JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 95, NO. BI2, PAGES 19,763-19,772, NOVEMBER 10, 1990

Localization of Intermediate-Term Earthquake Prediction V. G. Kossosoxov ̂ •u) V. i. K•nJs-Bo•ox

International Institute of Earthquake Prediction Theory and Mathematical Geophysics, Moscow

S. W. SMrm

Geophysics Program, University of Washington, Seattle

Relative seismic quiescence within a region which has already been diagnosed as having entered a "Ttrne of Increased Probability" (TIP) for the occurrence of a strong earthquake can be used to refine the locality in which the earthquake may be expected to occur. A simple algorithm with parameters fitted from the data in Northern California preceding the 1980 magnitude 7.0 earthquake offshore from Eureka depicts relative quiescence within the region of a TIP. The procedure was tested, without readaptation of parameters, on 17 other strong earthquake occu•ces in North America, Japan, and Eurasia, most of which were in regions for which a TIP had been previously diagnosed. The localization algorithm successfully outlined a region within which the subsequent earthquake occurred for 16 of these 17 strong earthquakes. The area of prediction in each ease was reduced significantly, ranging between 7% and 25% of the total area covered by the TIP.

INTRODUCTION

This study continues a series of papers aimed at diagnosis of Times of Increased Probability of strong earthqumes (TIPs) us- ing some concepts of pattern recognition. For a discussion of the rationale for this type of approach see Keilis.Borok, [1990]. In previous papers [Gabrielov et al., 1986; Keilis. Borok and Kossobokov, 1984, 1986, 1988], a TIP was defined for a time period of a few years and for an area of linear dimension much larger than the source of an incipient strong earthquake. Such predictions were called "long-term" ones. Recently, we mined to a more appropriate and commonly accepted expression, "intermediate-term" prediction [Wallace et al., 1984; Keilis- Borok et al., 1988].

Since one of the criticisms of the TIP approach has been that the area of a prediction is too large, the goal of the present study is to reduce the territorial uncertainty of the predictions. We will do this by exploiting the observation that seismic quies- cence often occurs in the vicinity of an incipient strong earth- quake. Specifically, the following problem is considered. For a region in which a TIP has been diagnosed for a strong earth- quake with magnitude greater than or equal to M 0, we seek to outline within this region a smaller area V where an incipient strong earthquake is most likely to occur. We shall call V "the area of prediction," and will evaluate the success of this pro- cedure based on how much smaller this area is than that of the entire TIP region.

The idea that seismic quiescence may be related to the oc- currence of future earthquakes is a well-established one, in the context of using it as a precursory phenomenon; see, for exam- ple, Wyss and Habertnann/1988]. Both our usage of the con- cept and our quantitative measure of seismic quiescence are somewhat different from those of earlier studies, however. We rely on the seismic activation as characterized by the M8 algo- rithm as the precursory phenomena indicating that a strong earthquake is imminent, and look to quiescence only as a means

Paper number 90JBOI28Z 0!484Y227•••1282 $05.00

to provide beuer spatial resolution for the prediction. Further- more, our definition of quiescence is cast in tern of a measur- able decrease in activity in a region previously active at a significantly higher level over the preceding 6 years. In this sense a seismic gap would not generally qualify as being an area of quiescence by our definition. In the process of investi- gathag quiescence, we were led to consider the alternate hy- pothesis, namely that seismic activity, rather than lack of activi- ty, might be used for the same purtx)ses. As will be discussed below, there is a rationale for this, but our results show that the

measure of relative quiescence appears to be a more effective tool for localization of a prediction.

Seismic quiescence was first recognized as a stage of the "se- ismic cycle" by Fedotov [1968]. He noted a number of ex- amples of seismic regions that exhibited marked reductions in seismicity in the years prior to strong earthquakes. The con- cept of quiescence as an intermediate-term precursor to ep- isodes of strong eartlxtuakes was timher developed by Mogi [1969] and has been the subject of numerous studies since then.

Many reported obse•'vations of quiescence appear to have problems, because of statistical inadequacies in the treatment of data as has been pointed out by Reasenberg and Matthews [1988], or •use of magnitude bias in earthquake catalogs as discussed by Habermann [1982]. We carmot claim that the results of this study are unaffected by bias which might be present in some earthquake catalogs. Refinement and improve- ment of catalogs need to be ongoing efforts, but are beyond the scope of this study. We hope that the catalogs used in our study are rather complete and were compiled in a uniform way. Despite these and other controversies that often arise in topics related to eanahquake prediction, there now seems to be a con- seasus among workers in the field that at least some subset of reported quiescences are genuine precursory phenomena [Wyss and Habermann, 1988]. Furthermore, the understanding of phy- sical mechanisms which might produce quiescence as a precur- sory phenomenon has improved. For example, Scholz [1988] has shown that intermediate-term quiescence may result from re- gional hardening or stress relaxation over volumes that are much larger tlum the size of the rupture zone of the subsequent earth- quake. His result provides encouragement for approaches to the problem that utilize seismicity characteristics over very large volumes, such as is the case for the study reported here.

19,763

19.764 Kosso•oKov BT AL.: LOCALIZATION OF •.RTi-IQ•AKE PR!!DICTION

LOCAIJ7ATION BY RELATIVE QUfESCENCE (MENDOCINO SceNnmo)

Data and Defin•io•

We started with analysis of seismicity near Cape Mendocino before the Eureka earthquake of 1980, M = 7.0. A TIP had been retroactively diagnosed for this earthquake by the algo- rithm M8 as defined by Gabrielov et al. [1986]. This algo- rithm depicts specific activation of the seismicity in a territory U which contains the epicenter of an incipient strong earthquake of magnitude M 0. In practice, U is considered to be either a circle or a square. The diameter of a circle or the side of a square D is taken to be a function of the specified strong earth- quake: D(M0)={exp (M0-5.6)+1 } in degrees of meridian. The motivation for scaling the dimensions of the region exponential- ly with magnitude is to take into account that the source dimen- sions, and thus the area of preparation for large earthquakes, are likely to scale this way. The lower threshold for earthquakes used in the M8 algorithm is taken as that magnitude which is exceeded on the average 20 times per year in the region con- sidered. The notation for this magnitude threshold is M(20). The choice of this threshold insures that comparable size data sets will be used when the algorithm is applied to regions which have quite different rams of seismicity. Aftershocks are routine- ly removed from the catalog before analysis. These and other parameters used in the M8 algorithm were established by data fitting for specific earthquakes, and then tested by applying the algorithm in other regions with parameters unchanged. For a brief summary of the analysis technique, see Keilis-Borok et al. [1988].

Application of the M8 algorithm to the U.S. Geological Sur- vey (USGS) catalog of earthquakes led to a retrospective diag- nosis of a TIP for an earthquake with magnitude gr. eater than 7.0 in Northern California starting in January of 1980. The specific region for which the TIP was indicated was a circle centered at 40.5øN, 124.0øW, with a diameter of D(Mo)=560 km. Within this area of more than 250,000 sq km, the seismic activity is highly variable, and there are a variety of different tectonic features present. In this study, we want to avoid mak- ing any specific conjectures about the influence of geology on future earthquakes, the whole idea being that since we do not understand the earthquake process in any detemfinistic fashion, we must reach our conclusions based only on the record of seismicity.

To explore the possibility of better localization of this predic- tion, we wish to characterize the rate of seismicity within small subdivisions of the region and look to see if there is a pattern of reduced seismicity, or quiescence, in the vicinity of the 1980 earthquake. To do this, we first subdivide the area of the TIP into 256 square cells [C;;} with a side equal to D/16=35 km (1 < i, j < 16). Activity ir•Jeach of these cells was then measured by simply counting the number of earthquakes with magnitude greater than a specified threshold, which for this case we chose to be magnitude 3.0. If we had used the threshold as defined by M(20) in the M8 algorithm, it would have been magnitude 3.5. Since there would not have been very many magnitude 3.5 earthquakes in each of our cells, this choice would have pro- duced a larger spatial uncertainty in the result. By using a cata- log of the local seismological network within which this earth- quake had occurred we were able to reduce the magnitude thres- hold to 3.0, [Smith et al., !981].

Several seismicity patterns seemed to distinguish an area where the Eureka earthquake of 1980 did occur. We defined

the simpliest among these patterns by the formal algoritlm named "Mendocino Scenario," which we will refer to in this pa. per as MS. It can be briefly described as follows.

We will be characterizing each cell of the TIP region by its rate of seismicity. Since the cells are only 35 km on a side there may not be enough earthquakes in a cell to reliably characterize its seismicity. To increase the reliability of this pro- cess, we choose to smooth the result over squares 3x3 cells in dimension. Thus the TIP region will be scanned by 256 over-

lapping uniform squares {Sii) with sides equal to 3/16 D, and each containing a correspon-ding Cij and its nearest eight neigh- bors. We will look at a sequence of overlapping 2-month time windows {t•} with a 1-month step, starting 6 years before the TIP. Six years was selected since that is the time interval used in algorithm M8. For each of these squares separately we will be looking for locally "anomalous quiescence." We postulate that the strong earthquake predicted by the TIP is most likely to occur in the region where a "sufficienfiy large" cluster of squares experiences such a quiescence. We will look for such clusters within the time interval that starts a year before the TIP was declared and ends either with strong earthquake or, if none occurred, with the end of the TIP. In the first case it will be considered a "confamed prediction" by the algorithm M8, and in the second it will be classified as a "false aim."

Now let us give formal definitions of "anomalous quiescence" and "sufficiently large cluster." Since we are not looking for a quiet area, but rather an area which has experienced a significant reduction in seismicity from a previously high level, we will define the measure in terms of the tenth percentile of the distribution extending back over the 6-year period prior to

the TIP. That is, a square $ij will be defined as "anomalously quiescent" during a particular 2-month interval if the number of earthquakes in that time is less than in 90% of all the other 2- month intervals in the data set.

The definition of clustering of squares in a state of quiescence is as follows. We denote $..- to be $.. at the time t•. We now say that squares S.. and Stm,n are "strongly connected" either when they belong•/• the same time tt and their central cells have common side, or when the central cells belong to consecu- tive times and coincide in space. We now consider all the squares which experienced anomalous quiescence Q= {Si;j•}. By our definition two squares Si.j• and St, n. n belong to •e same cluster if there is a sequenc• l of squares from Q stag with Sii • and ending at Stm n such that successive members of this se- quence are strongly •onnected. This definition divides Q into separate clusters Q. Q ...... Q. With these conventions we now define as "sufficiently large any cluster Q,n containing four or more strongly connected squares .Sij,•. The area of prediction is then simply the union of all sufficmntly large clusters.

The definitions here, as well as the parameters selected, were motivated by examining in detail the time-space variations in seismicity prior to the 1980 Eureka earthquake and are thus as- sured of successfully localizing the prediction for this one event. Having selected the process, which we call MS, the idea will be to test it in other regions that have experienced strong earth- quakes and for which TIPs have already been published, without adjusting any of the free parameters.

Data Analysis

Figure 1 illustrates an application of MS to the TIP associated with the Eureka earthquake of November 8, 1980. Sufficiently large clusters of locally anomalous quiescence were formed in June 1979, February-March 1980, and September-October 1980.

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Fig. 1. Applicatie• of Mendocino Scenario to the 1980 Eureka earthquake. Each 16x16 square shows the area of the TIP at the time t• indicated above. Central ceUs C.,i which experienced anomalous quiescence are shaded. The last two frames show the actual epicenter and the "rea of prediction" as defined in MS.

The union of these clusters is shown in the last frame of Figure 1, and does include the incipient epicenter. Note that in the bi- monthly frames shown in Figure 1, when a 3x3 cell square S.. is anomalously quiescent, it is only the central cell which is shad- ed. For the last frame which shows the area of prediction, how-

ever, the union of squares Sit., not just central cells, is shown as shaded.

To provide some additional insight into the variations in seismicity that produce the zones of anomalous quiescence shown in Figure 1, we show in Figure 2 the time variation of bimonthly averages of the number of earthquakes in cells close to the 1980 Eureka earthquake. Specifically, this figure shows the nmber of events exceeding magnitude 3.0 in those nine

overlapping squares Sij of dimension 105 kin, which contain the epicenter. Referring to Figure 1, we note that six cells show anomalous quiescence during June 1979. Now imagine that we superpose a square of nine cells on this pattern and slide it around to different positions while requiring that it always con- tain the center cell where the strong earthquake occurred later. For each position of the square, which we denote by S O , we keep track of whether or not one of the anomalously active cells is contained within that square. Carrying out this procedure for the June 1979 interval shown in Figure 1, one can see that it is only the squares Sa7 and Sag which contain cells that are

anomalously quiescent. Now referring to Figure 2 we can confirm that in June 1980 there is anomalously low seismicity on the two lower traces which represent squares S87 and S88. Conversely, looking at any of the upper traces of Figure 2, such as, for example, those representing S66 or S67 we see that during June 1989 these regions, although very quiet, did not qualify as anomalously quiescent because they did not show a sufficient reduction (to the tenth percentlie) from the previous level of seismicity.

Notice that in order to apply the criterion we have selected to the integer number of earthquakes that may occur during a 2- month interval, it is necessary that there be enough earthquakes in the entire sample, and enough variation in the rate of oc- currence, that the rate of seismicity for the tenth percenfile be at least one event per interval. In the case illustrated by Figure 2 the rates of activity are such that we can only define the tenth percenfile for the squares Ss6, $87, and S88. The average activity and stan 'da.rd deviations for these three cells (in the order given) are 6.0 + 3.0, 6.6 q- 3.2, and 5.9 + 3.0 and the tenth percentile corresponds to one event per 2 months. Thus to see more intui- tively what the tenth percentile criterion means here, we can see by looking at trace Ss7 in Figure 2 for the time interval 1979-1980, that it would rexluke at least two extra magnitude 3.0 events in a quiet bimonthly period in order to destroy an

19,766 Kossoso•ov m' •a•.: LocalJz•rma ov •uaxs Pasn•nON

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anomalous quiescence. Note that this would also be required for each of the three sufficienfiy large clusters in order to des- troy rite area of prediction shown in Figure 1.

As mentioned above, we used the patterns of seismicity prior to the Eureka earthquake to establish our hypothesis. Specifically, the MS algorithm includes several free parameters and two parameters that scale with the magnitude of the strong earthquake expected. The parameters that scale with magni- tude are cell size (D/16) where D(M0)={ex p (M0--5.6)+1}, and magnitude threshold (M0-4). The fixed parameters are time win- dow (2 months), smoothing window (3x3 cells), the percentile (tenth) used in definition of anomalous quiescence, and the number of strongly connected cells required for a "sufficienfiy large" cluster (>4). All of these parameters were adapted specifically for the Eureka earthquake and will be kept fixed as we test the algorithm on strong earthquakes in other parts of the world.

In specifying the algorithm we have tried to establish the magnitude threshold and the dimension of the region considered in a normalized way, so that these parameters can be transferred to other regions without re adaptation. Specifically, the magni- tude threshold is defined as four units less than magnitude of the strong earthquake being considered (M 0 ) so that we always deal with a fixed range of magnitudes. We also scale the linear di- mensions of the region considered exponentially with magni- tude M 0 reflecting that fact that earthquake source 'dimensions, and thus presumably the area of preparation for an earthquake, may scale in the same way, These choices were also motivated by the fact that with comparable rates of seismicity they will produce comparable numbers of earthquakes in the sample when applied to regions of different geographic size which may have strong earthquakes of different magnitude. This is, of course, not a strict normalization procedure, but we believe that it is a

useful way to be able to utilize earthquake catalogs in different tectonic situations for the test of the algorithm.

Figure 3 summarizes the test of MS for 17 more TIPs and strong earthquakes in different regions listed in Table 1. Only one epicenter falls outside the area of prediction, that is the earthquake of May 26, 1983 in Japan (Figure 3b). Some confu- sion may occur for cases when the TIPs for different earth- quakes overlap in space and time. For example, the earthquake of Decem• 1978 in Northern Japan also falls within the TIP for the Immp swarm of March 23-24, 1978. TMs is shown in Figure 3a where the earthquake in Japan is shown as a lightly shaded circle when plotted in the TIP region of the Immp swarm. Note also that the geographic coordinates for each different frame in Figure 3 are different; they are given in Table 1. As a result, the position of the earthquake in Japan being re- ferred to here will appear in a different relative position within the Imrup TIP. In Tien Shah, the earthquake of June 5, 1970, coincides with the TIP for the earthquake of February 11, 1969. In Northern California, the Coalinga earthquake of May 2, 1983, and the Morgan Hill earthquake of April 24, 1984, fall in the intersection of corresponding TIPs. In the last two cases, the area of prediction for the earlier earthquake included the epi- center of the later one. It is just this lack of spatial resolution for the TIPs that motivated the present study.

AS can be seen from Figure 3, application of MS significantly reduces the spatial uncertainty of prediction. The reduction in area ranges from a factor of about 4 for Kern County to a factor of 14 for the Immp swarm. These can be rough estimates only, because the epicentral distribution is not uniform, and only part of the territory of a TIP may be earthquake prone.

After completion of the draft of this manuscript, the October 18, 1989 Loma Prieta earthquake occurred. Since this earth- quake is of some special interest, a retrospective application of

Kosso•o•ov • •.: ••T• OP E.•m•• Pm•n-•oN !9767

MS has been carried out and the results included herein. TIPs for the California-Nevada region defined by another algorithm (CN) had been published by Keilis.Borok et al. [1988]. In that publication the TIP area was divided into northern and southern r•gions along a boundary in the neighborhood of 37øN. They showed that a TIP in Southern California was declared in 1983, but because the catalog available to them at that time terminated in April 1985, by definition of CN they could extend the TIP onJy 12 months forward, to April 1986. At the time of publica- c/on, they did not know whether or not the TIP would extend an additional 3 months and successfully predict the Chalfant Valley earthquake of July 21, 1986.

Subsequently, additional data were analyzed, and •e region of the TiPs in California was more precisely defined using algo- rithm M8 [Keilis-Borok and Kossobokov, 1988]. They defined the two regions in California shown in Figure 4 with TIPs de- clared in January 1985 for the northern area and in 1uly 1987 for the southern area (see Table 1). In June 1988 a special meet- ing of the National Earthquake Prediction Evaluation Council was held to review these results and other earthquake prediction research methodology [Updike, 1989].

Unforumately, MS was not applied to the more recent data set from California where the TIP was declared as mentioned

above until after the Loma Prieta earthquake had oecurre& The results shown in Figure 4 are rather provocative, since the large areas of the TIP are refined substantially, revealing two areas of relative quiescence. One such area includes the epicenter of the Loma Prieta shock, and the other is in the Sierra-Nevada and

the Owens Valley regions which were recenfiy marked by se- ismic activity related to the Mammoth Lakes earthquakes and the Chalfant Valley earthquake. The TIP for both these regions was formally closed by the occurrence of the Loma Prieta earth- quake according to our definition.

It should be noted that MS will determine the areas of predic- tion only in places of sufficiently high seismic activity where we can reasonably determine the tenth pereentile of the seismicity rate that is necessary to define anomalous quiescence. In areas of moderate seismic activity, only less restrictive quarttiles can be determined. And of course in places of low activity, quies- cence is not anomalous at all. Having this in mind, one might ask if MS is just a complex way of indicating places of high activation or their vicinity. This idea will be examined in detail in the following section.

LOCAl.wAgON BY SEISMIC AcIIvrI'Y

Let us try •o reduce the spatial uncertainty of prediction using an alternative hypothesis, namely that the epicenter of an inci- pient strong earthquake might be expected in the vicinity of only the most active places. To investigate this, we first display the density distribution of epicenters as illustrated in Figure 5. Here we have counted only earthquakes above a threshold of magnitude 3.5. This choice corresponds to M(20) for the Men- docino TIP declared by algorithm MS. We will use this definition of the threshold in a consistent manner for all the re-

gions considered in this paper as we compare the results from the different algorithms. In Figure 5 we note that the cell with the highest activity (21 events) is near the epicenter; thus we suggest the following algorithm as a possible alternative to MS.

Consider 6 years before the TIP, and count, as in Figure 5, the number of earthquakes which are located in elementary cells {Ci,/} and have magnitudes greater than M(20). The numbers of earthquakes represent an estimate for the density distribution of epicenters. Let us select 'the smallest area that contains 1/4 of all

the seismic activity, {C"ij}. In other words we count the most

active cells until the sum reaches 1/4 of the total activity in 6 years. The area of an incipient strong earthquake is then defined to be those squares centered on the counted cells; each square is formed by 25 elementary cells so that its side is 5/16 D. Let us illustrate this application using Figure 5 for the Cape Mendocino region. The total number of events exceeding mag- nitude 3.5 is 266. We start by collecting the highest activity cells, while summing the number of events contained therein, until we reach a sum of 1/4, which in this case would be 66. Carrying out this procedure, we find that we can collect only three cells, those which contain respectively 21, 18, and 17 events, before we would have a stun which exceeded 1/4 of the total. We now formally define the area of prediction as the area covered by all the squares of 25 cells centered on each of the three active cells. If the active cells are more than 5 units

apart, as is the case we are looking at, then the total area of prediction produced by the three active cells would be simply 75. In this particular case, however, one of the active cells is so close to the boundary of the TIP area that according to our for- real definition we cannot count those 5 cells which fall outside

the region, so our total area of prediction is only 70. If the ac- tive cells had been closer together, there could have been an overlap of the 25-cell squares which would thus have produced a smaller area of prediction. Since we are interested in reducing the area of prediction as much as possible, we will use the frac- tion by which the area of prediction is smaller than the total area of the TIP as our measure of how well the algorithm works. For the example illustrated, this fraction is 70/256, or 27%.

We applied this algorithm to the same TIPs as previously considered for MS. For each strong earthquake we count the number of cells in the area of prediction and also keep track of the cases when this area fails to contain the cell with incipient epicenter. The results are presented in Table 2. In case of the Chalfant Valley earthquake of July 21, 1986, the algorithm for- mally refused to give a prediction due to high concentration of activity, the single cell with maximal number of earthquakes al- ready conufins more than 1/4 of the total. It would be logical in such cases to take as an area of prediction the square centered on this single cell. For the Chalfant Valley earthq•e this would lead to a correct prediction. The area of prediction ranges from 20 to 117 cells, that is, from 8% to 46%. In 5 cases out of 17 the epicenter of the strong earthquake is missed by the algorithm. On average, the area of prediction occupies 22% of the territory of the TIP.

We reran the algorithm changing the level of 1/4 in our definition to 1/8 and then again to 1/3. The results of these tests are shown in Table 2. For the level 1/8 there are 10

missed epicenters; the area of prediction ranges from 9 to 63 cells covering in average about 12% of a territory of the TIP. For the level 1/3, the only missed epicenter is the same as the application of MS; the area of prediction ranges from 25 to 141 eelIs and occupies in average 28% of a territory of the TIP. We note that the choice of 1/8 in the activity algorithm produces about the same reduction in area of prediction as does MS, but many epicenters are missed. For the choice of 1/3, the same set of epicenters is predicted as for MS, but the area of prediction is twice as large on average.

An additional possibility to reduce the area of prediction is suggested by the fact that in all cases the epicenter was outside [C'..}, that is outside of the most active places. Furthermore, .t! ' in the epicentral maps considered, the strong earthquakes tend to appear on the periphery of active places, a phenomenon which has been noted by a number of others. So far we have not ex- ploreA this possibility.

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As the simplest alternative to MS we tried to consider .all nonempty cells as an area of prediction. The results of such lo- calization are presented in the last colran of Table 2. They are significantly worse than for both algorithms described above.

Although only 2 out of 17 epicenters were missed, the area of prediction is about 1/2 of the territory of the TIP thus making it not a very useful technique for localization.

For this simple localization by seismic activity method we

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TABLE 1. List of TIPs and Strong Earthquakes

D Limits of TIP Strong Earthquake Region M 0 M(20) km Latitude Longitude Tb Date Epicenter M

1 Taiwan 8.0 4.9 1333 16.0-28.0øN 114.9-127.8øE l/l/T/ 7/24/78 22.14øN 121.44øE 8.0 2 Mexico 8.0 4.4 1333 14.0-26.0øN 97.6-110.9øW 111184 9119185 18.19øN 103.59øW 8.2 3 Kern County 7.5 3.8 854 30.7-38.3øN 113.9-123.2øW 111152 7r21/52 35.00oN 119.00øw 7.7 4 Iturup (swarm) 7.5 4.8 854 40.2-47.8øN 140.7-151.3øE 1/1/78 3/23/78 44.93•I•T 148.44øE 7.5

3/24/78 44.24øN 148.86øE 7.6 5 Japan 7.5 4.9 854 36.2-43.8øN 138.0-148.0øE 7/1/77 6/12/78 38.19øN 142.03øE 7.7 6 ]'apan 7.5 4.9 854 39.2-46.8øN 137.8-148.2øE 7/1/77 6/12/78 44.59øN 146.58øE 7.5 7 Japan 7.5 4.9 854 34.2-41.8øN 136.1-145.9øE lil!80 5/26/83 40.46øN 139.10øE 7.7 8 Taiwan 7.5 4.6 854 21.2-28.8øN 120.8-129.2øE 10/1/86 11/14/86 23.90øN 121.57øE 7.8 9 Eureka 7.0 3.5 560 38.0-43.0"N 120.7-127.3øW 1/1/80 1118180 41.04øN 124.61øW 7.0

10 Tien Shan 6.5 3.0 384 40.3-43.7øN 75.7- 80.3øE 7ll/64 2/11169 41.50øN 79.30øE 6.6 11 Tien Shan 6.5 2.6 384 42.3-45.7øN 77.6- 82.4øE 111166 6/5170 42.46øN 78.80øE 6.8 12 Tien Shan 6.5 3.1 384 39.3-42.7øN 74.7- 79.3øE 1/1/70 7/26/71 39.84øN 77.30øE 6.7 13 Tien Shan 6.5 2.7 384 41.3-44.7øN 74.6- 79.4øE 7ll/75 3/24/78 42.86øN 78.58øE 7.0 14 Spitak (Armenia) 6.5 2.8 384 40.3-43.7øN 42.7- 47.3øE 111187 12.r7/88 40.80øN 44.10øE 6.9 15 Coalinga 6.0 2.7 276 35.0-37.5øN 118.8-121.8øW 5/2/82 5/2/82 36.23øN 120.30øW 6.7 16 Morgan Hill 6.0 2.6 276 36.0-38.5øN 120.1-123.2øW 4r24/83 4r24/84 37.30øN 121.67ow 6.2 17 Chalfant Valley 6.0 2.7 276 36.3-38.8øN 116.9-120.0øW 7/21/85 7/21/86 37.53øN 118.44øW 6.5 18 Loma Prieta ON-) 7.0 3.5 560 35.5-40.5øN 118.8-125.2øW 1/1/85 10/18/89 37.03øN 121.88øW 7.1 19 Loma Prieta (S) 7.0 3.7 560 33.5-38.5øN 116.9-123.1øW 7/1/87 10/18/89 37.03øN 121.88øW 7.1

Tb is the beginning time for the TIP as declared by the M8 algorithm. Note that in case numbers 15-17 the M8 algorithm had not been appli•, so Tb was arbitrarily assigned as I year prior to the earthquake. The area considered for these events was centered on the actual epicemer.

also tried to consider larger time intervals, starting them from the beginning of the catalog. The results were somewhat worm than the corresponding ones from Table 2, but the catalogs are so short and so different in completeness that we could not make any firm conclusions about the appropriateness of the time intervals used.

DISCUSSION

Both algorithms considered here do reduce the. territorial un- certainty of an earthquake prediction made by algorithm MS.

The area of prediction is smaller than the territo W for which the TIP had been diagnosed. In most cases (14 out of 17) MS gives a better result, with the average area of prediction reduced to 14% of the total territory of the TIP. This is to be compared with a reduction to 28% obtained with the algorithm of locali- zation by seismic activity which used 1/3 of the total seismicity as the cutoff in the definition of the most active cells.

Obviously, to evaluate the reduction gained by an algorithm, we have to compare the area of prediction not with the whole territory of the TIP, but with its seismically active part. As a

19,770 KossosoKov •r •t•: Ltw. m.•.•r•o• ov •zm•• PREmCnoN

_ w

lq

Fig. 4. Epicenter of the Loma Priers earthqu•l,e of 1989 and the area where it was predict• by algorithms M8 and MS. The circles labeled N and S refer to the two regions where a TIP had been diagnosed by MS. See Table I for details. The shaded areas refer to the 1ocadiz•m produc• by MS.

rough estimate of this part we may use the number of nonempty cells presented in the last column of Table 2. Comparison with the area of prediction obtained by both algorithms shows that MS as a rule additionally reduces territorial uncertainty more than by a factor of 3 while localization by seismic activity reduces it only to 60%.

The algorithm MS is based on temporal variations of seismi- city on a time scale of months, while the TIPs are diagnosed on a time scale of years. Nevertheless the prediction remains an intermediate-term one. Only an area, but not a duration, of the TIP can be reduced significantly by this algorithm.

It is worth reiterating that the quiescence described here is of quite different nature than that of a seismic gap as put forward, for example, by McCann et al. [1978]. It is more closely relat- ed to the early observations of Mogi [1969] in which he noted that some years before a great earthquake the future focal zone becomes relatively calm while the surrounding areas show in- creased activity. Kelleher and Savino [1975] also reviewed the characteristics of quiescence prior to large earthquakes. They

pointed out that within the quiescent zone corresponding to the entire rupture zone or aftershock area, there would often be a small concentration of seismicity clustered near the epicenter of the subsequent epicenter. This behavior is not inconsistent with our observations. In fact, one of the examples they used to illus- trate this was the Kern County earthquake of 1952, which is also one of the examples we have presented. In our context. quiescence is depicted on a rather short time scale (in incre- ments of 2 months) and is used only to refine the prediction area, not as an indication that an earthquake is imminent, that information being already contained in the diagnosis of the TIP. In the more traditional studies of quiescence, as referred to above, it has sometimes been reported that the duration of quiescence is related to the magnitude of the ensuing earth- quake. In our analysis, from the fundamental definition of the algorithm we have chosen, it is not possible to take duration into consideration explicifiy. From looking at Figure 1, however, we can see that individual episodes of quiescence appear to be quite short. We do not view this as being inconsistent with

KOSSOBOKO¾ E'r AL.: LOCALIZATION OF EARTHQUAKE PREDICTION' 19,771

126.48 125.65 124.83 124.00 123.17 122.34 121.51 120.69øW

43.03øN

42.39

41.76

41.13

40.50

39.87

39.24

38.61

37.98

I I 1 I ........... 5 9 I ............

17 2 3 I ........... 4 6 3 1 ........ 6 ß ß ß 3 3 2 1 .... 1 . . 14 . . . 1 . 1 l" . 3 ! . . . 4 . 1 . I 2 3 . I 2 2 I 1 ...... 4 3 5 16 14 21 6 2 ..... I . ....... I . I .... 1 . . . . . . . . . . . . . . 1 ........ 2 I . . 18 12 . ........ 2 I .....

......... 5 3 . I . .

........ 1 I 3 ....

........... 4 2 . .

Fig. 5 Distribution of earthquakes in the TIP area prior to the Eureka earthquake. Earthquakes above magnitude 3.5 from !974 to 1979 are shown. The solid circle marks the cell wifla the incipient Eureka earthquake of 1980. Note the southwest quadrant of the TIP area is the slable Pacific plate which is completely devoid of seismicity.

TABLE 2. Comparison of Alternative Algorithms Localization (% of TIP Area)

Algorithm Region M o Date 1/8 1/4 1/3 MS NE

1 Taiwan 8.0 7/24/'78 10 12 18 7 25 2 Mexico 8.0 9/19/85 10 22 23 13 30 3 Kern County 7.5 7/21/52 10 19 24 26 29 4 Immp (swarm) 7.5 3/23/78 10 16 23 7 50 5 3apart 7.5 6/12/78 4* 8* 31 11 42 6 3apart 7.5 6/12/78 6* 11 17 11 43 7 •'apan 7.5 5/26/83 21' 34* 41' 15' 57* 8 Taiwan 7.5 1!/14/86 -* 12 16 13 45 9 Eureka 7.0 11/8/80 10 27 31 11 27*

10 Tien Shah 6.5 2/11/69 25* 44* 55 20 80 11 Tien Shah 6.5 6/5/70 17 46 51 14 73 12 Tien Shan 6.5 7/26/71 8* 26 31 9 78 13 Tien Shah 6.5 3/24/78 6* 16 16 10 77

14 Spitale (Armenia) 6.5 7/12/88 20* 33 48 8 64 15 Coalinga 6.0 5/2/82 10' 12' 22 22 4! 16 Morgan Hill 6.0 4/24/83 10' 20* 27 19 40 17 Chalfant Valley 6.0 7/21/86 - - 10 16 41 18 Lc•a Prieta (N) 7.0 10118/89 -* -* -* 7 24

Larva Prieta (S) -* 10' 19* 14 28

Columns 1/8, 1/4, and 1/3 correspond to the cutoff used in the definition of most active cells in the algorithm of localization by seismic activity. Column MS corresl:gmds to Mendocino Scenario, and NE to the number of nonempty cells. The entries in each column are a measure of the size of the prediction area pro- duced by each localization algorithm expressed as a percentage of the original TiP area.

*Asterisks mark mislocatioris and dashes mark formal refusals.

some earlier studies, rather it is an indication that the precursory phenomenon being exposed by MS is more complicated than just a single episode of quiescence. It should also be remem- bered that the measure of quiescence formally defined here by MS only has meaning if it is applied to a region where a TIP is in progress.

At least two problems remain unexplored. The first concerns the physical meaning of the apparent success of MS. The type of quiescence which it determines may actually depict, indirect-

ly, some other phenomena such as a large temporal variation of activity or some other more adequate measure of local activa- tion.

The second problem concerns local seismotectonics. We could not allow for it in the framework of this paper. Both M8 and MS algorithms happen to be applicable in a uniform way without any re. adaptation in rather diverse tectonic cir- cumstances. That the method appe•s to work for a range of earthquake magnitudes and for a variety of regions including

19,772 KossonoKov BT AL.: LOCALIZATION OF F.•M•QUA.K!• PRI•ICTION

some characterized by transfomt faults and others by subdue- fion zones is encouraging. It extends the similarity of premoni- tory phenomena encountered in previous studies and allows one to test the hypothesis on world-wide data. This is of particular importance since it appears that it may not be necessary to await the reoeam'ence of strong earthquakes on the same smacture or in the same tectonic region in order to develop effective earth- quake prediction techniques.

Such uniformity is achieved, however, at the cost of obtaining robust and limited results which forrealize the diagnosis of rath- er simple universal phenomena. In the localization of prediction considered here we may be approaching a stage where we can account for local seismotectonics using existing methods. Specifically, the following data remain unexplored: activity of separate fault zones, seismicity in a lower magnitude range, cru- stal deformation, paleoseisrnic observations, and other geologic characterizations of seismogenic smactures.

However, even with all these reservations, the localization of intermediate-term prediction by the algorithms described here, particularly by MS, seem to deserve further test. Such testing can now be done by forward monitoring, as well as by more re- trospective studies for other territories. We believe it is impor- tant to do this both for methodological as well as practical pur-

•s

Fedotov, S. A., On seismic cycles, possibility of quantitative seismic re- gionalization and long-term prediction, in $eism/c Zon/ng of U$$R, edited by S. Medvedev, pp. 121-150, Nauka, Moscow, 1968.

C-abrielov, A.M., O. Dmitrieva, V. Keilis-Borok, V. Kossobokov, I. Kuznetsov, T. Levshina, K. Mirzoev, G. Molchan, S. Negmamllaev, V. Pisarenko, A. Prozoroff, W. Rinehart, L Rotwain, P. Shebalin, M. Shnirman, and S. Schreider, Algorithms of long-term earthquake pred- iction, in International School for Research Oriented to Earthquake Pre&'ction Algorithms, 61 pp., Centro Regional de Sismologia pars Am6fica del. Sur, Lima, Peru, 1986.

Habemumn, R. E., Consistency of teleseismic reporting since 1963, Bull. $eismol. Soc. Am., 71, 93-194, 1982,

Keilis-Borok, V. L, The lithosphere of the Earth as a nonlinear system with implications for earthquake prediction, Rev. Geophys., 28, 19-34, 1990.

Keilis-Borok, V. L, and V. Kossobokov, A complex of lotag-terrn pre- cursors for the strongest earthquakes of the world, in Proceedings of the 27th Geological Congress, vol. 61, pp. 56-66, Academy of Sci- ences, Moscow, 1984.

Keilis-Borok, V. I., and V. Kossobokov, Time of increased probably for the great earthquakes of the world, in Computational $eismology, 19, pp. 48-58, Academy of Sciences, Moscow, 1986.

Keilis-Borok, V. L, and V. Kossobokov, Premonitory activation of ismic flow: Algorithm M8, in Lecture Notes of the• Workshop on Glo. hal Geophysical lnformatics With Applications to Research in Earth. quake Prediction and Seismic Risk, International Institute for Theoreti- cal Physics, Trieste, 1988.

Keilis-Borok, V. L, L. Knopoff, I. Rotwain, and C. •'•dlen, Intermediae. term prediction of occurrence times of strong earthquakes, Nature, $$$$(6192), 690-694, 1988.

Kelleher, J., and J. Savino, Distribution of seismicity before large strike slip and thrust-type earthquakes, J. Geophys. Res., 80, 260-271, 1975.

McCann, W., S. Nishenko, L. Sykes, and J'. Krause, Seismic gaps and plate tectonics: Seismic potential for major plate boundaries, in Proceedings of Conference VII - Methodology for ldentOeying Seismic Gaps and Soon T Break Gaps, edited by J. Eveinclen, pp. 441-584 Office of Earlquake Studies, U.S. Geological Survey, Menlo Park, Calif., 1978.

Mogi, K., Some features of recent sdsmic activity in and near Japan, 2, Activity before and after great earthquakes, Bull. Earthquake Re•. ln•t. Tokyo Univ., 47, 395, 1969.

Reasenberg, P. A., and M. V. Matthews, Premrsory seismic quiescence: a preliminary assessment of the hypothesis, Pure Appl. Geophy•., 126(2-4), 373-406, 1988.

Scho!z, C. H., Mechanisms of Seismic Quiescence, Pure Appl. •,hys., 126(2-4), 701-718, 1988.

Smith, S. W., R. McPherson, and N. Severy, The Eureka earthquake of 1980, Breakup of the Gorda Plate, Earthquake Notes, 52, 44, 1981.

Updike, R. G., Proceedings of the National Earthquake Prediction Evaluation Council, June 6-7, 1988, U.S. Geol. Surv. Open File Rep. 89-144, 1989.

Wallace, R. E., J'. F. Davis, and K. C. MeNally, Terms for exp•g earthquake Potent, prediction, and probability, Bull. Seismol. Soc. Am., 74, 1819-1825, 1984.

Wyss, M., and R. Haberrnann, Precursory seismic quiescence, Pure Appl. Geophys., 126(2-4), 319-332, 1988.

,,

V. Kossobokov and V. Keilis-Borok, International Institute of Eanh- quake Prediction Theory and Mathematical Geophysics, Bolshaya Gruz- inskaya, #10, Moscow 123810, U.S.S.R..

S. W. Smith, Geophysics Program AK-50, University of Washingurn, Seattle, WA 98195.

(Received November 20, 1989; revised February 26, 1990;

accepted June 8, 1990.)