An array sonic logging signal Q value estimation method and related device

Abstract

The present application relates to the field of array acoustic logging, and discloses a method for estimating Q value of array acoustic logging signal and related equipment, the method comprises the following steps: acquiring array acoustic logging waveform data, and preprocessing the acquired array acoustic logging waveform data to obtain array acoustic logging waveform signal; performing frequency spectrum analysis on the array acoustic logging waveform signal through Fourier transform to obtain array acoustic logging waveform signal spectrum; calculating the Fourier amplitude spectrum ratio of adjacent receiver signals of the array acoustic logging to obtain the logarithm of the Fourier amplitude spectrum ratio; and obtaining the initial Q value between adjacent receivers through least square fitting according to the logarithm of the Fourier amplitude spectrum ratio; calculating the weight parameter of adjacent Q value by using maximum posterior probability for the initial Q value, updating the Q value, and completing the estimation of the Q value of the array acoustic logging signal, so that the final Q value estimation is more accurate.

Description

Technical Field

[0001] This invention relates to the field of array acoustic logging, specifically to a method for estimating the Q value of an array acoustic logging signal and related equipment. Background Technology

[0002] The acoustic attenuation quality factor (Q factor) of a formation is an important indicator for assessing rock elasticity, directly reflecting the rock's ability to absorb elastic waves. Accurately estimating the formation's Q factor has become one of the core topics in geophysical research. A high Q factor indicates that the rock absorbs elastic waves less and the waves propagate faster; while a low Q factor indicates that the rock absorbs waves more strongly, reflecting reduced wave velocity and increased attenuation. These characteristics not only relate to seismic wave propagation but also have significant application value in fields such as oil and gas exploration, groundwater resource management, and geological hazard assessment. Therefore, researchers continuously explore various methods to improve the accuracy and reliability of Q factor estimation.

[0003] In seismic exploration, quality factor estimation is typically divided into two aspects: time domain and frequency domain. Compared to time domain methods such as wavelet simulation and pulse rise time methods, frequency domain methods infer the acoustic properties and quality factor of rocks by analyzing the spectral characteristics of seismic signals. Common frequency domain methods include the logarithmic spectral ratio method, the center frequency shift method, and the peak frequency method. The logarithmic spectral ratio method proposed by Luo et al. in their paper "Q estimation by combining ISD with LSR method based on shaping-regularized inversion" estimates the Q factor by obtaining the signal spectrum through FFT and fitting the relationship between the logarithm of the Fourier transform spectral ratio and the frequency. The center frequency shift method and peak frequency shift method mentioned by Quan Y et al. in their paper "Seismicattenuation tomography using the frequency shift method" estimate the Q factor of formations by analyzing the changes in the center or peak frequency of the seismic wave spectrum. However, these methods are highly dependent on the type of seismic wavelet, and their effectiveness may vary significantly under different formation conditions or wavelet characteristics, thus affecting the accurate estimation of the Q factor.

[0004] In recent years, time-frequency domain-based Q-factor estimation methods have gradually attracted researchers' attention. Time-frequency domain analysis methods can simultaneously analyze signal characteristics in both time and frequency dimensions, and are better adapted to complex geological conditions and signal characteristics. Methods such as wavelet transform, S-transform, and generalized S-transform provide new approaches to Q-factor estimation. The wavelet transform, mentioned by Chakraborty et al. in their paper "Frequency-time decomposition if seismic data using wavelet-based methods," provides a frequency-varying "time-frequency" window with good accuracy in the low-frequency region. However, the basic wavelet requires admissibility conditions, and the selection of wavelets and their parameters, as well as the problem of multiple solutions, often lead to information loss during wavelet reconstruction. In his paper "Localization of the complex spectrum: the S transform", Stockwell proposed the S transform, which is a non-stationary signal analysis method between the short-time Fourier transform and the wavelet transform. It inherits the multi-resolution characteristics of the wavelet transform, maintains the connection with the Fourier transform, and has lossless reversibility in the transform. However, the fixed Gaussian window function of the S transform limits its practical application effect to a certain extent.

[0005] The shortcomings of existing methods are as follows: 1) The effectiveness of global statistics in frequency domain methods depends heavily on the signal-to-noise ratio of the data itself, especially when the signal-to-noise ratio of the data is low, the reliability is significantly reduced; 2) Time-frequency domain methods face the problem of universality of basic parameter settings and cannot be directly used for processing actual data, which limits their promotion in practical applications; 3) Existing methods rely on the accuracy of the first arrival of component waves, which greatly affects the selection of the calculation time window and thus affects the reliability of the estimation results.

[0006] In summary, optimizing existing frequency and time domain methods to improve their noise resistance and stability is an important direction for future research. Leveraging the advantages of data-driven approaches, developing new Q-factor estimation methods that adaptively extract features from large datasets can partially overcome the limitations of traditional methods in low signal-to-noise ratio scenarios. Summary of the Invention

[0007] The purpose of this invention is to provide a method and related equipment for estimating the Q value of array acoustic logging signals, so as to solve the technical problem of how to improve the accuracy of logging data under low signal-to-noise ratio conditions in the prior art.

[0008] This invention is achieved through the following technical solution: In a first aspect, the present invention provides a method for estimating the Q value of an array acoustic logging signal, comprising: Acquire array acoustic logging waveform data, and preprocess the acquired array acoustic logging waveform data to obtain array acoustic logging waveform signals; The spectrum of the array acoustic logging waveform signal is obtained by performing spectral analysis using Fourier transform. The logarithm of the ratio of the Fourier amplitude spectrum of the signals received by adjacent receivers in the array acoustic logging waveform signal spectrum is calculated. The initial Q value between adjacent receivers is obtained by least squares fitting based on the logarithm of the Fourier amplitude spectrum ratio. The weight parameters of adjacent Q values ​​are calculated using the maximum a posteriori probability for the initial Q value. The Q value is then updated using the weight parameters to complete the estimation of the Q value of the array acoustic logging signal.

[0009] Preferably, in the step of acquiring array acoustic logging waveform data, the array acoustic logging waveform data includes longitudinal waves, transverse waves, and Stoneley waves.

[0010] Preferably, in the step of preprocessing the acquired array acoustic logging waveform data to obtain the array acoustic logging waveform signal, the array acoustic logging waveform data is separated, denoised, and filtered using EMD signal decomposition and the Choi-Williams time-frequency distribution strategy to obtain the array acoustic logging waveform signal.

[0011] Preferably, the specific process of obtaining the spectrum of the array acoustic logging waveform signal by performing spectral analysis using Fourier transform is as follows: The continuous-time domain array acoustic logging waveform signal is discretized into multiple sampling points to form a discrete signal; The discrete Fourier transform algorithm is used to calculate the discrete signal, mapping each sampling point of the array acoustic logging waveform signal to a point in the frequency domain, thereby obtaining the frequency domain signal, i.e., the spectrum of the array acoustic logging waveform signal. Amplitude spectrum analysis and phase spectrum analysis are performed on the spectrum of the array acoustic logging waveform signal. Amplitude spectrum analysis obtains the intensity or energy distribution of the signal at different frequencies, while phase spectrum analysis obtains the phase information of the signal at different frequencies.

[0012] Preferably, in the step of calculating the logarithm of the Fourier amplitude spectrum ratio of the signals received by adjacent receivers in the array acoustic logging waveform signal spectrum, and obtaining the initial Q value between adjacent receivers through least squares fitting based on the logarithm of the Fourier amplitude spectrum ratio, the slope of the fitted line is obtained, and the Q value between adjacent receivers is obtained according to the Q value estimation formula of the spectrum ratio method and the obtained slope. The formula expression is as follows: , ,

[0013] Where m represents the slope of the straight line fitting; A(f) represents the amplitude of the Fourier spectrum; C represents a constant; Δt represents the time difference between the waveforms received by the two receivers; α represents the attenuation coefficient; v represents the velocity; f represents the frequency; and Q represents the quality factor.

[0014] Preferably, the initial Q-value spectral ratio method inversion Q-value induction is optimized, and the Q-value estimated by the spectral ratio method is used as a reference. The objective function expression is set as follows: min ‖Q_est - Q_true‖² Where Q_est is the Q value estimated by the spectral ratio method, and Q_true is the true Q value.

[0015] Preferably, the specific process of calculating the weight parameters of adjacent Q values ​​using the maximum a posteriori probability for the initial Q value, and updating the Q value using the weight parameters to complete the estimation of the Q value of the array acoustic logging signal is as follows: Define a prior distribution for the initial Q value, where the prior distribution includes the Gaussian distribution and the log-normal distribution; Construct a likelihood function, and determine the probability of the observed data occurring under the initial Q value based on the likelihood function; By combining the prior distribution and the likelihood function, Bayes' theorem is used to calculate the posterior distribution, and the Q value that maximizes the probability is found in the posterior distribution, which is the maximum posterior probability estimate. Determine the relationship between adjacent Q values, and calculate the weight parameters of adjacent Q values ​​based on the relationship between adjacent Q values ​​and the results of the maximum a posteriori probability estimation; The initial Q value is iteratively updated based on the weight parameters and the differences between adjacent Q values. In the iterative update, a new Q value estimate is calculated based on the weight parameters of the current Q value and adjacent Q values, thus completing the estimation of the Q value of the array acoustic logging signal.

[0016] Secondly, the present invention also provides a system for estimating the Q value of an array acoustic logging signal, comprising: The data preprocessing module is used to acquire array acoustic logging waveform data and preprocess the acquired array acoustic logging waveform data to obtain array acoustic logging waveform signals. The spectrum analysis module is used to perform spectrum analysis on the array acoustic logging waveform signal through Fourier transform to obtain the spectrum of the array acoustic logging waveform signal; The data calculation module is used to calculate the logarithm of the Fourier amplitude spectrum ratio of the signals received by adjacent receivers in the array acoustic logging waveform signal spectrum, and to obtain the initial Q value between adjacent receivers by least squares fitting based on the logarithm of the Fourier amplitude spectrum ratio. The data estimation module is used to calculate the weight parameters of adjacent Q values ​​using the maximum a posteriori probability of the initial Q value, and to update the Q value through the weight parameters, thus completing the estimation of the Q value of the array acoustic logging signal.

[0017] Thirdly, the present invention also provides a mobile terminal, 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 steps of the method for estimating the Q value of the array acoustic logging signal as described above.

[0018] Fourthly, the present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method for estimating the Q value of an array acoustic logging signal as described above.

[0019] Compared with the prior art, the present invention has the following beneficial technical effects: This invention provides a method for estimating the Q-value of array acoustic logging signals. By preprocessing and spectral analysis of the array acoustic logging waveform data, the characteristics of acoustic wave propagation in the formation can be precisely extracted. This allows for a more accurate understanding of the formation's physical properties. The initial Q-values ​​between adjacent receivers are calculated using the spectral ratio method, which provides a preliminary assessment of the formation's attenuation characteristics. This invention employs maximum a posteriori probability to calculate weighting parameters for adjacent Q-values. Updating the Q-values ​​using these weighting parameters results in a more robust and reliable final Q-value estimate.

[0020] Furthermore, using conventional logging data such as array acoustic waveforms, the quality factor Q of different types of signals (P-wave, S-wave, Stoneley wave) can be quickly and effectively calculated with high accuracy and efficiency. This achieves accurate calculation of the quality factor Q of array acoustic signals. Combined with other conventional logging curves (AC, Gamma, porosity, etc.), it can effectively identify reservoir sections with well-developed pores and fractures, improving the accuracy of fracture attributes in oil and gas reservoir exploration, avoiding misclassification and omission of oil and gas fractured reservoirs, and achieving accurate identification of effective reservoirs. In the absence of special logging projects such as electrical imaging and nuclear magnetic resonance, this invention can quickly and effectively calculate the reservoir quality factor Q using conventional array acoustic logging data, identify high-quality reservoirs, and achieve low cost. It can improve production efficiency, save exploration costs, and has good universality and promotion value. Attached Figure Description

[0021] Figure 1 This is a flowchart illustrating the method for estimating the Q value of an array acoustic logging signal in an embodiment of the present invention; Figure 2 This is a schematic diagram of array acoustic logging signals recorded by a monopole acoustic source in an embodiment of the present invention; Figure 3This is a schematic diagram illustrating the steps of the maximum a posteriori probability estimation Q-value algorithm in an embodiment of the present invention; Figure 4 This is a schematic diagram of the estimated Q-value results based on the maximum a posteriori probability algorithm in an embodiment of the present invention; Figure 5 This is a structural diagram of the array acoustic logging signal Q-value estimation system in an embodiment of the present invention; In the diagram: 1. Data preprocessing module; 2. Spectrum analysis module; 3. Data calculation module; 4. Data estimation module. Detailed Implementation

[0022] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0023] The purpose of this invention is to provide a method and related equipment for estimating the Q value of array acoustic logging signals, so as to solve the technical problem of how to improve the accuracy of logging data under low signal-to-noise ratio conditions in the prior art.

[0024] The present invention will now be described in further detail with reference to the accompanying drawings: Example 1 See Figure 1 In one embodiment of the present invention, a method for estimating the Q value of an array acoustic logging signal is provided, comprising: Step 1: Acquire array acoustic logging waveform data and preprocess the acquired array acoustic logging waveform data to obtain array acoustic logging waveform signals; Specifically, in the step of acquiring array acoustic logging waveform data, the array acoustic logging waveform data includes longitudinal waves, transverse waves, and Stoneley waves, according to... Figure 2 As shown, the first arrival of three waveforms is used as auxiliary information to extract longitudinal waves, transverse waves and Stoneley waves respectively.

[0025] Specifically, the array acoustic logging waveform signal is obtained by performing waveform separation, denoising, and filtering on the array acoustic logging waveform data through EMD signal decomposition and the Choi-Williams time-frequency distribution strategy.

[0026] Step 2: Perform spectral analysis on the array acoustic logging waveform signal using Fourier transform to obtain the spectrum of the array acoustic logging waveform signal; Specifically, the process of obtaining the spectrum of the array acoustic logging waveform signal by performing spectral analysis using Fourier transform is as follows: The continuous-time domain array acoustic logging waveform signal is discretized into multiple sampling points to form a discrete signal; The discrete Fourier transform algorithm is used to calculate the discrete signal, mapping each sampling point of the array acoustic logging waveform signal to a point in the frequency domain, thereby obtaining the frequency domain signal, i.e., the spectrum of the array acoustic logging waveform signal.

[0027] Among them, amplitude spectrum analysis and phase spectrum analysis are performed on the spectrum of the array acoustic logging waveform signal. Amplitude spectrum analysis obtains the intensity or energy distribution of the signal at different frequencies; phase spectrum analysis obtains the phase information of the signal at different frequencies.

[0028] Step 3: Calculate the logarithm of the ratio of the Fourier amplitude spectrum of the signals received by adjacent receivers in the array acoustic logging waveform signal spectrum. Based on the logarithm of the Fourier amplitude spectrum ratio, obtain the initial Q value between adjacent receivers through least squares fitting. Specifically, the slope of the fitted line is obtained, and the Q-value between adjacent receivers is obtained using the spectral ratio method and the obtained slope. The formula expression is as follows: , ,

[0029] Where m represents the slope of the straight line fitting; A(f) represents the amplitude of the Fourier spectrum; C represents a constant; Δt represents the time difference between the waveforms received by the two receivers; α represents the attenuation coefficient; v represents the velocity; f represents the frequency; and Q represents the quality factor.

[0030] Specifically, the initial Q-value spectral ratio method inversion Q-value induction is optimized. The Q-value estimated by the spectral ratio method is used as a reference, and the objective function expression is set as follows: min ‖Q_est - Q_true‖² Where Q_est is the Q value estimated by the spectral ratio method, and Q_true is the true Q value.

[0031] The Q-value inversion using the spectral ratio method is reduced to an optimization problem. Assuming that the first arrival point of the receiver wavelet follows a Gaussian distribution, the optimization problem to be inverted also follows a normal distribution.

[0032] Step 4: Calculate the weight parameters of adjacent Q values ​​using the maximum a posteriori probability for the initial Q value, update the Q value using the weight parameters, and complete the estimation of the Q value of the array acoustic logging signal.

[0033] Specifically, the process is as follows: Define a prior distribution for the initial Q value, where the prior distribution includes the Gaussian distribution and the log-normal distribution; Construct a likelihood function, and determine the probability of the observed data occurring under the initial Q value based on the likelihood function; By combining the prior distribution and the likelihood function, Bayes' theorem is used to calculate the posterior distribution, and the Q value that maximizes the probability is found in the posterior distribution, which is the maximum posterior probability estimate. Determine the relationship between adjacent Q values, and calculate the weight parameters of adjacent Q values ​​based on the relationship between adjacent Q values ​​and the results of the maximum a posteriori probability estimation; The initial Q value is iteratively updated based on the weight parameters and the differences between adjacent Q values. In the iterative update, a new Q value estimate is calculated based on the weight parameters of the current Q value and adjacent Q values, thus completing the estimation of the Q value of the array acoustic logging signal.

[0034] In this embodiment, the array acoustic logging tool transmitter transmits a signal at a certain depth. All receivers are spaced at the same interval, so the Q values ​​between adjacent electrodes should be similar. The Q value calculated with the previous electrode is used as prior information for calculating the Q value with the next electrode. The weight parameters of adjacent Q values ​​are calculated using the maximum a posteriori probability method, thereby continuously updating the Q values. The Q value calculated between electrodes that receive the signal later should include the Q value information calculated between receivers that receive the signal earlier.

[0035] Among them, according to Figure 3 As shown, firstly, the Q value between adjacent plates is calculated using the traditional spectral ratio method; secondly, considering the similarity of the Q values ​​between adjacent plates, the Q value between the plates that receive the signal first is used as prior information for the Q value between the plates that receive the signal later; finally, the Q value is iteratively updated using the maximum a posteriori probability algorithm, thereby improving the stability of the Q value compared to the spectral ratio method.

[0036] The following diagram illustrates the Q-value estimation results based on the maximum a posteriori probability algorithm, obtained through the above steps. Figure 4 Based on array acoustic logging data, an optimization algorithm for estimating the Q value of array acoustic logging signal attenuation has been developed. This algorithm can effectively solve the problem of large differences between the attenuation algorithms and processing results of longitudinal waves, transverse waves and Stoneley waves in array acoustic logging data by traditional processing platforms such as the spectral ratio method, bringing huge economic benefits.

[0037] In summary, this invention provides a method for estimating the Q-value of array acoustic logging signals. By preprocessing and spectral analysis of the array acoustic logging waveform data, the characteristics of acoustic wave propagation in the formation can be extracted precisely. This allows for a more accurate understanding of the formation's physical properties. The initial Q-values ​​between adjacent receivers are calculated using the spectral ratio method, which provides a preliminary assessment of the formation's attenuation characteristics. This invention employs maximum a posteriori probability to calculate weighting parameters for adjacent Q-values. Updating the Q-values ​​using these weighting parameters results in a more robust and reliable final Q-value estimate.

[0038] Example 2 according to Figure 5 As shown, the present invention also provides a system for estimating the Q value of an array acoustic logging signal, comprising: Data preprocessing module 1 is used to acquire array acoustic logging waveform data and preprocess the acquired array acoustic logging waveform data to obtain array acoustic logging waveform signals. Spectrum analysis module 2 is used to perform spectrum analysis on the array acoustic logging waveform signal through Fourier transform to obtain the spectrum of the array acoustic logging waveform signal; Data calculation module 3 is used to calculate the logarithm of the Fourier amplitude spectrum ratio of the signals received by adjacent receivers in the array acoustic logging waveform signal spectrum, and to obtain the initial Q value between adjacent receivers by least squares fitting based on the logarithm of the Fourier amplitude spectrum ratio. Data estimation module 4 is used to calculate the weight parameters of adjacent Q values ​​using the maximum a posteriori probability of the initial Q value, and update the Q value through the weight parameters to complete the estimation of the Q value of the array acoustic logging signal.

[0039] Example 3 The present invention also provides a mobile terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, such as a program for estimating the Q value of an array acoustic logging signal.

[0040] When the processor executes the computer program, it implements the steps of the above-described method for estimating the Q value of the array acoustic logging signal, for example: Acquire array acoustic logging waveform data, and preprocess the acquired array acoustic logging waveform data to obtain array acoustic logging waveform signals; The spectrum of the array acoustic logging waveform signal is obtained by performing spectral analysis using Fourier transform. The logarithm of the ratio of the Fourier amplitude spectrum of the signals received by adjacent receivers in the array acoustic logging waveform signal spectrum is calculated. The initial Q value between adjacent receivers is obtained by least squares fitting based on the logarithm of the Fourier amplitude spectrum ratio. The weight parameters of adjacent Q values ​​are calculated using the maximum a posteriori probability for the initial Q value. The Q value is then updated using the weight parameters to complete the estimation of the Q value of the array acoustic logging signal.

[0041] Alternatively, when the processor executes the computer program, it implements the functions of each module in the above system, for example: Data preprocessing module 1 is used to acquire array acoustic logging waveform data and preprocess the acquired array acoustic logging waveform data to obtain array acoustic logging waveform signals. Spectrum analysis module 2 is used to perform spectrum analysis on the array acoustic logging waveform signal through Fourier transform to obtain the spectrum of the array acoustic logging waveform signal; Data calculation module 3 is used to calculate the logarithm of the Fourier amplitude spectrum ratio of the signals received by adjacent receivers in the array acoustic logging waveform signal spectrum, and to obtain the initial Q value between adjacent receivers by least squares fitting based on the logarithm of the Fourier amplitude spectrum ratio. Data estimation module 4 is used to calculate the weight parameters of adjacent Q values ​​using the maximum a posteriori probability of the initial Q value, and update the Q value through the weight parameters to complete the estimation of the Q value of the array acoustic logging signal.

[0042] For example, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the mobile terminal.

[0043] For example, the computer program can be divided into a data preprocessing module 1, a spectrum analysis module 2, a data calculation module 3, and a data estimation module 4; The specific functions of each module are as follows: Data preprocessing module 1 is used to acquire array acoustic logging waveform data and preprocess the acquired array acoustic logging waveform data to obtain array acoustic logging waveform signals. Spectrum analysis module 2 is used to perform spectrum analysis on the array acoustic logging waveform signal through Fourier transform to obtain the spectrum of the array acoustic logging waveform signal; Data calculation module 3 is used to calculate the logarithm of the Fourier amplitude spectrum ratio of the signals received by adjacent receivers in the array acoustic logging waveform signal spectrum, and to obtain the initial Q value between adjacent receivers by least squares fitting based on the logarithm of the Fourier amplitude spectrum ratio. Data estimation module 4 is used to calculate the weight parameters of adjacent Q values ​​using the maximum a posteriori probability of the initial Q value, and update the Q value through the weight parameters to complete the estimation of the Q value of the array acoustic logging signal.

[0044] The mobile terminal can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. The mobile terminal may include, but is not limited to, a processor and memory.

[0045] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the mobile terminal, connecting various parts of the mobile terminal via various interfaces and lines.

[0046] The memory can be used to store the computer program and / or module. The processor implements various functions of the mobile terminal by running or executing the computer program and / or module stored in the memory and calling the data stored in the memory.

[0047] The memory may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a function (such as sound playback, image playback, etc.); the data storage area may store data created based on the use of the mobile phone (such as audio data, phonebook, etc.). Furthermore, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disks, RAM, plug-in hard disks, SmartMediaCards (SMC), Secure Digital (SD) cards, FlashCards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0048] Example 4 The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method for estimating the Q value of an array acoustic logging signal.

[0049] If the modules / units integrated in the mobile terminal are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0050] Based on this understanding, all or part of the processes in the above method can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of the above-described aggregated reinforcement learning resource scheduling method. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate form.

[0051] The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0052] It should be noted that the content contained in the computer-readable medium may be appropriately added to or subtracted from the content as required by the legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium may not include electrical carrier signals and telecommunication signals.

[0053] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for estimating the Q value of an array acoustic logging signal, characterized in that, include: Acquire array acoustic logging waveform data, and preprocess the acquired array acoustic logging waveform data to obtain array acoustic logging waveform signals; The spectrum of the array acoustic logging waveform signal is obtained by performing spectral analysis using Fourier transform. The logarithm of the ratio of the Fourier amplitude spectrum of the signals received by adjacent receivers in the array acoustic logging waveform signal spectrum is calculated. The initial Q value between adjacent receivers is obtained by least squares fitting based on the logarithm of the Fourier amplitude spectrum ratio. The weight parameters of adjacent Q values ​​are calculated using the maximum a posteriori probability for the initial Q value. The Q value is then updated using the weight parameters to complete the estimation of the Q value of the array acoustic logging signal.

2. The method for estimating the Q value of an array acoustic logging signal according to claim 1, characterized in that, In the step of acquiring array acoustic logging waveform data, the array acoustic logging waveform data includes longitudinal waves, transverse waves, and Stoneley waves.

3. The method for estimating the Q value of an array acoustic logging signal according to claim 1, characterized in that, In the step of preprocessing the acquired array acoustic logging waveform data to obtain the array acoustic logging waveform signal, the array acoustic logging waveform data is separated, denoised, and filtered by EMD signal decomposition and the Choi-Williams time-frequency distribution strategy to obtain the array acoustic logging waveform signal.

4. The method for estimating the Q value of an array acoustic logging signal according to claim 1, characterized in that, The specific process for obtaining the spectrum of the array acoustic logging waveform signal by performing spectral analysis using Fourier transform is as follows: The continuous-time domain array acoustic logging waveform signal is discretized into multiple sampling points to form a discrete signal; The discrete Fourier transform algorithm is used to calculate the discrete signal, mapping each sampling point of the array acoustic logging waveform signal to a point in the frequency domain, thereby obtaining the frequency domain signal, i.e., the spectrum of the array acoustic logging waveform signal. Amplitude spectrum analysis and phase spectrum analysis are performed on the spectrum of the array acoustic logging waveform signal. Amplitude spectrum analysis obtains the intensity or energy distribution of the signal at different frequencies, while phase spectrum analysis obtains the phase information of the signal at different frequencies.

5. The method for estimating the Q value of an array acoustic logging signal according to claim 1, characterized in that, In the step of calculating the logarithm of the Fourier amplitude spectrum ratio of the signals received by adjacent receivers in the array acoustic logging waveform signal spectrum, and obtaining the initial Q value between adjacent receivers through least squares fitting based on the logarithm of the Fourier amplitude spectrum ratio, the slope of the fitted line is obtained. The Q value between adjacent receivers is then obtained according to the Q value estimation formula of the spectrum ratio method and the obtained slope. The formula expression is as follows: ， ， Where m represents the slope of the straight line fitting; A(f) represents the amplitude of the Fourier spectrum; C represents a constant; Δt represents the time difference between the waveforms received by the two receivers; α represents the attenuation coefficient; v represents the velocity; f represents the frequency; and Q represents the quality factor.

6. The method for estimating the Q value of an array acoustic logging signal according to claim 1, characterized in that, The optimization of the initial Q-value spectral ratio method inversion Q-value induction is carried out by taking the Q-value estimated by the spectral ratio method as a reference, and setting the objective function expression as follows: min ‖Q_est - Q_true‖² Where Q_est is the Q value estimated by the spectral ratio method, and Q_true is the true Q value.

7. The method for estimating the Q value of an array acoustic logging signal according to claim 1, characterized in that, The specific process of estimating the Q-value of the array acoustic logging signal by calculating the weight parameters of adjacent Q-values ​​using the maximum a posteriori probability and updating the Q-value using the weight parameters is as follows: Define a prior distribution for the initial Q value, where the prior distribution includes the Gaussian distribution and the log-normal distribution; Construct a likelihood function, and determine the probability of the observed data occurring under the initial Q value based on the likelihood function; By combining the prior distribution and the likelihood function, Bayes' theorem is used to calculate the posterior distribution, and the Q value that maximizes the probability is found in the posterior distribution, which is the maximum posterior probability estimate. Determine the relationship between adjacent Q values, and calculate the weight parameters of adjacent Q values ​​based on the relationship between adjacent Q values ​​and the results of the maximum a posteriori probability estimation; The initial Q value is iteratively updated based on the weight parameters and the differences between adjacent Q values. In the iterative update, a new Q value estimate is calculated based on the weight parameters of the current Q value and adjacent Q values, thus completing the estimation of the Q value of the array acoustic logging signal.

8. A system for estimating the Q value of an array acoustic logging signal, characterized in that, include: The data preprocessing module is used to acquire array acoustic logging waveform data and preprocess the acquired array acoustic logging waveform data to obtain array acoustic logging waveform signals. The spectrum analysis module is used to perform spectrum analysis on the array acoustic logging waveform signal through Fourier transform to obtain the spectrum of the array acoustic logging waveform signal; The data calculation module is used to calculate the logarithm of the Fourier amplitude spectrum ratio of the signals received by adjacent receivers in the array acoustic logging waveform signal spectrum, and to obtain the initial Q value between adjacent receivers by least squares fitting based on the logarithm of the Fourier amplitude spectrum ratio. The data estimation module is used to calculate the weight parameters of adjacent Q values ​​using the maximum a posteriori probability of the initial Q value, and to update the Q value through the weight parameters, thus completing the estimation of the Q value of the array acoustic logging signal.

9. A mobile terminal, 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 steps of the method for estimating the Q value of the array acoustic logging signal as described in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for estimating the Q value of the array acoustic logging signal as described in any one of claims 1-7.

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