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In this paper we study a seismic sensing platform
using Shakebox, a low-noise and low-power 24- bit wireless accelerometer sensor.
The advances of wireless sensor offer the potential to monitor earthquake in California
at unprecedented spatial and temporal scales. We are exploring the possibility of
incorporating Shakebox into California Seismic Network (CSN), a new earthquake monitoring
system based on a dense array of low-cost acceleration seismic sensors. Compared
to the Phidget/Sheevaplug sensors currently used in CSN, the Shakebox sensors have
several advantages. However, Shakebox sensor collects 4K Bytes of seismic data per
second, giving around 0.4G Bytes of data in a single day. Therefore how to process
such large amount of seismic data becomes a new challenge. We adopt Hadoop/MapReduce,
a popular software framework for processing vast amounts of data in-parallel on
large clusters of commodity hardware. In this research, the test bed-generated seismic
data generation will be reported, the map and reduce function design will be presented,
the application of MapReduce on the testbed-generated data will be illustrated,
and the result will be analyzed.
This paper is concerned with anisotropic effects on seismic data and signal analysis for transversely isotropic rock media with vertical anisotropy. It is understood that these effects are significant in many practical applications, e.g. earthquake forecasting, materials exploration inside the Earth’s crust, as well as various practical works in oil industry. Under the framework of the most accepted anisotropic media model (i.e. VTI media, transverse isotropy with a vertical axis symmetry), with applications of a set of available anisotropic rock parameters for sandstone and shale, we have performed numerical calculations of the anisotropic effects. We show that for rocks with strong anisotropy, the induced relative depth error can be significantly large. Nevertheless, with an improved understanding of the seismic-signal propagation and proper data processing, the error can be reduced, which in turn may enhance the probability of forecasting accurately the various wave propagations inside the Earth’s crust, e.g. correctly forecasting the incoming earthquakes from the center of the Earth.