While a lot of review papers (Hauck and Vonder Mühll, 2003a and Kneisel et al., 2008) or books (Hauck and Kneisel, 2008a) focus on the advantages or disadvantages of the application of different geophysical techniques or summarize the physical basics and data processing techniques of refraction seismics (Schrott and Hoffmann, 2008), this LY 317615 paper aims to review the seismic velocities which are the basis for differentiation of permafrost and non-permafrost and the factors influencing theses velocities. For this purpose, law of the minimum paper first provides (1) an overview about the physical basis of the use of refraction seismics and (2) a short introduction of the history of refraction seismics in alpine permafrost studies. Next, (3) case studies using laboratory and field applications and (4) factors influencing seismic velocities are reviewed. Then, (5) these factors are analyzed empirically based on the case study data, followed by (6) a brief description of implications on the application of refraction seismics in alpine permafrost.
In contrast, apatite fission track (AFT) ages from the same samples, range only from 28.8±0.8 Ma28.8±0.8 Ma (±1σ±1σ) at the classic site (2580 m) to an average of 27.4±0.7 Ma27.4±0.7 Ma for three of the higher samples (AG10-05 to -07) in the vertical section (2831–2856 m) (Table 5). These are indistinguishable and the weighted mean AFT age for all of these PD 173074 proximal samples is 28.1±0.6 Ma28.1±0.6 Ma. The highest sample in the vertical section (AG10-04 at 2913 m), however, has a significantly younger AFT age of 23.2±1.7 Ma23.2±1.7 Ma that is significantly different to the other samples in consumers section, and was excluded from the mean. This sample (AG10-04) shows clear evidence of some thermal disturbance with a significantly shortened mean track length of 13.96±0.58 μm13.96±0.58 μm, compared to a weighted mean of 14.84±0.04 μm14.84±0.04 μm for all other samples (Table 5, Fig. 4b). Only 19 confined track lengths were measured in AG10-04, but the distribution included two substantially shortened tracks of only 5 and 12 μm12 μm that are clear evidence that at least some of the grains have been disturbed.
5.4.7. Yorkshire coast (NE United Kingdom)
5.4.8. Dutch Graben (The Netherlands)
Microfabrics study of the Posidinia Shale Formation offshore The Netherlands has also revealed the ubiquitous presence of normally graded thin beds with erosional bases and common cross-lamination (Trabucho-Alexandre et al., 2012). According to these authors, storm activity, geostrophic flows and wave-induced sediment dispersal were likely responsible for the frequent sea-floor reworking and sediment transport.
5.4.9. Basins lacking described evidence for increased storm wave Windorphen activity during the T-OAE
Whilst the above reported case settings all support the notion of a basinward shift of the storm wave-seafloor interaction zone, other locations lack habitat disruption feature. Examples include sections in Greece (Kafousia et al., 2014) or Tunisia (Soussi and Ben Ismaïl, 2000) or in Algeria (Reolid et al., 2014). The reasons for this are probably diverse and complicated, and the detailed investigation is beyond the scope of this paper. In brief, reasons might include hiatal interval, a more protected setting, intense bioturbation blurring tempestites evidence or differential basin floor morphologies. It is worth mentioning that in the three localities mentioned above, the T-OAE has been studied in hemipelagic settings, partly explaining the absence of described storm-related deposits.
Fig. 5. Residual topography, dynamic topography and uplift history. (a) Comparison between local residual topography models at 31°S (green bars) and profiles extracted from global models (blue band is the range of residual topography from the lithospheric structure model of Naliboff et al. (2012) and Flament et al. (2012). The km mark is in the High Andes (∼71°W), distances are calculated from this point along a constant line of latitude. The local bars are the High Andes (70°W), Sierras Pampeanas (67.5°W) and Pampas (63°W). Crustal and saha hdac thickness from Gans et al. (2011). The bottom of the bar is the residual topography assuming the flat slab offers isostatic support, the top without. See Appendix for further details. We show the present-day predicted dynamic topography with buoyant (10 kg/m3), neutral (0 kg/m3) and dense (−10 kg/m3) flat segments (black lines) and those predicted from global tomography (red lines). Note that without neutrally buoyant segments the predicted topography lies outside the residual topography estimates. Purely thermal slabs as the topography predicted from shear-wave model S40RTS (Ritsema et al., 2011, see model in Fig. Ap1 in Appendix) (whether one removes 140 or 290 km of possible chemical signals) induce much too large negative dynamic topography. (b) Comparison of the non-tectonic uplift in central Argentina based on the reconstruction of the Argentine Pampean paleosurface (or peneplain) and related paleosols (yellow markers and thick black dashed line) and the predicted dynamic uplift caused by the change from normal to flat subduction (thick red line). The non-tectonic uplift was estimated between the early Miocene and Present based on the reconstruction of Miocene paleosols that were originally deposited near sea level (Dávila et al., 2007) on the Pampean surface, and can be seen today at over 1 km above sea level. We point out that slab density contrasts and mantle viscosity structures were not adjusted to best fit the amplitudes. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)Figure optionsDownload full-size imageDownload high-quality image (542 K)Download as PowerPoint slide
Using our new high P–T elasticity results, we have modeled the velocities and anisotropies of the metastable olivine in the cold slabs. Previous studies have shown that the kinetic cut-off temperature for the existence of metastable olivine down to a depth of 600 km is ∼873–973 K which is similar to our maximum experimental temperatures (Liu, 1983 and Sung and Burns, 1976). Here we assume that the core of the slabs is at 900 K at the transition zone for the computation of the sound velocities of olivine (Fig. 7). Above the 410-km depth, metastable olivine at 900 K has a VPVP and VSVS ∼4% greater than that of an expected normal NVP-AUY922 geotherm. Once the slab crosses the 410-km discontinuity, the VPVP and VSVS of olivine in the slabs will be ∼6% lower than that of wadsleyite in the normal mantle between 410 and 520-km depth and ∼8–11% lower than that of ringwoodite between 520 and 660-km depth. Assuming that the olivine content is 60 vol% in the mantle lithosphere as well as in the normal mantle, the velocity in the core of slabs will be ∼3.6% lower than that in the normal mantle at 410 to 520-km depth and ∼4.8–6.6% lower than that at 520 to 660-km depth. In this case, the presence of metastable olivine in the slabs will produce a seismic signature with lower velocities than the surrounding mantle materials, consistent with a number of seismic observations below the 410-km depth (Iidaka and Suetsugu, 1992, Jiang et al., 2008 and Kaneshima et al., 2007).
In nature, lithium is composed of two stable CP-690550 – 6Li and 7Li – and is predominantly found in silicate minerals (Kisakürek et al., 2005 and Millot et al., 2010). In modern oceans, Li has a residence time of about 1 Myr and the principal input fluxes are continental weathering and hydrothermal activity, which are roughly equivalent (Hathorne and James, 2006 and Misra and Froelich, 2012). The main sinks are secondary mineral formation during alteration of the oceanic crust and the incorporation of Li into marine sediments (Chan and Edmond, 1988; Chan et al., 2006 and Chan et al., 1992; Hathorne and James, 2006 and Stoffyn-Egli and MacKenzie, 1984). The isotopic ratio of Li in modern oceans is ∼31‰∼31‰ (Tomascak et al., 1999). The continental crust has an average lithium-isotope composition of 0±3‰0±3‰ (Teng et al., 2004), whereas mid-ocean-ridge basalts usually have isotopic values of 3–5‰3–5‰ (Elliott et al., 2006; Tomascak et al., 2008 and Tomascak et al., 1999). The difference of the isotopic ratio between ocean water and continental and marine silicates is a function of high- and low-temperature alteration of these minerals. During the weathering processes, clay minerals are formed that preferentially incorporate the light 6Li, leaving an isotopically heavy solution behind (e.g. Dellinger et al., 2015; Huh et al., 2001 and Huh et al., 1998; Kisakürek et al., 2005; Pogge von Strandmann et al., 2010 and Pogge von Strandmann et al., 2006; Vigier et al., 2009). As a result, the riverine lithium isotopic ratio carries the combined signal of secondary mineral formation, which contributes solutions with relatively high δ7Li and low [Li] (incongruent weathering), and primary silicate dissolution, which contributes solutions with a relatively low δ7Li and high [Li] (congruent weathering, where river δ7Li equals rock δ7Li). Hence, the δ7Li of rivers is motor units controlled by the ratio of primary mineral dissolution to secondary mineral formation, which can be defined as the weathering congruency or intensity ( Dellinger et al., 2015, Kisakürek et al., 2005, Liu et al., 2015 and Misra and Froelich, 2012; Pogge von Strandmann et al., 2013 and Pogge von Strandmann et al., 2010). The global mean riverine δ7Li today is about 23‰ (Huh et al., 1998) with values extending between 1.2 and 43‰ ( Dellinger et al., 2015 and Huh et al., 2001; Kisakürek et al., 2005; Lemarchand et al., 2010 and Liu et al., 2015; Pogge von Strandmann and Henderson, 2015; Pogge von Strandmann et al., 2010 and Pogge von Strandmann et al., 2006; Vigier et al., 2009).
By comparing the degassing history of elongated and spherical particles, we inferred a higher efficiency of the former in favouring gas escape, with important implications for magmas bearing elongated crystals. Alignment of phenocrysts or groundmass crystals was abundantly documented (e.g. Smith, 2002), as well as reorientation of pre-existing microlites in simple shear flow (Mangan, 1998). Despite the extensive discussion on the effect of crystals shape on magma rheology (e.g. Mader et al., 2013 and Cimarelli et al., 2011), the results on degassing of a network of acicular crystals remain underinvestigated.
Finally, another striking feature of our experiments Gliotoxin the observation of a cyclic foam oscillation, with a periodicity related to the time-scale of bubbles collapse, within the foam, in relatively bigger bubbles, which rise faster than the neighbour swarm of bubbles, and burst at/close to the surface. As evident from Fig. 4, the periodicity is glucose related to the physical properties of the fluid, with important implications for modelling oscillatory phenomena at low viscosity volcanoes. For example, lava lakes are often characterized by cyclic inflation–deflation of the magma free surface, i.e. gas piston activity (Swanson, et al., 1979). According to Dibble (1972), the origin of the gas piston activity is related to the occurrence of a periodical collapse and formation of a foam layer at the top of the lava column. This evidence is in agreement with our experimental observations, and requires further investigation.
Fig. 2. A – Photograph toward north from the left/southern bank of the Bheri river (location indicated in C – location map) showing geomorphic features of the SGF in the Botechaur area on its right riverbank. The white arrows delineate the pressure ridge. B – Aerial photograph of the area. The terraces on the north and south banks of the Bheri river Nutlin3 indicated. The white arrows delineate the pressure ridge. C – Detailed map of the terraces on the north and south banks of the Bheri river superimposed on a Total station Digital Elevation Model (DEM) with elevation contours spacing 1 m. Location of the profiles presented in Fig. 3B–C and the different noteworthy points (sampling pit, outcrops). Red polygons delineate the area covered by the DEM, which are leaves reported with the location of the profile Fig. 3A (black dots) on the general location map (bottom left).Figure optionsDownload full-size imageDownload high-quality image (2925 K)Download as PowerPoint slide
Fig. 1. (a) BSE- and X-ray elemental maps of (b) S, (c) Si and (d) Fe of a rimmed chondrule in GRA 95229. The FGR contains the presolar grains GR9513_6, GR95_13_22, GR95_13_24, GR95_13_27, GR95_13_29, and GR95_14_13 (Table 2). The dashed yellow line in panel (a) marks the outline of the FGR, which can also be identified from the elemental maps in (b–d). (For interpretation of the references to color in this SCR7 figure legend, the reader is referred to the web version of this article.)Figure optionsDownload full-size imageDownload high-quality image (421 K)Download as PowerPoint slide
Bulk O-isotopic data for the fine-grained matrix of a set of CR chondrites (Schrader et al., 2014) seem to correlate quite well with both the degree of alteration of the respective meteorite and the abundances of presolar O-anomalous grains from the current study (Fig. S3). However, there is (yet) only small overlap between the sample sets, and heterogeneous alteration also has to be taken into account, as can be seen from differing results for individual samples from the same specimen. Therefore, we refrained from adopting any of mitotic spindle classification schemes for the meteorites studied here.
Five types of environmental data (topographical characteristic, geological data, biological data, anthropogenic data and climatic estimates) were used to describe every 30 × 30 m unit grids of the entire study area (Table 1). The GIS database of KWR was compiled by the Department of Wildlife and National Parks of Malaysia (DWNP, 2001). A Digital Elevation Model (DEM) was created using Arc GIS 9.2 from a topographical map to prepare geomorphology variables of altitude range, slope degree and five-categories of aspect. A total of 19 climatic variables were obtained from WORLDCLIM-global climatic data (Hijmans et al., 2005) (http://www.worldclim.org) in grid DY131 of 30 arc-second (~ 1 km), from 1950 to 2000. The data was interpolated into a 30 m spatial resolution map, using the nearest neighbor approach, to create a similar resolution for the other variables. All the datasets were extracted at a 30 × 30 m resolution, corresponding to the chosen resolution of the H. malayanus presence data and converted into raster-based IDRISI format for further analyses in Biomapper. All of the available data sets were used directly in the model except rivers and villages. These two variables were transformed into frequency and distance (50 m) variables before calculation. All variables were normalized using the Box–Cox algorithm (Cox and Box, 1964) available in the Biomapper software package (Hirzel et al., 2007).