Research On Seismic Random Noise Attenuation Method Based On Adaptive Fractal Conservation Law

Seismic exploration is an important means to explore the underground oil and gas resources reserves,and as an important strategic resource oil and gas resources are of great practical significance to support the rapid development of the national economy and ensure national energy security.At the same time,the reserve recoverable reserves of oil and gas resources in China are small,especially the recoverable reserves of high-quality oil resources are seriously insufficient,and the situation of oil and gas resources security facing China is very serious.In order to break the energy dilemma faced by China now,there is an urgent need to exploit the oil and gas resources buried in deep,thin and irregular strata,thus also putting forward higher requirements on the quality of exploration records.However,due to the influence of acquisition environment and acquisition conditions,the seismic records are often mixed with a large amount of random interference,showing obvious "weak signal,strong noise" characteristics,with complex noise types and high noise levels,which are difficult to reduce and bring adverse effects on the subsequent processing of the records.Therefore,it is of great importance to effectively suppress the random noise in seismic exploration data and improve the signal-to-noise ratio of exploration records in industrial exploration.For the complex noise suppression problem in seismic survey records,a large number of random noise suppression algorithms have been proposed by domestic and foreign researchers,such as Wiener filtering,f-x predictive filtering and time-frequency peak filtering(TFPF,Time frequency peak filtering).However,traditional noise reduction algorithms are not ideal for the reduction of complex random noise,and often have problems such as incomplete suppression of noise and low signal recovery accuracy.In view of the limitations of traditional methods and the demand for denoising of exploration records,this project combines the adaptive conservation law(FCL)with the smoothness characteristics of noise,and proposes a noise reduction algorithm based on the adaptive fractal conservation law,and successfully achieves the effective suppression of complex seismic random noise.In this study,the adaptive noise reduction framework is designed from the difference of signal and noise in smoothness,and the signal and noise regions are identified by the smoothness test method to obtain a reliable prior about the signal and noise regions.On this basis,the FCL algorithm is used to determine the filtering parameters of the signal and noise regions adaptively by combining the signal and noise characteristics to achieve effective suppression of the noise part and reliable reconstruction of the signal part,which improves the accuracy and intelligence of complex exploration data processing.To verify the performance of the algorithm,the proposed noise attenuation method is used to process simulated records and actual information,and the noise attenuation performance is compared with the classical noise cancellation algorithm.The results show that the proposed method can effectively improve the signal-to-noise ratio of the processed records and recover the weak reflection information that is drowned by strong random noise.Compared with the comparison methods,this paper has stronger noise cancellation ability and the recovered signal has obvious advantages in smoothness and continuity and the SNR has been improved more than15 d B.In summary,the research results of this paper are an active attempt to solve the problem of processing complex exploration data,which can help to improve the research related to noise attenuation algorithms and have good practical significance and application prospects.