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2019

117 record(s)
 
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    Count data (original counts and percentage abundances) from nannofossil assemblage analysis of Early Miocene samples. These form the prime data for a paper submitted to Palaeogeography, Palaeoclimatology, Palaeoecology (Young et al. submitted, 2019).

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    The 5km Hex GS Shrink Swell dataset shows a generalised view of the GeoSure Shrink Swell v8 dataset to a hexagonal grid resolution of 64.95km coverage area (side length of 5km). This dataset indicates areas of potential ground movement in a helpful and user-friendly format. The rating is based on a highest level of susceptibility identified within that Hex area: Low (1), Moderate (2), Significant (3). Areas of localised significant rating are also indicated. The summarising process via spatial statistics at this scale may lead to under or over estimation of the extent of a hazard. The supporting GeoSure reports can help inform planning decisions and indicate causes of subsidence. The Shrink Swell methodology is based on the BGS Digital Map (DiGMapGB-50) and expert knowledge of the behaviour of the formations so defined. This dataset provides an assessment of the potential for a geological deposit to shrink and swell. Many soils contain clay minerals that absorb water when wet (making them swell), and lose water as they dry (making them shrink). This shrink-swell behaviour is controlled by the type and amount of clay in the soil, and by seasonal changes in the soil moisture content (related to rainfall and local drainage). The rock formations most susceptible to shrink-swell behaviour are found mainly in the south-east of Britain. Clay rocks elsewhere in the country are older and have been hardened by burial deep in the earth and are less able to absorb water. The BGS has carried out detailed geotechnical and mineralogical investigations into rock types known to shrink, and are modelling their properties across the near surface. This research underpins guidance contained in the national GeoSure dataset, and is the basis for our responses to local authorities, companies and members of the public who require specific information on the hazard in their areas. The BGS is undertaking a wide-ranging research programme to investigate this phenomenon by identifying those areas most at risk and developing sustainable management solutions. Complete Great Britain national coverage is available.

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    Terrestrial laser scanning datasets (including GPS data for georeferencing), structure from motion datasets, and field photographs collected during the 2016 Central Italian Earthquake sequence. Data were collected from multiple localities to capture the co- and post- seismic near-fault deformation along the earthquake surface ruptures. See the scan_data.xlsx for individual details of each laser scan location, and readme.txt for a complete description. In each scan site folder you can find the unprocessed raw files and processed ascii files. Note, not all processed files are georeferenced.This is due to time constraints on field data collection. Georeferencing for these data can be achieved by point cloud matching to other georeferenced scans. Photos for structure from motion (SfM) datasets were collected with a Nikon camera. All original photos are provided. Field photographs taken in the aftermath of the 2016 Norcia Earthquake (30th October 2016, Mw6.6) are also provided. Relevant references are: Wilkinson, MW, KJW McCaffrey, RR Jones, GP Roberts, RE Holdsworth, LC Gregory, RJ Walters, LNJ Wedmore, H Goodall, F Iezzi (2017). Near-field fault slip of the 2016 Vettore Mw 6.6 earthquake (Central Italy) measured using low-cost GNSS. Scientific Reports 7: 4612. Walters, RJ, LC Gregory, LNJ Wedmore, TJ Craig, K McCaffrey, M Wilkinson, J Chen, Z Li, JR Elliott, H Goodall, F Iezzi, F Livio, AM Michetti, G Roberts, E Vittori (2018). Dual control of fault intersections on stop-start rupture in the 2016 Central Italy seismic sequence. Earth and Planetary Science Letters 500, 1-14. Wedmore, LNJ, LC Gregory, KJW McCaffrey, H Goodall, RJ Walters (in review). Partitioned off-fault deformation in the 2016 Norcia earthquake captured by differential terrestrial laser scanning. Submitted to Geophysical Research Letters.

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    This study explored the links between host rock composition, hydrothermal fluid composition (particularly pH), and the resulting ore minerals and deposits. The progressive water–rock reaction between 1 kg of initially acidic, condensed magmatic vapour and a series of different rock compositions was modelled with CHILLER (Reed, 1982, Reed, 1998), and follows the design of the water-rock reactions of Reed (1997). The thermodynamic data used in the numerical experiments are from the database SOLTHERM.H08 (Reed and Palandri, 2013). Data and calculations within SOLTHERM include: equilibrium constants calculated with SUPCRT92 (Johnson et al., 1992); mineral thermodynamic data for silicates, oxides, hydroxides, carbonates, gases (Holland and Powell, 1998) and sulphides (Shock, 2007). Mineral solid solutions are represented by end-member compositions that are mixed using an ideal multisite mixing scheme. Rock compositions used in the modelling represent a sub-alkaline andesitic control, and a number of alkaline compositions associated with world-class Au deposits. All starting rock compositions are derived from whole rock geochemical data, and have been recalculated to a 100% basis without TiO2 or P2O5 (excluded as minor phases with little to no effect on hydrothermal mineral assemblages). Original total Fe (as Fe2O3) has been recalculated to FeO and Fe2O3 using the method of Müller et al. (2001). The andesite is representative of calc-alkaline, silica saturated compositions, and is derived from and discussed in detail in Reed (1997). The Luise “Phonolite” (a trachyandesite using the Le Maitre et al., 1989 TAS plot; Fig. 1) and Trachyandesite are from the vicinity of the Ladolam epithermal Au deposit, Lihir Island, Papua New Guinea (Müller et al., 2001). The Porgera Mugearite and Feldspar Porphyry represent unaltered host rock compositions (Richards, 1990) from the Porgera Au deposit (Papua New Guinea). The Cripple Creek Phonolite is part of the host suite to the Cripple Creek epithermal Au deposit, Colorado (Kelley et al., 1998). The Savo trachyte (Smith et al., 2009) represents a typical host rock of the active hydrothermal system (Smith et al., 2010), on Savo island, Solomon Islands. With the exception of the Andesite, all compositions are alkaline using the total alkali versus silica definition of Irvine and Baragar (1971). The Savo sample is not associated with known epithermal Au mineralisation; this composition was selected on the grounds that it represents an evolved (SiO2-rich) silica-saturated, alkaline composition. The initial fluid composition is based on a condensate from Augustine volcano (Symonds et al., 1990) mixed 1:10 with pure water (Reed, 1997; Table 2). A single starting fluid for all models was chosen so as to demonstrate the effect of host rock alone.

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    The 5km Hex GS Compressible Ground dataset shows a generalised view of the GeoSure Compressible Ground v8 dataset to a hexagonal grid resolution of 64.95km coverage area (side length of 5km). This dataset indicates areas of potential ground movement in a helpful and user-friendly format. The rating is based on a highest level of susceptibility identified within that Hex area: Low (1), Moderate (2), Significant (3). Areas of localised significant rating are also indicated. The summarising process via spatial statistics at this scale may lead to under or over estimation of the extent of a hazard. The supporting GeoSure reports can help inform planning decisions and indicate causes of subsidence. The methodology is based on the BGS Digital Map (DiGMapGB-50) and expert knowledge of the behaviour of the formations so defined. This dataset provides an assessment of the potential for a geological deposit to compress under an applied load, a characteristic usually of superficial deposits such as peat or alluvium. Some types of ground may contain layers of very soft materials like clay or peat. These may compress if loaded by overlying structures, or if the groundwater level changes, potentially resulting in depression of the ground and disturbance of foundations. Complete Great Britain national coverage is available.

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    International Ocean Discovery Program Expedition 363, planktonic foraminifera range chart data Planktonic foraminifera range charts indicating: Column A: Sample ID Columns B and C: sample interval Columns D and E: top and bottom sample depth Column F: Zone (Wade et al., 2011) [W11] Column G: Zone name

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    Data from laboratory experiments conducted as part of project NE/K011464/1 (associated with NE/K011626/1) Multiscale Impacts of Cyanobacterial Crusts on Landscape stability. Soils were collected from two sites in eastern Australia and transferred to a laboratory at Griffith University, Queensland for conduct of experiments. Soils were A, a sandy loam, and B a loamy fine sand. Trays 120 mm x 1200 mm x 50 mm were filled with untreated soil that contained a natural population of biota. Soils were either used immediately for experiments (physical soil crust only: PC) or were placed in a greenhouse and spray irrigated until a cyanobacterial crust has grown from the natural biota. Growth was for a period of 5 days (SS), c.30 days (MS2) or c.60 days (MS1). Following the growing period (if applicable) trays were placed in a temperature/humidity controlled room at 35° and 30% humidity until soil moisture (measured 5 mm below the surface) was 5%. Trays were then subject to rainfall simulation. Rainfall intensity of 60 mm hr-1 was used and rainfall was applied for 2 minutes (achieving 2 mm application), 8 minutes (achieving 8 mm application) or 15 minutes (achieving 15 mm application). Following rainfall, trays were returned to the temperature/humidity-controlled room under UV lighting until soil moisture at 5 mm below the surface was 5%. A wind tunnel was then placed on top of each tray in turn and a sequential series of wind velocities (5, 7, 8.5, 10, 12 m s-1) applied each for one minute duration. On each tray the five wind velocities were run without saltation providing a cumulative dust flux. For the highest wind speed, an additional simulation run was conducted with the injection of saltation sands. Three replicates of each rainfall treatment were made. Variables measured include photographs, spectral reflectance, surface roughness, fluorescence, penetrometry, chlorophyll content, extracellular polysaccharide content, Carbon, Nitrogen and splash erosion and particle-size analysis (of wind eroded material). Details of rainfall simulator, growth of cyanobacteria, laser soil surface roughness characterisation and wind tunnel design and deployment in Strong et al., 2016; Bullard et al. 2018, 2019. Bullard, J.E., Ockelford, A., Strong, C.L., Aubault, H. 2018a. Impact of multi-day rainfall events on surface roughness and physical crusting of very fine soils. Geoderma, 313, 181-192. doi: 10.1016/j.geoderma.2017.10.038. Bullard, J.E., Ockelford, A., Strong, C.L., Aubault, H. 2018b. Effects of cyanobacterial soil crusts on surface roughness and splash erosion. Journal of Geophysical Research – Biogeosciences. doi: 10.1029/2018. Strong, C.S., Leys, J.F., Raupach, M.R., Bullard, J.E., Aubault, H.A., Butler, H.J., McTainsh, G.H. 2016. Development and testing of a micro wind tunnel for on-site wind erosion simulations. Environmental Fluid Mechanics, 16, 1065-1083.

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    High-pressure multi anvil synchrotron data from ID06-LVP at the ESRF. Contains diffraction, radiography, MHz ultrasonic and calibration data from experiments performed to ~ 13 GPa on CaSiO3 perovskite and Ca[Si60Ti40]O3 perovskite samples.

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    Ascii files or tables with earthquake source and model parameters determined for the Wells, Nevada earthquake

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    The data is from four three-component broadband seismometers deployed along the lower east rift zone during the 2018 Kilauea eruption for four months. The instruments were deployed towards the end of July before the eruption ceased, and were placed in locations that would complement the existing USGS seismic network.