2. MOTIVATION The distribution of interevent times of aftershocks suggests that they obey a Self Organized process (Bak et al, 2002). Numerical models such as ETAS (Ogata, 1988) take from the idea that every event is capable of spawning its own individual sequence of events. Thus, the sum of all sequences might be the reason behind the self organized or fractal behavior observed. However, spatial variations in the P value of the Omori Law (Wiemer and Katsumata, 1999; Enescu and Ito, 2003) may be indicative of different regimes actuating at different times and controlling the overall process. Here we attempt to gain information on the aftershock process from the temporal behavior of interevent times in order to answer the following questions: Can we identify differences in the generation of aftershocks which indicate different processes acting at different times? Are aftershocks self organized in a unique manner? Do principal processes exist which govern the generation of aftershocks? 1. ABSTRACT We have looked at the interevent time behavior as a function of time for aftershock sequences from mainshocks occurring in similar tectonic regimes but different regions with the aim of gaining information on the aftershock process. To this end we studied some well defined aftershock sequences from Alaska, New Zealand and Mexico and analyzed the distribution patterns of interevent times (also called waiting times) in both time and space. Several results emanate from this exercise. In most cases, more than one coherent process can be discerned from that acting following the main process. Interevent times within aftershock sequences as a reflection of different processes Ramón Zúñiga Centro de Geociencias, National Autonomous University of Mexico (UNAM), Juiriquilla, Mexico. 4. TEMPORAL AND SPATIAL DISTRIBUTIONS OF INTEREVENT TIMES 5. CONCLUSIONS Different processes, which can be identified through the temporal behavior of interevent times appear to actuate within aftershock sequences and which do not seem to obey a general principle. For most cases analyzed difference in trends might be the result of different coherent diffusion mechanism, with other mechanisms taking over at certain later time. The examples show that we can not generalize by stating that the initial process is contained within a localized region near the mainshock, nor can we relate spatially closer clusters with shorter interevent times, For every aftershock sequence, selected from the maximum affectation distance of Kagan (2002) we show the temporal behavior of the logarithm of interevent times (waiting times WT) and identify apparent changes in trend. We then look at the spatial location of events ocurring within the trends identified. 3. DATA EVENT AKaft /10/6/5/23 Ms 6.2 Depth 9.09 For this case two main trends can be observed, after which background seismicity seems to take over (wt oscillate around a constant value). EVENT NZaft2 1994/6/18/3/25 Ms 6.7 Depth 4.27 EVENT NZaft3 2001/12/7/19/27 Ms 6.2 Depth 5.0 EVENT MXaft1 2001/10/8/3/39 Ms 5.4 Depth 4.0 In order to better identify linearities in the time trend of the logarithm of interevent times, we plot the raw data, an equal interval interpolation, and a running average with 20 points. The normalized distribution of interevent times plotted for different magnitude cut-offs, does not indicate any departure from a single regime. Events occurring within the first trend (blue crosses) are tightly clustered around the mainshock while those in the second trend (green circles) are located in a wider region. Omori aftershock decay rate can be fitted by with a single P value, no other trend is apparent from the data. For this case the average shows larger scatter but nevertheless a second (intermediate) trend can be discerned. The location of events falling within the three trends appear to migrate inward. The background seismicity has not taken over the overall trend. Yet, only one regime is seen through the density distribution of interevent times. Omori Law is well fitted by means of one P value. No other trends are seen. The temporal behavior of WT for this event are similar to those of event AKaft1, again two main trends may be observed after which background seismicity (longer period oscillations around a constant value) takes over. Two separate clusters occurring within regions side by side are seen in this case. Notice that the events belonging to the second cluster (green circles) are closer to the mainshock and their inter-distance is no different from those of the first cluster and trend (blue crosses). For this case two linear trends are apparent in the density distribution indicating two correlation regimes. The aftershock decay rate can be fitted by and Omori law with a single P value. This event shows apparently only one trend before the background, although the magnitude resolution for these events is much larger than in the other cases (Mc ~ 3.6) which may preclude observing details in the immediate temporal vicinity of the mainshock. Notice also that the magnitude is smaller than other cases although aftershock production is similar. No pattern is distinguishable from the epicenter locations which can be correlated to the trends in WT and no difference can be made from background seismicity. The density distribution of interevent times is similar to that of a Poissonian process, again pointing to similarities between the sequence and background seismicity. An Omori relation is not clearly defined for this sequence and no single P value can be used to model the process. We selected some well defined aftershock sequences from Alaska, New Zealand and Mexico which occurred in similar tectonic situations All events are shallow continental type even though some of them they take place close to plate boundaries.