Geodynamic implications of deep mantle upwelling in the source of Tertiary volcanics from the Veneto region (South-Eastern Alps)

Major and trace element and Sr–Nd–Hf–Pb isotopic data for the most primitive Tertiary lavas from the Veneto region (South-Eastern Alps, Italy) show the typical features of HIMU hotspot volcanism, variably diluted by a depleted asthenospheric mantle component (⁸⁷Sr/⁸⁶Sr_i=0.70306–0.70378; _Ndi=+3.9 to +6.8; _Hfi=+6.4 to +8.1, ²⁰⁶Pb/²⁰⁴Pb_i=18.786–19.574). P-wave seismic tomography of the mantle below the Veneto region shows the presence of low-velocity anomalies at depth, which is consistent with possible upwellings of plume material. Between the depths of 100–250 km the velocity anomalies are approximately 2–2.5% slower than average, implying a temperature excess of about 220–280 K, in agreement with estimates for other mantle plumes in the world. In this context, the Veneto volcanics may represent the shallow expression of a mantle upflow. The presence of a HIMU-DM component in a collision environment has significant geodynamic implications. Slab detachment and ensuing rise of deep mantle material into the lithospheric gap is proposed to be a viable mechanism of hotspot magmatism in a subduction zone setting.     

Micrometeorological Observations of a Microburst in Southern Finland

On the afternoon of 3 July 2004 in Hyytiälä (Juupajoki, Finland), convective cells produced a strong downburst causing forest damage. The SMEAR II field station, situated near the damage site, enabled a unique micrometeorological analysis of a microburst with differences above and inside the canopy. At the time of the event, a squall line associated with a cold front was crossing Hyytiälä with a reflectivity maximum in the middle of the squall line. A bow echo, rear-inflow notch, and probable mesovortex were observed in radar data. The bow echo moved west-north-west, and its apex travelled just north of Hyytiälä. The turbulence data were analysed at two locations above the forest canopy and at one location at sub-canopy. At 1412 EET (Eastern European Time, UTC+2), the horizontal and vertical wind speed increased and the wind veered, reflecting the arrival of a gust front. At the same time, the carbon dioxide concentration increased due to turbulent mixing, the temperature decreased due to cold air flow from aloft and aerosol particle concentration decreased due to rain scavenging. An increase in the number concentration of ultra-fine particles (< 10 nm) was detected, supporting the new particle formation either from cloud outflow or due to rain. Five minutes after the gust front (1417 EET), strong horizontal and downward vertical wind speed gusts occurred with maxima of 22 and 15 m s^?1, respectively, reflecting the microburst. The turbulence spectra before, during and after the event were consistent with traditional turbulence spectral theory.     

On the relative nature of adequate measures: Media representations of the EU energy and climate package

This paper aims to contribute to media studies on climate change through a study of the ways in which the adequacy of transnational measures of tackling climate change are represented in media outlets in the UK, Ireland, Sweden and Finland. Media texts on the European Union Energy and Climate Package, introduced in January 2008 and approved in modified form in December 2008, are analyzed in-depth. Through an exploratory cross-societal comparison of media representations, the paper traces some of the dynamics of contemporary environmental debates. We argue that the adequacy of proposed transnational measures for tackling climate change becomes a relative concept in the media: measures are not adequate or inadequate per se, but contingent upon social context and relative with regard to time and to the comparisons made.     

Estimation of groundwater storage from seismic data using deep learning

Convolutional neural networks can provide a potential framework to characterize groundwater storage from seismic data. Estimation of key components, such as the amount of groundwater stored in an aquifer and delineate water table level, from active-source seismic data are performed in this study. The data to train, validate and test the neural networks are obtained by solving wave propagation in a coupled poroviscoelastic–elastic media. A discontinuous Galerkin method is applied to model wave propagation, whereas a deep convolutional neural network is used for the parameter estimation problem. In the numerical experiment, the primary unknowns estimated are the amount of stored groundwater and water table level, while the remaining parameters, assumed to be of less of interest, are marginalized in the convolutional neural network-based solution. Results, obtained through synthetic data, illustrate the potential of deep learning methods to extract additional aquifer information from seismic data, which otherwise would be impossible based on a set of reflection seismic sections or velocity tomograms.     

Identifying Rocky Seabed Using GIS-Modeled Predictor Variables

Mapping the seabed along the Norwegian coast is costly and time consuming. Hence, finding a modeling method to separate rocky seabed from other substrate types will provide digital maps that may be used to develop cost-effective sampling designs to predict species and habitat distribution. Our approach was to use geophysical data that were quantitative and objectively defined, generalized additive models (GAMs), and Akaike information criterion (AIC) to develop statistical models and select among them. We found that slope, terrain curvature, wave exposure, and depth predicted rocky seabed occurrence with a high degree of certainty.