Uranium and thorium series nuclides in oriented ferromanganese nodules: growth rates,turnover times and nuclide behavior

Three ferromanganese nodules handpicked from the tops of 2500 cm² area box cores taken from the north equatorial Pacific have been analysed for their U-Th series nuclides.²³⁰Th_exc concentrations in the surface 1–2 mm of the top side of the nodules indicate growth rates of 1.8–4.6 mm/10⁶ yr. In two of the nodules a significant discontinuity in the²³⁰Th_exc depth profile has been observed at ～0.3 m.y. ago, suggesting that the nodule growth has been episodic. The concentration profiles of²³¹Pa_exc (measured via²²⁷Th) yield growth rates similar to the²³⁰Th_exc data. The bottom sides of the nodules display exponential decrease of²³⁰Th_exc/²³²Th activity ratio with depth, yielding growth rates of 1.5–3.3 mm/10⁶ yr.The²³⁰Th_exc and²³¹Pa_exc concentrations in the outermost layer of the bottom face are significantly lower than in the outermost layer of the top face. Comparison of the extrapolated²³⁰Th_exc/²³²Th and²³⁰Th_exc/²³¹Pa_exc activity ratios for the top and bottom surfaces yields an “age” of (5?15) × 10⁴ yr for the bottom relative to the top. This “age” most probably represents the time elapsed since the nodules have attained the present orientation.The²¹⁰Pb concentration in the surface ～0.1 mm of the top side is in large excess over its parent²²⁶Ra. Elsewhere in the nodule, up to ～1 mm depth in both top and bottom sides,²¹⁰Pb is deficient relative to²²⁶Ra, probably due to²²²Rn loss. The absence of²¹⁰Pb_exc below the outermost layer of the top face rules out the possibility of a sampling artifact as the cause of the observed exponentially decreasing²³⁰Th_exc and²³¹Pa_exc concentration profiles. The flux of²¹⁰Pb_exc to the nodules ranges between 0.31 and 0.58 dpm/cm² yr. The exhalation rate of²²²Rn, estimated from the²²⁶Ra-²¹⁰Pb disequilibrium is ～570 dpm/cm² yr from the top side and >2000 dpm/cm² yr from the bottom side.²²⁶Ra is deficient in the top side relative to²³⁰Th up to ～0.5–1 mm and is in large excess throughout the bottom. The data indicate a net gain of²²⁶Ra into the nodule, corresponding to a flux of (24?46) × 10^?3 dpm/cm² yr. On a total area basis the gain of²²⁶Ra into the nodules is <20% of the²²⁶Ra escaping from the sediments. A similar gain of²²⁸Ra into the bottom side of the nodules is reflected by the high²²⁸Th/²³²Th activity ratios observed in the outermost layer in contact with sediments.     

Digital Image Based Approach for Three-Dimensional Mechanical Analysis of Heterogeneous Rocks

Summary This paper presents a digital image based approach for three-dimensional (3-D) numerical simulation and failure analysis of rocks by taking into account the actual 3-D heterogeneity. Digital image techniques are adopted to extract two-dimensional (2-D) material heterogeneity from material surface images. The 2-D image mesostructures are further extrapolated to 3-D cuboid mesostructures by assuming the material surface as a representation of the inner material heterogeneity within a very small depth. The iterative milling and scanning system is set up to generate the 3-D rock mesostructures. A Hong Kong granite specimen is used as an example to demonstrate the procedure of 3-D mesostructure establishment. The mechanical responses and failure process under the conventional Brazilian tensile test condition are examined through numerical analyses. The stress distribution, crack propagation process and failure model of heterogeneous material cases are simulated with a finite difference software. The numerical results indicate that material heterogeneity plays an important role in determining the failure behavior of rocks under external loading.     

Effect of common point selection on coordinate transformation parameter determination

The use of satellite positioning techniques commonly requires a transformation from a Conventional Terrestrial coordinate system to a Geodetic coordinate system, or vice versa. For such a transformation, the main problem is the determination of transformation parameters between these coordinate systems. The transformation parameters are estimated by a least-squares process using “common” points, i.e., those points whose coordinates are known in both systems. Therefore, the precision of so estimated transformation parameters is closely related to certain characteristics of the common points. In this contribution, we have formulated some theoretical relations between the transformation parameters and the number and the distribution of common points, and corroborated the theoretical results numerically, using a simulated geodetic network.     

On the use of IPCC-class models to assess the impact of climate on Living Marine Resources

The study of climate impacts on Living Marine Resources (LMRs) has increased rapidly in recent years with the availability of climate model simulations contributed to the assessment reports of the Intergovernmental Panel on Climate Change (IPCC). Collaboration between climate and LMR scientists and shared understanding of critical challenges for such applications are essential for developing robust projections of climate impacts on LMRs. This paper assesses present approaches for generating projections of climate impacts on LMRs using IPCC-class climate models, recommends practices that should be followed for these applications, and identifies priority developments that could improve current projections. Understanding of the climate system and its representation within climate models has progressed to a point where many climate model outputs can now be used effectively to make LMR projections. However, uncertainty in climate model projections (particularly biases and inter-model spread at regional to local scales), coarse climate model resolution, and the uncertainty and potential complexity of the mechanisms underlying the response of LMRs to climate limit the robustness and precision of LMR projections. A variety of techniques including the analysis of multi-model ensembles, bias corrections, and statistical and dynamical downscaling can ameliorate some limitations, though the assumptions underlying these approaches and the sensitivity of results to their application must be assessed for each application. Developments in LMR science that could improve current projections of climate impacts on LMRs include improved understanding of the multi-scale mechanisms that link climate and LMRs and better representations of these mechanisms within more holistic LMR models. These developments require a strong baseline of field and laboratory observations including long time series and measurements over the broad range of spatial and temporal scales over which LMRs and climate interact. Priority developments for IPCC-class climate models include improved model accuracy (particularly at regional and local scales), inter-annual to decadal-scale predictions, and the continued development of earth system models capable of simulating the evolution of both the physical climate system and biosphere. Efforts to address these issues should occur in parallel and be informed by the continued application of existing climate and LMR models.     

Jinshanjiangite and bafertisite from the Gremyakha-Vyrmes Alkaline Complex,Kola Peninsula

Jinshanjiangite (acicular crystals up to 2 mm in length) and bafertisite (lamellar crystals up to 3 × 4 mm in size) have been found in alkali granite pegmatite of the Gremyakha-Vyrmes Complex, Kola Peninsula. Albite, microcline, quartz, arfvedsonite, zircon, and apatite are associated minerals. The dimensions of a monoclinic unit cell of jinshanjiangite and bafertisite are: a = 10.72(2), b=13.80(2), c = 20.94(6) Å, β = 97.0(5)° and a = 10.654(6), b = 13.724(6), c = 10.863(8) Å, β = 94.47(8)°, respectively. The typical compositions (electron microprobe data) of jinshanjiangite and bafertisite are: (Na_0.57Ca_0.44)_Σ1.01(Ba_0.57K_0.44)_Σ1.01 (Fe_3.53Mn_0.30Mg_0.04Zn_0.01)_Σ3.88(Ti_1.97Nb_0.06Zr_0.01)_Σ2.04(Si_3.97Al_0.03O₁₄)O_2.00(OH_2.25F_0.73O_0.02)_Σ3.00 and (Ba_1.98Na_0.04K_0.03)_Σ2.05(Fe_3.43Mn_0.37Mg_0.03)_Σ3.83(Ti_2.02Nb_0.03)_Σ2.05 (Si_3.92Al_0.08O₁₄)(O_1.84OH_0.16)_Σ2.00(OH_2.39F_1.61)_Σ3.00, respectively. The minerals studied are the Fe-richest members of the bafertisite structural family.