SEISMICITY PATTERNS: ARE THEY ALWAYS RELATED TO NATURAL CAUSES?

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1 SEISMICITY PATTERNS: ARE THEY ALWAYS RELATED TO NATURAL CAUSES? F. Ramón Zúñiga 1 and Stefan Wiemer 2 1 Unidad de Investigación en Ciencias de la Tierra,/Instituto de Geofísica, UNAM. Campus Juriquilla, Querétaro, Qro., C.P , México. ramon@conin.unicit.unam.mx 2 Eidgenössische Technische Hochschule Zürich, Institute of Geophysics, ETH- Hönggerberg, 8093 Zürich, Switzerland. stefan@seismo.ifg.ethz.ch December 98 Abstract By analyzing data from two catalogs of seismicity, one local and one regional, examples are drawn that demonstrate artificial seismicity patterns which can arise when changes in magnitude reporting or in detection ability are introduced by changes in operating procedures of a seismic network. In the first case, a catalog which comprises seismic data for Guerrero, Mexico, is revised and a correction is applied to magnitudes suspected to be affected by different operative practices. Then, several time windows are analyzed comparing the seismicity rate within the window to that of the total record mapped as a function of space. The same procedure is applied to the original (i.e. uncorrected) catalog. A significant seismic quiescence apparent in the original data set in the center of the seismic network all but disappears in the corrected data, indicating that this anomaly is not naturally induced. In the second case, we investigate the homogeneity of seismicity reporting for the Interior of Alaska. We computed the standard deviate z as a function of time by comparing the overall seismicity rate with the rate in a 3-year window. The maps of z-values were inspected for all times. The most outstanding rate change is found around Since the b-value remained unchanged, the most reasonable explanation for the observed rate change 1

2 around mid-1992 is a decrease in the detection ability of the network in the Interior of Alaska. Both case studies demonstrate the usefulness of systematic comparisons of the cumulative and non-cumulative frequency-magnitude distribution and of spatial and temporal mapping of the seismicity rates as a tool to investigate the homogeneity of earthquake reporting. Introduction Seismicity patterns have through the years attracted the attention of researchers as a means to investigate the tectonic stress behavior of a region. Changes in the rate of earthquake production are believed to be closely related to changes in stress in a particular volume (Dieterich, 1994; Dieterich and Okubo, 1996; Kato et al., 1997). Precursory phenomena have been proposed to cause changes in seismicity rates, such as localized precursory seismic quiescence (Reasenberg and Matthews, 1988; Wyss, 1997), regional activation (Habermann and Creamer, 1994; Keilis-Borok and Kossobokov, 1990; Kossobokov and Carlson, 1995) and accelerated moment release (Bufe et al., 1994; Varnes, 1989). Any study of seismicity patterns whose goal is to detect variations in the background stress state has to start with the identification of a pattern as significant and then proceed to attempt inferring about the possible physical causes of the pattern. One, however, needs to be certain that the pattern in question is caused by natural activity rather than related to data acquisition or processing before any conclusions can be drawn on its significance or physical meaning. For this reason, it is of utmost importance to know the seismic record from which the pattern comes from to the finest detail possible and in addition provide some way of testing whether a pattern in question has indeed been generated by natural causes. Several studies have been carried out on the problem of artificial or man-made seismicity anomalies (Habermann, 1982; Zúñiga, 1989; Habermann, 1991; Wyss, 1991). Some of the most common sources of errors, as well as techniques to detect them can be found in Zúñiga and Wyss (1995). Other sources of error are continuously added to the list as we 2

3 progress in finding new ways of identifying artificial seismicity variations. Different techniques have been proposed to identify artificial rate changes. Some techniques rely on comparisons of seismicity rates, defined as the number of events per unit time, taken at different time intervals and for varying magnitude bands (i.e. Magnitude Signatures and GENAS algorithm, Habermann, 1983, 1987) as well as on its spatial mapping (i.e. ZMAPS, Wiemer and Wyss, 1994). Others have made use of b-value relations (Frohlich and Davis, 1993; Zúñiga and Wyss, 1995) or magnitude distributions (Perez and Scholz, 1984) in order to distinguish between natural and man-made seismicity anomalies. The objective of this study is to highlight the extent of the problem of artificial seismicity rate changes (ASR) by providing examples which show how spurious patterns can be produced by them. We also show ways in which we can discriminate these spurious patterns from the naturally induced ones. We specifically demonstrate that spatial and temporal mapping of seismicity rates can give important clues to the existence of ASR and help in identifying their causes. By doing so, we hope to emphasize the serious repercussions of not treating seismicity catalogs in a proper way. First case study: A local network record in the Guerrero Gap, Mexico We will discuss the catalog of Guerrero, Mexico, seismicity obtained from a telemetric network of short period seismographs which has operated from 1988 till 1996 in a basically continuous fashion. This network is still run by the Institute of Geophysics of the National University of Mexico (UNAM) and has been subjected, as have most networks, to various changes in the operative procedures throughout its history. The network covers a segment of the Mexican subduction thrust which has not experienced a large earthquake for over 90 years, the so-called Guerrero seismic gap. Figure 1 shows the seismic activity of the region together with the location of the stations. Not all stations have been in operation at all times, although all were installed during

4 It is immediately apparent that the seismicity is not distributed homogeneously, showing bands of dense activity parallel to the trench. These bands have been attributed to tensional and compression stresses within the subducting slab (Suárez et al., 1990) due to a varying dip. We first study the overall characteristics of the seismicity record by means of the cumulative number of events per time unit (hereafter referred to as seismicity rate). In this case we used a time unit equal to one month for our calculations. Figure 2 shows the aforementioned rate, for events with a magnitude M 2.0 (magnitude is determined from coda duration), which is somewhat lower than the minimum magnitude of completeness (~2.5 from the b- value distribution). Various trends in the data can be seen, of which three stand out: the beginning of the time series, from 1987 to 1989; the mid-time interval, from 1989 to 1993; and the most recent trend, from 1993 to The first trend is related to the start of cataloguing and operation of the network, and was subjected to many adjustments, so we excluded it out from our analysis. The second trend is a period during which the network ran with the least changes and in a consistent manner. The last trend is related to a final change in operating procedures, which is discussed later. We now proceed to "clean" the catalog in order to produce a more homogeneous data set. We start by declustering the catalog using the algorithm proposed by Reasenberg (1985), using the original parameter settings. We confirmed the main trends and determined their onset and ending with GENAS algorithm (Habermann, 1983) resulting in the following dates: , , and (we use decimal fractions of the year to facilitate calculations). We then compared the two last trends by means of their b-value distributions. Figure 3 shows the cumulative frequency-magnitude distribution for the two trends and the magnitude distribution histograms. The two frequency-magnitude distributions are distinctively different to the eye. One of the most common changes seen in earthquake catalogs is an effective additive variation in magnitudes measured after some time (magnitudes values increase or decrease 4

5 by a fixed amount), which has been referred to as a "magnitude shift" (Habermann, 1987). Such a change might produce the horizontal translation of the histogram observed in Figure 3b. However, a magnitude shift would not affect the b-value (slope of linear trend in Figure 3a) (Zúñiga and Wyss, 1995) as is the case here. We investigated if a more general linear change in magnitude, which is sometimes known as a "stretch" in magnitude, might suit the data better, employing the technique proposed in Zúñiga and Wyss (1995) to that effect. Figure 3c,d shows the results of this exercise. The best fit in a least squares sense between the earlier and later period is achieved by the following transformation: M new = 0.8 M old for events after We can see that a good match is obtained between the b-value curves for both intervals. The resulting cumulative number versus time curve is shown in Figure 2 as a solid line. A statistically significant change in rate for M 2.0 which was detected by comparing a running average rate with the previous rate by means of AS(t) function (Habermann, 1983; Wiemer and Wyss, 1994) for time is no longer detected after the correction.. To study the effect of the magnitude correction on seismicity patterns, we analyzed the spatial dependency of seismicity rate changes by comparing seismicity patterns before and after the correction. The patterns we will discuss are those obtained through the ZMAP (Wiemer and Wyss, 1994; Wyss and Wiemer, 1997) technique. In short, this method compares the rate within a specified time window to the seismicity rate of the entire seismic record, excluding the analysis window. The time window is selected such that its length is not significant with respect to the duration of the record. The area of study is subdivided by a densely spaced grid and for every grid node a fixed number of nearest earthquakes (here: 300) are selected to represent the seismicity around that particular point (that is, we form a subcatalog for every node in the grid which is taken as the seismicity record for that point in space). The same procedure could be performed using events which are located within a fixed distance from the node, but variations in the seismicity density make the former approach of constant sampling sizes statistically more robust. A statistical z-test using the LTA(t) function (Habermann, 1987, Wiemer and Wyss, 1994) is carried out for each time 5

6 series at each node. The resulting three-dimensional matrix of z-values (Latitude, Longitude and Time) can be sliced at any time. We analyzed time windows with a duration of 1-year, starting at the beginning of each year. Figure 4AB depicts the results of comparing a rate variation map for a time window (t w ) which starts at We can see that an apparent anomalous decrease (in all Figures, positive z-values indicate seismicity decreases while negative values are indicative of increases in seismicity) located to the center of the study area is enhanced by the correction. Z-values larger than 2.57 are significant at the 99% level. It is important to mention that although we corrected magnitudes only after , the rate analysis are carried out considering the whole span of the catalog as a basis for comparison. Therefore, we observe an increase in the z-value because the variance of the seismicity rates decreases because of the subsequent correction. Few changes between the original and corrected catalog are visible for the 1991 and 1992 slices (Figure 4C-F). The most significant differences between the original and corrected catalog can be identified when comparing the seismicity rates during the period with the overall background (Figure 4G, H). The original data set reveals a statistically important seismicity rate decrease in the center of the Guerrero Gap (Figure 4G). However, this rate change is no longer significant in the corrected data set (Figure 4H). Second case study: Seismicity rate change in the Interior of Alaska in mid-1992 As a second case study we investigate the homogeneity of seismicity reporting for crustal seismicity in the Interior of Alaska. The data used in our study are extracted from the Alaska Earthquake Information Center (AEIC) catalog of digital data. AEIC started recording digitally in the fall of 1988, but the earliest usable data are from January Earthquakes are routinely located with the computer program HYPOELLIPSE (Lahr, 1989). We investigated crustal seismicity (depths < 40km) for the period The catalog is declustered using (Reasenberg, 1985) algorithm with the standard parameter setting. However, the results presented here are largely independent of the declustering 6

7 applied. An epicenter map and the major tectonic features of the region are shown in Figure 5a. Interior Alaska is transected by major and great faults, for example the Denali fault, that have dimensions and morphological expressions similar to those of the San Andreas fault system. Some of these faults are thought to be capable of M>8 earthquakes, but they have not ruptured during the short historically recorded period in Alaska. Several faults that have ruptured during the last few decades have produced M>7 earthquakes (Fletcher and Christensen, 1996). Page et al. (1995) proposed a block rotation model for east-central Alaska, which is based on the NW-SE striking lineations seen in the crustal seismicity and in air-magnetic surveys. The largest earthquake recorded in the Interior of Alaska over the past ten years was the 1995 M w 6.2 Tatalina River earthquake, located approximately 50 km north of Fairbanks (Figure 5a). To investigate spatial and temporal homogeneity of the earthquake reporting, we sample the seismicity using overlapping cylindrical volumes each containing 1000 earthquakes. This large sample size is chosen to enhance robustness of the analysis and because we are mostly interested in larger scale changes in catalog homogeneity rather than a high-resolution study. As in the Guerrero case, we compute the standard deviate z as a function of time by comparing the overall seismicity rate with the rate, only that here we use a 2-year window. The maps of z-values are inspected for all times. The highest z-value (z=4), corresponding to the most significant rate change, is found around , and we show the corresponding spatial distribution of z-values in Figure 5b. The red colors north of the Denali fault indicate a rate decrease starting mid Blue and green colors indicate a slight increase in the number of events detected. The cumulative number of events as a function of time plot for the high z-value region (Figure 6a) shows a stable rate of events for the period (~300 events per year), then a drop of the rate by over 50% for the period This rate decrease is followed by the October 1995 Tatalina River earthquake and its subsequent aftershock activity, which produces a rate increase despite the declustering of the catalog. 7

8 One may propose that this rate decrease is causally linked to the upcoming mainshock. However, if we compare the number of events reported in each magnitude bin (Figure 6c) we find that the rate of magnitude 1.5 and above earthquakes is essentially unchanged, and only the number of M < 1.5 events decreased. The b-value remains unchanged (Figure 6b), and the most logical explanation for the observed rate change around mid-1992 is a decrease in the detection ability of the network in the Interior of Alaska. A plot of the magnitude of completeness as a function of time supports this interpretation (Figure 7): We find a step like increase in the magnitude of completeness around from Mc ~ 1.4 to Mc ~ 1.6. Discussion Seismicity rates vary with the applied stress and can serve as a remote stress sensor in the Earth s crust. However, seismicity rates are also highly susceptible to changes in operating procedure of seismic networks. In this paper, we show how by using two simple techniques artificially introduced rate changes can easily be detected. The first technique is the systematic comparison of the cumulative and non-cumulative frequency-magnitude distribution for two periods (Figures 3 and 6). Using a common sense approach, an investigator is often able to reconstruct and explain the history of seismicity rate changes based on these graphs. The second technique is the quantitative mapping of seismicity rates as a function of space and time. Most efforts of quantitative seismicity rate mapping have been focussed on the identification of possible earthquake precursors (e.g., Wiemer and Wyss, 1994; Wyss et al., 1996). In this paper we demonstrate for the first time the usefulness of spatial information for unraveling the reporting history of a seismic network. The change in seismicity rate observed around in the Guerrero case study (Figure 2) is likely to be a result of different operative practices which induced an ASR. It is difficult to think of a natural change in the earthquake production that would produce a drastic reporting change as seen in Figure 3. Such a change could, for example, consist of a decrease in medium size events (2 < M < 4) for unknown reasons, while simultaneously the number of larger events remains unchanged and an increase in the detection capability of the 8

9 network increases the number of small events detected (M < 2). Although not impossible per se, it is highly unlikely that such a complex change would occur. A much simpler explanation can be found when considering the operating procedures of the seismic network. As aforementioned, magnitudes have been determined from duration of the signal from the beginning of cataloguing. Around 1990 the signals were sent through an AD filter before being recorded in memory. Records were automatically stored in blocks. The operator had to retrieve the blocks from memory and display them in order to measure the duration. A truncation of the signal would sometimes prevent measuring the complete coda. Different operators used different ways to deal with the problem. One operator employed the decay trend and estimated the end of the signal. Another operator estimated the length by comparing with other records. The change in operators coincides with the time for which the seismicity variation is observed in the cumulative curves (Figure 2). Therefore, we believe that there is a direct connection between the two events. Even though the different operative practices might have introduced a change in the number of reported events (if, for instance, the operator had decided to exclude those events which appeared truncated) and not affect the determination of magnitudes, the curves and histogram in Figure 3 demonstrate that this was not the case, since such a change would not effectively produce the shift observed between both curves as well as in the histograms. The difference between the curves and histograms can only be attained by a change in magnitude determination, being a simple shift or a stretch. It is important to mention that some of the problems detected in this study might have been avoided if magnitudes had been determined based on amplitude and not on duration. The change in rates is most pronounced for the central region of the catalog (Figure 4 GH) and less obvious for outlying regions. One might assume that the center of a seismic network is less susceptible to ASR than outlying areas. However, we can understand this at first counterintuitive behavior by analyzing the magnitude of completeness as a function of space (Figure 8). As one would expect, the magnitude of completeness is with Mc ~ 2.0 lowest in the central part of the network, and increases to Mc > 2.5 in outlying areas. The change in reporting procedures has the greatest impact for smaller events (2 < M < 2.5), 9

10 because the change in magnitude reporting moves these events below the cut-off magnitude of M = 2. Consequently, the area with the lowest magnitude of completeness shows the most significant artificial rate change. We have shown that correcting a catalog after a suspicion of spurious changes may provide some indication of the nature of the change. For the cases analyzed, there are instances where apparent anomalies remain after the correction or even get enhanced by it. Other examples show that some anomalies are no longer significant or are not seen altogether after the correction. Thus, even if the correction is subjected to later refinements, preliminary results can be used as a tool to identify ASR. The Alaska case study also clearly demonstrates the usefulness of spatial and temporal mapping of the seismicity rates as a tool to investigate the homogeneity of earthquake reporting. The overall seismicity rate for crustal activity in Alaska shows little change, because the rate decrease in the Interior is at least partial compensated by detection capability increases in South Central Alaska (blue areas in Figure 5b). Consequently, the network operators were not aware that a change in the detection ability had occurred until our z-value mapping revealed this increase-decrease pattern. Subsequently it was discovered that in order to reduce the workload during the eruption of Mt. Spurr in the summer of 1992, the decision was reached (but unfortunately not documented) to change the triggering setup for the Interior subnet. One additional triggering station was subsequently required to declare an event, causing the number of small earthquakes detected to be drastically reduced (Figure 6c). This change did not influence the detection ability south of the Denali fault, which belongs to a different triggering subnet, thus we find no rate reduction (Figure 5b). The detection ability in the Interior was never restored and remains below the level despite the introduction of a new processing system in early 1997 (the Iceworm system; Lindquist, 1998). We conclude that our analysis of the homogeneity of reporting was able to detect and quantify a major change in the network capabilities previously unknown, and we suggest that a routine monitoring of the seismicity rates using the simple tools applied in this study can help to minimize the unnecessary loss of data by offering an early detection capability to identify rate changes. In these as in other cases studied, 10

11 experience indicate that for long term experiments, best results are obtained when a conservative approach is employed, i.e. introducing as few changes in the operation of a network as possible. Acknowledgments The authors would like to thank Paul Reasenberg and an anonymous reviewer for their comments and suggestions which improved the presentation of this study. Gerardo Suárez effort in keeping the Guerrero network alive for such a long time is gratefully acknowledged. Jaime Domínguez was very helpful in providing information related to operative practices of the Guerrero network. We acknowledge the Alaska Earthquake Information Center and the Institute of Geophysics-UNAM for providing seismicity catalogs used in this study. Support for this work has been provided by NSF Grant EAR and the Science and Technology Agency of Japan (SW) and by UNAM through its DGAPA-PAPIIT program (RZ). 11

12 Figures Figure 1: Epicenter map of the Guerrero region. Epicenters located with the local seismic network during the period are shown as black dots. Triangles mark the location of the seismic station, black lines indicate the bathymetry and the location of the Mid-America trench. Figure 2: Cumulative number of detected events (M 2) as a function of time from the Guerrero network. The dashed line indicates the original data after declustering, the solid line the corrected data set using the magnitude transformation given in the text. The vertical dashed line indicated the time when the change in reporting rate occurred. Figure 3: Cumulative number of events (frames a and c) and non-cumulative number of events (frames b and d) as a function of magnitude in Guerrero. The frames a and b correspond to the original data set, frames c and d to the corrected data set. Two periods are compared in each frame: (o) and (x). The numbers have been normalized by the duration of each period. Figure 4: Maps of the standard deviate z for four different times. The left frames correspond to the original data set, the right frames to the corrected data set. At each node of a densely spaced grid, the 300 nearest earthquakes have been sampled and investigated for seismicity rate changes. The color-coding corresponds to the z-value at this grid node, high z-values indicate a rate decrease. Z-values are computed for a window length of 1 year and compare the seismicity rate in the one-year window starting at the time indicated to the left with the overall seismicity rate excluding the one-year period. Figure 5: (Left) Map of south-central and Interior Alaska. Epicenter during the period are plotted as black dots, red lines mark major faults. (Right) z-value map comparing the seismicity rates in the period with the overall background rate. At each node, the nearest 1000 earthquakes were sampled. The red colors (high z-values) indicate an artificially 12

13 introduced decrease in the number of events detected in the Interior of Alaska that started around mid Figure 6: (a) Cumulative number of events as a function of time for the seismicity in the Interior of Alaska. The 1995 M6.2 Tatalina river earthquake is marked by a star. Note the rate decrease that started in (b) Cumulative and (c) non-cumulative number of events as a function of magnitude. Two periods are compared in each frame: (o) and (x). The numbers have been normalized by the duration of each period. Figure 7: Magnitude of completeness as a function of time for the seismicity in the Interior of Alaska. The completeness is computed based on the frequency-magnitude distribution and using overlapping windows containing 500 earthquakes each. Figure 8: Map of the Guerrero region contouring the overall magnitude of completeness of the Guerrero network. 13

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