The Research Of Spatiotemporal2-D Time-frequency Peak Filtering For Seismic Random Noise Suppression

Seismic prospecting is an important kind of geophysical method for oil, gas and mineralresources. It utilizes manual methods (using explosive or non-explosive hypocenters) tostimulate seismic waves and study the propagation law of seismic waves in strata toascertain the underground geological structure according to rock elasticity. In seismicprospecting, there are lots of noises in collected seismic data because of the influencescaused by the behaviors of people, environments, and instruments and so on. These noisesare interlaced with the related information of underground structure and lithology, andcause interference for the valid information to varying degrees. So this kind of seismicdata could not be used to do geological explanation directly. It must be conducted digitalprocessing and extracted related useful information, and then taken a series of subsequentprocessings. The data processing means of “three highs”(high signal-to-noise ratio (SNR),high resolution and high fidelity) and “one accuracy”(accurate imaging) are important forthe acquisition of valid information. The seismic noise reduction is one of the keyprocedures of seismic data processing. To suppress the seismic noise effectively andimprove the SNR, resolution and fidelity of seismic data are of great importance for theinterpretation of geological structure and exploitation of oil, gas and mineral resources.This article mainly studies the seismic random noise reduction. At present, there are somany effective methods to suppress the seismic random noise, and time-frequency domainalgorithms belong to the kind of method which has been verified and widely used. In thisarticle, we mainly introduce three algorithms of time-frequency domain, Chirplet,empirical mode decomposition (EMD) and time-frequency peak filtering (TFPF). Amongnumerous time-frequency domain methods, TFPF is especially prominent. TFPF is ahighly effective method in random noise reduction, and the conventional1-D TFPF wasapplied to the enhancement of newborn electroencephalogram signals originally and hasbeen applied in the seismic random noise reduction by the modern signal processinglaboratory of Jilin University.This article aims at the shortage in filtering aspect of the conventional1-D TFPF andtakes improvement measures by proposing three improved approaches. First, it aims at apair of contradiction in TFPF itself that is long window length can suppress random noise effectively but produces large attenuation for the amplitude of valid signal, on the contrary,short window length can preserve the amplitude of valid signal well but presents lack ofcapability in random noise reduction. That is to say, there is a large restriction to TFPF bythe selection of window length. If it can be flexible in the window length selection, it willget better filtering results and we need to obtain a good trade-off between the randomnoise reduction and the valid signal preservation. This article adopts EMD method toassist TFPF to obtain the good trade-off between noise suppression and amplitudepreservation. The concrete scheme is that it utilizes the decomposition characteristic ofEMD which can decompose a signal to a series of intrinsic mode functions (IMFs) fromhigh frequency to low frequency. These modes are components of the original signal, andwe judge and find out the modes need to be filtered by computing the correlation degree ofeach other, and then do TFPF for these modes by selecting different window length. Atlast, adding the filtered modes and the residual modes up to get the final filtering signal.This kind of method possesses great flexibility in the selection of window length for TFPF,that is to say, it could select window length for different frequency componentsdiscriminatingly: for the noise dominant components, it selects long window length tofilter, conversely, for the signal dominant components, it selects short window length tofilter, and for the pure signal mode, it doesn’t need to filter. By doing this, it not only cansuppress the random noise effectively, but also can preserve the amplitudes of validsignals. Through experiments on synthetic seismic signals and field seismic data, we haveobtained some comparatively ideal results thereby testified the superiority of this method.Next, this article develops the conventional1-D TFPF to be spatiotemporal2-D TFPF.Thus, it is a big improvement for suppressing seismic random noise, recovering reflectionevents and reducing filtering bias more effectively. The seismic data shows2-D features oftime and space, so we should adopt the filtering methods confirmed to the spatiotemporal2-D features of seismic data and this will bring very good results. In this article, the2-DTFPF method we studied utilizes the time-space correlation of seismic events andimplements spatiotemporal filtering by virtue of Radon transform. Radon transform is amethod which adds the original data up along predefined path, such as line, parabola orhyperbola et al. In view of Radon transform can identify the reflection events of theoriginal seismic records by presenting that these events are focused as energy waveletswith different locations in Radon domain, so it plays a role in representing the direction ofreflection events. Thus, it provides direction for TFPF processing and achieves thepurpose of filtering along the direction of valid reflection events. It breaks the limitation ofthe conventional TFPF which implements filtering along the channel direction and provides a new way for seismic random noise suppression. We demonstrate the validityand practicability of this method through experiments on synthetic seismic signals andfield seismic data, and extend the applicability to a wide range by proposing localspatiotemporal2-D TFPF method.At last, this article introduces the local spatiotemporal2-D TFPF method. Theapplicability of the local method is broader than the global method, so it is more effectivein processing the seismic data with relatively complicated situations of reflection events.The global Radon transform is very applicable to the seismic data with regular geometriesevents but incapable to the seismic data with irregular geometries events or interlacedevents with different slope or curvature parameter. At this time, we need to adopt localmethods to track the characteristic variation trend of the reflection events. The local Radontransform is the form of global Radon with spatiotemporal window actually. Thewindowed Radon transform could provide simplification for the situation of the seismicevents in local areas so that it is convenient to compute by adopting simple superpositionpaths. So, we adopt local Radon transform to identify the reflection events of the originalseismic record firstly, and then do TFPF in local Radon domain. This method is apopularizing way of the spatiotemporal TFPF and increases its universality and flexibilityin seismic random noise suppression. Therefore, it provides a new way for the processingof more complicated seismic data.