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野外采集的地震勘探原始记录中通常包含多种噪声。噪声的存在降低了地震资料的信噪比,影响地震资料处理成果品质,因此,去噪方法研究和应用是处理工作的重要环节之一。“加权中值滤波自动检测并压制强能量干扰方法”是一种在准噶尔盆地油气勘探中非常有效的噪声压制方法。该方法在地震数据原始记录频率域剖面上采用加权中值滤波的方法自动检测可能存在的强能量干扰,并针对性地对相应频段上的噪声信号进行压制,去噪效果较为理想。但该方法的算法运行过程中涉及大量的数据计算,开发的程序需要花费大量时间才能完成一次去噪过程。提高计算效率成为该噪声压制方法推广应用的关键。高质量图像处理用途的高端图形处理器(GPU)在大规模高带宽计算方面表现出色,近年来更多地应用于高性能计算工作。CUDA并行计算开发平台帮助应用人员开发高效率计算程序,使GPU能更容易应用于高性能计算。通过分析“加权中值滤波自动检测并压制强能量干扰方法”算法实现方式,发现该算法适宜利用GPU进行并行化改造。利用CUDA并行编程技术将该算法中部分串行执行的数据计算过程改造成适合GPU计算的并行计算过程,使整个去噪方法工作效率提升3倍。GPU并行计算技术能使油气勘探数据处理过程中类似应用有效并行化,利用较小成本实现高效计算效率。
The original seismic acquisition collected in the field usually contains a variety of noise. The existence of noise reduces the signal-to-noise ratio of seismic data and affects the quality of seismic data processing results. Therefore, the research and application of denoising methods are one of the important processes in the processing. “Weighted median filtering automatic detection and suppression of strong energy interference ” is a very effective noise suppression method in oil and gas exploration in the Junggar Basin. This method uses weighted median filtering method to automatically detect possible strong energy disturbances in the original recorded frequency domain profile of seismic data, and aims to suppress the noise signals in the corresponding frequency bands in a targeted manner. The denoising effect is ideal. However, the algorithm involved in this method runs a large amount of data and the developed program takes a long time to complete a denoising process. Increasing computational efficiency has become the key to popularizing and applying this noise suppression method. High-end graphics processors (GPUs) for high-quality image processing applications excel in large-scale, high-bandwidth computing and have been increasingly used in high-performance computing in recent years. CUDA parallel computing development platform to help applications developers to develop efficient computing programs, so that GPU can be more easily applied to high-performance computing. By analyzing the implementation of the method of “Automatic Detection and Suppression of Strong Energy Interference” by Weighted Mean Median Filter, it is found that the algorithm is suitable for GPU parallel transformation. The CUDA parallel programming technique is used to transform the partial serial data execution in the algorithm into a parallel computing process which is suitable for GPU computing. The efficiency of the whole denoising method is improved by three times. GPU parallel computing enables efficient parallelization of similar applications in the processing of oil and gas exploration data, enabling efficient computational efficiency with less cost.