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地震波能量补偿的并行反Q滤波方法研究
引用本文:张全,王一品,张伟,彭博,胥林.地震波能量补偿的并行反Q滤波方法研究[J].西南石油大学学报(自然科学版),2023,45(1):24-32.
作者姓名:张全  王一品  张伟  彭博  胥林
作者单位:1. 西南石油大学计算机科学学院, 四川 成都 610500;2. 电子科技大学信息与通信工程学院, 四川 成都 611731;3. 西南石油大学信息学院, 四川 南充 637001
基金项目:油气藏地质及开发工程国家重点实验室开放基金(PLN2022-51,PLN2021-21,PLN2021-25)
摘    要:在石油勘探地震资料处理中,反Q滤波方法能有效地对地震波进行振幅补偿和相位校正,为地震反演和储层预测提供更准确的信息。对于大规模的地震道集数据处理,反Q滤波方法在CPU计算平台上执行时间较长,影响了地震解释的效率。分析发现,反Q滤波方法大量时间消耗在振幅相位补偿与短时傅里叶变换。在GPU平台上,首先,对振幅相位补偿部分进行并行化;其次,对批量短时傅里叶变换用CUFFT库进行加速;最后,对批量短时傅里叶变换进一步优化并将其应用于反Q滤波方法。实验结果表明,相比CPU计算环境,基于CUFFT库的反Q滤波并行算法效率提升了3.9倍,优化后的批量短时傅里叶变换进一步将效率提升了12%。

关 键 词:反Q滤波  振幅补偿  傅里叶变换  并行计算  计算统一设备体系结构  
收稿时间:2021-02-03

Research on Parallel Inverse Q Filtering Methods for Seismic Wave Energy Compensation
ZHANG Quan,WANG Yipin,ZHANG Wei,PENG Bo,XU Lin.Research on Parallel Inverse Q Filtering Methods for Seismic Wave Energy Compensation[J].Journal of Southwest Petroleum University(Seience & Technology Edition),2023,45(1):24-32.
Authors:ZHANG Quan  WANG Yipin  ZHANG Wei  PENG Bo  XU Lin
Institution:1. School of Computer Science, Southwest Petroleum University, Chengdu, Sichuan 610500, China;2. School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China;3. School of Information, Southwest Petroleum University, Nanchong, Sichuan 637001, China
Abstract:In seismic data processing of petroleum exploration, the inverse Q filtering method can effectively perform amplitude compensation and phase correction on seismic waves to provide more accurate information for seismic inversion and reservoir prediction. In large-scale seismic data processing, the inverse Q filtering method takes longer operation time under the CPU computing platform, which affects the efficiency of seismic interpretation. After analysis, it is found that the inverse Q filtering method consumes a lot of time in the short-time Fourier transform and calculates the amplitude and dispersion compensation terms. On the GPU platform, we first parallelizes the amplitude and dispersion compensation calculations, and accelerates the batch short-time Fourier transform with the CUFFT library, and then further optimizes the batch short-time Fourier transform and applies it to the inverse Q filtering method. The results show that compared with the CPU computing environment, the efficiency of the inverse Q filtering parallel algorithm based on the CUFFT library is improved by 3.9 times, and the optimized batch short-time Fourier transform further improves the efficiency of the parallel inverse Q filtering method by 12%.
Keywords:Inverse Q filtering  amplitude compensation  Fourier transform  parallel computing  CUDA  
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