principles and applications of microearthquake networks

3.4. Evetit Processing 63
16-mm microfilms. The microcomputer prompts the analyst to perform
the measuring tasks. If an error is detected, the analyst is asked to repeat
the measurement. The microcomputer processes the measurements of
each earthquake into a phase list and calculates the hypocenter
eters for the earthquake. This system will be further discussed in
3.5.2.
param-
Sect ion
3.4.3.
Automated Methods: Determining First P-Arrival Times
Because most microearthquake networks are designed for precise determination
of earthquake hypocenters, it is crucial that any automated
system be able to determine the first P-arrival times accurately. Otherwise,
the automated system is no better than an event detection system.
In this subsection, we discuss some published techniques for automated
determination of first P-arrival times. Very little discussion will be given to
the simpler procedures for determining the directions of first motion, amplitudes,
and periods.
The waveforms of microearthquakes recorded at neighboring stations
have been observed to be remarkably dissimilar in general. This has led to
computer algorithms that process each incoming digital signal as an independent
time series. Before processing the signal, most investigators
apply bandpass filtering to reduce noise that is outside the frequency
range of interest and to eliminate the dc bias level.
The concept of characteristic functions is useful in designing computer
algorithms for determining first P-arrival times. The incoming signal in
digital form is seldom used directly in the algorithms. It is usually transformed
into one or more time series that are more suitable for determining
first P-arrival times. The transformed time series is referred to here as the
characteristic functionf(k), where k is a time index.
One of the simplest characteristic functions is obtained by performing a
difference operation on the incoming digital signal. Stewart et al. (1971)
and Crampin and Fyfe ( 1974) defined their characteristic function as the
absolute value of the difference between adjacent data points, i.e.,
(3.7)
where X k is the amplitude of the incoming signal at time f k, and k is an
index for counting the data points. This operation filters out the lowfrequency
portions of the incoming signal as shown in Fig. 16a. This
characteristic function works well in conditioning the incoming signal that
contains impulsive high-frequency seismic waves.
Crampin and Fyfe (1974) referred to Eq. (3.7) as the one-sample mechanism.
They also computed a four-sample mechanism and an eight-sample