بهينه‌سازي مكان‌يابي زمين‌لرزه‌ها در منطقه البرز مركزي با استفاده از تأخيرهاي زماني همبستگي شكل‌موج‌ها

Improving locations of earthquakes along the Central Alborz, Iran, using waveform cross-correlation-based time delays

در مطالعه حاضر، تمام شكل­ موج­ هاي موجود براي منطقه البرز و مناطق همجوار آن به منظور مكان­يابي بهينه زمين­ لرزه ­ها مورد بررسي قرار گرفته تا تصويري واضح ­تر از لرزه ­خيزي اين منطقه ارائه شود. در مرحله نخست مطالعه، همه فازهاي P و S زمين­ لرزه ­ها، كه در 41 ايستگاه لرزه ­نگاري و بين طول و عرض­ جغرافيايي 48 تا 54 و 33 تا 37 درجه در دسترس بودند، دوباره قرائت شدند. سپس 4152 زمين­ لرزه با روش جك ­نايف، كه قادر است ارزيابي قابل اعتمادي از خطاي اوليه را ارائه دهد، مكان­يابي اوليه انجام گرفت. در مرحله دوم، زمان رسيد فاز P از روي همبستگي متقاطع شكل­ موج ­ها، بهينه كردن قرائت­ ها و زمين­ لرزه ­هاي همبسته تصحيح شد. اين نوع زمين ­لززه­ها، جفت زمين ­لرزه­هايي هستند كه در فاصله معيني از يكديگر قرار دارند و شكل ­موج ­هاي مشابهي را در ايستگاه­ ها ايجاد مي­ كنند. در پايگاه داده مورد مطالعه، همه اين جفت زمين­ لرزه­ ها فاصله­ اي كمتر از 10 كيلومتر دارند و اختلاف زماني آنها معادلات خطي وزن­ داري را تشكيل مي­دهند كه از روي كيفيت قرائت فازي طبقه ­بندي شده ­اند. به طور تقريبي، بيش از 280000 اختلاف زماني از روي شكل­ موج­ ها محاسبه شده است كه از بين آنها تنها مواردي با ضرايب همبستگي بيش از 0/7 براي مكان­يابي زمين­ لرزه ­ها انتخاب شدند. در مرحله سوم، به منظور كاهش اثر خطاي احتمالي مدل پوسته، از روش اختلاف زماني دوگانه در مكان­يابي زمين ­لرزه ­ها بهره­ گيري شد. نتايج حاصل از 2409 زمين ­لرزه با مكان­يابي مجدد كه تنها از روي همبستگي امواج به دست آمده ­اند، جمع­ شدگي و انسجام زمين‌لرزه ­ها در اطراف گسل­ هاي مهم منطقه حكايت دارد.

In this study, a complete waveform database of Alborz and its surroundings was processed so as to ameliorate the locations of the earthquakes and obtain an enhanced picture of the past decade’s seismicity distribution. In the first step of this study, P- and S-wave arrival times were manually re-picked at 41 stations extending from 33° N to 37° N and from 48° E to 54° E. Our initial locations, including 4152 events, were implemented using Jackknife resampling method, normally employed for statistical inference. This up-to-date technique reliably estimates hypocentral errors by deleting one observation at a time. In order to ensure that the relocation would provide valid results, only events that met certain criteria were selected. The selection criteria were (1) largest primary azimuthal gap between stations less than 210°, (2) arrival time residuals less than 1 s, (3) number of recording stations no less than 6, and (4) initial event uncertainty in epicenter and depth of less than 10 km. The second step of this study focused on improving the arrival time pickings of event pairs utilizing P-wave cross-correlation-based time delays. Correlated events are those occurring within a few kilometers of one another to generate similar waveforms. All event pairs with separation distances less than 10 kilometers were processed. The differential times of event pairs with corresponding travel time residuals for all observations were combined into a system of linear equations and weighed based on the quality of arrival time picks. We computed a total of more than 280000 P-wave differential times and selected waveform pairs with coefficients of 0.7 or larger. In the third step, to minimize the effect of inaccurate velocity structure, we applied the double-difference location approach. The algorithm, hypoDD, determines relative locations within clusters of closely spaced events using double-difference method developed by Waldhauser and Ellsworth (2000). By relocating merely closely spaced events, this algorithm ameliorates relative location accuracy along with reducing the effects of unmodeled velocity structure. The nearest neighbor approach was applied so as to link events using a maximum search radius of 10 km and a minimum number of 8 links. Event linkage strongly controls how the dataset is broken into clusters for relative relocation in hypoDD. For example, a single link between two closely spaced events, but perhaps occurring along different faults, causes all linked events to collapse into a single cluster rather than forming two clusters. Because of the relatively small number of stations recording each event and due to the closely spaced known faults in Alborz region, we, instead, visually prescribed cluster identification. In this way, we used such essential documentary sources as seismotectonic maps, the hypocenter locations of seismic events in the initial locating procedure, and the expansion of the major faults. The distribution of 2409 relocated events delineated more coherent features, and in general, the relative relocations increased the agreement with major active faults. The absolute and relative relocations discussed in the present research are an improvement because of either the carefully re-picked P- and S-wave arrival times or the applied appropriate waveform phase-picking algorithm.