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Bulletin of the SeismologicalSocietyof Am erica,Vo l. 68, No. 5, pp. 1521-1532, Octob er 1978

A U T O M A T I C E A R T H Q U A K E R E C O G N I T I O N A N D T I M I N G F R O MS I N G L E T R A C E SB y R E X V . A L L E N

ABSTRACTA c o m p u t e r p r o g r a m h a s b e e n d e v e l o p e d f o r t h e a u t o m a t i c d e t e c t i o n a n d

t i m i n g o f e a r t h q u a k e s o n a s i n g l e s e i s m i c tr a c e . T h e p r o g ra m o p e r a t e s o n l in ea n d i s s u f f i c ie n t l y s i m p l e t h a t i t is e x p e c t e d t o w o r k i n i n e x p e n s i v e l o w - p o w e rm i c r o p r o c e s s o r s in f ie l d a p p l i c a t io n s . I n te s t s w i t h a n a l o g t a p e s o f e a r t h q u a k e st h e p r o g r a m c o r r e c t l y i d e n t if i e d a n d ti m e d t o w i t h i n 0 . 0 5 s e c a b o u t 7 0 p e r c e n to f th e e v e n t s w h i c h w o u l d n o r m a l l y b e t im e d i n o p e r a t io n o f a n e t w o r k . T h ep r o g r a m e v a l u a t e s t h e a c c u r a c y o f it s p i c k s a n d i t s e s t i m a t e s a p p e a r t o b e q u i t er e li a b le . T h e a l g o r it h m i s w o r k i n g a t p r e s e n t i n a 1 6 - b i t m i n i c o m p u t e r a n da p p e a r s t o b e c o m p a t i b le w i t h p r e s e n t l y a v a i la b l e m i c r o p r o c e s s o r s .

INTRODUCTIONA n e w c o m p u t e r p r o g r a m f o r t h e a u t o m a t i c p r o c e ss i n g o f s e is m ic d a t a h a s b e e n

d e v e l o p e d a n d t e s t e d b y t h e U . S . G e o lo g i c a l S u r v e y .T h e g o al h a s b e e n t o p r o d u c e a c o m p u t e r p r o g r a m t h a t c a n d u p l ic a t e t h e

p e r f o r m a n c e o f a h u m a n o p e r a t o r i n p r o c e s s in g P a r r iv a l s o f s in g le s e i s m o m e t e rt r a ce s . I t m u s t r e c o g n i ze e a r t h q u a k e s , t i m e t h e m t o a n a c c u r a c y o f b e t t e r t h a n 0 .0 5s ec , d e t e r m i n e a m p l i t u d e s , d i r e c t i o n o f f i rs t m o t i o n a n d a fe w o t h e r d e s c r i p t iv ep a r a m e t e r s , a n d f o r m u l a t e a n e s t i m a t e o f h o w r e l i ab l e th e p i c k t i m e i s t o b ec o n s i d er e d . T o b e a t a l l u s ef u l, t h e p r o g r a m m u s t b e c a p a b l e o f d i s ti n g u is h i n ge a r t h q u a k e s f r o m n o i se s o u r c e s s u c h a s w i n d o r v e h i c u l a r t ra f fi c , a n d i t m u s t b ea b l e t o o p e r a t e w i t h s m a l l w e a k e a r t h q u a k e s s u c h a s a r e c o m m o n l y u s e d i n r o u t i n eo p e r a t i o n o f a l o c a t i o n n e t w o r k s u c h a s t h e U S G S C e n t r a l C a l i f o r n ia N e t w o r k .

C o n t r a i n t s o n th e p r o g r a m a r e s im p l e , b u t r a t h e r s t ri n g e n t. I t m u s t b e s u i t a b le f o re v e n t u a l r e a l - t i m e o p e r a t i o n i n a n i n ex p e n s i v e lo w - p o w e r m i c r o p r o c e s s o r a t r e m o t ef ie ld l o c at io n s . T h i s c o n s t r a i n t m e a n s t h a t t h e p r o g r a m m a y n o t l o o k b a c k i n t i m e ,a n d i t m a y u s e o n l y a l i m i te d a m o u n t o f m e m o r y b o t h f o r p r o g r a m i n s tr u c t i o n s a n df o r a n y s c r a t c h - p a d s t o r a g e u s e d d u r i ng o p e r a t i o n . B y n o t l o o k in g b a c k , I m e a nt h a t e a c h s a m p l e i s p r o c e s s e d s e q u e n t i a l l y , a n d t h e p r o g r a m is n o t a l l o w e d t o a cc e s se a r l ie r p o i n t s u n l es s i t h a s s t o r e d t h e m i n a s e p a r a t e s c r a t c h p a d a r r a y .T h i s i s a n e x t r e m e l y r i g o r o u s se t o f r e q u i r e m e n t s , a n d i n m a n y c a s e s n o t a l l t h ec o n s t r a i n t s w i ll b e r e q u i r e d . I n s o m e c a s e s fo r i n s t a n c e , t h e a l g o r i t h m w i ll m e r e l y b er e q u i r e d t o re c o g n iz e a n e a r t h q u a k e f o r r e c o r d i n g b y a n e v e n t r e c o r d e r . I n t h i s c as ep r e ci s e t i m i n g a n d m o s t m e a s u r e m e n t s w il l b e m a d e l a t e r b y a h u m a n o p e r a t o r . I na n o t h e r p o s s i b le a p p l i c a t i o n a n u m b e r o f p r o c e s s o r s m i g h t b e l in k e d t o g e t h e r i np r o c e s s i n g t e l e m e t e r e d d a t a a t a c e n t r a l c o l le c t io n p o i n t f o r a r e a l - t i m e e a r t h q u a k el o c a t o r . I n t h i s u s e it w o u l d b e p o s s i b l e to c r o s s - r e f e r e n c e r e s u l t s b e t w e e n s t a t i o n sa n d t h u s s i m p l i f y t h e r e c o g n i t i o n p r o b l e m .

T h e a l g o r i th m h a s b e e n d e v e l o p e d a n d t e s t ed u n d e r t h e m o s t r ig o r ou s c o n s t r a in t sa n d i f s o m e c a n b e r e l a x e d i t s p e r f o r m a n c e s h o u l d b e e v e n b e t t e r .M i c r o p r o c e s s o r s h a v e c o m e i n t o u s e o n l y in t h e p a s t f e w ye a r s , a n d a r e s t il le v o l v i n g s o r a p i d l y t h a t i t i s d i f fi c u lt to s t a y a b r e a s t o f w h a t i s a v a i l a b l e . S o u c e k

Any use of trade nam es or trad em arks in this publication is for descriptive purposes only and does notconstitute endorsement b y the U.S. Geological Survey.1521

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1522 REX V. ALLEN(1976) reviews their capabilities and uses and discusses some of the representativeprocessors currently available. Program development for the system described herewas carried out part ly on CDC 6600 and 7600 computers and partly on the USGSCDC 1700 16-bit computer in the Menlo Park office.

When opera ting on the CDC 6600 and CDC 7600 machines, the program processespreviously digitized data sets up to 45 sec in length, digitized at 200 samples/sec. Inthis processing, the restriction of not looking back is strictly observed: In thisfashion, a ra the r good simulation of on-line operation can be achieved.Most of the illustrations in this paper are of these digital data sets, because it ispossible to store and later plot detailed time histories of the program operation,including operational parameters. Most of the program development and testing,however, was carried out on the CDC 1700 computer. Since this machine can digitizeand operate with analog input signals, it is possible to test a program with long runsof analog tape data sets or to operate in real time by monitoring selected telemet rychannels of the USGS Central California Network. This ability to use analog datasets is absolutely necessary in developing an algorithm that is resistant to noisesources. Digital records are unsatis factory because seismic report libraries containearthquake records, not noise, and the real test of an automatic picking algorithmis its ability to reject strong noise bursts while still recognizing weak earthquakes.

An illus tration of this program's effectiveness is provided in Figure 1, which plotsten arrivals from a magnitude 2.8 earthquake at distances ranging from 50 to 90 kmillustrating the accuracy which may be expected.

PROGR M OPER TIONIn the following discussion of program operation, I shall use the te rm event tomea n a section of a seismic record which is noticeably different in cha rac ter from

the immediately preceding record. An earthquake will be an event in this sense, aswill wind or cultura lly produced noise bursts. The charact er of a trace is describedby a specific function of trace amplitude and first time difference. If the seismictrace is regarded as a time series f i t ) with first difference ( t ) , then the characteristicfunction E ( t ) is defined as

E ( t ) = f ( t ) 2 + (2 2 + f ( t ) 2 .Ce is a weighting cons tant whose purpose is to vary the relative weight assigned theampl itude and first difference depending on digital sample rate and noise character-istics of the individual seismic station. This particular characteristic function wasselected because it incorporates quantities descriptive of both amplitude and fre-quency conten t of the seismic trace, varies rapidly with changes in either of theseparameters, is always positive, and is easy to compute.

The restriction tha t the program may not look back means tha t an event mustbe recognized immediately and its arrival time recorded. Anyone who has spentsome time looking at seismic records, however, will recognize that most events inthe sense defined above will turn out not to be earthquakes. The program musttherefore be able to determine very quickly that a spurious event is not anearthquake and return to monitoring the trace. If the event lasts fairly long, forexample, a passing vehicle or such, the program must store sufficient informationduring the event to enable it to decide at the end whether the event was anearthquake and worth recording, or merely noise that should be ignored.In understanding the logical operations followed by the program, it is easiest torefer to the two flow charts of Figures 2 and 3 and the parameter plot in Figure 4.

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A U T O M A T I C E A R T H Q U A K E R E C O G N I T I O N A N D T I M I N G F R O M S I N G L E T R A C E S 5 2 3F i g u r e 2 is a c o n v e n t i o n a l f l o w c h a r t a n d m o s t c l e a r l y i ll u s tr a t e s t h e p r o g r a m f l o ww i t h t h e v a r i o u s lo o p s a n d d e c i s i o n p o i n ts . F i g u r e 3 i s a c h a r t o f t h e f o r m s u g g e s t e dby S te w a r t 1 97 3) , a nd , by inc lud ing de ta i ls o f t r a ns fe r func t io n s a nd s o m e na r r a t iv e

I. . _ .

I

I

0 2 4 6 8 I 0T I M E , S E C O N D S

F IG . I . P r o g r a m P a r r i v a l p i ck s f o r m a g n i t u d e 2 . 8 e a r t h q u a k e . D o t t e d v e r t i c a l l i n e a t c e n t e r o f t r a c ei s p i c k p o i n t f o r e a c h t r a c e . E p i c e n t r a l d i s t a n c e s r a n g e f r o m 5 0 to 9 0 k m .F L O W C H A R T : M A I N P R O G R A ML I init ial ize and ~ 9 Reco rd reL t ime and_. . . . ~ . . . . t r s i i I ~ p o . p e o h v a .

f i l t e r , p r o . . . . L c r i t e r i o n , ~ [update parametersE , ~ , , 8 , ) ,S t o re R i ~ N i C 1 0 2 a > |Y E St0 2 1 S 0

4 D e c l a re e v e n t [ 1 0 2 2 - S + 1set f laqs , s to re onse tvalues & absolute t ime ~'J 1 0 . 3 C o m p u t e L J:5 Update p rov is iona l ~ 10 .4 S < Lp e a k v a l u e, P I , o

6 S a m p l e tracef i l te r , p rocess , ~YESu p d at e, s ta r e ~ J E v e nt l on g e n ou g h?Zero crossinq? Y ~__12 Cell OnolysJs routines I Present f ina l c r i te ria o f f re q ,gr ea te r th a n pr ov isi on al Olspersondsperson, e~c.etc.rpeak ? ~

1 3 . R e c o r d s u i t s d e s i re d II

F IG . 2 . P r o g r a m f l o w c h a r t .description it is easier to follow the detailed operation of the main program. Theb o x n u m b e r s o f F i g u r e 2 c o rr e s p o n d t o s t a t e m e n t n u m b e r s o f F i g u r e 3 f o r e a s y c r o s sr e f er e n c e . F i g u re 4 i s a t i m e h i s t o ry o f s o m e o f t h e o p e r a t i n g p a r a m e t e r s u s e d b y t h epr o g r a m dur ing a ty p ic a l e v e nt .

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1524 REX V ALLEN

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O

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AUTOMATIC EARTtIQUAKE RECOGNITION AND TIMING FROM SINGLE TRACES 15254 0 0 ~ - - . . ,

o- 4 0 0

6 . . . .

0 . l x , - : : : : ~ _ _ ~ 7 . . , N , l ~ t l ~ w , E / l l l ~ ' f f ' T V ' ' 1 ( ~ J t l v l l ~ . , . ~ L r l t ~ l ~ i' ~ i - ' v ' ~ ' ' ~ ~ V I2 .J

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Event declored 1~ o v e r 1 0 . 4 )

2 3 4 5 6 7 8 9 I 0T I M E , S E C O N D SFIG. 4. Operating parameters during an event. (a) Seismic trace. (b) Short-term average ~i, ofcharacteristic function and reference level, 7i. Points indicated as 4 refer to statem ent 4 in programflow charts. No te false onsets before arrival of real event. Ampli tude scale is logarithmic. (c) Short- termaverage ai, and the criteria level 3, carried during observation of an event. Logarithmic amplitude scale.(d) Quantit ies L and S, which determine point at which event is declared over.The following description will reference principally Figure 3, and I will attempt todiscuss the program in sufficient detail that an inte rested reade r can co de the

algorithm for any available computer. Paragraph numbers refer to the correspondingoperation in Figure 3. Parameters mentioned in the discussion are defined in theFigure 3 flow chart. Table 1 is an alphabetized parameter list which will aid inlocating the definitions in Figure 3.

PROGRAM DESCRIPTION1. This step is principally concerned with resetting M, the peak index number,the event relative time count, and, in some versions of the program, the

quant ity a, discussed below.2. In this sequence the seismic trace is sampled, digitized to produce the 12-bitinteger value N i at time i, and the integer value is processed to remove anyD.C. bias in the digital data. T he resulting value, R i is a floating-point numberin the present program version, with the constant C1 controlling the timeconst ant of the high-pass filter function.2.3 The weighted difference AR i is computed.2.4 The present value of Ni is stored for use with the next sample.2.5 The character istic function E is computed as discussed earlier.The program now computes a shor t-te rm ~0.01 sec) average ai and a long-term

~2 sec) average fl, of the quanti ty E i and multiplies fii by threshold constant C5 toproduce the reference level 7~.

3. If ~i > Yi, an event is declared Figure 4).The procedure of comparing short- and long-term averages is similar to thepicking algorithms described by Ambuter and Soloman 1974), and Stevenson1976), with the rather impor tant exception that the averages are carried out on thecharacteristic function rather that on seismic trace amplitudes. Thi s procedure is

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1526 R E X V A L L E NT A B L E 1

P A R A M E T E R L I S T A N D D E F IN I T I O N D I R E C T O R YP a r a m e t e r S t a t e m e n t P a r a m e t e r S t a t e m e n t

A 4 ,2 , 9 .2 P 5 .C1 2 . 2 R 2 . 2C 2 2 . 6 A R 2 . 3C3 2 . 7 S 1 0 . 2 . 1D 4 .3 T 4 .1 , 9 .1E 2 .5 a 2 .6i 2 . f l 2 .7L 10 .3 ~ 2 .8M 9 .1 8 10 .1N 2 .S t a t e m e n t n u m b e r s r e f er t o F i g u r e 3.

inherently more powerful, since the quantity E is sensitive to changes in bothamplitude and frequency and varies as the sum of the squares of these twoparameters. Stewart (1977) makes use of a characteristi c function that is a modifiedfirst difference term. The function described here is more powerful but could not beused in an application such as Stewart s in which 100 or more traces are to bemonitored by one processor.

4. Whe n an event is declared, the absolute time is recorded along with thepresent values of trace amplitude, derivitive, and ft. fi, the long-term averageof E, will be a measure of the background noise level at the time of detectionand will be used later in assigning a reliability weight to the pick.5. The program now enters a pair of nested loops in which it searches for a peakamplitude and the subsequent zero crossing. When the zero crossing isdetected, the program breaks out of the loop, records the zero-crossing timerelative to event onset and the signed amplitude of the preceding peak. Thetime and amplitude information is stored in a pair of scratchpad arrays forlater use by analysis routines.

10.0 and 10.2 At each zero crossing of Ri, the present value of a is comparedwith a cont inuat ion criterion level 8. If a < 3, the smallcount register, S, isincremented by 1. If a > 8, S is reset to 0. Therefore, S always contains thenumber of consecutive zero crossings that have occurred at which a is lessthan t he continuation criterion level.The criterion; 8, must start at a level comparable to the reference level ~/at thebeginning of an event, and it must increase during an event in some fashion to

ensure that the event is terminated even if the noise level should abruptly increaseand remain indefinitely at the new high level, as during a very sudden windstorm,for example.Furthermore, experience with earthquake swarms and aftershock sequences in-dicates that the observation loop during an event should be terminated as rapidlyas possible {well before the end of the coda) in order to enable the program to

resume searching for subsequent earthquakes partially buried in the coda of preced-ing events.I tried several methods in arriving at the present algorithm for computing 8,including linear and parabolic increase with time and with M, the number ofobserved peaks in the event. The most successful version is a two-segment parabolicincrease with M, in which the increase is made much steeper after M = 60. This

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A U T O M A T I C E A R T H Q U A K E R E C O G N I T I O N A N D T I M I N G F R O M S IN G L E T R A C E S 1527O ONSET EVEMTOV[R

V V w I V v v

b O N S T V N T O V R

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tm

F z a . 5 . S c h e m a t i c e a r t h q u a k e w i t h d a t a s t o r e d d u r i n g ev e n t s . a ) S e i s n fi c e v e n t w i t h o n s e t a n d e n dp o i n t s . b ) D o t s r e p r e s e n t z er o - c ro s s i n g t i m e s a n d p r e v i o u s p e a k a m p l i t u d e s . A m p l i t u d e b a r s i n d i c a t eb a c k g r o u n d n o i s e p r e c e d i n g o n s e t , a n d a r r o w g i v e s f i r st d if f e re n c e a t o n s e t . c ) D a t a s t o r e d f o r u s e b ya n a l y s i s r o u t i n e s a f t e r e v e n t t e r m i n a t e s .

scheme seems successful in terminating observations as soon as possible for smallvery close earthquakes without losing the m entirely and also in enabling theprogram to continue observation and recording of more dis tant events with typicallyloriger period :waves. In applications where measurement of coda length is veryimportant this method would obviously be unsatisfactory.i0.3 An event will be declared over when some number of zero crossings with a

> 8 have occurred. This terminat ion number L will be small typica lly 3at the start of an event to enable the program quickly to reject noise spikesor other very short:term interference. When the algorithm is well into alarger event however L must be considerably larger to ensure that anearthquake observation is not terminated too early during a quiet periodbetween phase arrivals. The present version of the program uses the relation

L = 3 M/ 3

where M is the cu rrent number of observed peaks in an event.10.4 This decision of whethe r the event is over is simply a comparsion of S andL with branches to continue the program in the observation loop or toterminate observation of the event as required.11. This is an elementa ry test to throw out very short events such as noisespikes. Typically the criteria for further consideration are th at the event

must have been longer than 1.5 sec and have recorded more than 40 peaks.During normal operation the progam will be producing false onsets aboutonce per minute and this test returns it quickly to the search loop withoutwasting time on analysis or using up recording space.

12. The analysis routines are a set of subroutines tha t examine the datarecorded during the event and produce quantitat ive descriptors of the event.They are discussed later.13. After the program calls the analysis routines thei r results may be examinedto determine whether the event was probably an earthquake and worth

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1 5 2 8 R E X V A L L E N

noting, or mere ly a long noise event which can be ignored. If it was catalogedas an earthquake, the required descriptors are recorded. The program thenreturns to (1) and reenters the search loop.When an event has been declared over, the program has stored a description ofthe event with which the analysis routines can work. The information available inthis data set is1. B, a measure of the noise level at event detection.2. A0, the trace amplitude at detection.3. D, the trace first difference at detection.4. The array A containing the peak amplitude of each peak in the observed wavetrain.5. The array T containing the time of each zero crossing relative to event onset.Figure 5 illustrates these quantities for a schematic earthquake.

N LYSIS SUBROUTINESI have tried a larger number of analysis techniques to examine this data set andcomplete description of even those I currently use would be too lengthy to include.Instead, I will briefly describe a few of the key routines to illustrate what can bedone with available stored data.

A key subroutine IPP UT (Peaks Per Unit Time) divides the observed coda intohalf-second intervals and counts the number of peaks in each interval, storing thenumber in a short scratchpad array. It is then easy to compute the mean frequencyof the seismic signal and measure the variation in frequency during the event bycomputing the mean and standard deviation of the array. Cultural noise such aspassing vehicles will typically be quite monochromatic compared to earthquakes.Another routine quantifies the shape of the observed coda in terms of relativesteepness of onsets and decays in the envelope shape. The routine selects the largestpeak in each half-second interval and compares it with the largest in the followinginterval. The difference in amplit ude is squared, and if the envelope is increasing inamplitude, the square is added to the variable UP ; if the envelope is decreasing,it is added to DOWN . When the entire coda has been processed, the numberSLOPE is computed as

SLOPE = SQRT (UP/DOWN).Earth quakes with characteristic steep onsets and slow decays tend to produce valuesof SLOPE of 1 or greater, whereas noise bursts tend to have values of 1 or less. TheSLOPE value in itself is not a sure discriminator of earthquakes, and especiallytends to break down for longer events.

Subroutine WEIGH T is used to determine the weight, or measure of reliability tobe placed on the P-arrival pick. It makes use of fi, a measure of background noiselevel at event onset, D, the first difference at observed onset, and peak amplitudesof the first 3 peaks. The exact form of the algorithm is still being changed, but, as anexample, to be weighted 0 , the highest quality, an event must meet the followingcriteria

(1) D>(2) A1 > 450(3) A 1 ~ > 4(4) A2 >6 ~o rA 3> 6.

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AUTOMATIC EARTHQUAKE RECOGNITION AND TIMING FROM SINGLE TRACES 529These criteria are successivelyrelaxed for weights of I, 2, and 3.

Magnitude determination is an unsolved problem. In principle, the magnitude ofa local earthquake can be estimated from a single seismogram by using the codalength Leeet al. 1972). This determination cannot be made by this program in itspresent version because of the decision discussed earlier to terminate observation ofan event as soon as possible. As a result, most of the coda is lost, especially in largerevents, and the reported duration is artificially shortened. Several alternativemethods have been tried including variations on the scheme described by DeNoyer1959). None has been entirely satisfactory, and this problem will require further

investigation.PERFORMANCE EVALUATION

Processing of digital data sets is useful in developing algorithms and for detailedexamination of program behavior, but for evaluation of program performance, thereis no substitute for the processing of analog signals. Only in this way is it possibleto operate a system in real time or to simulate real time operation by the use ofanalog tapes.

This program was tested on the USGS CDC 1700 computer using analog tapesas input. The 1700 system incorporates an analog-digital converter ADC) for inputand a Siemens multi-channel strip chart recorder for monitoring the input signaland p rogra m operation. A line prin ter is available for output of progr am results.

In a typical test run, the computer moni tors an analog signal from a tape reco rdedduring an aftershock sequence or an earthquake swarm. The tape will carry recordsfrom many telemetered seismic stations, plus two serial timing channels: WWVBand locally generated IRIG C. During test runs, the computer generates an eventflag that is set high at the onset of an event and then reset low when the event isdeclared over. This flag output is displayed on the strip chart recorder next to theseismic trace selected for monitoring and provides an easily read indication ofprogram status. The false-trigger rate can easily be observed and the program'saccuracy in picking P arrivals can be checked.If an event is observed which passes the length criteria {item 11 of Figure 3), theprogram prints out on the line printer the time of the arrival to the nearest second,as read from a time code translator, first motion, and results of the analysissubroutines.

The strip chart record is run at 25 mm/sec so it is possible to judge timing quiteaccurately by observing the program status flag. Detected first motion, weight, andother relevant par ameters required for program evaluation are available in the line-printer output. Figure 6 shows a section of strip chart record for a typical smallearthquake with the line printer output superimposed.

A test run normally consists of half an hour to an hour of an analog tape. Afterthe run is completed, the strip chart and line printer output are given to one of thepeople who normally scan records of the USGS seismic net for scoring.

To be scored a Hit , an event must be picked with proper first motion and witha timing error of less than 0.05 sec. A miss is scored for an event tha t norma llywould be picked in a net operati on and tha t th e program missed entirely. In addition,various forms of errors are tabulated , such as IM , indicating a wrong first moti onbut tol erable timing; T , indicating a timing error greater tha n 0.05 sec; or T? ,indicating a questionable bu t no t definitely wrong time in the case of emerg entarrivals. The score of the run is taken to be the number of hits divided by the totalnumber of earthquakes and generally runs about 60 to 80 per cent. It should be

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AUTOMATIC EARTHQUAKE RECOGNITION AND TIMING FROM SINGLE TRACES 1531realized that the figure is representat ive of the number of really reliable picks madeby th e p rogram. If marginal picks are included its performance figure is correspond-ingly improved. In addition, the program s criteria were so stringent as to excludealmost all false picks on noise bursts. If some false picks are allowed performancefigures again can be improved. It is not possible to assign a single performance indexwhich covers all cases since each application will have different requirements anddifferent evaluation criteria, but the 60 to 80 per cen t figure is proba bly conservativefor most applications.In the se tes t runs of an hour or less, the program usually makes no picks at all onnoise sources, so a different test scheme was used to evaluate this facet of itsperformance. A particularly noisy station in the Central California Network, PKH,was monitored in real time during several extended periods. In the longest test runof 44 hours, the program report ed 36 events, all of which were noise bursts. ( Therewas no earthquake recorded on this station during any of the t est runs.) Of these 36events, 33 were weighted 3 by the progr am and 3 were weighted 2. No events werereport ed with weights of I or 0, corresponding to reliable picks.

Bear in mind th at the p rogram was instructed to report all events longer than theminimum threshold. Although it called the analysis routines and reported theirresults, it did not make use of these results in deciding what should be repo rted. Useof these results can reduce considerably the num ber of false reports.In the preceding discussion, it has been assumed that the program will always berequired to operate independently, that is, that it will report only results, and th atthe seismic record will never be available for study by huma n operators. Ther e areindeed applications in which this mode of operation may be required. Operation ofa micro-earthquake network in deep ocean areas might be one example. In such acase, the bottom package might be required to operate independently for up toseveral months and to store information on several hundred earthquakes for laterretrieval. If the descriptors are restricted to a few hundred bits of information, thisbecomes a practical possibility.At another extreme, the program might record the entire seismic trace, eitheranalog or digital, for each event of sufficient length to qualify as a possibleearthq uake of interest. In this mode, the resulting output would be an edit ed tapecontaining only events. The advantage of this mode is that all trace detail ispreserved; the disadvantage, of course, is that a much larger body of data must berecorded, the hardware problems and costs are thereby multiplied.

In principle, if the recognition algorithm works properly, equipment can be builtto operate at either of these extremes or in some intermediate mode, if desired.

Fur the r tests and developmen t planned for the i mmediate future should demon-strate more clearly the strengths, weaknesses, and potential uses of this algorithm.

R E F E R E N C E SAmbuter, B. P. and S. C. Solomon (1974). An event-recordingsystem for monitoring small earthquakes,

B u l l . S e i s m . S o c . A m . 64, 1181-1188.DeNoyer, J. (1959). Determination of the energy n body and surface waves, Pt. II, B u l l . S e i s m . S o c . A m .49 1-10.Lee, W. H. K., R. E. Bennett, and K. L. Meagher (1972). A method of estimating magnitudelof ocalearthquake, from signal duration, U . S . G e o l. S u r v . O p e n F i l e R e p o r t .Stewart, G. W. (1973). n t r o d u c t i o n t o M a t r i x C o m p u t a t i o n s Academic Press, New York. iStewart, S. W. (1977). Real-time detectionand locationof local seismic events n central California,B u l l .S e i s m . S o c . A m . 67, 433-452.

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1 5 5 2 R E X V . A L L E NS t e v e n s o n , R . 1 97 6) . M i c r o e a r t h q u a k e s a t F l a t h e a d L a k e , M o n t a n a : A s t u d y u s i n g a u t o m a t i c e a r t h q u a k e

process ing , Bull. Seism. Soc. Am. 66, 61-79.Soucek, B. {1976}. Microprocessors and Microcomputers J o h n W i l e y a n d S o n s , N e w Y o r k.V.S . GEOLOGICAL SURVEY345 MIDDLEFIELD ROADMENLO PARK , CALIFORNIA 94025

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