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摘要: 反褶积是提高地震数据分辨率的重要方法。然而, 传统的反褶积方法在增强地震信号高频成分的同时, 也放大了高频噪声的能量, 降低了反褶积之后地震记录的信噪比。分辨率和信噪比的矛盾制约了现有反褶积方法表征薄层结构的能力。为此, 本文提出了一种信号自适应识别多道反褶积算法。该方法从原始地震数据中提取了地震信号识别算子, 并将其作为空间正则化约束引入多道反褶积的目标函数, 在一定程度上实现了具有信号自适应识别能力的高分辨率处理技术。基于地震信号的空间可预测性, 地震信号识别算子从地震数据本身进行估算和提取, 对地震记录具有较强的自适应性能力。模型数据与实际数据的测试分析表明, 本文方法能够有效地抑制高频噪声在反褶积过程中的放大效应, 在提高了分辨率的同时, 较好地保持了地震记录信噪比。Abstract: Deconvolution plays a critical role in enhancing the resolution of seismic data.However, conventional deconvolution methods, though boosting the high-frequency components of seismic signals, amplify the energy of high-frequency noise, thereby reducing the signal-to-noise ratios(SNRs) of seismic records after deconvolution.The contradiction between resolution and SNRs restricts the ability of existing deconvolution methods to characterize thin-layer structures.Hence, this study proposed a multi-channel deconvolution method for self-adaptive signal recognition.The method extracted seismic signal recognition operators from raw seismic data.It introduced them as spatial regularization constraints into the objective function of multi-channel deconvolution, somewhat achieving high-resolution processing with self-adaptive signal recognition capabilities.Based on the spatial predictability of seismic signals, their recognition operators were estimated and extracted directly from seismic data, demonstrating high adaptability to seismic records.As indicated by the test analysis of the model and actual data, the proposed method can effectively suppress the amplification effect of high-frequency noise during deconvolution, thus improving resolution and maintaining the SNRs of seismic records.
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Key words:
- deconvolution /
- signal recognition /
- high resolution /
- seismic data
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