DAS VSP data interference wave identification method and device, electronic equipment and medium

Abstract

The invention discloses a DAS VSP data interference wave identification method and device, electronic equipment and a medium. The method can comprise the steps that multi-parameter feature analysis is conducted on DAS VSP data; performing interference wave identification according to the analyzed multi-parameter characteristics; and outputting a data channel number containing the interference wave as a final interference wave identification result. According to the method, the DAS-VSP data is subjected to multi-parameter feature extraction, and the interference waves are analyzed and recognized based on the wave field features, so that simple and effective DAS-VSP data interference wave recognition is realized.

Description

Technical Field

[0001] This invention relates to the field of DAS VSP data processing, and more specifically, to a method, apparatus, electronic device, and medium for identifying interference waves in DAS VSP data. Background Technology

[0002] Distributed fiber acoustic sensing (DAS) is a novel signal acquisition technology that characterizes seismic signals by demodulating the phase changes of Rayleigh scattering of optical signals. Due to its advantages such as high density, low cost, high construction efficiency, and good data consistency, DAS VSP technology is increasingly being used in borehole geophysical applications.

[0003] Compared to traditional VSP data, DAS VSP data has a lower signal-to-noise ratio and contains a wide variety of interference types. These interferences include common noises found in conventional VSP data, such as wellbore waves and casing waves, as well as unique interference types specific to DAS VSP data, such as strong coherent fading noise generated by demodulation equipment and harmonic interference caused by poor fiber optic coupling (affecting the entire recorded wavefield). Interference in DAS VSP data severely impacts its quality.

[0004] Currently, a method for identifying interference waves in DAS VSP data still needs to be developed.

[0005] The information disclosed in the background section of this invention is intended only to enhance the understanding of the general background of this invention and should not be construed as an admission or in any way implying that such information constitutes prior art known to those skilled in the art. Summary of the Invention

[0006] This invention proposes a method, device, electronic device, and medium for identifying interference waves in DAS-VSP data. It can extract multi-parameter features from DAS-VSP data and then analyze and identify interference waves based on these wavefield features, thereby achieving simple and effective identification of interference waves in DAS-VSP data.

[0007] In a first aspect, embodiments of this disclosure provide a method for identifying interference waves in DAS VSP data, including:

[0008] Multi-parameter feature analysis was performed on DAS VSP data.

[0009] Interference wave identification is performed based on the analyzed multi-parameter characteristics.

[0010] The output data channel number containing the interference wave is the final interference wave identification result.

[0011] As a specific implementation of this disclosure, the multi-parameter features include the energy features, spectral features, and autocorrelation function features of each channel of DAS VSP data.

[0012] As a specific implementation of this disclosure, multi-parameter feature analysis of DAS VSP data includes:

[0013] Input DAS VSP data channels;

[0014] Calculate the root mean square energy, spectrum, autocorrelation function, and energy of the autocorrelation function for this data channel.

[0015] As a specific implementation of this disclosure, interference wave identification is performed based on the analyzed multi-parameter features:

[0016] Enter the data channel number of the DAS VSP data;

[0017] Calculate the energy anomaly index, spectrum anomaly index, and autocorrelation anomaly index for the data channel, and then calculate the total anomaly index for the data channel.

[0018] The presence of abnormal interference is determined by the total abnormality index of the data channel. If the total abnormality index is greater than the abnormality threshold, it is an abnormal interference channel; otherwise, it is a normal data channel.

[0019] As one specific implementation of this disclosure, the energy anomaly index is:

[0020] I E (i) = |E(i) - E'(i)| / E'(i)

[0021] Where E(i) is the root mean square energy of the data channel, and E'(i) is the energy value of the quadratic polynomial approximation of E(i);

[0022] The spectrum anomaly index is:

[0023]

[0024] Among them, P i P is the number of peak values. i f i (k) represents the corresponding peak frequency, k = 1, 2, ..., P i .

[0025] As a specific implementation of this disclosure, the autocorrelation anomaly index is:

[0026] I R (i)=|E R (i)-E' R (i)| / E'R (i)

[0027] Among them, E R (i) represents the energy of the autocorrelation function of the data channel, E' R (i) is E R The energy value of the quadratic polynomial approximation of (i).

[0028] As one specific implementation of this disclosure, the total anomaly index is:

[0029] I(i) = c E ·I E (i)+c F ·I F (i)+c R ·I R (i)

[0030] Among them, I E (i), I F (i) and I R (i) represent the energy anomaly index, the spectral anomaly index, and the autocorrelation anomaly index, respectively. E c F and c R These are the weighting coefficients for the energy anomaly index, the spectrum anomaly index, and the autocorrelation anomaly index, respectively.

[0031] Secondly, this disclosure also provides a DAS VSP data interference wave identification device, comprising:

[0032] The analysis module performs multi-parameter feature analysis on DAS VSP data;

[0033] The identification module identifies interference waves based on the analyzed multi-parameter characteristics.

[0034] The output module outputs the data channel number containing the interference wave, which is the final interference wave identification result.

[0035] As a specific implementation of this disclosure, the multi-parameter features include the energy features, spectral features, and autocorrelation function features of each channel of DAS VSP data.

[0036] As a specific implementation of this disclosure, multi-parameter feature analysis of DAS VSP data includes:

[0037] Input DAS VSP data channels;

[0038] Calculate the root mean square energy, spectrum, autocorrelation function, and energy of the autocorrelation function for this data channel.

[0039] As a specific implementation of this disclosure, interference wave identification is performed based on the analyzed multi-parameter features:

[0040] Enter the data channel number of the DAS VSP data;

[0041] Calculate the energy anomaly index, spectrum anomaly index, and autocorrelation anomaly index for the data channel, and then calculate the total anomaly index for the data channel.

[0042] The presence of abnormal interference is determined by the total abnormality index of the data channel. If the total abnormality index is greater than the abnormality threshold, it is an abnormal interference channel; otherwise, it is a normal data channel.

[0043] As one specific implementation of this disclosure, the energy anomaly index is:

[0044] I E (i) = |E(i) - E'(i)| / E'(i)

[0045] Where E(i) is the root mean square energy of the data channel, and E'(i) is the energy value of the quadratic polynomial approximation of E(i);

[0046] The spectrum anomaly index is:

[0047]

[0048] Among them, P i P is the number of peak values. i f i (k) represents the corresponding peak frequency, k = 1, 2, ..., P i .

[0049] As a specific implementation of this disclosure, the autocorrelation anomaly index is:

[0050] I R (i)=|E R (i)-E' R (i)| / E' R (i)

[0051] Among them, E R (i) represents the energy of the autocorrelation function of the data channel, E' R (i) is E R The energy value of the quadratic polynomial approximation of (i).

[0052] As one specific implementation of this disclosure, the total anomaly index is:

[0053] I(i) = c E ·I E (i)+c F·I F (i)+c R ·I R (i)

[0054] Among them, I E (i), I F (i) and I R (i) represent the energy anomaly index, the spectral anomaly index, and the autocorrelation anomaly index, respectively. E c F and c R These are the weighting coefficients for the energy anomaly index, the spectrum anomaly index, and the autocorrelation anomaly index, respectively.

[0055] Thirdly, embodiments of this disclosure also provide an electronic device, the electronic device comprising:

[0056] Memory, which stores executable instructions;

[0057] A processor that executes the executable instructions in the memory to implement the DAS VSP data interference wave identification method.

[0058] Fourthly, embodiments of this disclosure also provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the DAS VSP data interference wave identification method.

[0059] Its beneficial effects are as follows:

[0060] 1) Based on the multi-parameter feature extraction and analysis of DAS VSP data, the identification of interference waves is realized, avoiding the problem of low accuracy of single-parameter identification;

[0061] 2) This interference wave identification method is simple to implement and has good identification effect.

[0062] The methods and apparatus of the present invention have other features and advantages that will be apparent from or will be set forth in detail in the accompanying drawings and following detailed description, which together serve to explain the particular principles of the invention. Attached Figure Description

[0063] The above and other objects, features and advantages of the present invention will become more apparent from the more detailed description of exemplary embodiments of the invention in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same parts.

[0064] Figure 1A flowchart illustrating the steps of a DAS VSP data interference wave identification method according to an embodiment of the present invention is shown.

[0065] Figure 2 A schematic diagram illustrating the interference wave identification effect according to an embodiment of the present invention is shown.

[0066] Figure 3 A block diagram of a DAS VSP data interference wave identification device according to an embodiment of the present invention is shown.

[0067] Explanation of reference numerals in the attached figures:

[0068] 201. Analysis module; 202. Recognition module; 203. Output module. Detailed Implementation

[0069] Preferred embodiments of the invention will now be described in more detail. While preferred embodiments of the invention are described below, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0070] To facilitate understanding of the solutions and effects of the embodiments of the present invention, six specific application examples are given below. Those skilled in the art should understand that these examples are merely for the purpose of understanding the present invention, and any specific details therein are not intended to limit the present invention in any way.

[0071] Example 1

[0072] Figure 1 A flowchart illustrating the steps of a DAS VSP data interference wave identification method according to an embodiment of the present invention is shown.

[0073] like Figure 1 As shown, the DAS VSP data interference wave identification method includes:

[0074] Step 101: Perform multi-parameter feature analysis on the DAS VSP data;

[0075] Step 102: Identify interference waves based on the analyzed multi-parameter features;

[0076] Step 103: The output data channel number containing the interference wave is the final interference wave identification result.

[0077] In one example, the multi-parameter features include the energy features, spectral features, and autocorrelation function features of each channel of the DAS VSP data.

[0078] In one example, multi-parameter feature analysis of DAS VSP data includes:

[0079] Input DAS VSP data channels;

[0080] Calculate the root mean square energy, spectrum, autocorrelation function, and energy of the autocorrelation function for this data channel.

[0081] In one example, interference wave identification is performed based on the analyzed multi-parameter features:

[0082] Enter the data channel number of the DAS VSP data;

[0083] Calculate the energy anomaly index, spectrum anomaly index, and autocorrelation anomaly index for the data channel, and then calculate the total anomaly index for the data channel.

[0084] The presence of abnormal interference is determined by the total abnormality index of the data channel. If the total abnormality index is greater than the abnormality threshold, it is an abnormal interference channel; otherwise, it is a normal data channel.

[0085] In one example, the energy anomaly index is:

[0086] I E (i) = |E(i) - E'(i)| / E'(i)

[0087] Where E(i) is the root mean square energy of the data channel, and E'(i) is the energy value of the quadratic polynomial approximation of E(i);

[0088] The spectrum anomaly index is:

[0089]

[0090] Among them, P i P is the number of peak values. i f i (k) represents the corresponding peak frequency, k = 1, 2, ..., P i .

[0091] In one example, the autocorrelation anomaly index is:

[0092] I R (i)=|E R (i)-E' R (i)| / E' R (i)

[0093] Among them, E R (i) represents the energy of the autocorrelation function of the data channel, E' R (i) is E R The energy value of the quadratic polynomial approximation of (i).

[0094] In one example, the total anomaly index is:

[0095] I(i) = c E ·IE (i)+c F ·I F (i)+c R ·I R (i)

[0096] Among them, I E (i), I F (i) and I R (i) represent the energy anomaly index, the spectral anomaly index, and the autocorrelation anomaly index, respectively. E c F and c R These are the weighting coefficients for the energy anomaly index, the spectrum anomaly index, and the autocorrelation anomaly index, respectively.

[0097] Specifically, input DAS VSP data and perform multi-parameter feature analysis on the DAS VSP data; the multi-parameter features mainly include the energy characteristics, spectral characteristics, and autocorrelation function characteristics of each DAS VSP data channel; input any data channel x of the DAS VSP data. i (t), where i = 1, 2, 3, ..., M are the channel numbers of the DAS VSP data, and t is the time; calculate the root mean square energy E(i) of this data channel:

[0098] E(i)=∫x i (t) 2 dt

[0099] Calculate the spectrum F of this data channel. i (f), where f is the frequency and P is the number of peaks in the statistical spectrum. i and the corresponding peak frequency f i (k), k = 1, 2, ..., P i Calculate the autocorrelation function of this data channel:

[0100] R i (τ)=∫x i (t)x(t+τ)dt

[0101] Then calculate the energy of the autocorrelation function:

[0102] E R (i)=∫R i (τ) 2 dτ

[0103] Repeat the above steps to complete the calculation of all data channels.

[0104] Interference wave identification based on wavefield characteristic analysis; input any channel number i = 1, 2, 3, ..., M of the DAS VSP data, where i = 1, 2, 3, ..., M is the channel number of the DAS VSP data and the anomaly threshold (this threshold is a constant and can be manually set as needed). Calculate the energy anomaly index I of this data channel. E (i):

[0105] I E (i) = |E(i) - E'(i)| / E'(i)

[0106] Where E(i) is the root mean square energy of the data channel, and E'(i) is the energy value of the quadratic polynomial approximation of E(i).

[0107] Calculate the spectral anomaly index I of this data channel. F (i):

[0108]

[0109] Among them, P i P is the number of peak values. i f i (k) represents the corresponding peak frequency, k = 1, 2, ..., P i .

[0110] Calculate the autocorrelation anomaly index I of this data channel. R (i):

[0111] I R (i)=|E R (i)-E' R (i)| / E' R (i)

[0112] Among them, E R (i) represents the energy of the autocorrelation function of the data channel, E' R (i) is E R The energy value of the quadratic polynomial approximation of (i).

[0113] Calculate the total anomaly index of this data channel:

[0114] I(i) = c E ·I E (i)+c F ·I F (i)+c R ·I R (i)

[0115] Among them, I E (i), I F (i) and I R(i) represent the energy anomaly index, the spectral anomaly index, and the autocorrelation anomaly index, respectively. E c F and c R These are the weighting coefficients for the energy anomaly index, the spectrum anomaly index, and the autocorrelation anomaly index, respectively, and the weighting coefficients are given manually.

[0116] The presence of abnormal interference is determined based on the total abnormality index of the data channel. That is, if I(i) is greater than the abnormal threshold, it is an abnormal interference channel, otherwise it is a normal data channel.

[0117] The final interference wave identification result is the data channel number containing the interference wave.

[0118] Example 2

[0119] The present invention also provides a DAS VSP data interference wave identification device, comprising:

[0120] The analysis module performs multi-parameter feature analysis on DAS VSP data;

[0121] The identification module identifies interference waves based on the analyzed multi-parameter characteristics.

[0122] The output module outputs the data channel number containing the interference wave, which is the final interference wave identification result.

[0123] In one example, the multi-parameter features include the energy features, spectral features, and autocorrelation function features of each channel of the DAS VSP data.

[0124] In one example, multi-parameter feature analysis of DAS VSP data includes:

[0125] Input DAS VSP data channels;

[0126] Calculate the root mean square energy, spectrum, autocorrelation function, and energy of the autocorrelation function for this data channel.

[0127] In one example, interference wave identification is performed based on the analyzed multi-parameter features:

[0128] Enter the data channel number of the DAS VSP data;

[0129] Calculate the energy anomaly index, spectrum anomaly index, and autocorrelation anomaly index for the data channel, and then calculate the total anomaly index for the data channel.

[0130] The presence of abnormal interference is determined by the total abnormality index of the data channel. If the total abnormality index is greater than the abnormality threshold, it is an abnormal interference channel; otherwise, it is a normal data channel.

[0131] In one example, the energy anomaly index is:

[0132] I E (i) = |E(i) - E'(i)| / E'(i)

[0133] Where E(i) is the root mean square energy of the data channel, and E'(i) is the energy value of the quadratic polynomial approximation of E(i);

[0134] The spectrum anomaly index is:

[0135]

[0136] Among them, P i P is the number of peak values. i f i (k) represents the corresponding peak frequency, k = 1, 2, ..., P i .

[0137] In one example, the autocorrelation anomaly index is:

[0138] I R (i)=|E R (i)-E' R (i)| / E' R (i)

[0139] Among them, E R (i) represents the energy of the autocorrelation function of the data channel, E' R (i) is E R The energy value of the quadratic polynomial approximation of (i).

[0140] In one example, the total anomaly index is:

[0141] I(i) = c E ·I E (i)+c F ·I F (i)+c R ·I R (i)

[0142] Among them, I E (i), I F (i) and I R (i) represent the energy anomaly index, the spectral anomaly index, and the autocorrelation anomaly index, respectively. E c F and c R These are the weighting coefficients for the energy anomaly index, the spectrum anomaly index, and the autocorrelation anomaly index, respectively.

[0143] Specifically, input DAS VSP data and perform multi-parameter feature analysis on the DAS VSP data; the multi-parameter features mainly include the energy characteristics, spectral characteristics, and autocorrelation function characteristics of each DAS VSP data channel; input any data channel x of the DAS VSP data. i (t), where i = 1, 2, 3, ..., M are the channel numbers of the DAS VSP data, and t is the time; calculate the root mean square energy E(i) of this data channel:

[0144] E(i)=∫x i (t) 2 dt

[0145] Calculate the spectrum F of this data channel. i (f), where f is the frequency and P is the number of peaks in the statistical spectrum. i and the corresponding peak frequency f i (k), k = 1, 2, ..., P i Calculate the autocorrelation function of this data channel:

[0146] R i (τ)=∫x i (t)x(t+τ)dt

[0147] Then calculate the energy of the autocorrelation function:

[0148] E R (i)=∫R i (τ) 2 dτ

[0149] Repeat the above steps to complete the calculation of all data channels.

[0150] Interference wave identification based on wavefield characteristic analysis; input any channel number i = 1, 2, 3, ..., M of the DAS VSP data, where i = 1, 2, 3, ..., M is the channel number of the DAS VSP data and the anomaly threshold (this threshold is a constant and can be manually set as needed). Calculate the energy anomaly index I of this data channel. E (i):

[0151] I E (i) = |E(i) - E'(i)| / E'(i)

[0152] Where E(i) is the root mean square energy of the data channel, and E'(i) is the energy value of the quadratic polynomial approximation of E(i).

[0153] Calculate the spectral anomaly index I of this data channel. F (i):

[0154]

[0155] Among them, P i P is the number of peak values. i f i (k) represents the corresponding peak frequency, k = 1, 2, ..., P i .

[0156] Calculate the autocorrelation anomaly index I of this data channel. R (i):

[0157] I R (i)=|E R (i)-E' R (i)| / E' R (i)

[0158] Among them, E R (i) represents the energy of the autocorrelation function of the data channel, E' R (i) is E R The energy value of the quadratic polynomial approximation of (i).

[0159] Calculate the total anomaly index of this data channel:

[0160] I(i) = c E ·I E (i)+c F ·I F (i)+c R ·I R (i)

[0161] Among them, I E (i), I F (i) and I R (i) represent the energy anomaly index, the spectral anomaly index, and the autocorrelation anomaly index, respectively. E c F and c R These are the weighting coefficients for the energy anomaly index, the spectrum anomaly index, and the autocorrelation anomaly index, respectively, and the weighting coefficients are given manually.

[0162] The presence of abnormal interference is determined based on the total abnormality index of the data channel. That is, if I(i) is greater than the abnormal threshold, it is an abnormal interference channel, otherwise it is a normal data channel.

[0163] The final interference wave identification result is the data channel number containing the interference wave.

[0164] Example 3

[0165] This embodiment utilizes the proposed method for identifying interference waves in DAS-VSP data. In this embodiment, actual DAS-VSP data is directly used to verify the method.

[0166] Figure 2 The diagram illustrates the interference wave identification performance of this method. It shows that the method effectively identifies data channels in DAS VSP data affected by interference waves. The identified interference waves, primarily optical cable harmonics, are shown within the dashed box in the figure. Actual data testing demonstrates the effectiveness of the interference wave identification method of this invention.

[0167] Example 4

[0168] Figure 3 A block diagram of a DAS VSP data interference wave identification device according to an embodiment of the present invention is shown.

[0169] like Figure 3 As shown, the DAS VSP data interference wave identification device includes:

[0170] Analysis module 201 performs multi-parameter feature analysis on DAS VSP data;

[0171] The identification module 202 identifies interference waves based on the analyzed multi-parameter features;

[0172] The output module 203 outputs the data channel number containing the interference wave, which is the final interference wave identification result.

[0173] In one example, the multi-parameter features include the energy features, spectral features, and autocorrelation function features of each channel of the DAS VSP data.

[0174] In one example, multi-parameter feature analysis of DAS VSP data includes:

[0175] Input DAS VSP data channels;

[0176] Calculate the root mean square energy, spectrum, autocorrelation function, and energy of the autocorrelation function for this data channel.

[0177] In one example, interference wave identification is performed based on the analyzed multi-parameter features:

[0178] Enter the data channel number of the DAS VSP data;

[0179] Calculate the energy anomaly index, spectrum anomaly index, and autocorrelation anomaly index for the data channel, and then calculate the total anomaly index for the data channel.

[0180] The presence of abnormal interference is determined by the total abnormality index of the data channel. If the total abnormality index is greater than the abnormality threshold, it is an abnormal interference channel; otherwise, it is a normal data channel.

[0181] In one example, the energy anomaly index is:

[0182] I E (i) = |E(i) - E'(i)| / E'(i)

[0183] Where E(i) is the root mean square energy of the data channel, and E'(i) is the energy value of the quadratic polynomial approximation of E(i);

[0184] The spectrum anomaly index is:

[0185]

[0186] Among them, P i P is the number of peak values. i f i (k) represents the corresponding peak frequency, k = 1, 2, ..., P i .

[0187] In one example, the autocorrelation anomaly index is:

[0188] I R (i)=|E R (i)-E' R (i)| / E' R (i)

[0189] Among them, E R (i) represents the energy of the autocorrelation function of the data channel, E' R (i) is E R The energy value of the quadratic polynomial approximation of (i).

[0190] In one example, the total anomaly index is:

[0191] I(i) = c E ·I E (i)+c F ·I F (i)+c R ·I R (i)

[0192] Among them, I E (i), I F (i) and I R (i) represent the energy anomaly index, the spectral anomaly index, and the autocorrelation anomaly index, respectively. E c F and c R These are the weighting coefficients for the energy anomaly index, the spectrum anomaly index, and the autocorrelation anomaly index, respectively.

[0193] Example 5

[0194] This embodiment provides an electronic device, which includes: a memory storing executable instructions; and a processor that executes the executable instructions in the memory to implement the above-described DAS VSP data interference wave identification method.

[0195] An electronic device according to an embodiment of the present disclosure includes a memory and a processor.

[0196] This memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.

[0197] The processor may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment of this disclosure, the processor is used to execute computer-readable instructions stored in the memory.

[0198] Those skilled in the art will understand that, in order to solve the technical problem of how to achieve a good user experience, this embodiment may also include well-known structures such as communication buses and interfaces, and these well-known structures should also be included within the protection scope of this disclosure.

[0199] For a detailed description of this embodiment, please refer to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0200] Example 6

[0201] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the DAS VSP data interference wave identification method.

[0202] A computer-readable storage medium according to embodiments of the present disclosure stores non-transitory computer-readable instructions. When these non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the methods described in the foregoing embodiments of the present disclosure are performed.

[0203] The aforementioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or portable hard drive), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).

[0204] Those skilled in the art should understand that the above description of the embodiments of the present invention is only intended to illustrate the beneficial effects of the embodiments of the present invention, and is not intended to limit the embodiments of the present invention to any of the examples given.

[0205] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A method for identifying DAS VSP data interference waves, characterized in that, The method comprises the following steps: performing multi-parameter feature analysis on DAS VSP data; performing interference wave identification according to the analyzed multi-parameter features; outputting the data channel number containing the interference wave as the final interference wave identification result.

2. The DAS VSP data interference wave identification method of claim 1, wherein, The multi-parameter features include energy features, frequency spectrum features and autocorrelation function features of each data channel of the DAS VSP.

3. The DAS VSP data interference wave identification method of claim 1, wherein, The multi-parameter feature analysis on the DAS VSP data comprises the following steps: inputting a data channel of the DAS VSP data; calculating the root mean square energy, frequency spectrum and autocorrelation function of the data channel and the energy of the autocorrelation function.

4. The DAS VSP data interference wave identification method of claim 1, wherein, The interference wave identification according to the analyzed multi-parameter features comprises the following steps: inputting a data channel number of the DAS VSP data; calculating the energy anomaly index, frequency spectrum anomaly index and autocorrelation anomaly index of the data channel respectively, and then calculating the total anomaly index of the data channel; judging whether there is abnormal interference according to the total anomaly index of the data channel, and if the total anomaly index is greater than an anomaly threshold, the data channel is an abnormal interference channel, otherwise, the data channel is a normal data channel.

5. The DAS VSP data interference wave identification method of claim 4, wherein, The energy anomaly index is: I E (i) = E(i) - E'(i) / E'(i) wherein E(i) is the root mean square energy of the data channel, and E'(i) is the energy value of the quadratic polynomial approximation of E(i); The frequency spectrum anomaly index is: where P i is the number of peaks P i , f i (k) is the corresponding peak frequency, k = 1, 2,..., P i .

6. The DAS VSP data interference wave identification method of claim 4, wherein, The autocorrelation anomaly index is: I R (i) = E R (i) - E' R (i) / E' R (i) where E R (i) is the energy of the autocorrelation function of the data channel, E R (i) is the energy of the quadratic polynomial approximation of E R (i).

7. The DAS VSP data interference wave identification method of claim 4, wherein, The total anomaly index is: I(i) = c E • I E (i) + c F • I F (i) + c R • I R (i) wherein I E (i), I F (i) and I R (i) are the energy anomaly index, the spectral anomaly index and the autocorrelation anomaly index, respectively, c E , c F and c R are the weight coefficients of the energy anomaly index, the spectral anomaly index and the autocorrelation anomaly index, respectively.

8. A DAS VSP data interference wave identification apparatus, characterized by, The method comprises the following steps: an analysis module for performing multi-parameter feature analysis on DAS VSP data; an identification module for performing interference wave identification according to the analyzed multi-parameter features; an output module for outputting the data channel number containing the interference wave as the final interference wave identification result.

9. An electronic device, comprising: The electronic device comprises: a memory storing executable instructions; a processor running the executable instructions in the memory to implement the DAS VSP data interference wave identification method in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, which is executed by a processor to implement the DAS VSP data interference wave identification method in any one of claims 1-7.

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