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

Compilation of 59 sonic and density logs from 51 oil test wells

ABSTRACT Several relatively thick (>3 km deep) Cenozoic basins, including the Cupertino, Evergreen, Livermore, and San Pablo basins, may locally enhance strong ground motions in the San Francisco Bay area, California. As part of a crustal-scale, three-dimensional seismic velocity and density model for the Bay area, we have compiled data from sonic and density logs from oil test wells in the Bay area to better understand strong motion resonances generated by these basins. We have compiled the velocities and densities of sediments and rocks within these Cenozoic basins using 59 sonic and density logs from 51 oil test wells. The well data are primarily from the Livermore, Concord, and Los Medanos oil fields, and the Sacramento-San Joaquin delta, and provide measurements from the surface to as much as 5.3 km subsurface. Only a few logs from the South Bay are included in this compilation. The logs were hand digitized at non-uniform intervals between 3 and 30 m to capture the significant variations of the logs with depth for frequencies up to 2 Hz. Linear regression through 41 sonic logs yields Vp (km/s) = 2.24 + 0.599Z, where Z is depth in km. Shallow borehole data, generally from the South Bay, and from less than 30 m deep, indicate that the average surficial P-wave velocity at 10 holes in weathered Tertiary sedimentary units ranges from 2.21 and 2.32 km/s and is in close agreement with extrapolated P-wave velocities inferred from the oil test wells. A sonic log for Eocene sediments from Butano Ridge in San Mateo County shows that at a given depth, velocities are approximately 0.5 km/s higher than those near Livermore. The higher P-wave velocities for the Tertiary sedimentary rocks at Butano Ridge probably result from a combination of dense volcanic clasts in conglomerates plus very tight compaction of the sandstones. Density logs in Cenozoic sedimentary rocks show higher scatter. Linear regression of 18 density logs yield p (g/cm3) = 2.25 + 0.065Z. Average densities of weathered Tertiary sedimentary rocks measured on core samples from 5 shallow boreholes in the South Bay lie between 2.20 and 2.25 g/cm3 , in close agreement with the surficial density inferred from linear regression of oil well data. This report presents the locations, elevations, depths, stratigraphic and other information about the oil test wells, provides plots showing the density and sonic velocities as a function of depth for each well log, and compiles all data to better understand the velocities and densities of Cenozoic sedimentary rocks in the Bay area. CONTENT

Drift stabilization of ballooning modes in an inward-Shifted LHD configuration

A drift-magnetohydrodynamic theory is applied to a background anisotropic pressure equilibrium state to generate a drift corrected ballooning mode equation. The ratio of the mode frequency to the hot particle drift frequency constitutes the critical expansion parameter. The fast particles thus contribute weakly to the instability driving mechanism and also to the diamagnetic drift stabilisation. This equation is used to model the inward-shifted Large Helical Device (LHD) configuration. In the single-fluid limit, a weakly ballooning unstable band that encompasses a third of the plasma volume develops in the core of the plasma at low $left$ that becomes displaced towards the edge of the plasma at the experimentally achieved $leftsimeq 5$. Finite diamagnetic drifts (mainly due to the thermal ions) effectively stabilise these ballooning structures at all values of $left$. The validity of the large hot particle drift approximation is verified for hot to thermal ion density ratios that remain smaller than 2%

Images of Crust Beneath Southern California Will Aid Study of Earthquakes and Their Effects

The Whittier Narrows earthquake of 1987 and the Northridge earthquake of 1991 highlighted the earthquake hazards associated with buried faults in the Los Angeles region. A more thorough knowledge of the subsurface structure of southern California is needed to reveal these and other buried faults and to aid us in understanding how the earthquake-producing machinery works in this region

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