A three-dimensional approach to fault seal analysis: fault-block juxtaposition & argillaceous smear modelling

Three‐dimensional (3D) numerical modelling of fault displacement enables the building of geological models to represent the complex 3D geometry and geological properties of faulted sedimentary basins. Using these models, cross‐fault juxtaposition relationships are predicted in 3D space and through time, based on the geometries of strata that are cut by faults. Forward modelling of fault development allows a 3D prediction of fault‐zone argillaceous smear using a 3D application of the Shale Gouge Ratio. Numerical models of the Artemis Field, Southern North Sea, UK and the Moab Fault, Utah, USA are used to demonstrate the developed techniques and compare them to traditional one‐ and two‐dimensional solutions. These examples demonstrate that a 3D analysis leads to significant improvements in the prediction of fault seal, the analysis of the interaction of the sealing properties of multiple faults, and the interpretation of fault seal within the context of sedimentary basin geometry.     

Production of a molybdophore during metal-targeted dissolution of silicates by soil bacteria

Although many bioessential metals are scarce in natural water and rock systems, microbial secretion of high-affinity ligands for metal extraction from solid phases has only been documented for Fe. However, we have discovered that Mo is extracted from a silicate by a high-affinity ligand (a possible “molybdophore”) secreted by an N₂-fixing soil bacterium. The putative molybdophore, aminochelin, is secreted as a siderophore under Fe-depleted conditions, but is also secreted under Fe-sufficient, Mo-depleted conditions. Presumably, molybdophore production facilitates uptake of Mo for use in Mo enzymes. In contrast, an Fe-requiring soil bacterium without a special Mo requirement only enhances the release of Fe from the silicate. Fractionation of Mo stable isotopes during uptake to cells may provide a “fingerprint” for the importance of chelating ligands in such systems. Many such metal-specific ligands secreted by prokaryotes for extraction of bioessential metals, their effects on Earth materials, and their possible utility in the recovery of economic metals remain to be discovered.     

6: Classification of VMS deposits: Lessons from the South Uralides

VMS deposits of the South Urals developed within the evolving Urals palaeo-ocean between Silurian and Late Devonian times. Arc-continent collision between Baltica and the Magnitogorsk Zone (arc) in the south-western Urals effectively terminated submarine volcanism in the Magnitogorsk Zone with which the bulk of the VMS deposits are associated. The majority of the Urals VMS deposits formed within volcanic-dominated sequences in deep seawater settings. Preservation of macro and micro vent fauna in the sulphide bodies is both testament to the seafloor setting for much of the sulphides but also the exceptional degree of preservation and lack of metamorphic overprint of the deposits and host rocks. The deposits in the Urals have previously been classified in terms of tectonic setting, host rock associations and metal ratios in line with recent tectono-stratigraphic classifications. In addition to these broad classes, it is clear that in a number of the Urals settings, an evolution of the host volcanic stratigraphy is accompanied by an associated change in the metal ratios of the VMS deposits, a situation previously discussed, for example, in the Noranda district of Canada.Two key structural settings are implicated in the South Urals. The first is seen in a preserved marginal allochthon west of the Main Urals Fault where early arc tholeiites host Cu–Zn mineralization in deposits including Yaman Kasy, which is host to the oldest macro vent fauna assembly known to science. The second tectonic setting for the South Urals VMS is the Magnitogorsk arc where study has highlighted the presence of a preserved early forearc assemblage, arc tholeiite to calc-alkaline sequences and rifted arc bimodal tholeiite sequences. The boninitc rocks of the forearc host Cu–(Zn) and Cu–Co VMS deposits, the latter hosted in fragments within the Main Urals Fault Zone (MUFZ) which marks the line of arc-continent collision in Late Devonian times. The arc tholeiites host Cu–Zn deposits with an evolution to more calc-alkaline felsic volcanic sequences matched with a change to Zn–Pb–Cu polymetallic deposits, often gold-rich. Large rifts in the arc sequence are filled by thick bimodal tholeiite sequences, themselves often showing an evolution to a more calc-alkaline nature. These thick bimodal sequences are host to the largest of the Cu–Zn VMS deposits.The exceptional degree of preservation in the Urals has permitted the identification of early seafloor clastic and hydrolytic modification (here termed halmyrolysis sensu lato) to the sulphide assemblages prior to diagenesis and this results in large-scale modification to the primary VMS body, resulting in distinctive morphological and mineralogical sub-types of sulphide body superimposed upon the tectonic association classification.It is proposed that a better classification of seafloor VMS systems is thus achievable using a three stage classification based on (a) tectonic (hence bulk volcanic chemistry) association, (b) local volcanic chemical evolution within a single edifice and (c) seafloor reworking and halmyrolysis.     

Detecting trends in seagrass abundance using aerial photograph interpretation: Problems arising with the evolution of mapping methods

In the late 1970s and early 1980s an inventory of seagrass distribution in southeast Australia was made from multiscale aerial photographs using a simple analog mapping method, referred to here as theCamera lucida technique. Four priority estuaries were remapped in 2003, also using multi-scale aerial photographs, but using a geographic information system (GIS)-based analog mapping technique. We quantified the degree of error that arose when estimates of seagrass distribution were compared for the two techniques be ressessing. with GIS, the same aerial photographs used earlier for four estuaries: Port Hacking, Bermagui River, Merimbula Lake, and St Georges Basin.Camera lucida produced a larger estimate of total seagrass area than GIS in all four locations: 8% of total seagrass area or 12.9 ha in Port Hacking, 15% or 5 ha in Bermagui River, 20% or 38.0 ha in Merimbula Lake, and 243% or 502.2 ha in St Georges Basin. The consistent and moderate discrepancy in Port Hacking, Bermagui River, and Merimbula Lake was attributed mainly to differences betweenCamera lucida and GIS, such as the tendency ofCamera lucida to overestimate seagrass area by amalgamating disjunct seagrass patches into continuous meadows. The large discrepancy at St Georges Basin was attributed to interoperator error; i.e., the operators who mapped photographs taken in 1979 had widely differing interpretations of the aerial photographs. A 20-yr trend was calculated for each estuary using either theCamera lucida or revised GIS value as the start point. Perceived management status of the seagrass depended greatly on the initial data used. In St Georges Basin, a catastrophic decline (65%, 553.9 ha) occurred based on theCamera lucida-GIS comparison but only a small decline (14.7%, 51.8 ha) when all photos were GIS assessed, In view of the technical developments in habitat mapping, evaluation of change in seagrass abundance using remote sensing needs an assessment of intra-operator error, inter-operator error, and systemic error before maps produced by superseded technology are considered useful baselines.