Crack identification method, device, equipment and medium
By preprocessing and eliminating interference in array acoustic logging data, and extracting and weighting instantaneous frequencies, the problem of reduced fracture identification accuracy was solved, achieving a highly efficient fracture identification effect.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA NAT PETROLEUM CORP
- Filing Date
- 2024-12-31
- Publication Date
- 2026-06-30
AI Technical Summary
Existing fracture identification methods are affected by interference factors, leading to reduced identification accuracy. Especially in complex geological environments, interference factors such as instrument errors, wellbore effects, and geological inhomogeneity cause fluctuations and instability in logging data, affecting the accuracy of fracture identification.
By preprocessing the array acoustic logging data, extracting the direct Stoneley wave signal, performing interference cancellation processing, extracting the instantaneous amplitude and frequency, and improving the signal-to-noise ratio through weighted processing, the fracture is finally identified based on the target instantaneous frequency.
It improves the accuracy of fracture identification, reduces identification costs, and is consistent with the results of high-cost electrical imaging logging, effectively solving the problem of reduced identification accuracy caused by interference.
Smart Images
Figure CN122307735A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of crack recognition technology, specifically to a crack recognition method, crack recognition device, electronic device, and machine-readable storage medium. Background Technology
[0002] Fractures, as important oil and gas reservoir spaces and fluid flow channels, play a crucial role in enhancing reservoir permeability. Currently, commonly used fracture identification methods include core analysis, thin section identification, and conventional logging (such as sonic logging, neutron logging, density logging, and resistivity logging).
[0003] Commonly used fracture identification methods can be affected by various interference factors, thus reducing the accuracy of identification. For example, conventional logging methods typically rely on measurements of downhole physical properties to infer formation characteristics, including the presence and nature of fractures. However, these measurements can be affected by noise sources such as instrument errors, wellbore effects, drilling fluid interference, and geological inhomogeneities, thereby impacting the reliability of the results.
[0004] In complex geological environments, especially where there are fractures, interlayer fluids, or uneven porosity distribution, interference factors may cause fluctuations and instability in well logging data, thereby increasing the error in fracture identification and affecting the accuracy of well logging data interpretation. Summary of the Invention
[0005] The purpose of this application is to provide a crack identification method, crack identification device, equipment, and medium to solve the problem of reduced crack identification accuracy caused by interference in the prior art.
[0006] To achieve the above objectives, the first aspect of this application provides a crack identification method, the method comprising: The acquired raw array waveform data is processed to extract the direct Stoneley wave signal and obtain the direct Stoneley wave waveform. The direct Stoneley wave waveform is subjected to interference cancellation processing to obtain the target waveform data; Extract the instantaneous amplitude and instantaneous frequency from the target waveform data; The instantaneous frequency is processed to improve the signal-to-noise ratio, resulting in an optimized instantaneous frequency; The target instantaneous frequency is determined by weighting the optimized instantaneous frequency using the instantaneous amplitude. Crack identification is performed based on the target instantaneous frequency to obtain the crack identification result corresponding to the original array waveform data.
[0007] In this embodiment of the application, the acquired raw array waveform data is processed to extract the direct-to-Stoneley wave signal, resulting in a direct-to-Stoneley wave waveform, including: The original array waveform data is preprocessed to retain the Stoneley wave signal, resulting in preprocessed waveform data. Wave field separation is performed on the preprocessed waveform data to extract the direct Stoneley wave signal and obtain the direct Stoneley wave waveform.
[0008] In this embodiment of the application, the original array waveform data is preprocessed to retain the Stoneley wave signal, resulting in preprocessed waveform data, including: The original array waveform data is subjected to gain and delay recovery processing to obtain the first preprocessed data; The first preprocessed data is subjected to DC component removal processing to obtain the second preprocessed data; The second preprocessed data is subjected to bandpass filtering to obtain the preprocessed waveform data.
[0009] In this embodiment of the application, interference cancellation processing is performed on the direct-to-Stoneley wave waveform to obtain target waveform data, including: The direct Stoneley wave waveform is subjected to interference cancellation processing using the predictive deconvolution method to obtain the target waveform data.
[0010] In this embodiment of the application, extracting the instantaneous amplitude and instantaneous frequency from the target waveform data includes: Perform a Hilbert transform on the target waveform data to obtain Hilbert transform data; The instantaneous amplitude and the instantaneous frequency are determined based on the target waveform data and the Hilbert transform data.
[0011] In this embodiment of the application, the instantaneous frequency is processed to improve the signal-to-noise ratio to obtain an optimized instantaneous frequency, including: A pre-whitening factor is introduced into the instantaneous frequency to obtain the optimized instantaneous frequency.
[0012] In this embodiment of the application, the target instantaneous frequency is determined by weighting the optimized instantaneous frequency using the instantaneous amplitude, including: Determine the time radius; Determine the summation interval based on the stated time radius; Based on the summation interval, the optimized instantaneous frequency is weighted by the instantaneous amplitude to determine the target instantaneous frequency.
[0013] A second aspect of this application provides a crack detection device, the device comprising: The data processing module is used to process the acquired raw array waveform data, extract the direct Stoneley wave signal, and obtain the direct Stoneley wave waveform. An interference cancellation module is used to perform interference cancellation processing on the direct Stoneley wave waveform to obtain target waveform data. The attribute extraction module is used to extract the instantaneous amplitude and instantaneous frequency from the target waveform data; The first attribute optimization module is used to improve the signal-to-noise ratio of the instantaneous frequency to obtain an optimized instantaneous frequency; The second attribute optimization module is used to perform weighted processing on the optimized instantaneous frequency based on the instantaneous amplitude to determine the target instantaneous frequency; The crack identification module is used to identify cracks based on the target instantaneous frequency and obtain the crack identification result corresponding to the original array waveform data.
[0014] In this embodiment of the application, the data processing module includes: The first data processing unit is used to preprocess the original array waveform data, retain the Stoneley wave signal therein, and obtain the preprocessed waveform data. The second data processing unit is used to perform wave field separation on the preprocessed waveform data, extract the direct Stoneley wave signal, and obtain the direct Stoneley wave waveform.
[0015] In this embodiment of the application, the interference cancellation module is specifically used for: The direct Stoneley wave waveform is subjected to interference cancellation processing using the predictive deconvolution method to obtain the target waveform data.
[0016] In this embodiment of the application, the attribute extraction module includes: A data transformation unit is used to perform a Hilbert transform on the target waveform data to obtain Hilbert transformed data; The attribute extraction unit is used to determine the instantaneous amplitude and the instantaneous frequency based on the target waveform data and the Hilbert transform data.
[0017] In this embodiment of the application, the first attribute optimization module is specifically used for: A pre-whitening factor is introduced into the instantaneous frequency to obtain the optimized instantaneous frequency.
[0018] In this embodiment of the application, the second attribute optimization module includes: Time radius determination unit, used to determine the time radius; The summation interval determination unit is used to determine the summation interval based on the time radius; The target instantaneous frequency determination unit is used to determine the target instantaneous frequency by weighting the optimized instantaneous frequency with the instantaneous amplitude based on the summation interval.
[0019] A third aspect of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the crack identification method described in the first aspect above.
[0020] A fourth aspect of this application provides a machine-readable storage medium storing instructions that, when executed by a processor, configure the processor to perform the crack identification method described in the first aspect above.
[0021] The fracture identification method, apparatus, equipment, and medium provided in this application process the raw array waveform data obtained by array acoustic logging to extract the direct Stoneley wave signal, thereby improving signal accuracy. Then, interference cancellation processing is applied to the extracted direct Stoneley wave waveform to reduce interference from reflected signals. Next, the instantaneous amplitude and instantaneous frequency of the target waveform data obtained after interference cancellation are extracted, and the signal-to-noise ratio of the instantaneous frequency, which is relatively sensitive to fracture response, is improved. The optimized instantaneous frequency is then weighted by the instantaneous amplitude, and finally, fracture identification is performed based on the weighted instantaneous frequency. This effectively solves the problem of reduced fracture identification accuracy caused by existing interference, improves the fracture identification capability based on array acoustic logging, and reduces fracture identification costs.
[0022] Other features and advantages of the embodiments of this application will be described in detail in the following detailed description section. Attached Figure Description
[0023] The accompanying drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the following detailed description to explain the embodiments of this application, but do not constitute a limitation on the embodiments of this application. In the drawings: Figure 1 The schematic diagram illustrates a flow chart of a crack identification method according to an embodiment of this application; Figure 2 This illustration shows the effect of wavefield separation using median filtering in an embodiment of this application. Figure 3 This illustration schematically shows the results of extracting the three instantaneous attributes of the Stoneley wave waveform at a single depth point according to an embodiment of this application. Figure 4 The illustration shows a comparison diagram of fracture identification using the method provided in the embodiments of this application and the electrical imaging logging method; Figure 5 This illustration shows a data processing flowchart of an application example in an embodiment of this application; Figure 6The schematic diagram illustrates the structural block diagram of a crack identification device according to an embodiment of this application; Figure 7 The diagram illustrates the internal structure of a computer device according to an embodiment of this application.
[0024] Explanation of reference numerals in the attached figures A01 - Processor; A02 - Network Interface; A03 - Internal Memory; A04 - Display Screen; A05 - Input Device; A06 - Non-volatile Storage Media; B01 - Operating System; B02 - Computer Program. Detailed Implementation
[0025] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for illustration and explanation of the embodiments of this application and are not intended to limit the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.
[0026] It should be noted that if the embodiments of this application involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.
[0027] Furthermore, if the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.
[0028] Currently, commonly used fracture identification methods include core analysis, thin section identification, and conventional logging (such as acoustic, neutron, density, and resistivity logging), among which: Core analysis and thin section identification methods are intuitive and reliable, but due to the lack of spatial continuity in core data, it is difficult to fully understand the development of fractures in the entire area or the entire target layer, thus affecting the accurate identification of fractures.
[0029] Conventional logging methods are economical and practical, and the logging series is relatively complete. However, their detection depth is limited and the vertical resolution is low. Especially when the reservoir is mainly composed of pores, the response of pores to the logging curve is usually much greater than the influence of fractures, which makes fracture information easy to be masked and difficult to identify accurately.
[0030] Furthermore, microresistivity imaging logging offers high resolution and spatial continuity, providing a direct visual representation of fracture development and making it suitable for precise fracture identification. However, its high cost and limited data volume are major limiting factors in practical applications, preventing its widespread use in fracture identification.
[0031] In view of the problem that interference reduces the accuracy of crack identification in related technologies, this application provides a crack identification method, apparatus, device and medium. The crack identification method, apparatus, device and medium provided in this application will be described in detail below with reference to the accompanying drawings and through specific embodiments and implementation methods.
[0032] Figure 1 A schematic flowchart illustrating the crack identification method of an embodiment of this application is shown. Figure 1 As shown in one embodiment of this application, a crack identification method is provided, which may include the following steps.
[0033] Step 100: Process the acquired raw array waveform data to extract the direct Stoneley wave signal and obtain the direct Stoneley wave waveform.
[0034] The original array waveform data is the data obtained by array acoustic logging within the logging interval.
[0035] Specifically, multipole array acoustic logging technology can acquire multiple modes of wave signals in a single well run, making it suitable for monitoring and assessment of the wellbore at various life stages. It also boasts low operating costs, thus its widespread application in fracture identification. This application embodiment selects multipole array acoustic logging technology (such as using multiple receivers in an array acoustic instrument) to acquire the original array waveform data, which is complete acoustic waveform data.
[0036] Stoneley wave reflection coefficients can be used for fracture identification, but formation interfaces can also generate Stoneley wave reflections, which may lead to aliasing of reflected signals and affect the accuracy of fracture identification. When using the energy attenuation changes of P-waves, S-waves, and Stoneley waves for fracture identification, data from only a single frequency point is usually used, making the method susceptible to noise interference. S-wave anisotropy analysis can achieve fracture and stress analysis, but anisotropy is subject to many constraints (such as formation heterogeneity and the complexity of fracture characteristics), which may produce multiple solutions, requiring correction by combining other information. Based on this, the embodiments of this application determine the direct Stoneley wave waveform to improve signal accuracy, thereby ensuring the accuracy of fracture identification.
[0037] In one specific example, step 100 includes the following steps.
[0038] Step 110: Preprocess the original array waveform data, retain the Stoneley wave signal, and obtain the preprocessed waveform data.
[0039] Specifically, the purpose of preprocessing the original array waveform data is to retain the Stoneley wave signal in the monopole array waveform in order to remove the interference from other components.
[0040] Step 110 includes the following steps.
[0041] Step 111: Perform gain and delay recovery processing on the original array waveform data to obtain the first preprocessed data.
[0042] Step 112: Perform DC component removal processing on the first preprocessed data to obtain the second preprocessed data.
[0043] Step 113: Perform bandpass filtering on the second preprocessed data to obtain the preprocessed waveform data.
[0044] Specifically, the main purpose of gain and delay recovery processing (i.e., eliminating gain and delay) is to restore the waveform to the true waveform of the formation; DC component removal processing refers to subtracting the average value of the waveform data at the fixed tail points from the overall waveform data to suppress the DC noise component in the original waveform; the purpose of bandpass filtering processing is to remove the interference of other mode waves in the waveform and retain the unipolar Stoneley wave component, thereby improving the accuracy of subsequent processing.
[0045] In this specific example, each specific step in step 110 can be implemented using existing methods, and this application does not limit them.
[0046] Step 120: Perform wave field separation on the preprocessed waveform data, extract the direct Stoneley wave signal, and obtain the direct Stoneley wave waveform.
[0047] Specifically, the purpose of wavefield separation is to further remove noise and obtain various wavefields that reflect different information about the strata.
[0048] In this specific example, step 120 can separate the direct Stoneley wave and the reflected Stoneley wave (i.e., separate the direct wave and the reflected wave) by median filtering, and extract the direct Stoneley wave signal for further processing.
[0049] Median filtering is a nonlinear smoothing technique based on ordination statistics theory. It monotonically sorts the input data according to their magnitude and takes the median value after sorting as the output result. For array acoustic wave data, the specific steps of median filtering include: selecting an appropriate filter window length; based on the waveform data of a given depth segment, selecting waveform data from m adjacent depths centered on a certain depth point, and recombining them to form an input numerical sequence; each time sampling point corresponds to a column of data, which is monotonically arranged according to their magnitude, and the median value after sorting is taken as the median filtering result for that depth point; adjusting the processing depth and repeating the above steps to complete the filtering.
[0050] A schematic diagram illustrating the effect of wavefield separation using median filtering is shown below. Figure 2 As shown, Figure 2 In the diagram, the first channel is the depth channel, the second channel is the original Stoneley wave waveform (i.e., the preprocessed waveform data), and the third channel is the direct Stoneley wave waveform obtained after median filtering of the second channel waveform. A schematic diagram of the three-instantaneous attribute extraction results of the direct Stoneley wave waveform at a single depth point is shown below. Figure 3 As shown, Figure 3 In the diagram, from bottom to top, are the direct-to-Stoneley wave waveform, instantaneous amplitude, instantaneous phase, and instantaneous frequency. Instantaneous amplitude reflects the energy change of the signal and is a measure of reflection intensity; instantaneous phase describes the continuity of the in-phase axis, and regardless of amplitude, instantaneous phase accurately represents the phase characteristics of the signal, even clearly displaying low-amplitude signals; instantaneous frequency is the rate at which the instantaneous phase changes over time, revealing the change in signal frequency.
[0051] according to Figure 2 and Figure 3 As can be seen, step 120 can effectively remove the artifacts of crack features caused by reflected waves or noise by extracting the direct Stoneley wave signal, thereby improving the accuracy of the signal.
[0052] Step 200: Perform interference cancellation processing on the direct Stoneley wave waveform to obtain the target waveform data.
[0053] In a specific example, step 200 uses a predictive deconvolution method to perform interference cancellation processing on the direct Stoneley wave waveform to obtain the target waveform data. The specific operation is as follows.
[0054] Assuming the predictor is Based on the current value of the input array waveform (i.e., the direct Stoneley wave waveform is) ) and before Within a time period (of which, The waveform value (as a preset constant) is used for... The waveform at subsequent times is then predicted, as shown in the results. The predicted value at time t is expressed using the convolution formula as follows:
[0055] in, express Predicted value at time, This represents the convolution operation.
[0056] The actual array waveform contains effective reflected signals and multiple invalid reflections and noise interference. In contrast, effective reflected signals generated by fractures or layer interfaces appear randomly and vary in time and space, while the occurrence of multiple invalid reflections and noise interference follows a certain pattern throughout the time period and is predictable. Therefore, subtracting the predicted value from the actual waveform can further eliminate the interference caused by multiple reflections during the propagation of the acoustic signal in the well. The optimized waveform signal is as follows:
[0057] in, express The optimized waveform signal (i.e., the target waveform data) corresponding to the given time. express The actual array waveform corresponding to the time. express The predicted value (i.e., the disturbance) at that time.
[0058] Step 200 further optimizes the calculation by introducing predictive deconvolution theory to eliminate interference caused by multiple reflections of the acoustic signal during its propagation in the well, so that it can more realistically reflect the characteristics of the well wall fractures.
[0059] Step 300: Extract the instantaneous amplitude and instantaneous frequency from the target waveform data.
[0060] In one specific example, step 300 includes the following steps.
[0061] Step 310: Perform Hilbert transform on the target waveform data to obtain Hilbert transform data.
[0062] Step 320: Determine the instantaneous amplitude and the instantaneous frequency based on the target waveform data and the Hilbert transform data.
[0063] Among them, based on target waveform data Extracted instantaneous amplitude It can be represented as:
[0064] Based on target waveform data Extracted instantaneous frequency It can be represented as:
[0065] in,
[0066] in, Indicates based on target waveform data The obtained Hilbert transform data, Indicates the instantaneous phase.
[0067] Based on the chain rule and arctangent function, instantaneous frequency By converting the calculation formula, we can obtain:
[0068] in, , They represent , The derivative of .
[0069] according to Figure 3 It can be seen that the instantaneous frequency is most sensitive to the crack response. Based on this, the embodiments of this application refine the instantaneous frequency calculation to improve its application effect.
[0070] Step 400: Improve the signal-to-noise ratio of the instantaneous frequency to obtain an optimized instantaneous frequency.
[0071] In one specific example, step 400 obtains the optimized instantaneous frequency by introducing a pre-whitening factor into the instantaneous frequency. It can be represented as:
[0072] By introducing a pre-whitening factor, the signal-to-noise ratio of the instantaneous frequency signal can be further improved, effectively enhancing the fault characteristics (i.e., crack characteristics) in the signal, thereby amplifying the instantaneous frequency response to cracks.
[0073] Step 500: The optimized instantaneous frequency is weighted by the instantaneous amplitude to determine the target instantaneous frequency.
[0074] In one specific example, step 500 includes the following steps.
[0075] Step 510: Determine the time radius.
[0076] Step 520: Determine the summation interval based on the time radius.
[0077] Step 530: Based on the summation interval, the optimized instantaneous frequency is weighted by the instantaneous amplitude to determine the target instantaneous frequency.
[0078] In this specific example, the instantaneous frequency of the target can be expressed as:
[0079] in, This represents the instantaneous frequency after weighted processing (i.e., the target instantaneous frequency). The time radius is a preset constant. Indicates the time before weighted processing The magnitude of the instantaneous frequency at that point; Indicates time The instantaneous amplitude at that point.
[0080] Step 500, by introducing a time radius (i.e., using it as a local time window) and combining it with the instantaneous amplitude to weight the optimized instantaneous frequency, can effectively suppress interference from irrelevant noise, thereby improving the accuracy of crack identification.
[0081] Step 600: Crack identification is performed based on the target instantaneous frequency to obtain the crack identification result corresponding to the original array waveform data.
[0082] In a specific example, after obtaining the target instantaneous frequency, the crack identification result corresponding to the original array waveform data is determined using current analysis tools. Of course, it is understood that after obtaining the target instantaneous frequency, it can be visualized, and those skilled in the art can also derive the crack identification result based on the visualization.
[0083] The following application example will be used to explain the crack identification method provided in the embodiments of this application.
[0084] The natural gamma logging data of a certain actual well is as follows Figure 4The first one in the middle, Figure 4 The second channel is the depth channel. The measured data is then analyzed as follows: Figure 5 The process shown is executed, and its corresponding unipolar Stoneley wave and three instantaneous attributes (instantaneous amplitude, instantaneous phase, and target instantaneous frequency) are: Figure 4 The third to sixth channels in the data. Using a high-cost electrical imaging logging method, fracture identification was performed based on this measured data, resulting in dynamic and static electrical imaging images. Figure 4 The 7th and 8th lanes.
[0085] Stoneley waves are guided waves that propagate along the wellbore. They are reflected and transmitted at locations where the elastic properties and permeability of the formation change, and their waveforms contain response characteristics to wellbore fractures. By extracting and analyzing the three instantaneous attributes of the waveform, this response can be amplified (i.e., the influence of fractures on the waveform can be magnified), especially the instantaneous frequency. At fracture sites, a distinct "chaotic" phenomenon appears at the tail of the wave, serving as an indicator of the presence of fractures.
[0086] according to Figure 4 As can be seen from the sixth line, there is obvious disorder at the tail end of 1600m, indicating abnormal attenuation of the waveform data at this point, which is characteristic of wellbore fractures; according to Figure 4 The 7th and 8th lines show that a crack has developed at 1600m. According to... Figure 4 As can be seen in track 6, below 1604m, the disorder in the instantaneous frequency results becomes more pronounced, consistent with the fracture indication location in the electrical imaging results. In other well sections where fractures are not well-developed, the instantaneous frequency results appear as several relatively regular vertical bands.
[0087] As can be seen from the above comparison, in well sections with wellbore fractures, the instantaneous frequency result of the direct Stoneley wave (i.e., the target instantaneous frequency) obtained by the method provided in the embodiments of this application has obvious response characteristics and is consistent with the results of electrical imaging logging, which can effectively identify wellbore fractures.
[0088] As can be seen, the fracture identification method provided in this application embodiment can further mine the waveform information in the array acoustic logging data without increasing the cost of on-site logging operations, and is highly consistent with the results of electrical imaging logging processing, effectively reducing the cost of fracture identification.
[0089] Figure 1 This is a flowchart illustrating a crack identification method in one embodiment. It should be understood that, although... Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise explicitly stated herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.
[0090] Figure 6 A schematic block diagram of a crack identification device according to an embodiment of this application is shown. Figure 6 As shown in one embodiment of this application, a crack identification device is provided, which may include the following functional modules.
[0091] The data processing module is used to process the acquired raw array waveform data, extract the direct Stoneley wave signal, and obtain the direct Stoneley wave waveform.
[0092] The interference cancellation module is used to perform interference cancellation processing on the direct Stoneley wave waveform to obtain the target waveform data.
[0093] The attribute extraction module is used to extract the instantaneous amplitude and instantaneous frequency from the target waveform data.
[0094] The first attribute optimization module is used to improve the signal-to-noise ratio of the instantaneous frequency to obtain an optimized instantaneous frequency.
[0095] The second attribute optimization module is used to perform weighted processing on the optimized instantaneous frequency using the instantaneous amplitude to determine the target instantaneous frequency.
[0096] The crack identification module is used to identify cracks based on the target instantaneous frequency and obtain the crack identification result corresponding to the original array waveform data.
[0097] In this embodiment of the application, the data processing module includes: The first data processing unit is used to preprocess the original array waveform data, retain the Stoneley wave signal, and obtain the preprocessed waveform data.
[0098] The second data processing unit is used to perform wave field separation on the preprocessed waveform data, extract the direct Stoneley wave signal, and obtain the direct Stoneley wave waveform.
[0099] In this embodiment of the application, the first data processing unit includes: The first data processing subunit is used to perform gain and delay recovery processing on the original array waveform data to obtain the first preprocessed data.
[0100] The second data processing subunit is used to perform DC component removal processing on the first preprocessed data to obtain the second preprocessed data.
[0101] The third data processing subunit is used to perform bandpass filtering on the second preprocessed data to obtain the preprocessed waveform data.
[0102] In this embodiment of the application, the interference cancellation module is specifically used for: The direct Stoneley wave waveform is subjected to interference cancellation processing using the predictive deconvolution method to obtain the target waveform data.
[0103] In this embodiment of the application, the attribute extraction module includes: The data transformation unit is used to perform Hilbert transform on the target waveform data to obtain Hilbert transformed data.
[0104] The attribute extraction unit is used to determine the instantaneous amplitude and the instantaneous frequency based on the target waveform data and the Hilbert transform data.
[0105] In this embodiment of the application, the first attribute optimization module is specifically used for: A pre-whitening factor is introduced into the instantaneous frequency to obtain the optimized instantaneous frequency.
[0106] In this embodiment of the application, the second attribute optimization module includes: The time radius determination unit is used to determine the time radius.
[0107] The summation interval determination unit is used to determine the summation interval based on the time radius.
[0108] The target instantaneous frequency determination unit is used to determine the target instantaneous frequency by weighting the optimized instantaneous frequency with the instantaneous amplitude based on the summation interval.
[0109] Since the crack identification device provided in this application is a virtual device corresponding to the crack identification method in the above embodiments, it can also solve the problem of reduced crack identification accuracy caused by interference in the prior art.
[0110] This application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the crack identification method described in the above embodiments.
[0111] The electronic device provided in this application embodiment includes a processor capable of running the crack identification method of the aforementioned embodiment, and therefore can also solve the problem of reduced crack identification accuracy caused by interference in the prior art.
[0112] This application provides a machine-readable storage medium storing instructions that, when executed by a processor, configure the processor to perform the crack identification method described in the above embodiments.
[0113] The machine-readable storage medium provided in this application embodiment stores instructions for causing the machine to execute the crack identification method of the above embodiment, thus also solving the problem of reduced crack identification accuracy caused by interference in the prior art.
[0114] Figure 7 The diagram schematically illustrates the internal structure of a computer device according to an embodiment of this application. Figure 7 As shown in one embodiment of this application, a computer device is provided, which can be a terminal. The computer device includes a processor A01, a network interface A02, a display screen A04, an input device A05, and a memory (not shown) connected via a system bus. The processor A01 provides computing and control capabilities. The memory includes internal memory A03 and a non-volatile storage medium A06. The non-volatile storage medium A06 stores an operating system B01 and a computer program B02. The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 stored in the non-volatile storage medium A06. The network interface A02 is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor A01, it implements a crack detection method. The display screen A04 can be a liquid crystal display or an e-ink display. The input device A05 can be a touch layer covering the display screen, or buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse, etc.
[0115] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.
[0116] In one embodiment, the crack identification device provided in this application can be implemented as a computer program, which can be implemented in, for example... Figure 7 The device operates on the computer device shown. The computer device's memory can store various program modules that make up the crack detection device, and the computer program composed of the various program modules causes the processor to execute the steps in the crack detection methods of the various embodiments of this application described in this specification.
[0117] Figure 7 The computer equipment shown can be used as follows Figure 6 The data processing module in the crack identification device shown executes step 100, the interference elimination module executes step 200, the attribute extraction module executes step 300, the first attribute optimization module executes step 400, the second attribute optimization module executes step 500, and the crack identification module executes step 600.
[0118] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0119] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0120] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0121] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0122] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.
[0123] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.
[0124] Computer-readable media include both permanent and non-permanent, removable and non-removable media, which can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.
[0125] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.
[0126] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.
Claims
1. A crack identification method, characterized in that, The method includes: The acquired raw array waveform data is processed to extract the direct Stoneley wave signal and obtain the direct Stoneley wave waveform. The direct Stoneley wave waveform is subjected to interference cancellation processing to obtain the target waveform data; Extract the instantaneous amplitude and instantaneous frequency from the target waveform data; The instantaneous frequency is processed to improve the signal-to-noise ratio, resulting in an optimized instantaneous frequency; The target instantaneous frequency is determined by weighting the optimized instantaneous frequency using the instantaneous amplitude. Crack identification is performed based on the target instantaneous frequency to obtain the crack identification result corresponding to the original array waveform data.
2. The method according to claim 1, characterized in that, The acquired raw array waveform data is processed to extract the direct-to-Stoneley wave signal, resulting in the direct-to-Stoneley wave waveform, including: The original array waveform data is preprocessed to retain the Stoneley wave signal, resulting in preprocessed waveform data. Wave field separation is performed on the preprocessed waveform data to extract the direct Stoneley wave signal and obtain the direct Stoneley wave waveform.
3. The method according to claim 2, characterized in that, The original array waveform data is preprocessed to retain the Stoneley wave signal, resulting in preprocessed waveform data, including: The original array waveform data is subjected to gain and delay recovery processing to obtain the first preprocessed data; The first preprocessed data is subjected to DC component removal processing to obtain the second preprocessed data; The second preprocessed data is subjected to bandpass filtering to obtain the preprocessed waveform data.
4. The method according to claim 1, characterized in that, Interference cancellation processing is performed on the direct Stoneley wave waveform to obtain target waveform data, including: The direct Stoneley wave waveform is subjected to interference cancellation processing using the predictive deconvolution method to obtain the target waveform data.
5. The method according to claim 1, characterized in that, Extracting the instantaneous amplitude and instantaneous frequency from the target waveform data includes: Perform a Hilbert transform on the target waveform data to obtain Hilbert transform data; The instantaneous amplitude and the instantaneous frequency are determined based on the target waveform data and the Hilbert transform data.
6. The method according to claim 1, characterized in that, The instantaneous frequency is processed to improve the signal-to-noise ratio, resulting in an optimized instantaneous frequency, including: A pre-whitening factor is introduced into the instantaneous frequency to obtain the optimized instantaneous frequency.
7. The method according to claim 1, characterized in that, The target instantaneous frequency is determined by weighting the optimized instantaneous frequency using the instantaneous amplitude, including: Determine the time radius; Determine the summation interval based on the stated time radius; Based on the summation interval, the optimized instantaneous frequency is weighted by the instantaneous amplitude to determine the target instantaneous frequency.
8. A crack detection device, characterized in that, The device includes: The data processing module is used to process the acquired raw array waveform data, extract the direct Stoneley wave signal, and obtain the direct Stoneley wave waveform. An interference cancellation module is used to perform interference cancellation processing on the direct Stoneley wave waveform to obtain target waveform data. The attribute extraction module is used to extract the instantaneous amplitude and instantaneous frequency from the target waveform data; The first attribute optimization module is used to improve the signal-to-noise ratio of the instantaneous frequency to obtain an optimized instantaneous frequency; The second attribute optimization module is used to perform weighted processing on the optimized instantaneous frequency based on the instantaneous amplitude to determine the target instantaneous frequency; The crack identification module is used to identify cracks based on the target instantaneous frequency and obtain the crack identification result corresponding to the original array waveform data.
9. The apparatus according to claim 8, characterized in that, The data processing module includes: The first data processing unit is used to preprocess the original array waveform data, retain the Stoneley wave signal therein, and obtain the preprocessed waveform data. The second data processing unit is used to perform wave field separation on the preprocessed waveform data, extract the direct Stoneley wave signal, and obtain the direct Stoneley wave waveform.
10. The apparatus according to claim 8, characterized in that, The interference cancellation module is specifically used for: The direct Stoneley wave waveform is subjected to interference cancellation processing using the predictive deconvolution method to obtain the target waveform data.
11. The apparatus according to claim 8, characterized in that, The attribute extraction module includes: A data transformation unit is used to perform a Hilbert transform on the target waveform data to obtain Hilbert transformed data; The attribute extraction unit is used to determine the instantaneous amplitude and the instantaneous frequency based on the target waveform data and the Hilbert transform data.
12. The apparatus according to claim 8, characterized in that, The first attribute optimization module is specifically used for: A pre-whitening factor is introduced into the instantaneous frequency to obtain the optimized instantaneous frequency.
13. The apparatus according to claim 8, characterized in that, The second attribute optimization module includes: Time radius determination unit, used to determine the time radius; The summation interval determination unit is used to determine the summation interval based on the time radius; The target instantaneous frequency determination unit is used to determine the target instantaneous frequency by weighting the optimized instantaneous frequency with the instantaneous amplitude based on the summation interval.
14. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the crack identification method according to any one of claims 1 to 7.
15. A machine-readable storage medium storing instructions thereon, characterized in that, When executed by a processor, this instruction causes the processor to be configured to perform the crack identification method according to any one of claims 1 to 7.