Wide angle seismic recordings from the 2002 Georgia Basin Geohazards Initiative, Northwestern Washington and British Columbia

This report describes the acquisition and processing of shallow-crustal wide-angle seismicreflection and refraction data obtained during a collaborative study in the Georgia Strait, western Washington and southwestern British Columbia. The study, the 2002 Georgia Strait Geohazards Initiative, was conducted in May 2002 by the Pacific Geoscience Centre, the U.S. Geological Survey, and the University of Victoria. The wide-angle recordings were designed to image shallow crustal faults and Cenozoic sedimentary basins crossing the International Border in southern Georgia basin and to add to existing wide-angle recordings there made during the 1998 SHIPS experiment. We recorded, at wide-angle, 800 km of shallow penetration multichannel seismic-reflection profiles acquired by the Canadian Coast Guard Ship (CCGS) Tully using an air gun with a volume of 1.967 liters (120 cu. in.). Prior to this reflection survey, we deployed 48 Refteks onshore to record the airgun signals at wide offsets. Three components of an oriented, 4.5 Hz seismometer were digitally recorded at all stations. Nearly 160,300 individual air gun shots were recorded along 180 short seismic reflection lines. In this report, we illustrate the wide-angle profiles acquired using the CCGS Tully, describe the land recording of the air gun signals, and summarize the processing of the land recorder data into common-receiver gathers. We also describe the format and content of the archival tapes containing the SEGY-formated, common-receiver gathers for the Reftek data. Data quality is variable but the experiment provided useful data from 42 of the 48 stations deployed. Three-fourths of all stations yielded useful first-arrivals to source-receiver offsets beyond 10 km: the average maximum source-receiver offset for first arrivals was 17 km. Six stations yielded no useful data and useful firstarrivals were limited to offsets less than 10 km at five stations. We separately archived our recordings of 86 local and regional earthquakes ranging in magnitude from 0.2 to 4.3 and 16 teleseisms ranging in magnitude 5.5 to 6.5

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

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