在示波器上使用DSP滤波技术的优点和缺点

本文介绍了不同DSP滤波器的用途,以及相关的优点和缺点。

An adaptive noise attenuation method for edge and amplitude preservation

Noise intensity distributed in seismic data varies with different frequencies or frequency bands; thus, noise attenuation on the full-frequency band affects the dynamic properties of the seismic reflection signal and the subsequent seismic data interpretation, reservoir description, hydrocarbon detection, etc. Hence, we propose an adaptive noise attenuation method for edge and amplitude preservation, wherein the wavelet packet transform is used to decompose the full-band seismic signal into multiband data and then process these data using nonlinear anisotropic dip-oriented edge-preserving fi ltering. In the fi ltering, the calculated diffusion tensor from the structure tensor can be exploited to establish the direction of smoothing. In addition, the fault confidence measure and discontinuity operator can be used to preserve the structural and stratigraphic discontinuities and edges, and the decorrelation criteria can be used to establish the number of iterations. These parameters can minimize the intervention and subjectivity of the interpreter, and simplify the application of the proposed method. We applied the proposed method to synthetic and real 3D marine seismic data. We found that the proposed method could be used to attenuate noise in seismic data while preserving the effective discontinuity information and amplitude characteristics in seismic refl ection waves, providing high-quality data for interpretation and analysis such as high-resolution processing, attribute analysis, and inversion.

Random noise attenuation by f–x spatial projection-based complex empirical mode decomposition predictive filtering

The frequency–space（f–x） empirical mode decomposition（EMD） denoising method has two limitations when applied to nonstationary seismic data. First, subtracting the first intrinsic mode function（IMF） results in signal damage and limited denoising. Second, decomposing the real and imaginary parts of complex data may lead to inconsistent decomposition numbers. Thus, we propose a new method named f–x spatial projection-based complex empirical mode decomposition（CEMD） prediction filtering. The proposed approach directly decomposes complex seismic data into a series of complex IMFs（CIMFs） using the spatial projection-based CEMD algorithm and then applies f–x predictive filtering to the stationary CIMFs to improve the signal-to-noise ratio. Synthetic and real data examples were used to demonstrate the performance of the new method in random noise attenuation and seismic signal preservation.

Inversion-based data-driven time-space domain random noise attenuation method

Conventional time-space domain and frequency-space domain prediction filtering methods assume that seismic data consists of two parts, signal and random noise. That is, the so-called additive noise model. However, when estimating random noise, it is assumed that random noise can be predicted from the seismic data by convolving with a prediction error filter. That is, the source-noise model. Model inconsistencies, before and after denoising, compromise the noise attenuation and signal-preservation performances of prediction filtering methods. Therefore, this study presents an inversion-based time-space domain random noise attenuation method to overcome the model inconsistencies. In this method, a prediction error filter （PEF）, is first estimated from seismic data; the filter characterizes the predictability of the seismic data and adaptively describes the seismic data＇s space structure. After calculating PEF, it can be applied as a regularized constraint in the inversion process for seismic signal from noisy data. Unlike conventional random noise attenuation methods, the proposed method solves a seismic data inversion problem using regularization constraint; this overcomes the model inconsistency of the prediction filtering method. The proposed method was tested on both synthetic and real seismic data, and results from the prediction filtering method and the proposed method are compared. The testing demonstrated that the proposed method suppresses noise effectively and provides better signal-preservation performance.