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Blended noise suppression using a hybrid median filter, normal moveout and complex curvelet transform approach
Studia Geophysica Et Geodaetica ( IF 0.9 ) Pub Date : 2020-04-06 , DOI: 10.1007/s11200-020-0269-9
Lieqian Dong , Changhui Wang , Mugang Zhang , Deying Wang , Xiaofeng Liang

The high-density acquisition technique can improve subsurface imaging accuracy. However, it increases production cost rapidly and limits the wide application in practice. To solve this issue, the high productivity blending acquisition technology has emerged as a promising way to significantly increase the efficiency of seismic acquisition and reduce production cost. The great challenge of the blending acquisition technology lies in the severe interference noise of simultaneous sources. Therefore, the success of the blending acquisition technology relies heavily on the effectiveness of separating effective energy from the blended noise. We propose a blended noise suppression approach by using a hybrid median filter, normal moveout (NMO), and complex curvelet transform (CCT) approach. First, median filter is applied to original data after NMO correction. Second, the CCT-based thresholding denoising method is used to extract the remained effective energy from the data after median filtering to get the preliminary de-blended result. Next, the updated data are obtained by subtracting the pseudo-de-blended data of the de-blended result from the original data, and the process iterates. Last, the final de-blended result is obtained by adding the retrieved energy at each iteration until the signal-to-noise ratio satisfies the desired level. We demonstrate the effectiveness of the proposed approach on simulated synthetic and field data examples.



中文翻译:

使用混合中值滤波器,法向偏移和复杂Curvelet变换方法的混合噪声抑制

高密度采集技术可以提高地下成像精度。但是,它迅速增加了生产成本,并限制了其在实践中的广泛应用。为了解决这个问题,高生产率的混合采集技术已经成为一种有望显着提高地震采集效率并降低生产成本的有前途的方法。混合采集技术的最大挑战在于同步源的严重干扰噪声。因此,混合采集技术的成功很大程度上取决于将有效能量与混合噪声分离的有效性。我们提出了一种使用混合中值滤波器,法向运动(NMO)和复曲线波变换(CCT)的混合噪声抑制方法。第一,NMO校正后,中值滤波器将应用于原始数据。其次,基于CCT的阈值去噪方法被用来从中值滤波后的数据中提取剩余的有效能量,以获得初步的去混合结果。接下来,通过从原始数据中减去去混合结果的伪去混合数据来获得更新后的数据,并且处理进行迭代。最后,通过在每次迭代中添加所获取的能量,直到信噪比满足所需水平,才能获得最终的混合结果。我们在模拟的合成数据和现场数据示例上证明了该方法的有效性。通过从原始数据中减去去混合结果的伪去混合数据来获得更新后的数据,并且过程进行迭代。最后,通过在每次迭代中添加所获取的能量,直到信噪比满足所需水平,才能获得最终的混合结果。我们在模拟的合成数据和现场数据示例上证明了该方法的有效性。通过从原始数据中减去去混合结果的伪去混合数据来获得更新后的数据,并且过程进行迭代。最后,通过在每次迭代中添加所获取的能量,直到信噪比满足所需水平,才能获得最终的混合结果。我们在模拟的合成数据和现场数据示例上证明了该方法的有效性。

更新日期:2020-04-22
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