WO2024016572A1 - Procédé et appareil d'identification de bruit, et dispositif et support d'enregistrement - Google Patents

Procédé et appareil d'identification de bruit, et dispositif et support d'enregistrement Download PDF

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WO2024016572A1
WO2024016572A1 PCT/CN2022/138648 CN2022138648W WO2024016572A1 WO 2024016572 A1 WO2024016572 A1 WO 2024016572A1 CN 2022138648 W CN2022138648 W CN 2022138648W WO 2024016572 A1 WO2024016572 A1 WO 2024016572A1
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component data
data
amplitude envelope
value
similarity
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Chinese (zh)
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曹中林
王光银
王克斌
李乐
何光明
陈丹
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中国石油天然气集团有限公司
中国石油集团东方地球物理勘探有限责任公司
中油油气勘探软件国家工程研究中心有限公司
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Publication of WO2024016572A1 publication Critical patent/WO2024016572A1/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
    • G01V1/36—Effecting static or dynamic corrections on records, e.g. correcting spread; Correlating seismic signals; Eliminating effects of unwanted energy
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
    • G01V1/36—Effecting static or dynamic corrections on records, e.g. correcting spread; Correlating seismic signals; Eliminating effects of unwanted energy
    • G01V1/364—Seismic filtering
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/38—Seismology; Seismic or acoustic prospecting or detecting specially adapted for water-covered areas
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/10—Pre-processing; Data cleansing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/22—Matching criteria, e.g. proximity measures
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/30—Assessment of water resources

Definitions

  • the present application relates to the technical field of geophysical exploration, and in particular to a noise identification method, device, equipment and storage medium.
  • Seabed seismic exploration technology is a type of offshore seismic exploration technology, which also consists of seismic sources and acquisition instruments. Seabed seismic exploration technology includes ocean bottom node seismometer exploration technology (Ocean Bottom Node, referred to as OBN).
  • OBN Ocean Bottom Node
  • OBN records four components, including water detection p component, land detection x component, land detection y component and land detection z component.
  • the existing method performs TAUP transformation on the water detection p component and the land detection z component, scales the transformed amplitude envelope, and then performs noise prediction and suppression based on the scaled amplitude envelope.
  • the existing method only performs a simple scaling comparison on the TAUP transformation results. Although most significant noises can be identified, the identification accuracy of weaker noises is not high.
  • This application provides a noise identification method, device, equipment and storage medium to solve the problem that the existing technology cannot effectively identify noise.
  • this application provides a noise identification method, including:
  • the corresponding shear wave leakage noise data is determined according to the shear wave leakage noise data interval and the transformed land detection z component data.
  • this application provides a noise identification device, including:
  • the processing unit is used to obtain the water detection component data and the land detection z-component data corresponding to the four-component data, and perform TAUP transformation on the water detection component data and the land detection z-component data respectively, and obtain the transformed water detection component data and Transformed land survey z-component data;
  • a determination unit configured to determine the corresponding first amplitude envelope component data based on the converted water detection component data, and to determine the corresponding second amplitude envelope component data based on the transformed land detection z component data;
  • a determining unit further configured to determine the corresponding similarity coefficient according to the first amplitude envelope component data and the second amplitude envelope component data
  • the processing unit is also used to perform median filtering on the similarity coefficients to determine the corresponding shear wave leakage noise data interval;
  • the determining unit is also configured to determine the corresponding shear wave leakage noise data according to the shear wave leakage noise data interval and the transformed land detection z component data.
  • this application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;
  • the memory stores computer execution instructions
  • the processor executes computer execution instructions stored in the memory, so that the processor executes the method described in the first aspect.
  • the present application provides a computer-readable storage medium.
  • Computer-executable instructions are stored in the computer-readable storage medium. When executed by a processor, the computer-executable instructions are used to implement the method as described in the first aspect. .
  • the noise identification method, device, equipment and storage medium obtain the water detection component data and land detection z-component data corresponding to the four-component data, and perform TAUP on the water detection component data and land detection z-component data respectively.
  • Transform obtain the transformed water detection component data and the transformed land detection z-component data; determine the corresponding first amplitude envelope component data according to the transformed water detection component data, and determine the corresponding first amplitude envelope component data according to the transformed land detection component data
  • the z component data determines the corresponding second amplitude envelope component data; determines the corresponding similarity coefficient according to the first amplitude envelope component data and the second amplitude envelope component data; performs median filtering processing on the similarity coefficient to Determine the corresponding shear wave leakage noise data interval; determine the corresponding shear wave leakage noise data according to the shear wave leakage noise data interval and the transformed land inspection z component data, and further identify the noise data through the similarity of the amplitude envelope component data , can identify noisy data more effectively, whether
  • Figure 1 is a schematic diagram of the network architecture of the noise identification method provided by this application.
  • Figure 2 is a schematic flow chart of the noise identification method provided in Embodiment 1 of the present application.
  • Figure 3 is a schematic flow chart of the noise identification method provided in Embodiment 2 of the present application.
  • Figure 4 is a schematic flowchart of the noise identification method provided in Embodiment 7 of the present application.
  • FIG. 5 is a schematic flowchart of the noise identification method provided in Embodiment 8 of the present application.
  • Figure 6 is a schematic structural diagram of a noise identification device according to an embodiment of the present application.
  • Figure 7 is a block diagram of an electronic device used to implement the noise identification method according to the embodiment of the present application.
  • Seabed seismic exploration technology is a type of offshore seismic exploration technology, which also consists of seismic sources and acquisition instruments.
  • Seabed seismic exploration technology includes ocean bottom node seismometer exploration technology (Ocean Bottom Node, referred to as OBN).
  • OBN Ocean Bottom Node
  • node seismic instruments underwater without cable power supply and without communication.
  • Each node seismic instrument operates autonomously and is completely independent of all other nodes.
  • OBN records four components, including water detection p component, land detection x component, land detection y component and land detection z component.
  • the existing method performs TAUP transformation on the water detection p component and the land detection z component, scales the transformed amplitude envelope, and then performs noise prediction and suppression based on the scaled amplitude envelope. Or simply add the transformed amplitude envelopes, and the resulting difference is considered noise.
  • the existing method only performs a simple scaling comparison or subtraction on the TAUP transformation results. Although most significant noises can be identified, the identification accuracy of weaker noises is not high, and the land detection z component cannot be accurately identified. noise.
  • the inventor found in the research that TAUP transformation is performed on the water detection component data and the land detection z component data respectively, and the transformed water detection component data and the transformed land detection z component data are obtained.
  • noise data thereby identifying noise data.
  • noise data can be further identified through the similarity of amplitude envelope component data, which can more effectively identify noise data, whether it is significant noise or weak noise. can be identified very well.
  • the inventor proposes the technical solution of the embodiment of the present application based on the above creative discovery.
  • the network architecture and application scenarios of the noise identification method provided by the embodiments of this application are introduced below.
  • the network architecture corresponding to the noise identification method provided by the embodiment of the present application includes: submarine node 1 and electronic device 2.
  • the submarine node 1 is arranged on the seabed, and the submarine node 1 communicates with the electronic device 2 .
  • Seabed node 1 is used to collect four-component data.
  • the electronic device 2 obtains the four-component data collected by the seabed node 2.
  • the electronic device 2 performs TAUP transformation on the water detection component data and the land detection z-component data in the four-component data respectively, and obtains the transformed water detection component data and the transformed land detection z-component data.
  • the electronic device 2 further performs TAUP transformation according to the transformed water detection component data and the transformed land detection z-component data.
  • the water detection component data determines the first amplitude envelope component data and determines the second amplitude envelope component data according to the transformed land detection z component data.
  • the electronic device 2 calculates the first amplitude envelope component data and the second amplitude envelope component data.
  • the electronic device 2 performs median filtering on the similarity coefficient to determine the shear wave leakage noise data interval, and further determines the corresponding shear wave leakage noise based on the shear wave leakage noise data interval and the land inspection z component data after TAUP transformation. data to identify noisy data. Compared with the existing technology, noise data is further identified through the similarity of amplitude envelope component data, and noise data can be identified more effectively. Whether it is significant noise or weak noise, it can be well identified.
  • FIG 2 is a schematic flowchart of the noise identification method provided in Embodiment 1 of the present application.
  • the execution subject of the noise identification method provided in this embodiment is a noise identification device.
  • the noise identification device is located in an electronic device. Therefore, the present invention
  • the noise identification method provided by the embodiment includes the following steps:
  • Step 101 Obtain the water test component data and the land test z-component data corresponding to the four-component data, and perform TAUP transformation on the water test component data and the land test z-component data respectively, and obtain the transformed water test component data and the transformed land test component data.
  • Check z component data Obtain the water test component data and the land test z-component data corresponding to the four-component data, and perform TAUP transformation on the water test component data and the land test z-component data respectively, and obtain the transformed water test component data and the transformed land test component data.
  • the four-component data is collected from the seabed node OBN, and the four-component data includes the water detection component and the land detection z-component data.
  • the land inspection z-component data includes noise and effective signals caused by shear wave energy leakage. It is necessary to find the noise in the land inspection z-component data.
  • the noise caused by shear wave energy leakage has a small impact on the water inspection component data.
  • the water inspection component data This part of noise does not exist, so the noise data existing in the land survey z-component data can be found based on the water survey component data. In the time domain, the shear wave leakage noise and the effective signal are superimposed and cannot be separated.
  • TAUP transformation is also called linear RADON transformation and oblique superposition transformation. TAUP transformation can remove linear noise, enhance effective wave energy, and significantly improve the signal-to-noise ratio of the superimposed profile.
  • the TAUP transformation formula is as follows:
  • Pt is the transformed water detection component data
  • t represents time
  • x represents the transverse direction
  • y represents the longitudinal direction
  • ⁇ represents the time intercept after TAUP transformation
  • p x represents the slow speed in the x direction after TAUP transformation.
  • Zt is the z component data of the land survey after transformation
  • t represents time
  • x represents the transverse direction
  • y represents the longitudinal direction
  • ⁇ represents the time intercept after TAUP transformation
  • p x represents the x direction after TAUP transformation.
  • Slowness p y represents the slowness in the y direction after TAUP transformation.
  • Step 102 Determine the corresponding first amplitude envelope component data based on the transformed water detection component data, and determine the corresponding second amplitude envelope component data based on the transformed land detection z-component data.
  • the transformed water detection component data is subjected to slicing processing and Hilbert transformation to obtain the corresponding first amplitude envelope component data
  • the transformed land detection z component data is subjected to slicing processing and Hilbert transformation.
  • the corresponding second amplitude envelope component data is obtained by transformation.
  • Step 103 Determine the corresponding similarity coefficient based on the first amplitude envelope component data and the second amplitude envelope component data.
  • the noise caused by the leakage of shear wave energy has a small impact on the water detection component data and a greater impact on the land detection z component data. Therefore, the similarity between the data can be calculated.
  • the component data and the second amplitude envelope component data respectively determine the corresponding mean similarity, variance similarity and covariance similarity, and further calculate the similarity coefficient based on the mean similarity, variance similarity and covariance similarity.
  • the similarity coefficient is used to measure The similarity between the first amplitude envelope component data and the second amplitude envelope component data in the TAUP domain.
  • Step 104 Perform median filtering on the similarity coefficients to determine the corresponding shear wave leakage noise data interval.
  • median filtering is performed on the similar coefficients.
  • Median filtering has a good filtering effect on impulse noise. In particular, while filtering out noise, it can protect the edge of the signal from being blurred, thereby determining the corresponding The shear wave leakage noise data interval.
  • the similarity coefficient is an array with a value range of 0-1. The larger the value, the more similar the values are. The smaller the value, the less similar. The more dissimilar area is the shear wave leakage noise data interval.
  • Step 105 Determine the corresponding shear wave leakage noise data based on the shear wave leakage noise data interval and the transformed land survey z-component data.
  • the TAUP domain shear wave leakage noise data is obtained based on the shear wave leakage noise data interval and the transformed land detection z component data.
  • the shear wave leakage noise data in the TAUP domain is further subjected to inverse TAUP transformation to obtain the shear wave leakage noise data.
  • the shear wave leakage noise data is The data is the identified noise data, that is, the noise caused by the leakage of shear wave energy.
  • the water detection component data and the land detection z-component data are respectively subjected to TAUP transformation to obtain the transformed water detection component data and the transformed land detection z-component data, and the first step is determined based on the transformed water detection component data.
  • the amplitude envelope component data is used to determine the second amplitude envelope component data based on the transformed z-component data, the similarity between the first amplitude envelope component data and the second amplitude envelope component data is calculated, and the similarity coefficient is calculated Median filtering process is used to determine the shear wave leakage noise data interval, and further determine the corresponding shear wave leakage noise data based on the shear wave leakage noise data interval and the land inspection z component data after TAUP transformation, thereby identifying the noise data.
  • the noise data can be further identified through the similarity of the amplitude envelope component data, which can more effectively identify the noise data, whether it is significant noise or weak noise, it can be well identified.
  • Figure 3 is a schematic flow chart of the noise identification method provided in Embodiment 2 of the present application. As shown in Figure 3, based on the noise identification method provided in Embodiment 1 of the present application, the transformed water detection component in step 102 is The data to determine the corresponding first amplitude envelope component data is further refined, including the following steps:
  • Step 1021 Perform slicing processing on the transformed water test component data to obtain corresponding water test component slice data.
  • the transformed water detection component data is sliced along the transverse direction to obtain corresponding water detection component slice data.
  • Step 1022 Perform Hilbert transform on the water detection component slice data to obtain the corresponding first amplitude envelope component data.
  • Hilbert transform is performed on the water detection component switching data to obtain the corresponding first amplitude envelope component data.
  • step 102 of determining the corresponding second amplitude envelope component data based on the transformed land survey z-component data is further refined, including the following steps:
  • Step 1023 Perform slicing processing on the transformed land survey z-component data to obtain corresponding land survey z-component slice data.
  • the transformed water detection component data is sliced horizontally to obtain corresponding land detection z-component slice data.
  • Step 1024 Perform Hilbert transform on the z-component slice data of the land survey to obtain the corresponding second amplitude envelope component data.
  • Hilbert transform is performed on the z-component switching data of the land detector to obtain the corresponding second amplitude envelope component data.
  • the amplitude envelope component data is used to calculate the similarity between the water detection component data and the land detection z-component data, which can effectively identify the noise data.
  • step 103 is further refined, including the following steps:
  • Step 1031 Determine the corresponding mean similarity based on the first amplitude envelope component data and the second amplitude envelope component data, determine the corresponding variance similarity based on the first amplitude envelope component data and the second amplitude envelope component data, and The corresponding covariance similarity is determined based on the first amplitude envelope component data and the second amplitude envelope component data.
  • the mean similarity between the first amplitude envelope component data and the second amplitude envelope component data is calculated, and the variance similarity between the first amplitude envelope component data and the second amplitude envelope component data is calculated, And calculate the covariance similarity between the first amplitude envelope component data and the second amplitude envelope component data.
  • Step 1032 Determine the corresponding similarity coefficient based on the mean similarity, variance similarity and covariance similarity.
  • the mean similarity, variance similarity and covariance similarity are substituted into the formula to calculate the total similarity, and the total similarity is determined as the similarity coefficient.
  • the formula is expressed as:
  • V(Pa,Za) A 1 (Pa,Za) ⁇ A 2 (Pa,Za) ⁇ A 3 (Pa,Za)Formula (3)
  • V is the similarity coefficient
  • a 1 is the mean similarity
  • a 2 is the variance similarity
  • a 3 is the covariance similarity
  • Pa represents the first amplitude envelope component data
  • Za represents the second amplitude envelope component data.
  • the similarity coefficient is determined based on the mean similarity, variance similarity, and covariance similarity. Using the similarity coefficient can better represent the similarity between the two amplitude envelope component data.
  • the determination of the corresponding mean similarity based on the first amplitude envelope component data and the second amplitude envelope component data in step 1031 is further refined, specifically including: Following steps:
  • Step 1031a Calculate the first mean value corresponding to the first amplitude envelope component data, and calculate the second mean value corresponding to the second amplitude envelope component data.
  • the corresponding first mean value is calculated according to the first amplitude envelope component data, where the first amplitude envelope component data is composed of a plurality of first amplitude envelope component signals, and the plurality of first amplitude envelope component signals are calculated.
  • the first mean corresponding to the signal, the first mean formula is expressed as:
  • u P is the first mean value
  • P is the first amplitude envelope component signal
  • the corresponding second mean value is calculated according to the second amplitude envelope component data, wherein the second amplitude envelope component data is composed of a plurality of second amplitude envelope component signals, and the corresponding second average value of the plurality of second amplitude envelope component signals is calculated.
  • the second mean, the second mean formula is expressed as:
  • u Z is the second mean value
  • Z is the second amplitude envelope component signal
  • Step 1031b Calculate the corresponding mean similarity based on the first mean and the second mean.
  • the first mean and the second mean are substituted into the formula to calculate the corresponding mean similarity.
  • the mean similarity formula is expressed as:
  • a 1 is the mean similarity
  • u P is the first mean
  • u Z is the second mean
  • C 1 is the first constant.
  • the similarity between the two amplitude envelope component data can be well represented by the mean similarity.
  • the determination of the corresponding variance similarity based on the first amplitude envelope component data and the second amplitude envelope component data in step 1031 is further refined, specifically including: Following steps:
  • Step 1031c Calculate the first variance corresponding to the first amplitude envelope component data, and calculate the second variance corresponding to the second amplitude envelope component data.
  • the corresponding first variance is calculated based on the first amplitude envelope component data, where the first amplitude envelope component data is composed of a plurality of first amplitude envelope component signals, and the plurality of first amplitude envelope component signals are calculated.
  • the first variance corresponding to the component signal, the first variance formula is expressed as:
  • ⁇ P is the first variance
  • u P is the first mean
  • P is the first amplitude envelope component signal
  • the corresponding second variance is calculated according to the second amplitude envelope component data, wherein the second amplitude envelope component data is composed of a plurality of second amplitude envelope component signals, and the corresponding second variance of the plurality of second amplitude envelope component signals is calculated.
  • the second variance, the second variance formula is expressed as:
  • ⁇ z is the second variance
  • u Z is the second mean
  • Z is the second amplitude envelope component signal
  • Step 1031d Calculate the corresponding variance similarity based on the first variance and the second variance.
  • the variance similarity formula is expressed as:
  • a 2 is the variance similarity
  • ⁇ P is the first variance
  • ⁇ z is the second variance
  • C 2 is the second constant.
  • the similarity between the two amplitude envelope component data can be well represented by the variance similarity.
  • the determination of the corresponding covariance similarity based on the first amplitude envelope component data and the second amplitude envelope component data in step 1031 is further refined. Specifically, Includes the following steps:
  • Step 1031e Calculate the covariance corresponding to the first amplitude envelope component data and the second amplitude envelope component data, and determine the corresponding covariance similarity based on the corresponding covariance.
  • the covariance corresponding to the first amplitude envelope component data and the second amplitude envelope component data is calculated, where the first amplitude envelope component data is composed of a plurality of first amplitude envelope component signals, and the second amplitude envelope component data is composed of a plurality of first amplitude envelope component signals.
  • the envelope component data consists of multiple second amplitude envelope component signals, and the covariance formula is expressed as:
  • ⁇ PZ is the covariance
  • u P is the first mean
  • P is the first amplitude envelope component signal
  • u Z is the second mean
  • Z is the second amplitude envelope component signal.
  • the covariance similarity formula is expressed as:
  • a 3 is the covariance similarity
  • ⁇ PZ is the covariance
  • ⁇ P is the first variance
  • ⁇ z is the second variance
  • C3 is the third constant.
  • the similarity between the two amplitude envelope component data can be well represented by the covariance similarity.
  • Figure 4 is a schematic flow chart of the noise identification method provided in Embodiment 7 of the present application. As shown in Figure 4, on the basis of the noise identification method provided in Embodiments 1 to 6 of the present application, step 104 is further refined. Specifically, it includes the following steps:
  • Step 1041 Perform median filtering on each value in the similarity coefficient to determine the corresponding output value.
  • median filtering is performed on each value in the similarity coefficient. Specifically, according to the initial window length, the preset maximum window length, the minimum value corresponding to the current window length corresponding to the value, the corresponding window center value and the corresponding The maximum value further determines the corresponding output value.
  • Step 1042 determine all corresponding output values as corresponding shear wave leakage noise data intervals.
  • each output value corresponding to each value in the similarity coefficient is determined as the corresponding shear wave leakage noise data interval, and further the TAUP domain shear wave leakage noise data is obtained based on the shear wave leakage noise data interval and the transformed land inspection z component data.
  • the shear wave leakage noise data is the identified noise data.
  • median filtering has a good filtering effect on impulse noise.
  • it can protect the edge of the signal from being blurred and obtain a more accurate shear wave leakage noise data interval.
  • FIG. 5 is a schematic flow chart of the noise identification method provided in Embodiment 8 of the present application. As shown in Figure 5, based on the noise identification method provided in Embodiment 7 of the present application, step 1041 is further refined, specifically including the following step:
  • Step 10411 Obtain the initial window length and the preset maximum window length, determine the initial window length as the current window length, and obtain the minimum value, the corresponding window center value, and the corresponding maximum value corresponding to the current window length.
  • the initial window length and the preset maximum window length are obtained, where the initial window length is predefined and the initial window length is an odd number.
  • the preset maximum window length is set according to the actual situation, where the initial window length is smaller than the preset maximum window length. Determine the initial window length as the current window length, and obtain the minimum value, the corresponding window center value, and the corresponding maximum value corresponding to the current window length corresponding to each value in the similarity coefficient.
  • Step 10412 Determine whether the window center value corresponding to the numerical value is greater than the corresponding minimum value and less than the corresponding maximum value; if so, perform step 10413; if not, perform step 10414.
  • the window center value corresponding to the numerical value is both greater than the corresponding minimum value and smaller than the corresponding maximum value. According to the minimum value corresponding to the current window length corresponding to each numerical value in the similarity coefficient, the corresponding window center value and the corresponding The size relationship between the maximum values further determines the corresponding output value.
  • Step 10413 Determine the corresponding output value based on the numerical value, the minimum value corresponding to the numerical value, and the maximum value corresponding to the numerical value.
  • the window center value corresponding to a certain value satisfies the condition that it is greater than the corresponding minimum value and less than the corresponding maximum value, then the size relationship between the value, the minimum value corresponding to the value, and the maximum value corresponding to the value is further determined. the corresponding output value.
  • Step 10414 Adjust the current window length corresponding to the numerical value, and determine the corresponding output value based on the adjusted window length corresponding to the numerical value and the preset maximum window length.
  • the window center value corresponding to a certain value does not meet the conditions of being greater than the corresponding minimum value and less than the corresponding maximum value, then the current window length corresponding to the value is adjusted, the window length is increased, and the adjusted value corresponding to the value is The window length and the preset maximum window length determine the corresponding output value.
  • median filtering can be used to identify the noise data in the water detection component data, filter the effective signals in the water detection component data, and obtain more accurate noise data.
  • step 10413 is further refined, specifically including the following steps:
  • Step 10413a determine whether the value is greater than the corresponding minimum value and less than the corresponding maximum value.
  • the corresponding output value is further determined based on the relationship between the numerical value, the minimum value corresponding to the numerical value, and the maximum value corresponding to the numerical value. Specifically, it is determined whether the numerical value is both greater than the corresponding minimum value and less than the corresponding maximum value.
  • Step 10413b if yes, determine the value as the corresponding output value.
  • the value satisfies the condition that it is greater than the corresponding minimum value and less than the corresponding maximum value, then the value is determined as the output value corresponding to the value under the current window length.
  • Step 10413c if not, determine the window center value corresponding to the value as the corresponding output value.
  • the window center value under the current window length is determined as the output value corresponding to the value.
  • the noise data in the water detection component data can be identified by using median filtering, and more accurate noise data can be obtained.
  • step 10414 is further refined, specifically including the following steps:
  • Step 10414a if the adjusted window length corresponding to the numerical value is equal to the preset maximum window length, determine the window center value corresponding to the adjusted window length as the corresponding output value.
  • the adjusted window length corresponding to the numerical value is equal to the preset maximum window length. If the adjusted window length corresponding to the numerical value is equal to the preset maximum window length, it means that the window length has reached the maximum value and the window can no longer be modified. The length is adjusted, and the window center value corresponding to the adjusted window length is determined as the corresponding output value.
  • Step 10414b if the adjusted window length corresponding to the numerical value is less than the preset maximum window length, determine the adjusted window length as the current window length, and execute to obtain the minimum value and the corresponding window center corresponding to the current window length corresponding to the numerical value. value and the corresponding maximum value step.
  • the adjusted window length corresponding to the value is less than the preset maximum window length, it means that the window length is not the maximum value, and the window length can be adjusted later, and the adjusted window length is further determined as the current window length. , repeat the steps of obtaining the minimum value, the corresponding window center value, and the corresponding maximum value corresponding to the current window length corresponding to the value.
  • the noise data in the water detection component data can be identified by using median filtering, and more accurate noise data can be obtained.
  • step 105 is further refined, specifically including the following steps:
  • Step 1051 Determine the shear wave leakage noise data to be converted based on the shear wave leakage noise data interval and the transformed land detection z component data.
  • shear wave leakage noise data interval and the TAUP transformed ground inspection z component data are substituted into the formula to calculate the shear wave leakage noise data to be converted.
  • the formula is expressed as:
  • Ht is the shear wave leakage noise data to be converted
  • Zt is the transformed land detection z component data
  • K is the shear wave leakage noise data interval.
  • K multiplied by Zt represents the effective signal part in the ground-based z component data
  • the effective signal part subtracted from Zt is the noise data part in the ground-based z-component data
  • Step 1052 Perform inverse TAUP transformation on the shear wave leakage noise data to be converted, obtain the transformed shear wave leakage noise data, and determine the transformed shear wave leakage noise data as the corresponding shear wave leakage noise data.
  • the shear wave leakage noise data to be converted is subjected to inverse TAUP transformation to obtain the inversely transformed shear wave leakage noise data, and the inversely transformed shear wave leakage noise data is determined as the corresponding shear wave leakage noise data.
  • H is the shear wave leakage noise data
  • t represents time
  • x represents the transverse direction
  • y represents the longitudinal direction
  • ⁇ represents the time intercept after TAUP transformation
  • p x represents the slowness in the x direction after TAUP transformation
  • p y represents the slowness in the y direction after TAUP transformation.
  • noise data can be identified more effectively. Whether it is significant noise or weak noise, it can be well identified and more accurate noise data can be obtained.
  • step 105 Based on the noise identification method provided in Embodiment 1 of this application, after step 105, the following steps are also included:
  • Step 106 Perform denoising processing on the land detection z-component data and the shear wave leakage noise data according to the shear wave leakage noise data to obtain denoised land detection z-component data.
  • the denoising process mainly involves calculating the difference between the land detection z-component data and the shear wave leakage noise data.
  • the difference is the denoised land detection z-component data.
  • the noise data can be more effectively identified, and the noise caused by a large amount of shear wave energy leakage in the land survey z-component data can be effectively removed, thereby obtaining more accurate denoised land survey z-component data.
  • Figure 6 is a schematic structural diagram of a noise identification device provided by an embodiment of the present application.
  • the noise identification device provided by this embodiment includes a processing unit 201 and a determination unit 202.
  • the processing unit 201 is used to obtain the water test component data and land test z-component data corresponding to the four-component data, and perform TAUP transformation on the water test component data and land test z-component data respectively, and obtain the transformed water test component data. and the transformed z-component data of the land survey.
  • the determining unit 202 is configured to determine the corresponding first amplitude envelope component data based on the transformed water detection component data, and determine the corresponding second amplitude envelope component data based on the transformed land detection z-component data.
  • the determining unit 202 is also configured to determine the corresponding similarity coefficient based on the first amplitude envelope component data and the second amplitude envelope component data.
  • the processing unit 201 is also used to perform median filtering on the similarity coefficients to determine the corresponding shear wave leakage noise data interval.
  • the determining unit 202 is also used to determine the corresponding shear wave leakage noise data based on the shear wave leakage noise data interval and the transformed ground inspection z-component data.
  • the determination unit is also configured to perform slicing processing on the transformed water detection component data to obtain corresponding water detection component slice data; perform Hilbert transformation on the water detection component slice data to obtain the corresponding first amplitude. Envelope component data.
  • the determination unit is also used to perform slicing processing on the transformed land survey z-component data to obtain the corresponding land survey z-component slice data; perform Hilbert transformation on the land survey z-component slice data to obtain the corresponding Second amplitude envelope component data.
  • the determining unit is also configured to determine the corresponding mean similarity based on the first amplitude envelope component data and the second amplitude envelope component data, and determine the corresponding mean similarity based on the first amplitude envelope component data and the second amplitude envelope component data.
  • the corresponding variance similarity is determined based on the first amplitude envelope component data and the second amplitude envelope component data; the corresponding similarity coefficient is determined based on the mean similarity, variance similarity and covariance similarity.
  • the determining unit is also configured to calculate a first mean value corresponding to the first amplitude envelope component data, and to calculate a second mean value corresponding to the second amplitude envelope component data; to calculate the corresponding mean value according to the first mean value and the second mean value.
  • Mean similarity is also configured to calculate a first mean value corresponding to the first amplitude envelope component data, and to calculate a second mean value corresponding to the second amplitude envelope component data; to calculate the corresponding mean value according to the first mean value and the second mean value.
  • the determining unit is also configured to calculate the first variance corresponding to the first amplitude envelope component data, and calculate the second variance corresponding to the second amplitude envelope component data; calculate according to the first variance and the second variance Corresponding variance similarity.
  • the determining unit is also configured to calculate the covariance corresponding to the first amplitude envelope component data and the second amplitude envelope component data, and determine the corresponding covariance similarity based on the corresponding covariance.
  • the processing unit is also configured to perform median filtering on each value in the similarity coefficient to determine the corresponding output value; and determine all corresponding output values as corresponding shear wave leakage noise data intervals.
  • the processing unit is also used to obtain the initial window length and the preset maximum window length, determine the initial window length as the current window length, and obtain the minimum value corresponding to the current window length, the corresponding window center value, and The corresponding maximum value; determine whether the window center value corresponding to the value is greater than the corresponding minimum value and less than the corresponding maximum value; if so, determine the corresponding output value based on the value, the minimum value corresponding to the value, and the maximum value corresponding to the value; if not , then adjust the current window length corresponding to the value, and determine the corresponding output value based on the adjusted window length corresponding to the value and the preset maximum window length.
  • the processing unit is also used to determine whether the value is greater than the corresponding minimum value and less than the corresponding maximum value; if so, determine the value as the corresponding output value; if not, then determine the window center value corresponding to the value Determine the corresponding output value.
  • the processing unit is also configured to determine the window center value corresponding to the adjusted window length as the corresponding output value if the adjusted window length corresponding to the value is equal to the preset maximum window length; if the adjusted window length corresponding to the value If the adjusted window length is less than the preset maximum window length, the adjusted window length is determined as the current window length, and the steps of obtaining the minimum value, the corresponding window center value, and the corresponding maximum value corresponding to the current window length corresponding to the value are performed. .
  • the determining unit is also configured to determine the shear wave leakage noise data to be converted based on the shear wave leakage noise data interval and the transformed ground inspection z component data; perform inverse TAUP transformation on the shear wave leakage noise data to be converted, and obtain the transformed The shear wave leakage noise data is obtained, and the transformed shear wave leakage noise data is determined as the corresponding shear wave leakage noise data.
  • the processing unit is also configured to perform denoising processing on the ground detection z-component data based on the shear wave leakage noise data to obtain denoised ground detection z-component data.
  • FIG. 7 is a block diagram of an electronic device used to implement the noise identification method according to the embodiment of the present application.
  • the electronic device 300 includes: a memory 301 and a processor 302 .
  • Memory 301 stores computer execution instructions
  • the processor executes the computer execution instructions stored in the memory 302, so that the processor executes the method provided by any of the above embodiments.
  • a computer-readable storage medium is also provided.
  • Computer-executable instructions are stored in the computer-readable storage medium, and the computer-executable instructions are used by a processor to execute the method in any of the above embodiments.
  • a computer program product including a computer program, the computer program being used by a processor to execute the method in any of the above embodiments.

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Abstract

Procédé et appareil d'identification de bruit (200), et dispositif (300) et support d'enregistrement. Le procédé consiste à : réaliser respectivement une transformation TAUP sur des données de composants de détection d'eau et des données de composants z de détection de terrain, et acquérir les données de composants de détection d'eau transformées et les données de composants z de détection de terrain transformées ; déterminer des premières données de composants d'enveloppe d'amplitude correspondantes selon les données de composants de détection d'eau transformées, et déterminer des secondes données de composant d'enveloppe d'amplitude correspondantes selon les données de composants z de détection de terrain transformées (102) ; déterminer un coefficient de similarité correspondant selon les premières données de composants d'enveloppe d'amplitude et les secondes données de composants d'enveloppe d'amplitude (103) ; réaliser un traitement de filtrage médian sur le coefficient de similarité, de façon à déterminer un intervalle de données de bruit de fuite d'onde transversale correspondant (104) ; et déterminer des données de bruit de fuite d'onde transversale correspondantes selon l'intervalle de données de bruit de fuite d'onde transversale et les données de composants z de détection de terrain transformées (105). Des données de bruit sont en outre identifiées au moyen du degré de similarité entre des données de composants d'enveloppe d'amplitude, de telle sorte que les données de bruit peuvent être efficacement identifiées.
PCT/CN2022/138648 2022-07-20 2022-12-13 Procédé et appareil d'identification de bruit, et dispositif et support d'enregistrement WO2024016572A1 (fr)

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