Methods for improving the signal-to-noise ratio of acoustic wave long-range detection data

Abstract

The present invention provides a method for improving the signal-to-noise ratio of acoustic wave long-distance detection data, and the method for improving the signal-to-noise ratio of acoustic wave long-distance detection data comprises: step 1: collecting acoustic wave long-distance detection data; step 2: selecting a seismic data; step 3: selecting the first sampling point of the seismic data; step 4: calculating the square of the amplitude of the sampling point; step 5: calculating the square of the amplitude of all sampling points of the seismic data; step 6: processing all data according to steps 2-5; step 7: performing filtering processing; step 8: outputting the acoustic wave long-distance detection result profile. The method for improving the signal-to-noise ratio of acoustic wave long-distance detection data improves the acoustic wave data imaging signal-to-noise ratio while filtering out noise without losing useful signals, and can achieve the improvement of the acoustic wave long-distance detection data imaging signal-to-noise ratio.

Description

Technical Field

[0001] The invention relates to the field of oil and gas exploration acoustic wave long-range detection data processing, and in particular to a method for improving the signal-to-noise ratio of acoustic wave long-range detection data. Background Art

[0002] There are two methods to improve the signal-to-noise ratio: the frequency filtering method based on the difference in the frequency range of the signal and the noise, and the frequency wavenumber filtering method based on the difference in the apparent speed of the signal and the noise. The frequency filtering method of noise first performs Fourier transform on the data to transform the data from the time domain to the frequency domain, and then separates the signal and noise in the frequency domain according to the different frequency distributions of the signal and the noise. Finally, the inverse Fourier transform is performed to transform the data from the frequency domain to the time domain to complete the frequency filtering of the data. In the case where the frequency distribution of the noise and the signal does not overlap, frequency filtering can achieve ideal results, and the signal-to-noise ratio of the data is high. If the frequency distribution of the noise and the signal overlaps, the frequency filtering will lose part of the effective signal while filtering out the noise, and the frequency filtering effect is not ideal. The frequency wavenumber filtering method first performs a two-dimensional Fourier transform on the data to transform the data from the time-space domain to the frequency wavenumber domain, then separates the signal and noise in different frequency wavenumber domains according to their different apparent velocities, and finally performs an inverse Fourier transform to transform the data from the frequency wavenumber domain to the time-space domain to complete the frequency wavenumber filtering of the data. When the apparent velocities of the noise and the signal do not overlap, the frequency wavenumber filtering can achieve ideal results and the signal-to-noise ratio of the data is high. If the apparent velocities of the noise and the signal overlap, the frequency wavenumber filtering will lose part of the effective signal while filtering out the noise, and the frequency wavenumber filtering effect is not ideal.

[0003] In the Chinese patent application with application number: CN200710052423.8, a method for detecting the first wave of the acoustic wave transmission method is involved, which determines the width of the front time interval and the width of the rear time interval according to the resonant frequency of the acoustic wave sensor; calculates the average absolute value of the front time interval of the acoustic wave waveform, and records the average absolute value of the front time interval; calculates the average absolute value of the rear time interval of the acoustic wave waveform at each moment, and records the maximum value of the average absolute value of the rear time interval; calculates the characteristic amplitude ratio at each moment, and records the maximum value of the characteristic amplitude ratio; calculates the equivalent signal-to-noise ratio; determines the existence of the first wave according to the numerical relationship between the maximum value of the characteristic amplitude ratio and the equivalent signal-to-noise ratio; when it is determined that the first wave exists, the half-wave where the data corresponding to the maximum value is located is the detected first wave.

[0004] In the Chinese patent application with application number: CN201710271863.6, a sound wave communication processing method, device and electronic device are involved. After obtaining the effective sound signal of the current scene, since the effective sound signal includes the ambient sound signal of the current scene and the sound wave signal played by the electronic device, the present application calculates the signal-to-noise ratio of the sound wave communication in the current scene by obtaining the corresponding ambient sound energy value and sound wave energy value at at least one frequency point in the effective sound signal, and then adjusts the sound wave configuration parameters of the sound player based on the signal-to-noise ratio to ensure that the amplitude of the sound wave signal played by the sound player reconfigured according to the adjusted sound wave configuration parameters adapts to the current scene, avoids the played sound wave signal from being masked by the ambient noise, ensures that the receiver can effectively detect the sound wave signal, and improves the correctness and reliability of the resulting transmission data.

[0005] In the Chinese patent application with application number: CN201310484692.7, a method for picking up well-constrained velocity spectrum of low signal-to-noise ratio seismic data is involved, which belongs to the field of seismic data processing. The method includes: (1) well logging preprocessing: correcting the input acoustic wave time difference or acoustic wave velocity to obtain the corrected acoustic wave velocity; (2) performing shallow layer compensation and downward extension processing on the corrected acoustic wave velocity of acoustic logging data to obtain the acoustic wave velocity after shallow layer compensation and downward extension; (3) performing time-depth conversion on the acoustic wave velocity after shallow layer compensation and downward extension to obtain layer velocity, and then performing root mean square velocity conversion on the layer velocity to obtain the root mean square velocity; (4) obtaining the projection of the root mean square velocity on the velocity spectrum.

[0006] The above existing technologies are quite different from the present invention and fail to solve the technical problem we want to solve. Therefore, how to filter out noise without losing signal is an inevitable requirement for improving the signal-to-noise ratio of acoustic wave long-range detection data imaging. To this end, we invented a method for improving the signal-to-noise ratio of acoustic wave long-range detection data to solve the above technical problems. Summary of the invention

[0007] The purpose of the present invention is to provide a method for improving the signal-to-noise ratio of acoustic wave data imaging by filtering out noise without losing useful signals, and to achieve a method for improving the signal-to-noise ratio of acoustic wave long-range detection data imaging.

[0008] The object of the present invention can be achieved by the following technical measures: a method for improving the signal-to-noise ratio of acoustic wave long-range detection data, the method for improving the signal-to-noise ratio of acoustic wave long-range detection data comprising:

[0009] Step 1: Collect acoustic wave remote detection data;

[0010] Step 2: Select a seismic data;

[0011] Step 3: Select the first sampling point of the seismic data;

[0012] Step 4: Calculate the square of the amplitude of the sampling point;

[0013] Step 5: Calculate the square of the amplitude of all sampling points of the seismic data;

[0014] Step 6: Process all data according to steps 2-5;

[0015] Step 7: Perform filtering;

[0016] Step 8: Output the acoustic wave long-range detection result profile.

[0017] The purpose of the present invention can also be achieved by the following technical measures:

[0018] In step 1, the collected acoustic wave long-range detection data is an acoustic wave signal with certain morphological regular characteristics.

[0019] In step 2, a seismic data is selected. The seismic data is composed of a superposition of effective signal and noise. The effective signal is a signal with a frequency of f. 1 ,...f n It is generated by superposition of sine waves.

[0020] In step 2, the seismic data selected are:

[0021] x=s+n

[0022]

[0023] x is the amplitude of seismic data, s is the signal amplitude, n is the noise amplitude, f is the frequency of the s amplitude spectrum, is the initial phase corresponding to frequency f in the s phase spectrum.

[0024] In step 3, the first sampling point of the seismic data is selected, and the amplitude of the sampling point is generated by superimposing the first amplitude of the sine wave of f and the amplitude of the noise.

[0025] In step 3, the sampling point is:

[0026] x(t 1 )=s(t 1 )+n(t 1 )

[0027]

[0028] x(t 1 ) is the amplitude of the first sampling point of the seismic data, s(t 1 ) is the amplitude of the first sampling point of the signal, n(t 1) is the amplitude of the first sampling point of noise, t 1 The time corresponding to the first sampling point, f is the frequency of the s amplitude spectrum, is the initial phase corresponding to frequency f in the s phase spectrum.

[0029] In step 4, the square of the amplitude of the sampling point is calculated using the following formula:

[0030] A 1 =A·A

[0031] A 1 is the output amplitude value, A is the input amplitude value;

[0032] A 1 =x=s+n

[0033]

[0034] According to the frequency shift formula of the signal, we can get

[0035]

[0036] x is the amplitude of seismic data, s is the signal amplitude, n is the noise amplitude, f is the frequency of the s amplitude spectrum, is the initial phase corresponding to frequency f in the s phase spectrum.

[0037] In step 5, the square of the amplitude of all sampling points of the data is calculated, and the formula is as follows:

[0038] A 1i =A i ·A i

[0039] i=1,2,3...,N

[0040]

[0041] x 1i is the i-th output amplitude of seismic data, s is the signal amplitude, n is the noise amplitude, f is the frequency of the s amplitude spectrum, is the initial phase corresponding to frequency f in the s phase spectrum;

[0042] A 1i is the i-th output amplitude value, A i is the i-th input amplitude value, N is the number of sampling points, and n is 1.

[0043] In step 7, when performing filtering processing, a zero-phase low-frequency filter is selected to output low-frequency data.

[0044] The method for improving the signal-to-noise ratio of acoustic wave long-distance detection data in the present invention utilizes the multiplication of the amplitude of seismic data according to the frequency shift formula of the signal. After the multiplication, the seismic data undergoes frequency shift, and the frequencies are respectively shifted to 0 Hz and 2 times the original frequency. The amplitude of random noise becomes 0 in the product, and the frequency of 0 Hz is filtered by a low-pass filter, so that the signal-to-noise ratio is enhanced. The frequency shift processing method of seismic data can improve the signal-to-noise ratio of acoustic wave long-distance detection data imaging. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 A flowchart of a specific embodiment 1 of a method for improving the signal-to-noise ratio of acoustic wave long-range detection data of the present invention;

[0046] Figure 2 It is a schematic diagram of data that is not processed by the present invention in a specific embodiment 1 of the present invention;

[0047] Figure 3 This is a schematic diagram of data processed by the present invention in a specific embodiment 1 of the present invention;

[0048] Figure 4 A flowchart of a specific embodiment 2 of a method for improving the signal-to-noise ratio of acoustic wave long-range detection data of the present invention;

[0049] Figure 5 It is a schematic diagram of data that is not processed by the present invention in a specific embodiment 2 of the present invention;

[0050] Figure 6 It is a schematic diagram of data processed by the present invention in a specific embodiment 2 of the present invention;

[0051] Figure 7 A flowchart of a specific embodiment 3 of a method for improving the signal-to-noise ratio of acoustic wave long-range detection data of the present invention;

[0052] Figure 8 It is a schematic diagram of data that is not processed by the present invention in a specific embodiment 3 of the present invention;

[0053] Fig. 9 It is a schematic diagram of data processed by the present invention in a specific embodiment 3 of the present invention. DETAILED DESCRIPTION

[0054] In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings.

[0055] The present invention provides a method for improving the signal-to-noise ratio of acoustic wave remote detection seismic data, which improves the imaging signal-to-noise ratio of acoustic wave remote detection data by using a signal frequency shift processing method. The main steps include:

[0056] Step 1: Input the acoustic wave remote detection data;

[0057] Step 2: Select a data. This seismic data is composed of the superposition of effective signal and noise. The effective signal is the frequency f 1 ,...f n It is generated by superposition of sine waves.

[0058] x=s+n

[0059]

[0060] x is the amplitude of seismic data, s is the signal amplitude, n is the noise amplitude, f is the frequency of the s amplitude spectrum, is the initial phase corresponding to frequency f in the s phase spectrum.

[0061] Step 3: Select the first sampling point of the data. The amplitude of the sampling point is generated by superimposing the first amplitude of the sine wave of f and the amplitude of the noise.

[0062] x(t 1 )=s(t 1 )+n(t 1 )

[0063]

[0064] x(t 1 ) is the amplitude of the first sampling point of the seismic data, s(t 1 ) is the amplitude of the first sampling point of the signal, n(t 1 ) is the amplitude of the first sampling point of noise, t 1 The time corresponding to the first sampling point, f is the frequency of the s amplitude spectrum, is the initial phase corresponding to frequency f in the s phase spectrum;

[0065] Step 4: Calculate the square of the amplitude of the sampling point.

[0066] A 1 =x=s+n

[0067]

[0068] According to the frequency shift formula of the signal, we can get

[0069]

[0070] x is the amplitude of seismic data, s is the signal amplitude, n is the noise amplitude, f is the frequency of the s amplitude spectrum, is the initial phase corresponding to frequency f in the s phase spectrum;

[0071] Step 5: Calculate the square of the amplitude of all sampling points of the data.

[0072]

[0073] x 1i is the i-th output amplitude of seismic data, s is the signal amplitude, n is the noise amplitude, f is the frequency of the s amplitude spectrum, is the initial phase corresponding to frequency f in the s phase spectrum.

[0074] A 1i is the i-th output amplitude value, A i is the i-th input amplitude value, N is the number of sampling points, and n is 1.

[0075] Step 6: Process all data according to steps 2-5;

[0076] Step 7: Filtering, select a zero-phase low-frequency filter and output low-frequency data.

[0077] Step 8: Output the result profile.

[0078] The following are several specific embodiments of the present invention.

[0079] Embodiment 1:

[0080] In a specific embodiment 1 of the present invention, Figure 1 As shown, Figure 1 The present invention is a flow chart of a method for improving the signal-to-noise ratio of acoustic wave long-range detection data.

[0081] Step 101, inputting acoustic wave remote detection data;

[0082] Step 102, select a piece of data;

[0083] Step 103, selecting the first sampling point of the data;

[0084] Step 104, calculating the square of the amplitude of the sampling point;

[0085] Step 105, calculating the square of the amplitude of all sampling points of the data track;

[0086] Step 106, process all data according to steps 2-5;

[0087] Step 107, filtering process;

[0088] Step 108, output the result profile.

[0089] The schematic diagram of the data processing of acoustic wave remote detection using the present invention is as follows Figure 3 The schematic diagram of data processing without using the present invention is shown in Figure 2As shown. It can be seen from the figure that the signal-to-noise ratio of the data not processed by the present invention is low, while the signal-to-noise ratio of the imaging data processed by the present invention is high. The method of improving the signal-to-noise ratio of acoustic wave long-range detection data of the present invention is correct. Since the conventional denoising method also loses part of the signal during filtering, the effect is not ideal.

[0090] Embodiment 2:

[0091] In a specific embodiment 2 of the present invention, as Figure 4 As shown, Figure 4 The present invention is a flow chart of a method for improving the signal-to-noise ratio of acoustic wave long-range detection data.

[0092] Step 101, input noise-free data;

[0093] Step 102, select a piece of data;

[0094] Step 103, selecting the first sampling point of the data;

[0095] Step 104, calculating the square of the amplitude of the sampling point;

[0096] Step 105, calculating the square of the amplitude of all sampling points of the data track;

[0097] Step 106, process all data according to steps 2-5;

[0098] Step 107, filtering process;

[0099] Step 108, output the result profile.

[0100] The schematic diagram of the data processing of acoustic wave remote detection using the present invention is as follows Figure 6 The schematic diagram of data processing without using the present invention is shown in Figure 5 As shown. It can be seen from the figure that the signal-to-noise ratio of the data not processed by the present invention is low, while the signal-to-noise ratio of the imaging data processed by the present invention is high. The method of improving the signal-to-noise ratio of acoustic wave long-range detection data of the present invention is correct. Since the conventional denoising method also loses part of the signal during filtering, the effect is not ideal.

[0101] Embodiment 3:

[0102] In a specific embodiment 3 of the present invention, as Figure 7 As shown, Figure 7 The present invention is a flow chart of a method for improving the signal-to-noise ratio of acoustic wave long-range detection data.

[0103] Step 101, input noisy data;

[0104] Step 102, select a piece of data;

[0105] Step 103, selecting the first sampling point of the data;

[0106] Step 104, calculating the square of the amplitude of the sampling point;

[0107] Step 105, calculating the square of the amplitude of all sampling points of the data track;

[0108] Step 106, process all data according to steps 2-5;

[0109] Step 107, filtering process;

[0110] Step 108, output the result profile.

[0111] The schematic diagram of the data processing of acoustic wave remote detection using the present invention is as follows Fig. 9 The schematic diagram of data processing without using the present invention is shown in Figure 8 As shown. It can be seen from the figure that the signal-to-noise ratio of the data not processed by the present invention is low, while the signal-to-noise ratio of the imaging data processed by the present invention is high. The method of improving the signal-to-noise ratio of acoustic wave long-range detection data of the present invention is correct. Since the conventional denoising method also loses part of the signal during filtering, the effect is not ideal.

[0112] The present invention has an ideal effect in processing the actual acoustic wave long-range detection data. This is because the present invention realizes the processing of acoustic wave long-range detection data, and filters out the acoustic wave long-range detection data noise through frequency shift processing without losing effective signals, and the acoustic wave long-range detection signal-to-noise ratio improvement method is superior to the conventional signal-to-noise ratio improvement method.

[0113] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions recorded in the aforementioned embodiments or replace some of the technical features therein with equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0114] Except for the technical features described in the specification, all other technical features are known technologies to those skilled in the art.

Claims

1. A method for improving the signal-to-noise ratio of acoustic wave long-range detection data, characterized in that: The method for improving the signal-to-noise ratio of acoustic wave long-range detection data comprises: Step 1: Collect acoustic wave remote detection data; Step 2: Select a seismic data; Step 3: Select the first sampling point of the seismic data; Step 4: Calculate the square of the amplitude of the sampling point; Step 5: Calculate the square of the amplitude of all sampling points of the seismic data; Step 6: Process all data according to steps 2-5; Step 7: Perform filtering; Step 8: Output the acoustic wave long-range detection result profile; In step 4, the square of the amplitude of the sampling point is calculated using the following formula: A1=A·A A1 is the output amplitude value, A is the input amplitude value; According to the frequency shift formula of the signal, we can get x is the amplitude of seismic data, s is the signal amplitude, n is the noise amplitude, f is the frequency of the s amplitude spectrum, is the initial phase corresponding to frequency f in the s phase spectrum; In step 7, when performing filtering processing, a zero-phase low-frequency filter is selected to output low-frequency data.

2. The method for improving the signal-to-noise ratio of acoustic wave long-range detection data according to claim 1, characterized in that: In step 1, the collected acoustic wave long-range detection data is an acoustic wave signal with certain morphological regular characteristics.

3. The method for improving the signal-to-noise ratio of acoustic wave long-range detection data according to claim 1, characterized in that: In step 2, a seismic data is selected. The seismic data is composed of a superposition of effective signals and noise. The effective signal is composed of frequencies f1,...f n It is generated by superposition of sine waves.

4. The method for improving the signal-to-noise ratio of acoustic wave long-range detection data according to claim 3, characterized in that: In step 2, the seismic data selected are: x=s+n x is the amplitude of seismic data, s is the signal amplitude, n is the noise amplitude, f is the frequency of the s amplitude spectrum, is the initial phase corresponding to frequency f in the s phase spectrum.

5. The method for improving the signal-to-noise ratio of acoustic wave long-range detection data according to claim 1, characterized in that: In step 3, the first sampling point of the seismic data is selected, and the amplitude of the sampling point is generated by superimposing the first amplitude of the sine wave of f and the amplitude of the noise.

6. The method for improving the signal-to-noise ratio of acoustic wave long-range detection data according to claim 5, characterized in that: In step 3, the sampling point is: x(t1)=s(t1)+n(t1) x(t1) is the amplitude of the first sampling point of the seismic data, s(t1) is the amplitude of the first sampling point of the signal, n(t1) is the amplitude of the first sampling point of the noise, t1 is the time corresponding to the first sampling point, f is the frequency of the amplitude spectrum of s, is the initial phase corresponding to frequency f in the s phase spectrum.

7. The method for improving the signal-to-noise ratio of acoustic wave long-range detection data according to claim 1, characterized in that: In step 5, the square of the amplitude of all sampling points of the data is calculated, and the formula is as follows: A 1i =A i ·A i i=1,2,3...,N x 1i is the i-th output amplitude of seismic data, s is the signal amplitude, n is the noise amplitude, f is the frequency of the s amplitude spectrum, is the initial phase corresponding to frequency f in the s phase spectrum; A 1i is the i-th output amplitude value, A i is the i-th input amplitude value, N is the number of sampling points, and n is 1.

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