The moon: Composition determined by nebular processes

The bulk composition of the Moon was determined by the conditions in the solar nebula during its formation, and may be quantitatively estimated from the premise that the terrestrial planets were formed by cosmochemical processes similar to those recorded in the chondrites. The calculations are based on the Ganapathy-Anders 7-component model using trace element indicators, but incorportate improved geophysical data and petrological constraints.A model Moon with 40 ppb U, a core 2% by weight (1.8% metal with 35% Ni and 0.2% FeS) and Mg/(Fe²⁺+Mg)0.75 meets the trace element restrictions, and has acceptable density, heat flow and moment of inertia ratio. The high Ni content of the core permits low-Ti mare basalts to equilibrate with metal, yet still retain substantial Ni. The silicate resembles the Taylor-Jake composition (and in some respects the waif Ganapathy-Anders Model 2a), but has lower SiO₂.Minor modifications of the model composition (U=30–35 ppb) yield a 50% melt approximating Apollo 15 green glass and a residuum of olivine plus 3 to 4% spinel; the low SiO₂, favors spinel formation, and, contrary to expectation, Cr is not depleted in the liquid. There may no longer be any inconsistency between the cosmochemical approach and arguments based on experimental petrology.Paper dedicated to Professor Hannes Alfvén on the occasion of his 70th birthday, 30 May, 1978.Unless otherwise stated, this ratio refers to themolar ratio throughout this paper.     

Geology of the Atlantis Massif (Mid-Atlantic Ridge, 30° N): Implications for the evolution of an ultramafic oceanic core complex

Marine Geophysical Research - The oceanic core complex comprising Atlantis Massif was formed within the past 1.5–2 Myr at the intersection of the Mid-Atlantic Ridge, 30° N, and the...     

TOPEX/Poseidon and Jason-1: Absolute Calibration in Bass Strait, Australia

Updated absolute calibration results from Bass Strait, Australia, are presented for the TOPEX/Poseidon (T/P) and Jason-1 altimeter missions. Data from an oceanographic mooring array and coastal tide gauge have been used in addition to the previously described episodic GPS buoy deployments. The results represent a significant improvement in absolute bias estimates for the Bass Strait site. The extended methodology has allowed comparison between the altimeter and in situ data on a cycle-by-cycle basis over the duration of the dedicated calibration phase (formation flight period) of the Jason-1 mission. In addition, it has allowed absolute bias results to be extended to include all cycles since the T/P launch, and all Jason-1 data up to cycle 60. Updated estimates and formal 1-sigma uncertainties of the absolute bias computed throughout the formation flight period are 0 ± 14 mm for T/P and +152 + 13 mm for Jason-1 (for the GDR POE orbits). When JPL GPS orbits are used for cycles 1 to 60, the Jason-1 bias estimate is 131 mm, virtually identical to the NASA estimate from the Harvest Platform off California calculated with the GPS orbits and not significantly different to the CNES estimate from Corsica. The inference of geographically correlated errors in the GDR POE orbits (estimated to be approximately 17 mm at Bass Strait) highlights the importance of maintaining globally distributed verification sites and makes it clear that further work is required to improve our understanding of the Jason-1 instrument and algorithm behavior.     

Theory of interaction intensities,buffer capacities,and pH stability in aqueous systems,with application to the pH of seawater and a heterogeneous model ocean system

The choice of convenient basic constituents for evaluating pH stability of aqueous systems is discussed, and two useful interaction parameters are defined and related to the buffer capacity: the interaction capacity, $\text{δ′}{}_{\mathrm{X,Y}}\text{=}\text{?}\text{p}\text{X}\text{?}\text{TOT}\text{Y}$, and the interaction intensity, $\text{δ}{}_{\mathrm{X,\; Y}}\text{=}\text{?}\text{p}\text{X}\text{?}\text{pTOT}\text{Y}$; for pH and TOTH, δ′_{H, H} = ?β_H^?1, where β_H is the pH buffer capacity. A method is presented for the computation of exact values of all interaction capacities and intensities through inversion of the Jacobian matrix of the system of non-linear equations describing the aqueous system. The major species of an aqueous system (H₂O, H⁺, solid phases, gases, and the most abundant solute species) are shown to constitute a useful set of basic constituents for evaluation of approximate pH buffer capacities according to a simple rule: the major-minor species rule for zeroth order pH-TOTH interaction. The concepts of buffering and pH-statting are examined and contrasted; it is demonstrated that the buffer capacity of an aqueous system cannot be infinite: it is limited by the concentration of solutes in solution. The effect upon pH of variations in constituents other than H⁺ is described in terms of first order interactions via complex formation and solid formation; approximate formulas for calculation are derived. Higher order interactions are derived from combinations of first order ones. The pH stability of the ocean system is examined in terms of an aqueous phase model including ion-association reactions and a heterogeneous model incorporating CO₂ in the gas phase, quartz, kaolinite, calcite, chlorite, and illite, in addition to the aqueous phase. There is an approximately three-fold enhancement of buffer capacity in the aqueous model as a consequence of ion-association. Only a few interaction pathways are of quantitative significance in establishing the buffer capacity. Results for the heterogeneous ocean model lend quantitative support to Sillén's notion of pH stability: the buffer capacity is about four hundred times greater than that of the aqueous phase model.     

Correlation coefficients and sea-floor spreading an automated analysis of magnetic profiles

A computer correlation technique is described for comparing observed magnetic anomaly profiles to model profiles generated from a paleomagnetic time scale. A model is computed for a given spreading rate, divided into small segments, and each segment then compared to a corresponding unit of the data profile. Each model segment is systematically shifted and correlated with the data, and the resulting correlation functions are displayed in an offset-age diagram. Interpretation of this diagram reveals possible histories involving faulting or changes of spreading rate that may be overlooked by the visual method, and quantitative estimates of the uncertainties in the spreading rates may be made.