Frozen magma lenses below the oceanic crust

Author Posting. © The Authors, 2005. This is the author's version of the work. It is posted here by permission of Nature Publishing Group for personal use, not for redistribution. The definitive version was published in Nature 436 (2005): 1149-1152, doi:10.1038/nature03944.The Earth's oceanic crust crystallizes from magmatic systems generated at mid-ocean ridges. Whereas a single magma body residing within the mid-crust is thought to be responsible for the generation of the upper oceanic crust, it remains unclear if the lower crust is formed from the same magma body, or if it mainly crystallizes from magma lenses located at the base of the crust. Thermal modelling, tomography, compliance and wide-angle seismic studies, supported by geological evidence, suggest the presence of gabbroic-melt accumulations within the Moho transition zone in the vicinity of fast- to intermediate-spreading centres. Until now, however, no reflection images have been obtained of such a structure within the Moho transition zone. Here we show images of groups of Moho transition zone reflection events that resulted from the analysis of approximately 1,500 km of multichannel seismic data collected across the intermediate-spreading-rate Juan de Fuca ridge. From our observations we suggest that gabbro lenses and melt accumulations embedded within dunite or residual mantle peridotite are the most probable cause for the observed reflectivity, thus providing support for the hypothesis that the crust is generated from multiple magma bodies

Shear wave velocity prediction using seismic attributes and well log data

Formation’s properties can be estimated indirectly using joint analysis of compressional and shear wave velocities. Shear wave data isnot usually acquired during well logging, which is most likely for costsaving purposes. Even if shear data is available, the logging programs provide only sparsely sampled one-dimensional measurements: this informationis inadequate to estimate reservoir rock properties. Thus, if the shear wave data can be obtained using seismic methods, the results can be used across the field to estimate reservoir properties. The aim of this paper is to use seismic attributes for prediction of shear wave velocity in a field located in southern part of Iran. Independent component analysis(ICA) was used to select the most relevant attributes to shear velocity data. Considering the nonlinear relationship between seismic attributes and shear wave velocity, multi-layer feed forward neural network was used for prediction of shear wave velocity and promising results were presented

Derivation of consistent hard rock (1000<Vs<3000 m/s) GMPEs from surface and down-hole recordings: Analysis of KiK-net data

A key component in seismic hazard assessment is the estimation of ground motion for hard rock sites, either for applications to installations built on this site category, or as an input motion for site response computation. Empirical ground motion prediction equations (GMPEs) are the traditional basis for estimating ground motion while VS30 is the basis to account for site conditions. As current GMPEs are poorly constrained for VS30 larger than 1000 m/s, the presently used approach for estimating hazard on hard rock sites consists of “host-to-target” adjustment techniques based on VS30 and κ0 values. The present study investigates alternative methods on the basis of a KiK-net dataset corresponding to stiff and rocky sites with 500 < VS30 < 1350 m/s. The existence of sensor pairs (one at the surface and one in depth) and the availability of P- and S-wave velocity profiles allow deriving two “virtual” datasets associated to outcropping hard rock sites with VS in the range [1000, 3000] m/s with two independent corrections: 1/down-hole recordings modified from within motion to outcropping motion with a depth correction factor, 2/surface recordings deconvolved from their specific site response derived through 1D simulation. GMPEs with simple functional forms are then developed, including a VS30 site term. They lead to consistent and robust hard-rock motion estimates, which prove to be significantly lower than host-to-target adjustment predictions. The difference can reach a factor up to 3–4 beyond 5 Hz for very hard-rock, but decreases for decreasing frequency until vanishing below 2 Hz

Hierarchical Models in the Brain

This paper describes a general model that subsumes many parametric models for continuous data. The model comprises hidden layers of state-space or dynamic causal models, arranged so that the output of one provides input to another. The ensuing hierarchy furnishes a model for many types of data, of arbitrary complexity. Special cases range from the general linear model for static data to generalised convolution models, with system noise, for nonlinear time-series analysis. Crucially, all of these models can be inverted using exactly the same scheme, namely, dynamic expectation maximization. This means that a single model and optimisation scheme can be used to invert a wide range of models. We present the model and a brief review of its inversion to disclose the relationships among, apparently, diverse generative models of empirical data. We then show that this inversion can be formulated as a simple neural network and may provide a useful metaphor for inference and learning in the brain

It’s Not a Bug, It’s a Feature: Functional Materials in Insects

Over the course of their wildly successful proliferation across the earth, the insects as a taxon have evolved enviable adaptations to their diverse habitats, which include adhesives, locomotor systems, hydrophobic surfaces, and sensors and actuators that transduce mechanical, acoustic, optical, thermal, and chemical signals. Insect‐inspired designs currently appear in a range of contexts, including antireflective coatings, optical displays, and computing algorithms. However, as over one million distinct and highly specialized species of insects have colonized nearly all habitable regions on the planet, they still provide a largely untapped pool of unique problem‐solving strategies. With the intent of providing materials scientists and engineers with a muse for the next generation of bioinspired materials, here, a selection of some of the most spectacular adaptations that insects have evolved is assembled and organized by function. The insects presented display dazzling optical properties as a result of natural photonic crystals, precise hierarchical patterns that span length scales from nanometers to millimeters, and formidable defense mechanisms that deploy an arsenal of chemical weaponry. Successful mimicry of these adaptations may facilitate technological solutions to as wide a range of problems as they solve in the insects that originated them.Insects have evolved manifold optimized solutions to everyday problems. The diversity and precision of their hierarchical material adaptations often outsmart and outperform current man‐made approaches. These materials hence provide an excellent basis for the inspiration of new technological approaches by taking design cues from nature’s solutions.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/143760/1/adma201705322.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/143760/2/adma201705322_am.pd