دانلود رایگان مقاله انگلیسی تطبیق پیگیری داده های لرزه ای به صورت موازی – الزویر ۲۰۱۷
|عنوان فارسی:||تطبیق پیگیری داده های لرزه ای به صورت موازی|
|عنوان انگلیسی:||Matching pursuit parallel decomposition of seismic data|
|تعداد صفحات مقاله انگلیسی : 23||تعداد صفحات ترجمه فارسی : ترجمه نشده|
|سال انتشار : 2017||نشریه : الزویر - Elsevier|
|فرمت مقاله انگلیسی : PDF||کد محصول : E8150|
|محتوای فایل : PDF||حجم فایل : mb 1|
|رشته های مرتبط با این مقاله: ژئوفیزیک|
|گرایش های مرتبط با این مقاله: لرزه نگاری|
|مجله: کامپیوترها و علوم زمین - Computers and Geosciences|
|دانشگاه: School of Geophysics and Information Technology - China University of Geosciences (Beijing) - China|
|کلمات کلیدی: پیگیری تطبیقی؛ سرعت محاسبه؛ الگوریتم موازی؛ داده های لرزه ای|
In order to improve the computation speed of matching pursuit decomposition of seismic data, a matching pursuit parallel algorithm is designed in this paper. We pick a fixed number of envelope peaks from the current signal in every iteration according to the number of compute nodes and assign them to the compute nodes on average to search the optimal Morlet wavelets in parallel. With the help of parallel computer systems and Message Passing Interface, the parallel algorithm gives full play to the advantages of parallel computing to significantly improve the computation speed of the matching pursuit decomposition and also has good expandability. Besides, searching only one optimal Morlet wavelet by every compute node in every iteration is the most efficient implementation.
The matching pursuit decomposition of seismic data is performed trace by trace. Actually, for a seismic profile or a 3D seismic data volume, the traces can be directly assigned to the compute nodes of parallel computer systems on average to implement decompositions in parallel. We use a seismic profile as shown in Figure 9 to compare this coarse-grained parallel strategy and our parallel algorithm of matching pursuit. The operating environment of these two parallel programs is still an Intel multiprocessor computer whose main frequency of processors is 2.40GHz.
First, those 800 traces are assigned to 1, 2, 4, 8, 16 and 32 processors on average to decompose in parallel, respectively. Then we implement our parallel algorithm to the traces by setting 1, 2, 4, 8, 16, 32 processors, respectively, and searching only one optimal wavelet by every processor. In these two parallel methods, we define the same threshold value to end the decompositions for fair comparisons.
After all the implementations are completed, we calculate the speedup and efficiency of these two methods for different numbers of processors, respectively, as shown in Figure 10. The speedup of the coarse-grained parallel strategy is apparently lower than that of our parallel algorithm of matching pursuit, and the difference between these two is increased with the increasing number of processors. Meanwhile, the overall efficiency of the coarse-grained parallel strategy is also much lower than that of our parallel algorithm. That’s because the number of iterations and optimal Morlet wavelets of seismic traces are unknown before the decomposition according to the matching pursuit algorithm. The coarse-grained parallel strategy easily leads to uneven jobs for compute nodes even though we assign the same number of traces to every compute node, affecting the overall efficiency of the parallel computing.