CN108680958B - Seismic data noise reduction method based on peak value transformation - Google Patents
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Abstract
The invention discloses a seismic data noise reduction method based on peak value transformation, and belongs to the field of oil and gas geophysical exploration. Carrying out peak value transformation on the seismic data to obtain a signal after the peak value transformation; performing wavelet decomposition on the data after the peak value transformation, performing threshold denoising processing on the obtained wavelet component, and performing wavelet reconstruction on the obtained new wavelet component; and obtaining the seismic data after noise reduction processing through inverse peak value transformation. The method provided by the invention processes the seismic data by combining the peak value transformation and the wavelet analysis, realizes the effect of suppressing the noise of the seismic data, and the processed signals can meet the processing and interpretation work of deep seismic data.
Description
Technical Field
The invention belongs to the field of oil and gas geophysical exploration, and particularly relates to a seismic data noise reduction method based on peak value transformation.
Background
With the continuous deepening of oil and gas exploration, the structure and environment of an exploration target area become more and more complex, and seismic wave data acquired by a seismic receiver inevitably contain some interference noise due to the influences of factors such as observation environment, measurement error, ground micro-vibration and the like, so that the processing and interpretation work of seismic data is seriously influenced, and the judgment of the geological structure and the oil and gas reservoir condition of the target area is influenced. Therefore, it is often necessary to perform noise reduction processing on the acquired seismic data so that the effective seismic information is clearer and the seismic data processing and interpretation are facilitated.
Disclosure of Invention
The invention aims to provide a seismic data noise reduction method based on peak value transformation, which makes up the defects of the existing seismic data noise reduction method and provides a more effective seismic processing method.
In order to achieve the purpose, the technical scheme adopted by the invention is a seismic data noise reduction method based on peak value transformation, and the method comprises the following steps:
s1, carrying out peak value transformation on the seismic data to obtain a peak value transformed signal;
s2, performing wavelet decomposition on the data after the peak value transformation, performing threshold denoising processing on the obtained wavelet component, and performing wavelet reconstruction on the obtained new wavelet component;
and S3, obtaining the seismic data after noise reduction processing through inverse peak value transformation.
The peak value transformation in the step S1 refers to selecting n major inflection points of the seismic data, i.e., peak points, as truncation points, dividing the signal into n-1 segments, splicing the odd-numbered signal segments and the even-numbered signal segments together, and recording the position information of the peak points.
The wavelet decomposition in S2 refers to performing wavelet decomposition on the peak-transformed signal, selecting an appropriate wavelet and determining a decomposition level, performing threshold denoising processing on a high-frequency coefficient, i.e., a detail component, of the wavelet decomposition, and reconstructing the processed wavelet coefficient.
The inverse peak transform in S3 refers to connecting the line segments in the order in the "peak transform" position information in S1 (inverse peak transform).
The method provided by the invention processes the seismic data by combining the peak value transformation and the wavelet analysis, realizes the effect of suppressing the noise of the seismic data, and the processed signals can meet the processing and interpretation work of deep seismic data.
Drawings
FIG. 1 is a flow chart of a method for peak transform based seismic data noise reduction according to the present invention.
FIG. 2 is a graph of raw seismic data.
FIG. 3 is a graph of denoised seismic data.
Fig. 4 is a filtered noise map.
Detailed Description
The following further description of the present invention, in order to facilitate understanding of those skilled in the art, is provided in conjunction with the accompanying drawings and is not intended to limit the scope of the present invention. In the present application, the embodiments and various aspects of the embodiments may be combined with each other without conflict. The drawings in the following description are only some embodiments of the invention and other drawings may be derived from those drawings by a person skilled in the art without inventive effort.
Referring to fig. 1, the invention provides a seismic data noise reduction method based on peak value transformation, comprising the following steps:
And step 306, arranging the reciprocal signals obtained by inverse peak value transformation according to the original trace order to obtain the seismic wave image after peak value transformation processing.
Setting a seismic signal containing noise as:
s(n)＝f(n)+σe(n) (1)
in the formula (1), s (n) is the collected seismic signal, f (n) represents the seismic signal containing effective information, e (n) is noise, and sigma is the noise intensity. The wavelet transform is to suppress e (n) to recover f (n), thereby achieving the purpose of removing noise.
Wavelet transformation mathematical process:
fkis discretely sampled data of signal f (t), fk＝c0,kThen, the decomposition formula of the orthogonal wavelet transform of the signal f (t) is:
wherein, cj,kIs a scale factor; dj,kIs a wavelet coefficient; h. g is a pair of Quadrature Mirror Filterbanks (QMF); j is the number of decomposition layers; n is the number of discrete sampling points;
the wavelet reconstruction process is the inverse operation of the decomposition process, and the corresponding reconstruction formula is
Wavelet threshold denoising process:
s1 calculates an orthogonal wavelet transform of the noisy signal. Selecting proper wavelet and wavelet decomposition layer number j, and performing wavelet decomposition on the noise-containing signal to j layers by using a formula (2) to obtain a corresponding wavelet decomposition coefficient.
S＝cA1+cD1＝cA2+cD2+cD1＝cA3+cD3+cD2+cD1＝...＝cAj+cDj+...+cD3+cD2+cD1 (4)
Where cAi is the approximate part of the decomposition and cDi is the detailed part of the decomposition. J, the noise part is usually contained in cD1, cD2, and cD3, and wavelet coefficients are processed by using a threshold, and the reconstructed signal can be used for denoising.
S2, performing threshold processing on the wavelet coefficient obtained by decomposition, wherein the threshold processing method comprises two methods:
hard threshold method:
soft threshold method:
and denoising by adopting a soft threshold, wherein the used threshold principle is a heuristic threshold principle.
And S3, performing wavelet inverse transformation. And (4) reconstructing the wavelet coefficient subjected to threshold processing by using a formula (3) to obtain a restored original signal estimation value.
Processing the heuristic threshold principle, namely the signal-to-noise ratio is small according to an unbiased likelihood estimation principle; when the signal noise is large, a fixed threshold form is adopted.
In this embodiment, it should be noted that the above steps can be implemented by computer programming, so as to further implement the denoising enhancement effect of the seismic wave data signal.
The analysis of the processing result can refer to fig. 2, fig. 3 and fig. 4, where fig. 2 is the original seismic data, fig. 3 is the seismic data after the seismic processing method/apparatus provided by the present application, fig. 4 is the noise filtered by peak value transformation, and it can be seen by comparing fig. 2 and fig. 3 that the noise of the seismic wave image is reduced and the information originally covered by the noise is revealed, and the denoising effect is obvious.
Although the present application provides method steps as described in an embodiment or flowchart, more or fewer steps may be included based on conventional or non-inventive means. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. When an apparatus or client product in practice executes, it may execute sequentially or in parallel (e.g., in a parallel processor or multithreaded processing environment, or even in a distributed data processing environment) according to the embodiments or methods shown in the figures. The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, the presence of additional identical or equivalent elements in a process, method, article, or apparatus that comprises the recited elements is not excluded.
The devices or modules and the like explained in the above embodiments may be specifically implemented by a computer chip or an entity, or implemented by a product with certain functions. For convenience of description, the above devices are described as being divided into various modules by functions, and are described separately. Of course, in implementing the present application, the functions of each module may be implemented in one or more pieces of software and/or hardware, or a module that implements the same function may be implemented by a combination of a plurality of sub-modules, and the like. The above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical division, and other divisions may be realized in practice, for example, a plurality of modules or components may be combined or integrated into another system, or some features may be omitted, or not executed.
Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may therefore be considered as a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.
The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, classes, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
From the above description of the embodiments, it is clear to those skilled in the art that the present application can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solutions of the present application may be essentially implemented or contribute to the prior art in the form of a software product, which may be stored in a storage medium, such as R0M/RAM, a magnetic disk, an optical disk, etc., and includes several instructions for causing a computer device (which may be a personal computer, a mobile terminal, a server, or a network device, etc.) to execute the method described in the embodiments or some parts of the embodiments of the present application.
The embodiments in the present specification are described in a progressive manner, and the same or similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. The application is operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet-type devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.
While the present application has been described with examples, those of ordinary skill in the art will appreciate that there are numerous variations and permutations of the present application without departing from the spirit of the application, and it is intended that the appended claims encompass such variations and permutations without departing from the present application.
Claims (2)
1. A seismic data noise reduction method based on peak value transformation is characterized in that: the method comprises the steps of (1) carrying out,
step 300, decomposing the acquired seismic data according to the number of tracks;
step 301, performing peak value transformation processing on the acquired seismic wave data to convert high-frequency signals into low-frequency signals;
step 302, performing wavelet decomposition on the signal after the peak value transformation, selecting a proper wavelet basis and a proper decomposition layer number, and decomposing the signal into an approximate component and a detail component;
step 303, selecting a proper threshold value, and performing zero setting processing on a part of wavelet coefficients to obtain a new wavelet component;
step 304, reconstructing the obtained new wavelet component to obtain a time domain signal of peak value transformation arranged according to a maximum value and a minimum value;
step 305, performing inverse peak value transformation on the denoised peak value transformation signal according to the position information stored before;
step 306, arranging reciprocal signals obtained by inverse peak value transformation according to an original trace sequence to obtain a seismic wave image after peak value transformation processing;
setting a seismic signal containing noise as:
s(n)＝f(n)+σe(n) (1)
in the formula (1), s (n) is an acquired seismic signal, f (n) represents a seismic signal containing effective information, e (n) is noise, and sigma is noise intensity; the wavelet transform is to suppress e (n) to recover f (n), thereby achieving the purpose of removing noise;
wavelet transformation process:
fkis discretely sampled data of signal f (t), fk＝c0,kThen, the decomposition formula of the orthogonal wavelet transform of the signal f (t) is:
wherein, cj,kIs a scale factor; dj,kIs a wavelet coefficient; h. g is a pair of quadrature mirror filterbanks QMF; j is the number of decomposition layers; n is the number of discrete sampling points;
the wavelet reconstruction process is the inverse operation of the decomposition process, and the corresponding reconstruction formula is
Wavelet threshold denoising process:
selecting proper wavelets and wavelet decomposition layer number j, and performing wavelet decomposition on the noise-containing signals to the layer j by using the formula (2) to obtain corresponding wavelet decomposition coefficients;
S＝cA1+cD1＝cA2+cD2+cD1＝cA3+cD3+cD2+cD1＝...＝cAj+cDj+...+cD3+cD2+cD1(4)
where cAi is the approximate part of the decomposition and cDi is the detailed part of the decomposition; j, the noise part is usually contained in cD1, cD2, and cD3, wavelet coefficients are processed by using a threshold, and the reconstructed signal can achieve the purpose of denoising;
the wavelet coefficient obtained by decomposition is subjected to threshold processing, and the threshold processing method comprises two methods: hard threshold method:
soft threshold method:
denoising by adopting a soft threshold, wherein the used threshold principle is a heuristic threshold principle;
and (4) performing wavelet inverse transformation, and reconstructing the wavelet coefficient subjected to threshold processing by using a formula (3) to obtain a restored original signal estimation value.
2. The method of claim 1 for peak transform-based seismic data noise reduction, wherein: processing the heuristic threshold principle, namely the signal-to-noise ratio is small according to an unbiased likelihood estimation principle; when the signal noise is large, a fixed threshold form is adopted.
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