Noise Identification Method, Device, Equipment and Storage Medium

Abstract

The present application provides a noise recognition method, device, equipment and storage medium. The method includes: respectively performing TAUP transformation on the water inspection component data and the land inspection z-component data to obtain the transformed water inspection component data and the transformed land inspection z-component data; determining corresponding first amplitude envelope component data according to the transformed water inspection component data, and determining corresponding second amplitude envelope component data according to the transformed land inspection z-component data; determining corresponding similarity coefficients according to the first amplitude envelope component data and the second amplitude envelope component data; performing median filtering on the similarity coefficients to determine the corresponding shear wave leakage noise data interval; and determining corresponding shear wave leakage noise data according to the shear wave leakage noise data interval and the transformed land inspection z-component data. The method of the present application further identifies noise data through the similarity of the amplitude envelope component data, and can more effectively identify noise data.

Description

Technical Field

[0001] The present application relates to the technical field of geophysical exploration, and in particular, to a method, device, equipment and storage medium for noise recognition. Background Art

[0002] Seabed seismic exploration technology is a kind of offshore seismic exploration technology, which also consists of a seismic source and acquisition instruments. Seabed seismic exploration technology includes Ocean Bottom Node (OBN) seismic exploration technology.

[0003] OBN records four components, including a hydrophone p component, a geophone x component, a geophone y component and a geophone z component. However, due to factors such as the uneven seabed causing the nodes to not be horizontally placed and the coupling between the nodes and the seabed, there is a large amount of low-speed and low-frequency regular noise caused by the leakage of shear wave energy in the geophone z component. The existing method performs Tau-P transformation on the hydrophone p component and the geophone z component, scales the amplitude envelope of the transformation, and then performs noise prediction and suppression based on the scaled amplitude envelope.

[0004] In the existing method, only a simple scaling comparison is performed on the Tau-P transformation result. Although most significant noises can be identified, the recognition accuracy for weak noises is not high. Summary of the Invention

[0005] The present application provides a method, device, equipment and storage medium for noise recognition to solve the problem that the prior art cannot effectively identify noises.

[0006] In a first aspect, the present application provides a method for noise recognition, including:

[0007] Obtain the hydrophone component data and the geophone z component data corresponding to the four-component data, and perform TAUP transformation on the hydrophone component data and the geophone z component data respectively to obtain the transformed hydrophone component data and the transformed geophone z component data;

[0008] Determine the corresponding first amplitude envelope component data according to the transformed hydrophone component data, and determine the corresponding second amplitude envelope component data according to the transformed geophone z component data;

[0009] Determine the corresponding similarity coefficient according to the first amplitude envelope component data and the second amplitude envelope component data;

[0010] Perform median filtering on the similarity coefficient to determine the corresponding shear wave leakage noise data interval;

[0011] Determine the corresponding shear wave leakage noise data according to the shear wave leakage noise data interval and the transformed geophone z component data.

[0012] In a second aspect, the present application provides a noise recognition device, including:

[0013] A processing unit, configured 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 to obtain the transformed water detection component data and the transformed land detection z-component data;

[0014] A determination unit, configured to determine corresponding first amplitude envelope component data according to the transformed water detection component data, and determine corresponding second amplitude envelope component data according to the transformed land detection z-component data;

[0015] The determination unit is further configured to determine a corresponding similarity coefficient according to the first amplitude envelope component data and the second amplitude envelope component data;

[0016] The processing unit is further configured to perform median filtering on the similarity coefficient to determine a corresponding shear wave leakage noise data interval;

[0017] The determination unit is further configured to determine corresponding shear wave leakage noise data according to the shear wave leakage noise data interval and the transformed land detection z-component data.

[0018] In a third aspect, the present invention provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0019] The memory stores computer-executable instructions;

[0020] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the method described in the first aspect.

[0021] In a fourth aspect, the present invention provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, they are used to implement the method described in the first aspect.

[0022] The noise identification method, apparatus, device and storage medium provided in the present application obtain water detection component data and land detection z component data corresponding to four-component data, and perform TAUP transformation on the water detection component data and land detection z component data respectively to obtain the transformed water detection component data and the transformed land detection z component data; 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; determine the corresponding similarity coefficient based on the first amplitude envelope component data and the second amplitude envelope component data; perform median filtering on the similarity coefficient to determine the corresponding shear wave leakage noise data interval; determine the corresponding shear wave leakage noise data based on the shear wave leakage noise data interval and the transformed land detection z component data, and further identify the noise data through the similarity of the amplitude envelope component data, so as to more effectively identify the noise data, whether it is significant noise or weak noise. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0024] Figure 1 Schematic diagram of the network architecture of the noise recognition method provided by the present invention;

[0025] Figure 2 1 is a flow chart of a noise identification method provided in the first embodiment of the present invention;

[0026] Figure 3 1 is a flow chart of a noise identification method provided in the second embodiment of the present invention;

[0027] Figure 4 1 is a flow chart of a noise identification method provided in Embodiment 7 of the present invention;

[0028] Figure 5 1 is a flow chart of a noise identification method provided in Embodiment 8 of the present invention;

[0029] Figure 6 1 is a structural diagram of a noise identification device according to an embodiment of the present invention;

[0030] Figure 7 is a block diagram of an electronic device for implementing the noise recognition method according to an embodiment of the present invention.

[0031] The above drawings illustrate specific embodiments of the present application, which will be described in more detail below. These drawings and the textual description are not intended to limit the scope of the present application in any way, but rather to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments. Detailed implementation manners

[0032] Here, exemplary embodiments will be described in detail, and examples thereof are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numerals in different drawings represent the same or similar elements. The implementation manners described in the following exemplary embodiments do not represent all implementation manners consistent with the present application. On the contrary, they are merely examples of apparatuses and methods consistent with some aspects of the present application as detailed in the appended claims.

[0033] To clearly understand the technical solution of the present application, the solutions of the prior art will be introduced in detail first.

[0034] Submarine seismic exploration technology is a kind of offshore seismic exploration technology, and also consists of a seismic source and acquisition instruments. Submarine seismic exploration technology includes Ocean Bottom Node (OBN) seismic exploration technology. In OBN, node seismic instruments are placed underwater without cables for power supply and without communication. Each node seismic instrument operates independently and is completely independent of all other nodes.

[0035] OBN records four components, including a hydrophone p component, a geophone x component, a geophone y component, and a geophone z component. However, affected by factors such as the rugged changes on the seabed resulting in the nodes not being horizontally placed and the coupling between the nodes and the seabed, there is a large amount of low-speed and low-frequency regular noise caused by the leakage of shear wave energy in the Z-axis component. The existing method performs TAUP transformation on the hydrophone p component and the geophone z component, scales the amplitude envelope of the transformation, and then performs noise prediction and suppression based on the scaled amplitude envelope. Or simply add the amplitude envelopes of the transformation, and the obtained difference is considered as noise.

[0036] In the existing method, only simple scaling comparison or subtraction is performed on the Tau-P transformation result. Although most significant noises can be identified, the recognition accuracy of weaker noises is not high, and the noise in the geophone z component cannot be accurately identified.

[0037] Therefore, in view of the problem that the prior art cannot effectively identify noise, the inventors found in their research that by separately performing TAUP transformation on the water detection component data and the land detection z-component data, obtaining the transformed water detection component data and the transformed land detection z-component data, determining the first amplitude envelope component data based on the transformed water detection component data and determining the second amplitude envelope component data based on the transformed land detection z-component data, calculating the similarity between the first amplitude envelope component data and the second amplitude envelope component data, performing median filtering on the similarity coefficient to determine the shear wave leakage noise data interval, and further determining the corresponding shear wave leakage noise data based on the shear wave leakage noise data interval and the land detection z-component data after TAUP transformation, noise data can be identified. Compared with the prior art, noise data is further identified through the similarity of the amplitude envelope component data, and noise data can be more effectively identified, and both significant noise and weak noise can be well identified.

[0038] Therefore, based on the above creative discovery, the inventors proposed the technical solution of the embodiments of the present invention. The network architecture and application scenarios of the noise identification method provided by the embodiments of the present invention will be introduced below.

[0039] As Figure 1 shown, the network architecture corresponding to the noise identification method provided by the embodiments of the present invention includes: a subsea node 1 and an electronic device 2. The subsea node 1 is arranged on the seabed, and the subsea node 1 is communicatively connected to the electronic device 2. The subsea node 1 is used to collect four-component data. The electronic device 2 obtains the four-component data collected by the subsea node 2. The electronic device 2 separately performs TAUP transformation on the water detection component data and the land detection z-component data in the four-component data to obtain the transformed water detection component data and the transformed land detection z-component data. The electronic device 2 further determines the first amplitude envelope component data based on the transformed water detection component data and determines the second amplitude envelope component data based on the transformed land detection z-component data. The electronic device 2 calculates the similarity between 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 data based on the shear wave leakage noise data interval and the land detection z-component data after TAUP transformation, so as to identify the noise data. Compared with the prior art, noise data is further identified through the similarity of the amplitude envelope component data, and noise data can be more effectively identified, and both significant noise and weak noise can be well identified.

[0040] The embodiments of the present invention will be specifically described below with reference to the accompanying drawings.

[0041] Embodiment 1

[0042] Figure 2It is a schematic flowchart of the noise recognition method provided in the first embodiment of the present invention. As Figure 2 shown, the execution subject of the noise recognition method provided in this embodiment is a noise recognition device, and this noise recognition device is located in an electronic device. Then, the noise recognition method provided in this embodiment includes the following steps:

[0043] Step 101: Obtain the hydrophone component data and the land-based z-component data corresponding to the four-component data, and perform TAUP transformation on the hydrophone component data and the land-based z-component data respectively to obtain the transformed hydrophone component data and the transformed land-based z-component data.

[0044] In this embodiment, the four-component data is collected by an ocean bottom node (OBN). The four-component data includes the hydrophone component and the land-based z-component data. Among them, the land-based z-component data includes the noise caused by the leakage of shear wave energy and the effective signal. It is necessary to find the noise in the land-based z-component data. The noise caused by the leakage of shear wave energy has little influence on the hydrophone component data, and there is no such part of the noise in the hydrophone component data. Therefore, the noise data existing in the land-based z-component data can be found based on the hydrophone component data. In the time domain, the shear wave leakage noise and the effective signal are superimposed together and cannot be separated. It is necessary to transform the time domain to the TAUP domain for matching processing to find the noise interference. First, obtain the hydrophone component data and the land-based z-component data, perform three-dimensional TAUP transformation on the hydrophone component data to obtain the transformed hydrophone component data, that is, the three-dimensional data volume Pt in the TAUP domain, and perform three-dimensional TAUP transformation on the hydrophone component data to obtain the transformed hydrophone component data, that is, the three-dimensional data volume Zt in the TAUP domain. Among them, the TAUP transformation is also called the linear RADON transformation and the slant stack transformation. The TAUP transformation can remove the linear noise, enhance the energy of the effective wave, and significantly improve the signal-to-noise ratio of the stacked section.

[0045] Among them, the TAUP transformation formula is as follows:

[0046]

[0047] Among them, Pt is the transformed hydrophone component data, t represents time, x represents the transverse direction, y represents the longitudinal direction, τ represents the time intercept after TAUP transformation, and p x represents the slowness in the x direction after TAUP transformation, and p y represents the slowness in the y direction after TAUP transformation.

[0048]

[0049] Among them, Zt is the transformed land-based z-component data, t represents time, x represents the transverse direction, y represents the longitudinal direction, τ represents the time intercept after TAUP transformation, and p xDenoted as the slowness in the x - direction after the TAUP transform, p y Denoted as the slowness in the y - direction after the TAUP transform.

[0050] Step 102: Determine the corresponding first amplitude envelope component data according to the transformed hydrophone component data, and determine the corresponding second amplitude envelope component data according to the transformed land - based z - component data.

[0051] In this embodiment, slice processing and Hilbert transform are performed on the transformed hydrophone component data to obtain the corresponding first amplitude envelope component data, and slice processing and Hilbert transform are performed on the transformed land - based z - component data to obtain the corresponding second amplitude envelope component data.

[0052] Step 103: Determine the corresponding similarity coefficient according to the first amplitude envelope component data and the second amplitude envelope component data.

[0053] In this embodiment, the noise caused by shear - wave energy leakage has less influence on the hydrophone component data and greater influence on the land - based z - component data. Therefore, the similarity between the data can be calculated. Specifically, the mean similarity, variance similarity, and covariance similarity are respectively determined according to the first amplitude envelope component data and the second amplitude envelope component data, and further the similarity coefficient is calculated according to 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.

[0054] Step 104: Perform median filtering on the similarity coefficient to determine the corresponding shear - wave leakage noise data interval.

[0055] In this embodiment, median filtering is performed on the similarity coefficient. Median filtering has a good effect on filtering impulse noise. Especially when filtering noise, it can protect the edges of the signal from being blurred, thereby determining the corresponding shear - wave leakage noise data interval. The similarity coefficient is an array, and the value range is 0 - 1. The larger the value, the more similar it is, and the smaller the value, the less similar it is. The less similar area is the shear - wave leakage noise data interval.

[0056] Step 105: Determine the corresponding shear - wave leakage noise data according to the shear - wave leakage noise data interval and the transformed land - based z - component data.

[0057] In this embodiment, the shear - wave leakage noise data in the TAUP domain is obtained according to the shear - wave leakage noise data interval and the transformed land - based z - component data. Further, an inverse TAUP transform is performed on the shear - wave leakage noise data in the TAUP domain to obtain the shear - wave leakage noise data. The shear - wave leakage noise data is the identified noise data, that is, the noise caused by shear - wave energy leakage.

[0058] In this embodiment, TAUP transformation is respectively performed on the water inspection component data and the land inspection z-component data to obtain the transformed water inspection component data and the transformed land inspection z-component data. The first amplitude envelope component data is determined based on the transformed water inspection component data, and the second amplitude envelope component data is determined based on the transformed land inspection z-component data. The similarity between the first amplitude envelope component data and the second amplitude envelope component data is calculated, and median filtering is performed on the similarity coefficient to determine the shear wave leakage noise data interval. Further, the corresponding shear wave leakage noise data is determined based on the shear wave leakage noise data interval and the land inspection z-component data after TAUP transformation, so as to identify the noise data. Compared with the prior art, the noise data is further identified through the similarity of the amplitude envelope component data, and the noise data can be more effectively identified. Whether it is significant noise or weak noise, it can be well identified.

[0059] Embodiment 2

[0060] Figure 3 It is a schematic flowchart of the noise identification method provided in Embodiment 2 of the present invention. As Figure 3 shown, based on the noise identification method provided in Embodiment 1 of the present invention, the step of determining the corresponding first amplitude envelope component data according to the transformed water inspection component data in step 102 is further refined, including the following steps:

[0061] Step 1021: Perform slicing processing on the transformed water inspection component data to obtain the corresponding water inspection component slice data.

[0062] In this embodiment, the transformed water inspection component data is sliced along the transverse direction to obtain the corresponding water inspection component slice data.

[0063] Step 1022: Perform Hilbert transform on the water inspection component slice data to obtain the corresponding first amplitude envelope component data.

[0064] In this embodiment, Hilbert transform is performed on the water inspection component switching data to obtain the corresponding first amplitude envelope component data.

[0065] Optionally, the step of determining the corresponding second amplitude envelope component data according to the transformed land inspection z-component data in step 102 is further refined, including the following steps:

[0066] Step 1023: Perform slicing processing on the transformed land inspection z-component data to obtain the corresponding land inspection z-component slice data.

[0067] In this embodiment, the transformed water inspection component data is sliced along the transverse direction to obtain the corresponding land inspection z-component slice data.

[0068] Step 1024: Perform Hilbert transform on the land inspection z-component slice data to obtain the corresponding second amplitude envelope component data.

[0069] In this embodiment, perform Hilbert transform on the land inspection z-component switching data to obtain the corresponding second amplitude envelope component data.

[0070] In this embodiment, calculating the similarity between the water inspection component data and the land inspection z-component data using the amplitude envelope component data can effectively identify noise data.

[0071] Embodiment Three

[0072] Based on the noise identification method provided in Embodiment One of the present invention, step 103 is further refined, including the following steps:

[0073] Step 1031: Determine the corresponding mean similarity according to the first amplitude envelope component data and the second amplitude envelope component data, determine the corresponding variance similarity according to the first amplitude envelope component data and the second amplitude envelope component data, and determine the corresponding covariance similarity according to the first amplitude envelope component data and the second amplitude envelope component data.

[0074] In this embodiment, calculate the mean similarity between the first amplitude envelope component data and the second amplitude envelope component data, calculate the variance similarity between the first amplitude envelope component data and the second amplitude envelope component data, and calculate the covariance similarity between the first amplitude envelope component data and the second amplitude envelope component data.

[0075] Step 1032: Determine the corresponding similarity coefficient according to the mean similarity, variance similarity, and covariance similarity.

[0076] In this embodiment, substitute the mean similarity, variance similarity, and covariance similarity into the formula to calculate the total similarity, and determine the total similarity as the similarity coefficient. The formula is expressed as:

[0077] V(Pa,Za) = A1(Pa,Za) × A2(Pa,Za) × A3(Pa,Za) Formula (3)

[0078] Wherein, V is the similarity coefficient, A1 is the mean similarity, A2 is the variance similarity, A3 is the covariance similarity, Pa represents the first amplitude envelope component data, and Za is the second amplitude envelope component data.

[0079] In this embodiment, determine the similarity coefficient according to the mean similarity, variance similarity, and covariance similarity. Using the similarity coefficient can better represent the similarity between the two amplitude envelope component data.

[0080] Embodiment Four

[0081] Based on the noise recognition method provided in the third embodiment of the present invention, the determination of the corresponding mean similarity according to the first amplitude envelope component data and the second amplitude envelope component data in step 1031 is further refined, and specifically includes the following steps:

[0082] Step 1031a, calculate the first mean corresponding to the first amplitude envelope component data, and calculate the second mean corresponding to the second amplitude envelope component data.

[0083] In this embodiment, the first mean corresponding to the first amplitude envelope component data is calculated. Among them, the first amplitude envelope component data is composed of multiple first amplitude envelope component signals, and the first mean corresponding to the multiple first amplitude envelope component signals is calculated. The first mean formula is expressed as:

[0084]

[0085] Among them, u P is the first mean, and P is the first amplitude envelope component signal.

[0086] Furthermore, the second mean corresponding to the second amplitude envelope component data is calculated. Among them, the second amplitude envelope component data is composed of multiple second amplitude envelope component signals, and the second mean corresponding to the multiple second amplitude envelope component signals is calculated. The second mean formula is expressed as:

[0087]

[0088] Among them, u Z is the second mean, and Z is the second amplitude envelope component signal.

[0089] Step 1031b, calculate the corresponding mean similarity according to the first mean and the second mean.

[0090] In this embodiment, 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:

[0091]

[0092] Among them, A1 is the mean similarity, u P is the first mean, u Z is the second mean, and C1 is the first constant.

[0093] In this embodiment, the mean similarity can well represent the similarity between the two amplitude envelope component data

[0094] Embodiment Five

[0095] Based on the noise recognition method provided in the third embodiment of the present invention, the determination of the corresponding variance similarity in step 1031 according to the first amplitude envelope component data and the second amplitude envelope component data is further refined, specifically including the following steps:

[0096] 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.

[0097] In this embodiment, calculate the corresponding first variance according to the first amplitude envelope component data. Among them, the first amplitude envelope component data is composed of multiple first amplitude envelope component signals. Calculate the first variance corresponding to the multiple first amplitude envelope component signals. The first variance formula is expressed as:

[0098]

[0099] Among them, σ P is the first variance, u P is the first mean, and P is the first amplitude envelope component signal.

[0100] Furthermore, calculate the corresponding second variance according to the second amplitude envelope component data. Among them, the second amplitude envelope component data is composed of multiple second amplitude envelope component signals. Calculate the second variance corresponding to the multiple second amplitude envelope component signals. The second variance formula is expressed as:

[0101]

[0102] Among them, σ z is the second variance, u Z is the second mean, and Z is the second amplitude envelope component signal.

[0103] Step 1031d, calculate the corresponding variance similarity according to the first variance and the second variance.

[0104] In this embodiment, substitute the first variance and the second variance into the formula to calculate the corresponding variance similarity. The variance similarity formula is expressed as:

[0105]

[0106] Among them, A2 is the variance similarity, σ P is the first variance, σ z is the second variance, and C2 is the second constant.

[0107] In this embodiment, the variance similarity can well represent the similarity between two amplitude envelope component data.

[0108] Embodiment Six

[0109] Based on the noise recognition method provided in the third embodiment of the present invention, the determination of the corresponding covariance similarity in step 1031 according to the first amplitude envelope component data and the second amplitude envelope component data is further refined, and specifically includes the following steps:

[0110] 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 according to the corresponding covariance.

[0111] In this embodiment, calculate the covariance corresponding to the first amplitude envelope component data and the second amplitude envelope component data. Among them, the first amplitude envelope component data is composed of multiple first amplitude envelope component signals, and the second amplitude envelope component data is composed of multiple second amplitude envelope component signals. The covariance formula is expressed as:

[0112]

[0113] Among them, σ PZ is the covariance, u P is the first mean value, P is the first amplitude envelope component signal, u Z is the second mean value, and Z is the second amplitude envelope component signal.

[0114] Furthermore, calculate the corresponding covariance similarity according to the covariance. The covariance similarity formula is expressed as:

[0115]

[0116] Among them, A3 is the covariance similarity, σ PZ is the covariance, σ P is the first variance, σ z is the second variance, and C3 is the third constant.

[0117] In this embodiment, the covariance similarity can well represent the similarity between two amplitude envelope component data.

[0118] Embodiment Seven

[0119] Figure 4 is a schematic flowchart of the noise recognition method provided in the seventh embodiment of the present invention. As Figure 4 shown, based on the noise recognition methods provided in the first to sixth embodiments of the present invention, step 104 is further refined, and specifically includes the following steps:

[0120] Step 1041, perform median filtering on each value in the similarity coefficient to determine the corresponding output value.

[0121] In this embodiment, median filtering is performed on each value in the similarity coefficient. Specifically, the corresponding output value is further determined according to the initial window length, the preset maximum window length, the minimum value corresponding to the current window length of the value, the corresponding window center value, and the corresponding maximum value.

[0122] Step 1042: Determine all the corresponding output values as the corresponding shear wave leakage noise data intervals.

[0123] In this embodiment, each output value corresponding to each value in the similarity coefficient is determined as the corresponding shear wave leakage noise data interval. Further, according to the shear wave leakage noise data interval and the transformed land inspection z-component data, the shear wave leakage noise data in the TAUP domain is obtained. An inverse TAUP transform is performed on the shear wave leakage noise data in the TAUP domain to obtain the shear wave leakage noise data, which is the identified noise data.

[0124] In this embodiment, median filtering has a good filtering effect on impulse noise. Especially when filtering noise, it can protect the edges of the signal from being blurred, and a more accurate shear wave leakage noise data interval can be obtained.

[0125] Embodiment Eight

[0126] Figure 5 is a schematic flowchart of the noise identification method provided in Embodiment Eight of the present invention. As Figure 5 shown, on the basis of the noise identification method provided in Embodiment Seven of the present invention, step 1041 is further refined, specifically including the following steps:

[0127] Step 10411: Obtain the initial window length and the preset maximum window length, and determine the initial window length as the current window length. Obtain the minimum value, the corresponding window center value, and the corresponding maximum value corresponding to the current window length of the value.

[0128] In this embodiment, the initial window length and the preset maximum window length are obtained. Among them, the initial window length is predefined and is an odd number. The preset maximum window length is set according to the actual situation, and the initial window length is less than the preset maximum window length. The initial window length is determined as the current window length, and the minimum value, the corresponding window center value, and the corresponding maximum value corresponding to each value in the similarity coefficient are obtained.

[0129] Step 10412: 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, execute step 10413; if not, execute step 10414.

[0130] In this embodiment, it is determined whether the window center value corresponding to a value is greater than the corresponding minimum value and less than the corresponding maximum value, and the corresponding output value is further determined according to the magnitude relationship among the minimum value, the corresponding window center value, and the corresponding maximum value corresponding to each value in the similarity coefficient.

[0131] Step 10413: Determine the corresponding output value according to the value, the minimum value corresponding to the value, and the maximum value corresponding to the value.

[0132] In this embodiment, if the window center value corresponding to a certain value satisfies the condition of being greater than the corresponding minimum value and less than the corresponding maximum value, then the corresponding output value is further determined according to the magnitude relationship among the value, the minimum value corresponding to the value, and the maximum value corresponding to the value.

[0133] Step 10414: Adjust the current window length corresponding to the value, and determine the corresponding output value according to the adjusted window length corresponding to the value and the preset maximum window length.

[0134] In this embodiment, if the window center value corresponding to a certain value does not satisfy the condition of being greater than the corresponding minimum value and less than the corresponding maximum value, then adjust the current window length corresponding to the value, increase the window length, and determine the corresponding output value according to the adjusted window length corresponding to the value and the preset maximum window length.

[0135] In this embodiment, median filtering can be used to identify the noise data in the water detection component data, filter the valid signals in the water detection component data, and obtain more accurate noise data.

[0136] Embodiment Nine

[0137] Based on the noise identification method provided in Embodiment Eight of the present invention, step 10413 is further refined, which specifically includes the following steps:

[0138] Step 10413a: Determine whether the value is greater than the corresponding minimum value and less than the corresponding maximum value.

[0139] In this embodiment, the corresponding output value is further determined according to the magnitude relationship among the value, the minimum value corresponding to the value, and the maximum value corresponding to the value. Specifically, it is determined whether the value is greater than the corresponding minimum value and less than the corresponding maximum value.

[0140] Step 10413b: If so, determine the value as the corresponding output value.

[0141] In this embodiment, if the value satisfies the condition of being 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.

[0142] Step 10413c, if not, then determine the window center value corresponding to the value as the corresponding output value.

[0143] In this embodiment, if the value satisfies the condition of being greater than the corresponding minimum value and less than the corresponding maximum value, then determine the window center value under the current window length as the output value corresponding to the value.

[0144] In this embodiment, median filtering can identify the noise data in the water inspection component data and obtain more accurate noise data.

[0145] Embodiment Ten

[0146] Based on the noise recognition method provided in Embodiment Eight of the present invention, step 10414 is further refined, specifically including the following steps:

[0147] Step 10414a, if the adjusted window length corresponding to the value is equal to the preset maximum window length, then determine the window center value corresponding to the adjusted window length as the corresponding output value.

[0148] In this embodiment, determine whether 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 is equal to the preset maximum window length, it means that the window length has reached the maximum value and cannot be adjusted further. Determine the window center value corresponding to the adjusted window length as the corresponding output value.

[0149] Step 10414b, if the adjusted window length corresponding to the value is less than the preset maximum window length, then determine the adjusted window length as the current window length, and execute the steps of obtaining the minimum value, the corresponding window center value, and the corresponding maximum value corresponding to the current window length of the value.

[0150] In this embodiment, if 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 can be adjusted further. Further determine the adjusted window length as the current window length, and repeat the steps of obtaining the minimum value, the corresponding window center value, and the corresponding maximum value corresponding to the current window length of the value.

[0151] In this embodiment, median filtering can identify the noise data in the water inspection component data and obtain more accurate noise data.

[0152] Embodiment Eleven

[0153] Based on the noise recognition method provided in Embodiment One of the present invention, step 105 is further refined, specifically including the following steps:

[0154] Step 1051: Determine the shear-wave leakage noise data to be converted according to the shear-wave leakage noise data interval and the transformed land-based inspection z-component data.

[0155] In this embodiment, substitute the shear-wave leakage noise data interval and the land-based inspection z-component data after TAUP transformation into the formula to calculate the shear-wave leakage noise data to be converted. The formula is expressed as:

[0156] Ht = Zt - K × Zt Formula (12)

[0157] Wherein, Ht is the shear-wave leakage noise data to be converted, Zt is the transformed land-based inspection z-component data, and K is the shear-wave leakage noise data interval.

[0158] In this embodiment, K multiplied by Zt represents the effective signal part in the land-based inspection z-component data, and what is obtained by subtracting the effective signal part from Zt is the noise data part in the land-based inspection z-component data.

[0159] 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 corresponding shear-wave leakage noise data.

[0160] In this embodiment, perform inverse TAUP transformation on the shear-wave leakage noise data to be converted, obtain the shear-wave leakage noise data after inverse transformation, and determine the corresponding shear-wave leakage noise data.

[0161] Among them, the inverse TAUP transformation formula is as follows:

[0162]

[0163] Wherein, H is the shear-wave leakage noise data, t represents time, x represents the transverse direction, y represents the longitudinal direction, τ represents the time intercept after TAUP transformation, and p x represents the slowness in the x direction after TAUP transformation, and p y represents the slowness in the y direction after TAUP transformation.

[0164] In this embodiment, noise data can be more effectively identified, and both significant noise and weak noise can be well identified to obtain relatively accurate noise data.

[0165] Embodiment Twelve

[0166] Based on the noise identification method provided in Embodiment 1 of the present invention, after step 105, the following steps are further included:

[0167] Step 106: Denoise the land detector z-component data and the shear wave leakage noise data based on the shear wave leakage noise data to obtain the denoised land detector z-component data.

[0168] In this embodiment, the denoising process mainly calculates the difference between the land detector z-component data and the shear wave leakage noise data, and this difference is the denoised land detector z-component data.

[0169] In this embodiment, noise data can be more effectively identified, and the noise caused by a large amount of shear wave energy leakage in the land detector z-component data can be effectively removed, so as to obtain more accurate denoised land detector z-component data.

[0170] Figure 6 It is a schematic structural diagram of a noise recognition device provided by an embodiment of the present invention. As Figure 6 shown, the noise recognition device provided in this embodiment includes a processing unit 201 and a determination unit 202.

[0171] Among them, the processing unit 201 is used to obtain the hydrophone component data and the land detector z-component data corresponding to the four-component data, and perform TAUP transformation on the hydrophone component data and the land detector z-component data respectively to obtain the transformed hydrophone component data and the transformed land detector z-component data. The determination unit 202 is used to determine the corresponding first amplitude envelope component data according to the transformed hydrophone component data, and determine the corresponding second amplitude envelope component data according to the transformed land detector z-component data. The determination unit 202 is further used to determine the corresponding similarity coefficient according to the first amplitude envelope component data and the second amplitude envelope component data. The processing unit 201 is further used to perform median filtering on the similarity coefficient to determine the corresponding shear wave leakage noise data interval. The determination unit 202 is further used to determine the corresponding shear wave leakage noise data according to the shear wave leakage noise data interval and the transformed land detector z-component data.

[0172] Optionally, the determination unit is further used to perform slicing processing on the transformed hydrophone component data to obtain the corresponding hydrophone component slice data; perform Hilbert transform on the hydrophone component slice data to obtain the corresponding first amplitude envelope component data.

[0173] Optionally, the determination unit is further used to perform slicing processing on the transformed land detector z-component data to obtain the corresponding land detector z-component slice data; perform Hilbert transform on the land detector z-component slice data to obtain the corresponding second amplitude envelope component data.

[0174] Optionally, the determination unit is further configured to determine a corresponding mean similarity according to the first amplitude envelope component data and the second amplitude envelope component data, determine a corresponding variance similarity according to the first amplitude envelope component data and the second amplitude envelope component data, and determine a corresponding covariance similarity according to the first amplitude envelope component data and the second amplitude envelope component data; determine a corresponding similarity coefficient according to the mean similarity, the variance similarity, and the covariance similarity.

[0175] Optionally, the determination unit is further configured to calculate a first mean corresponding to the first amplitude envelope component data, and calculate a second mean corresponding to the second amplitude envelope component data; calculate a corresponding mean similarity according to the first mean and the second mean.

[0176] Optionally, the determination unit is further configured to calculate a first variance corresponding to the first amplitude envelope component data, and calculate a second variance corresponding to the second amplitude envelope component data; calculate a corresponding variance similarity according to the first variance and the second variance.

[0177] Optionally, the determination unit is further configured to calculate a covariance corresponding to the first amplitude envelope component data and the second amplitude envelope component data, and determine a corresponding covariance similarity according to the corresponding covariance.

[0178] Optionally, the processing unit is further configured to perform median filtering on each value in the similarity coefficient to determine a corresponding output value; determine all corresponding output values as a corresponding shear wave leakage noise data interval.

[0179] Optionally, the processing unit is further configured to obtain an initial window length and a preset maximum window length, and determine the initial window length as the current window length, obtain a minimum value, a corresponding window center value, and a corresponding maximum value corresponding to the current window length of the 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 a corresponding output value according to the value, the minimum value corresponding to the value, and the maximum value corresponding to the value; if not, adjust the current window length corresponding to the value, and determine a corresponding output value according to the adjusted window length corresponding to the value and the preset maximum window length.

[0180] Optionally, the processing unit is further configured 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, determine the window center value corresponding to the value as the corresponding output value.

[0181] Optionally, the processing unit is further 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 is less than the preset maximum window length, determine the adjusted window length as the current window length, and perform the steps of obtaining the minimum value, the corresponding window center value, and the corresponding maximum value corresponding to the current window length of the value.

[0182] Optionally, the determining unit is further configured to determine the shear wave leakage noise data to be converted according to the shear wave leakage noise data interval and the transformed land-based inspection z-component data; perform an inverse TAUP transform on the shear wave leakage noise data to be converted to 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.

[0183] Optionally, the processing unit is further configured to perform noise reduction processing on the land-based inspection z-component data according to the shear wave leakage noise data to obtain the denoised land-based inspection z-component data.

[0184] Figure 7 is a block diagram of an electronic device for implementing the noise recognition method according to an embodiment of the present invention, as Figure 7 shown. The electronic device 300 includes: a memory 301 and a processor 302.

[0185] The memory 301 stores computer execution instructions;

[0186] The processor 302 executes the computer execution instructions stored in the memory, so that the processor executes the method provided in any one of the above embodiments.

[0187] In an exemplary embodiment, there is also provided a computer-readable storage medium storing computer execution instructions, and the computer execution instructions are executed by a processor to perform the method in any one of the above embodiments.

[0188] In an exemplary embodiment, there is also provided a computer program product including a computer program, and the computer program is executed by a processor to perform the method in any one of the above embodiments.

[0189] Those skilled in the art will readily think of other implementations of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any variations, uses, or adaptations of the present application, which follow the general principles of the present application and include known common knowledge or conventional technical means in the technical field not disclosed in the present application. The specification and embodiments are only regarded as exemplary, and the true scope and spirit of the present application are pointed out by the following claims.

[0190] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present application is only limited by the appended claims.

Claims

1. A noise recognition method, characterized in that, The method includes: Obtaining the water inspection component data and the land inspection z-component data corresponding to the four-component data, and respectively performing TAUP transformation on the water inspection component data and the land inspection z-component data to obtain the transformed water inspection component data and the transformed land inspection z-component data; Determining corresponding first amplitude envelope component data according to the transformed water inspection component data, and determining corresponding second amplitude envelope component data according to the transformed land inspection z-component data; Determining a corresponding similarity coefficient according to the first amplitude envelope component data and the second amplitude envelope component data; Performing median filtering on the similarity coefficient to determine a corresponding shear wave leakage noise data interval; Determining corresponding shear wave leakage noise data according to the shear wave leakage noise data interval and the transformed land inspection z-component data.

2. The method according to claim 1, wherein The determining corresponding first amplitude envelope component data according to the transformed water inspection component data includes: Performing slicing processing on the transformed water inspection component data to obtain corresponding water inspection component slice data; Performing Hilbert transform on the water inspection component slice data to obtain corresponding first amplitude envelope component data.

3. The method according to claim 1, characterized in that, The determining a corresponding similarity coefficient according to the first amplitude envelope component data and the second amplitude envelope component data includes: Determining a corresponding mean similarity according to the first amplitude envelope component data and the second amplitude envelope component data, determining a corresponding variance similarity according to the first amplitude envelope component data and the second amplitude envelope component data, and determining a corresponding covariance similarity according to the first amplitude envelope component data and the second amplitude envelope component data; Determining a corresponding similarity coefficient according to the mean similarity, the variance similarity, and the covariance similarity.

4. The method according to claim 3, wherein The determining a corresponding mean similarity according to the first amplitude envelope component data and the second amplitude envelope component data includes: Calculating a first mean corresponding to the first amplitude envelope component data, and calculating a second mean corresponding to the second amplitude envelope component data; Calculating a corresponding mean similarity according to the first mean and the second mean.

5. The method according to claim 3, characterized in that, The determining a corresponding variance similarity according to the first amplitude envelope component data and the second amplitude envelope component data includes: Calculating a first variance corresponding to the first amplitude envelope component data, and calculating a second variance corresponding to the second amplitude envelope component data; Calculating a corresponding variance similarity according to the first variance and the second variance.

6. The method according to claim 3, wherein The determining a corresponding covariance similarity according to the first amplitude envelope component data and the second amplitude envelope component data includes: Calculating the covariance corresponding to the first amplitude envelope component data and the second amplitude envelope component data, and determining the corresponding covariance similarity according to the corresponding covariance.

7. The method according to any one of claims 1 to 6, characterized in that, The performing median filtering on the similarity coefficient to determine a corresponding shear wave leakage noise data interval includes: Performing median filtering on each value in the similarity coefficient to determine a corresponding output value; Determining all the corresponding output values as the corresponding shear wave leakage noise data interval.

8. The method according to claim 7, characterized in that, Performing median filtering on each value in the similarity coefficient to determine the corresponding output value includes: Obtaining an initial window length and a preset maximum window length, determining the initial window length as the current window length, and obtaining the minimum value, the corresponding window center value, and the corresponding maximum value corresponding to the current window length of the value; Determining 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, determining the corresponding output value according to the value, the corresponding minimum value, and the corresponding maximum value of the value; If not, adjusting the current window length corresponding to the value, and determining the corresponding output value according to the adjusted window length corresponding to the value and the preset maximum window length.

9. The method according to claim 8, characterized in that, The determining the corresponding output value according to the value, the corresponding minimum value, and the corresponding maximum value of the value includes: Determining whether the value is greater than the corresponding minimum value and less than the corresponding maximum value; If so, determining the value as the corresponding output value; If not, determining the window center value corresponding to the value as the corresponding output value.

10. The method according to claim 8, wherein The determining the corresponding output value according to the adjusted window length corresponding to the value and the preset maximum window length includes: If the adjusted window length corresponding to the value is equal to the preset maximum window length, determining 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 less than the preset maximum window length, determining the adjusted window length as the current window length, and performing the steps of obtaining the minimum value, the corresponding window center value, and the corresponding maximum value corresponding to the current window length of the value.

11. The method according to claim 1, wherein The determining the corresponding shear-wave leakage noise data according to the shear-wave leakage noise data interval and the transformed land detection z-component data includes: Determining the shear-wave leakage noise data to be converted according to the shear-wave leakage noise data interval and the transformed land detection z-component data; Performing inverse TAUP transformation on the shear-wave leakage noise data to be converted, obtaining the transformed shear-wave leakage noise data, and determining the transformed shear-wave leakage noise data as the corresponding shear-wave leakage noise data.

12. The method according to claim 1, characterized in that, After the determining the corresponding shear-wave leakage noise data according to the shear-wave leakage noise data interval and the transformed land detection z-component data, it further includes: Performing noise reduction processing on the land detection z-component data according to the shear-wave leakage noise data to obtain the denoised land detection z-component data.

13. A noise recognition device, characterized in that, The device includes: A processing unit, configured to obtain the hydrophone component data and the land detection z-component data corresponding to the four-component data, and perform TAUP transformation on the hydrophone component data and the land detection z-component data respectively to obtain the transformed hydrophone component data and the transformed land detection z-component data; A determination unit, configured to determine the corresponding first amplitude envelope component data according to the transformed hydrophone component data, and determine the corresponding second amplitude envelope component data according to the transformed land detection z-component data; The determination unit is 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 further configured to perform median filtering on the similarity coefficient to determine the corresponding shear wave leakage noise data interval; The determining unit is further configured to 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.

14. An electronic device, comprising: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor executes the method according to any one of claims 1 to 12.

15. A computer-readable storage medium, characterized in that, Computer-executable instructions are stored in the computer-readable storage medium, and when the computer-executable instructions are executed by a processor, they are used to implement the method according to any one of claims 1 to 12.

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