chert, ‐19 to ‐42 % in the upper part of the lower bedded chert, ‐36 to ‐42 % in the lower black shale, ‐28 to ‐35 % in the massive chert, manganese ore and upper black shale, and ‐23±5 % in the upper bedded chert. Thus, there is a marked negative shift in δ³⁴S values in the lower bedded chert, and an upward‐increasing trend in δ³⁴S through the manganese ore horizon. The present data provide evidence for a change in the paleoceanographic environmental resulting from inflow of oxic deepwater into the stagnant anoxic ocean floor below the manganese ore horizon. This event is likely to have triggered the precipitation of manganese oxyhydroxides. The redistribution of redox‐sensitive elements through the formation of metalliferous black shale and manganese carbonate ore may have occurred in association with bacterial decomposition of organic matter during early diagenesis of initial manganese oxyhydroxides.     

Chemical Profiles across a Jurassic Stratiform Manganese Deposit at Katsuyama in the Mino Terrane of Central Japan: Implications for Depositional Environment, Diagenetic Metal Redistribution and Paleoceanography

Abstract. Inorganic chemical compositions are determined for a series of rocks crossing an Early Jurassic stratiform manganese ore deposit in a chert‐dominant sequence at Katsuyama, in the Mino Terrane of central Japan. The lithology in the vicinity of the manganese ore bed is classified into lower bedded chert, black shale, massive chert, manganese ore and upper bedded chert, in ascending order. The rocks surrounding the manganese deposit are anomalously high in certain elements: Pb (max. 29 ppm), Ni (1140) and Co (336) in the lower bedded chert, Mo (438), As (149), Tl (29) and U (12) in the black shales, V (210) and Cr (87) in the massive chert, and MnO and W (24) in the manganese ore. The aluminum‐normalized profiles reveal a distinct zonation of redox‐sensitive elements: Pb‐Zn, Ni‐Co‐Cu(‐Zn) and U‐Cr in the lower bedded chert, Mo‐As‐Tl in the black shale, V(‐Cr) in the massive chert, and Mn‐Fe‐Ba‐W in the manganese ore, in ascending order. The lower and upper bedded cherts and manganese ore generally exhibit flat rare earth element patterns with positive Ce anomalies, whereas the uppermost part of the lower bedded chert, the black shale and massive chert have flat patterns with weak or nonexistent negative Ce anomalies and weak positive Eu anomalies. The strong enrichment in Ni, Co, W, Tl and As detected in the Katsuyama section is not recognized in other sediments, including those of anoxic deposition origin, but is identified in modern ferromanganese nodules, suggesting that metal enrichment in the Katsuyama section is essentially due to the formation of ferromanganese nodules rather than to deposition in an anoxic environment. The observed elemental zonation is well explained by equilibrium calculations, reflecting early diagenetic formation and associated gradual reduction with depth. The concentration profiles in combination with litho‐ and biostratigraphical features suggest that formation of these bedded manganese deposits was triggered by an influx of warm, saline and oxic water into a stagnant deep ocean floor basin in Panthalassa at the end of the middle Early Jurassic. Paleoceanographic environmental controls thus appear to be important factors in the formation and preservation of this type of stratiform manganese deposit.     

深圳市某建筑深基坑支护滑塌事故剖析

以深圳某建筑深基坑滑塌为例,在现场实物和相关资料调查的基础上,从基坑支护的设计、施工、监测以及质量安全管理等方面对基坑滑塌事故的原因进行了较为全面的分析和探讨,认为导致该基坑支护工程滑塌的主要原因是土钉和锚索抗拔力不足.最后针对目前深基坑工程中普遍存在的问题提出了加强设计方案的审查、加强施工管理、加强土钉墙研究的建议.     

深直井旋转钻柱空转功率消耗的数学建模

通过对深井旋转钻柱空转功率消耗因素的分析，考虑空转时离心力作用在井壁上引起的摩擦力导致的功率消耗，并将钻井液视为牛顿液体，运用柱坐标系建立了旋转钻柱在深井作业条件下空转功率消耗的分析模型。综合考虑空转功率与钻柱旋转的角速度、钻柱与井眼结构参数、钻井液粘度系数等因素间的关系，即旋转钻柱运动过程中的离心力和钻井液作用在钻柱上的剪切应力对功率的影响，建立了更加符合实际工况的数学模型。     

Comparison of runoff characteristics of two adjacent basins in a tropical rainforest using a modified hydrologic cycle model with outflow

We propose a new runoff model including an outflow process that was applied to two adjacent basins (CL, TL) located in Lambir Hills National Park in north‐central Sarawak, Malaysia. Rainfall, runoff, topography, and soil layer thickness were observed. About 19% of annual runoff was observed in the CL basin (21.97 ha), whereas about 46% was observed in the TL basin (23.25 ha). It was inferred that the CL basin has an outflow because of low base flow, small runoff peak, and excessive water loss. By incorporating the outflow process into the HYdrological CYcle MODEL, good agreement between the data generated by the model and that observed was shown, with the exception of the data from the rainless period. Then, the fitting parameters for each basin were exchanged, except for the outflow parameter, and the characteristics of each basin were compared by calculating virtual runoff. As a result, the low base flow of the CL basin was estimated by the movement of the rainwater that escaped from the basin as deep percolation or lateral flow (11% of rainfall). The potential of the CL basin for mitigating flood and drought appeared to be higher than that of the TL basin. This is consistent with the topographic characteristics of the CL basin, which has a gentler slope than the TL basin. Copyright © 2012 John Wiley & Sons, Ltd.     

Analytical modeling on consolidation of stiffened deep mixed column-reinforced soft soil under embankment

Stiffened deep mixed (SDM) column is a new ground improvement technique to improve soft soil, which can be used to increase bearing capacity, reduce deformation, and enhance stability of soft soil. This technique has been successfully adopted to support the highway and railway embankments over soft soils in China and other countries. However, there have been limited investigations on its consolidation under embankment loading. This paper developed an analytical solution for the consolidation of embankment over soft soil with SDM column in which core pile is equal to or shorter than outer DM column. The consolidation problem was simplified as a consolidation of composite soil considering the load shear effect of core pile. The developed solution was verified by a comparison with the results computed by three-dimensional (3-D) finite element analysis. A parametric study based on the derived solution was conducted to investigate influence factors—length of core pile, diameter of core pile, diameter of SDM column, modulus of DM column, and permeability coefficient of DM column—on the consolidation behavior of SDM column-supported embankment over soft soil. The developed solution was applied to a case history of SDM column-supported embankment, and a good agreement was found between the predictions and the field measurements.     

Water mining from the deep critical zone by apple trees growing on loess

There have been significant recent advances in understanding the ecohydrology of deep soil. However, the links between root development and water usage in the deep critical zone remains poorly understood. To clarify the interaction between water use and root development in deep soil, we investigated soil water and root profiles beyond maximum rooting depth in five apple orchards planted on farmland with stand ages of 8, 11, 15, 18, and 22 years in a subhumid region on the Chinese Loess Plateau. Apple trees rooted progressively deeper for water with increasing stand age and reached 23.2 ± 0.8 m for the 22‐year‐old trees. Soil water deficit in deep soil increased with tree age and was 1,530 ± 43 mm for a stand age of 22 years. Measured root deepening rate was far great than the reported pore water velocity, which demonstrated that trees are mining resident old water. The deficits are not replenished during the life‐span of the orchard, showing a one‐way mining of the critical zone water. The one‐way root water mining may have changed the fine root profile from an exponential pattern in the 8‐year‐old orchard to a relative uniform distribution in older orchards. Our findings enhance our understanding of water‐root interaction in deep soil and reveal the unintended consequences of critical zone dewatering during the lifespan of apple trees.     

Simulation of site-scale water fluxes in desert and natural oasis ecosystems of the arid region in Northwest China

The study of water fluxes is important to better understand hydrological cycles in arid regions. Data-driven machine learning models have been recently applied to water flux simulation. Previous studies have built site-scale simulation models of water fluxes for individual sites separately, requiring a large amount of data from each site and significant computation time. For arid areas, there is no consensus as to the optimal model and variable selection method to simulate water fluxes. Using data from seven flux observation sites in the arid region of Northwest China, this study compared the performance of random forest (RF), support vector machine (SVM), back propagation neural network (BPNN), and multiple linear regression (MLR) models in simulating water fluxes. Additionally, the study investigated inter-annual and seasonal variation in water fluxes and the dominant drivers of this variation at different sites. A universal simulation model for water flux was built using the RF approach and key variables as determined by MLR, incorporating data from all sites. Model performance of the SVM algorithm (R² = 0.25–0.90) was slightly worse than that of the RF algorithm (R² = 0.41–0.91); the BPNN algorithm performed poorly in most cases (R² = 0.15–0.88). Similarly, the MLR results were limited and unreliable (R² = 0.00–0.66). Using the universal RF model, annual water fluxes were found to be much higher than the precipitation received at each site, and natural oases showed higher fluxes than desert ecosystems. Water fluxes were highest during the growing season (May–September) and lowest during the non-growing season (October–April). Furthermore, the dominant drivers of water flux variation were various among different sites, but the normalized difference vegetation index (NDVI), soil moisture and soil temperature were important at most sites. This study provides useful insights for simulating water fluxes in desert and oasis ecosystems, understanding patterns of variation and the underlying mechanisms. Besides, these results can make a contribution as the decision-making basis to the water management in desert and oasis ecosystems.     

Site characterization model using least‐square support vector machine and relevance vector machine based on corrected SPT data (Nc)

Statistical learning algorithms provide a viable framework for geotechnical engineering modeling. This paper describes two statistical learning algorithms applied for site characterization modeling based on standard penetration test (SPT) data. More than 2700 field SPT values (N) have been collected from 766 boreholes spread over an area of 220 sqkm area in Bangalore. To get N corrected value (N_c), N values have been corrected (N_c) for different parameters such as overburden stress, size of borehole, type of sampler, length of connecting rod, etc. In three‐dimensional site characterization model, the function N_c=N_c (X, Y, Z), where X, Y and Z are the coordinates of a point corresponding to N_c value, is to be approximated in which N_c value at any half‐space point in Bangalore can be determined. The first algorithm uses least‐square support vector machine (LSSVM), which is related to a ridge regression type of support vector machine. The second algorithm uses relevance vector machine (RVM), which combines the strengths of kernel‐based methods and Bayesian theory to establish the relationships between a set of input vectors and a desired output. The paper also presents the comparative study between the developed LSSVM and RVM model for site characterization. Copyright © 2009 John Wiley & Sons, Ltd.