principles and applications of microearthquake networks

58 3. Data Processitig PrmedureLs
is more advantageous to implement an event detector using digital technology
rather than analog components, In the digital approach, the event
detector is implemented as an algorithm for an on-line computer. Stewart
e? ul. (1971) noted that the algorithm must be simple and must not require
much analysis of the past data so that the computer can keep up with the
incoming flow of new data. Far example, if the incoming analog signals
are digitized at 100 sampledsec, and if 100 seismic stations are monitored
by one central processing unit in real time, then the time available for
processing each data sample is only 100 psec. Many existing computers
can perform such a data processing task with ease. However, most microearthquake
networks can only afford a modest computer system at a
cost of about $100,000. Under such a monetary constraint, a computer
suitable for on-line data processing has a cycle time of the order of I psec
and an addressable random access memory of approximately 64 kbytes.
Therefore, to monitor 100 stations digitized at 100 samplesisec, the computer
cannot perform more than a few tens of basic instructions on each
data sample, nor can it store more than about 1 sec of the incoming
digitized data in its main memory.
Although event detection based on waveform correlation has been
applied successfully for teleseismic events recorded by large seismic arrays
(e.g., Capon, 19731, this technique does not seem to be practical for
microearthquake networks. The reason is that the waveforms of microearthquakes
recorded at neighboring stations are remarkably dissimilar in
general. Moreover, the processing times for correlation methods are
rather excessive.
Stewart et ul. (197 1) used a small digital computer to implement a realtime
detection and processing system for the USGS Central California
Microearthquake Network. Their detection technique is summarized in
Fig. 16. In this figure, xk is the raw input signal to the computer at time ?k,
wherek is an index for counting the digitized samples. In order to filter out
the lower frequency noise in the input signal, a difference operation is
performed, i.e.,
(3.6) DXI, = ( Xk -
where DXk is the conditioned input signal. This difference operation is
analogous to the filtering and rectifying operations usually performed by
analog event detectors. The conditioned input signal DXk is used in the
same manner as the ~A(T)/ in Eq. (3.1). Wk is a recursive approximation to
a 10 point moving time average of DXk. Because in this example the input
signal is digitized at a rate of 50 sampleshec, a 10 point time average is
equivalent to a time window of 0.2 sec. Wk
is intended to approximate the
short-term average a(?) as given in Eq. (3.1) with T~ = 0.2 sec. Similarly,