【摘要】 在对振幅值动态范围分布较大的地震数据进行强能量去噪处理时,针对常规方法通常会面临的阈值求取不准导致效果不理想、需要反复测试模块参数以及需要多轮迭代联合去噪才能达到预期效果等问题,本文提出了基于数据驱动的分贝准则小波域强能量振幅压制方法。与常规方法相比,该方法不直接对异常强振幅能量值进行统计分析,而是对振幅的能量级指数进行统计分析来确定去噪阈值,即分贝判定准则。本文采用小波变换在时频域选取最佳有效信号分布时窗进行阈值统计,然后分频压制,以进一步提升去噪效果。理论和实际数据测试表明,该方法能有效压制地震数据中的强能量振幅,对强能量振幅分布动态范围适应广、时窗依赖程度低、保幅性好,具有良好的应用前景。更多还原

【Abstract】 To suppress the strong noise in seismic data with wide range of amplitudes, commonly used methods often yield unsatisfactory denoising results owing to inappropriate thresholds and require parametric testing as well as iterations to achieve the anticipated results. To overcome these problems, a data-driven strong amplitude suppression method based on the decibel criterion in the wavelet domain(ISANA) is proposed. The method determines the denoising threshold based on the decibel criterion and statistically analyzes the amplitude index rather than the abnormally high amplitudes. The method distinguishes the frequency band distributions of the valid signals in the time–frequency domain based on the wavelet transformation and then calculates thresholds in selected time windows, eventually achieving frequency-divided noise attenuation for better denoising. Simulations based on theoretical and real-world data verify the adaptability and low dependence of the method on the size of the time window. The method suppresses noise without energy loss in the signals.更多还原