Near-surface quality factor analysis method, system, device, medium, and program

Abstract

This application relates to a method, system, device, storage medium, and computer program for near-surface quality factor analysis. The method includes: dividing the near-surface strata into layers based on the first arrival time and thickness of each layer of formation waves; randomly selecting one type of near-surface formation as the target layer; performing a Fourier transform on each signal in the target layer to obtain the amplitude spectrum of each signal; using the signal at a preset micro-wellhead location as the reference signal; calculating the first arrival time difference between each signal in the target layer and the reference signal; calculating the logarithmic spectrum ratio of each frequency point in the amplitude spectrum based on the amplitude spectrum and the first arrival time difference; linearly fitting the first arrival time difference to the logarithmic spectrum ratio of each frequency point to obtain the quality factor of each frequency point; and weighted averaging the obtained quality factors to obtain the final quality factor of the target layer. This improves the accuracy of the quality factor calculation results.

Description

Technical Field

[0001] This application relates to the field of seismic exploration, and in particular to a method, system, apparatus, medium, and procedure for near-surface quality factor analysis. Background Technology

[0002] In seismic exploration, the near-surface is a special stratigraphic category, generally referring to the low-velocity medium zone below the surface that has not formed into rock, with a thickness ranging from a few meters to hundreds of meters. Besides differences in structure, physical properties, water content, weathering degree, and formation age, near-surface strata also exhibit significant variations in elastic properties under different environmental conditions, temperatures, and climates. The near-surface is of great importance for deep oil and gas exploration. Complex near-surface seismic geological conditions affect the quality of seismic data acquisition and final data processing. In particular, loose surface geological conditions often cause strong absorption and attenuation of seismic wave energy, while also generating interference noise such as low-frequency surface waves, reducing the signal-to-noise ratio of seismic data.

[0003] Currently, the most widely used methods for near-surface structure surveys are micrologging and small refraction. Micrologging is a geophysical survey method that involves excitation in a well passing through a low-velocity zone and reception at the surface (or vice versa). It uses the vertical time-distance curve of the transmitted wave to calculate parameters such as formation velocity and thickness, thereby delineating low-velocity zones. Simultaneously, micrologging data can also be used to calculate the near-surface absorption quality factor Q, allowing for inverse Q filtering to achieve energy compensation and improve resolution. However, due to inconsistencies in excitation lithology, energy levels, and detector coupling, coupled with factors such as the near-field of the source and interference waves, the results of obtaining the quality factor based on micrologging data remain unsatisfactory. Some near-surface quality factor estimation methods based on dual-well micrologging have emerged, showing better results than traditional methods. However, dual-well micrologging also suffers from differences in excitation wavelet and detector coupling.

[0004] Currently, in some areas with harsh near-surface conditions, single-well micrologging is still the primary method for near-surface surveys. However, existing technologies for single-well micrologging data suffer from low accuracy in calculating quality factors. Summary of the Invention

[0005] This application provides a method, system, device, medium, and procedure for near-surface quality factor analysis to address the problem of low accuracy in the calculation results of quality factors.

[0006] Firstly, this application provides a method for analyzing near-surface quality factors, including:

[0007] The near-surface strata are divided by using the first arrival time and thickness of the strata waves of each layer. The near-surface strata are classified, and one of the near-surface strata is randomly selected as the target layer.

[0008] Perform a Fourier transform on each signal in the target layer to obtain the amplitude spectrum of each signal;

[0009] Using the signal from the preset micro-logging wellhead position as the reference trace signal, the first arrival time difference between each trace signal in the target layer and the reference trace signal is calculated.

[0010] The logarithmic spectral ratio of each frequency point in the amplitude spectrum is calculated based on the amplitude spectrum of each signal and the first arrival time difference.

[0011] The quality factor of each frequency point is obtained by linearly fitting the first arrival time difference with the logarithmic spectrum ratio at each frequency point.

[0012] The final quality factor of the target layer is obtained by weighted averaging of the quality factors at each frequency point.

[0013] In some embodiments, the step of dividing the near-surface strata using the arrival times and thicknesses of the first-arrival waves of each stratum layer to obtain a near-surface stratum classification includes:

[0014] The velocity of the ground wave in each layer of the near-surface strata is obtained by quoting the arrival time of the ground wave in each layer and the thickness of each layer.

[0015] Determine whether the formation wave velocity in each layer is less than a preset velocity threshold;

[0016] If a formation wave velocity is less than the velocity threshold, then determine whether the formation thickness of the near-surface formation corresponding to the formation wave velocity less than the velocity threshold is greater than a preset thickness threshold.

[0017] If the formation thickness of the near-surface stratum corresponding to a formation wave velocity less than the velocity threshold is greater than the thickness threshold, then the near-surface stratum with a formation wave velocity less than the velocity threshold and a stratum thickness greater than the thickness threshold is classified as a deceleration layer.

[0018] If the formation thickness of the near-surface stratum corresponding to a formation wave velocity less than the velocity threshold is less than or equal to the thickness threshold, then the near-surface stratum with a formation wave velocity less than the velocity threshold and a formation thickness less than or equal to the thickness threshold is classified as a low-velocity layer.

[0019] If a formation wave velocity is greater than or equal to the velocity threshold, then the near-surface formation with a formation wave velocity greater than or equal to the velocity threshold is considered a high-velocity layer.

[0020] The deceleration layer, the low-velocity layer, and the high-velocity layer are grouped into a near-surface stratum classification.

[0021] In some embodiments, performing a Fourier transform on each signal in the target layer to obtain the amplitude spectrum of each signal includes:

[0022] Each signal in the target layer is treated as a time-domain signal;

[0023] Perform a Fourier transform on the time-domain signal to obtain a complex array;

[0024] The amplitude information of each frequency component in the complex array is extracted to obtain the amplitude of each signal.

[0025] The amplitude of each signal is plotted to obtain the amplitude spectrum of each signal.

[0026] In some embodiments, calculating the first arrival time difference between each signal in the target layer and the reference signal includes:

[0027] Obtain the first arrival time of the reference track signal;

[0028] The arrival time difference is obtained by subtracting the arrival time of each signal in the target layer from the arrival time of the reference signal.

[0029] In some embodiments, calculating the logarithmic spectral ratio of each frequency point in the amplitude spectrum based on the amplitude spectrum of each signal and the first arrival time difference includes:

[0030] Obtain the amplitude spectrum of the reference channel signal;

[0031] The amplitude spectrum of each signal is divided by the amplitude spectrum of the reference signal to obtain the spectral ratio of each frequency point in the amplitude spectrum of each signal.

[0032] Take the logarithm of the spectral ratio to obtain the logarithmic spectral ratio at each frequency point.

[0033] In some embodiments, the step of linearly fitting the first-arrival time difference with the logarithmic spectrum ratio at each frequency point to obtain the quality factor at each frequency point includes:

[0034] The slope of the fitted line is obtained by linearly fitting the first arrival time difference with the logarithmic spectral ratio at each frequency point.

[0035] The quality factor for each frequency point is calculated using the slope of the fitted straight line and the frequency of each frequency point.

[0036] Secondly, this application provides a near-surface quality factor analysis system, including:

[0037] The stratigraphic classification module is used to divide the near-surface strata by using the first arrival time and thickness of the stratigraphic wave of each layer of the near-surface strata to obtain the near-surface stratigraphic classification, and randomly select one of the near-surface stratigraphic classifications as the target layer.

[0038] The amplitude spectrum acquisition module is used to perform Fourier transform on each signal in the target layer to obtain the amplitude spectrum of each signal.

[0039] The time difference calculation module is used to use the signal at the preset micro-wellhead position as the reference trace signal to calculate the first arrival time difference between each trace signal in the target layer and the reference trace signal.

[0040] The spectral ratio calculation module is used to calculate the logarithmic spectral ratio of each frequency point in the amplitude spectrum based on the amplitude spectrum of each signal and the first arrival time difference;

[0041] The linear fitting module is used to perform linear fitting between the first arrival time difference and the logarithmic spectrum ratio at each frequency point to obtain the quality factor at each frequency point.

[0042] The weighted average module is used to perform a weighted average of the quality factors at each frequency point to obtain the final quality factor of the target layer.

[0043] Thirdly, this application provides a computer device including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described above.

[0044] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the above aspects.

[0045] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the methods described above.

[0046] This application provides a method, system, equipment, medium, and program for near-surface quality factor analysis. It classifies near-surface strata by utilizing the first arrival time and thickness of each layer of formation waves, resulting in near-surface stratum classifications. One of these classifications is randomly selected as the target layer. A Fourier transform is performed on each signal in the target layer to obtain its amplitude spectrum. The signal from a preset micro-wellhead location is used as the reference signal. The first arrival time difference between each signal in the target layer and the reference signal is calculated. The logarithmic spectrum ratio at each frequency point in the amplitude spectrum is calculated based on the amplitude spectrum and the first arrival time difference. A linear fit is performed between the first arrival time difference and the logarithmic spectrum ratio at each frequency point to obtain the quality factor at each frequency point. Finally, a weighted average of the quality factors at each frequency point is calculated to obtain the final quality factor of the target layer. This method primarily addresses the problem of low accuracy in the calculated quality factor. Attached Figure Description

[0047] The present application will be described in more detail below based on embodiments and with reference to the accompanying drawings:

[0048] Figure 1 A flowchart illustrating a near-surface quality factor analysis method provided in this application embodiment;

[0049] Figure 2 A schematic diagram of the functional modules of a near-surface quality factor analysis system provided in this application embodiment;

[0050] Figure 3 This is a schematic diagram of the structure of an electronic device for near-surface quality factor analysis provided in an embodiment of this application.

[0051] In the accompanying drawings, the same parts are referred to by the same reference numerals, and the drawings are not drawn to scale. Detailed Implementation

[0052] To enable those skilled in the art to better understand the technical solutions of this application, and to fully understand and implement the process of how this application uses technical means to solve technical problems and achieve corresponding technical effects, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, not all of them. The embodiments of this application and the various features within them can be combined with each other without conflict, and the resulting technical solutions are all within the protection scope of this application. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of this application.

[0053] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0054] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0055] Based on the above background, this scheme proposes a near-surface quality factor analysis method. It utilizes the first arrival time and thickness of each near-surface stratum wave to classify the strata, obtaining near-surface stratum classifications. One of these classifications is randomly selected as the target layer. Fourier transform is performed on each signal in the target layer to obtain its amplitude spectrum. The signal from a preset micro-wellhead location is used as the reference signal. The first arrival time difference between each signal in the target layer and the reference signal is calculated. The logarithmic spectrum ratio of each frequency point in the amplitude spectrum is calculated based on the amplitude spectrum and the first arrival time difference. A linear fit is performed between the first arrival time difference and the logarithmic spectrum ratio at each frequency point to obtain the quality factor at each frequency point. Finally, a weighted average of the quality factors at each frequency point is calculated to obtain the final quality factor of the target layer, thus improving the accuracy of the quality factor calculation results.

[0056] Example 1

[0057] Figure 1 A flowchart illustrating a near-surface quality factor analysis method provided in this application embodiment is shown below. Figure 1 As shown, a method for analyzing near-surface quality factors includes:

[0058] S1. The near-surface strata are divided using the first arrival time and thickness of each stratum wave obtained from the near-surface strata to obtain near-surface strata classification. One of the near-surface strata classifications is randomly selected as the target layer.

[0059] In this embodiment of the invention, the propagation velocity of each stratum wave is analyzed based on its first arrival time, and velocity boundaries are defined according to the distribution of first arrival times. Near-surface strata can be divided into different layers, such as: low-velocity layers: typically loose soil or sediment layers near the surface, with low propagation velocities; decreasing-velocity layers: possibly thicker sedimentary layers where wave velocities may decrease at certain depths; and high-velocity layers: typically bedrock layers, with higher propagation velocities.

[0060] In detail, the near-surface strata are divided using the first arrival time and thickness of each stratum wave obtained from the near-surface strata to obtain a near-surface stratum classification, including:

[0061] The velocity of the ground wave in each layer of the near-surface strata is obtained by quoting the arrival time of the ground wave in each layer and the thickness of each layer.

[0062] Determine whether the formation wave velocity in each layer is less than a preset velocity threshold;

[0063] If a formation wave velocity is less than the velocity threshold, then determine whether the formation thickness of the near-surface formation corresponding to the formation wave velocity less than the velocity threshold is greater than a preset thickness threshold.

[0064] If the formation thickness of the near-surface stratum corresponding to a formation wave velocity less than the velocity threshold is greater than the thickness threshold, then the near-surface stratum with a formation wave velocity less than the velocity threshold and a stratum thickness greater than the thickness threshold is classified as a deceleration layer.

[0065] If the formation thickness of the near-surface stratum corresponding to a formation wave velocity less than the velocity threshold is less than or equal to the thickness threshold, then the near-surface stratum with a formation wave velocity less than the velocity threshold and a formation thickness less than or equal to the thickness threshold is classified as a low-velocity layer.

[0066] If a formation wave velocity is greater than or equal to the velocity threshold, then the near-surface formation with a formation wave velocity greater than or equal to the velocity threshold is considered a high-velocity layer.

[0067] The deceleration layer, the low-velocity layer, and the high-velocity layer are grouped into a near-surface stratum classification.

[0068] In detail, the formation wave velocity of each near-surface stratum is obtained by quoting the arrival time of the first wave in each stratum and the thickness of each stratum. The calculation formula is shown below:

[0069]

[0070] Where: V i D represents the formation wave velocity of the i-th layer. i ΔT represents the thickness of the i-th layer. iThis represents the initial arrival time difference between the top and bottom of the i-th layer.

[0071] By utilizing a dual judgment mechanism of velocity and thickness thresholds, near-surface strata of different properties are effectively distinguished. This helps identify special layers within the near-surface strata, such as decreasing velocity layers, low-velocity layers, and high-velocity layers, allowing for a more accurate revelation of changes in subsurface geological structures and improving the precision of stratigraphic division. Clear classification of near-surface strata provides a reliable basis for subsequent geological analysis and resource exploration, optimizing the decision-making process and reducing risks.

[0072] S2. Perform a Fourier transform on each signal in the target layer to obtain the amplitude spectrum of each signal.

[0073] In this embodiment of the invention, performing a Fourier transform on each signal in the target layer to obtain the amplitude spectrum of each signal includes:

[0074] Each signal in the target layer is treated as a time-domain signal;

[0075] Perform a Fourier transform on the time-domain signal to obtain a complex array;

[0076] The amplitude information of each frequency component in the complex array is extracted to obtain the amplitude of each signal.

[0077] The amplitude of each signal is plotted to obtain the amplitude spectrum of each signal.

[0078] In detail, the Fourier transform of the time-domain signal is calculated using the following formula:

[0079]

[0080] Where f represents frequency, x(t) represents time-domain signal, j represents imaginary unit, t represents time variable, and X(f) represents complex array.

[0081] In detail, the amplitude information of each frequency component in the complex array is extracted to obtain the amplitude spectrum of each signal. The calculation formula is as follows:

[0082] A(f)=|X(f)|

[0083] Where |X(f)| represents the modulus of the complex array, and A(f) represents the amplitude of each signal.

[0084] By performing Fourier transform on each signal in the target layer and extracting the amplitude spectrum, the characteristics of the signal in the frequency domain can be effectively analyzed, thereby more accurately calculating the quality factor of the near-surface strata. This not only improves the accuracy of the quality factor calculation, but also reveals the attenuation characteristics of different near-surface strata, identifies noise, enhances the time-frequency analysis capability of seismic data, and optimizes the seismic inversion process, ultimately helping to more accurately understand the underground structure and geological characteristics.

[0085] S3. Using the signal from the preset micro-logging wellhead position as the reference trace signal, calculate the first arrival time difference between each trace signal in the target layer and the reference trace signal.

[0086] In this embodiment of the invention, calculating the first arrival time difference between each signal in the target layer and the reference signal includes:

[0087] Obtain the first arrival time of the reference track signal;

[0088] The arrival time difference is obtained by subtracting the arrival time of each signal in the target layer from the arrival time of the reference signal.

[0089] In detail, the first arrival time difference is obtained by subtracting the first arrival time of each signal in the target layer from the first arrival time of the reference signal. The calculation formula is as follows:

[0090] Δt n =t n -t1

[0091] Where, Δt n Indicates the time difference between arrival and departure, t n t1 represents the initial arrival time of the nth channel signal, and t1 represents the initial arrival time of the reference channel signal.

[0092] By using the signal from the preset wellhead position of the micro-logging well as the reference trace signal, the first arrival time of the reference trace signal is obtained. By comparing the first arrival time of each trace signal in the target layer with the first arrival time of the reference trace signal, the time difference can be accurately identified. This helps to optimize the comparative analysis of signals, reveal potential time errors or offsets in the system, and thus provide a basis for subsequent accuracy improvement.

[0093] S4. Calculate the logarithmic spectral ratio of each frequency point in the amplitude spectrum based on the amplitude spectrum of each signal and the first arrival time difference.

[0094] In this embodiment of the invention, the step of calculating the logarithmic spectral ratio of each frequency point in the amplitude spectrum based on the amplitude spectrum of each signal and the first arrival time difference includes:

[0095] Obtain the amplitude spectrum of the reference channel signal;

[0096] The amplitude spectrum of each signal is divided by the amplitude spectrum of the reference signal to obtain the spectral ratio of each frequency point in the amplitude spectrum of each signal.

[0097] Take the logarithm of the spectral ratio to obtain the logarithmic spectral ratio at each frequency point.

[0098] In detail, the amplitude spectrum of each signal is divided by the amplitude spectrum of the reference signal to obtain the spectral ratio at each frequency point in the amplitude spectrum of each signal. The calculation formula is as follows:

[0099]

[0100] Among them, A n Let A1 represent the amplitude spectrum of the nth channel signal, A1 represent the amplitude spectrum of the reference channel signal, and f represent the frequency.

[0101] In detail, the logarithm of the spectral ratio is taken to obtain the logarithmic spectral ratio at each frequency point, and the calculation formula is as follows:

[0102]

[0103] Where f represents the frequency, Z represents the depth of each signal, Z1 represents the depth of the reference signal, and Z... n The depth of the nth channel signal is represented by A, and the amplitude spectrum is represented by A. n Let A1 represent the amplitude spectrum of the nth channel signal, A1 represent the amplitude spectrum of the reference channel signal, C represent a constant, Q represent the quality factor, and t represent the amplitude spectrum of the reference channel signal. n t1 represents the initial arrival time of the nth channel signal, and t1 represents the initial arrival time of the reference channel signal.

[0104] By calculating the ratio of the amplitude spectrum of each signal to the amplitude spectrum of the reference signal, the spectral ratio at each frequency point is obtained. Taking the logarithm of the spectral ratio yields the logarithmic spectral ratio, which is helpful for attenuation analysis. It can effectively eliminate the influence of geological conditions, wave propagation paths, and other factors in different channels, thus more accurately reflecting the changes in the quality factor. It can efficiently extract attenuation information from multi-channel seismic data, eliminate noise interference, and improve the accuracy of quality factor calculation.

[0105] S5. Perform linear fitting between the initial arrival time difference and the logarithmic spectrum ratio at each frequency point to obtain the quality factor at each frequency point.

[0106] In this embodiment of the invention, the step of linearly fitting the first-arrival time difference with the logarithmic spectrum ratio at each frequency point to obtain the quality factor at each frequency point includes:

[0107] The slope of the fitted line is obtained by linearly fitting the first arrival time difference with the logarithmic spectral ratio at each frequency point.

[0108] The quality factor for each frequency point is calculated using the slope of the fitted straight line and the frequency of each frequency point.

[0109] In detail, the initial arrival time difference t is set. n Using -t1 as the independent variable x and the logarithmic spectral ratio as the dependent variable y, a linear fit is performed between the initial arrival time difference and the logarithmic spectral ratio at each frequency point. The equation for the linear fit is shown below:

[0110] y=k*(t n -t1)+b

[0111] Where k represents the slope of the fitted line, t n -t1 represents the initial arrival time difference, and b represents the intercept of the fitted line.

[0112] The slope of the fitted line is obtained from the linear fitting equation, and the quality factor at each frequency point is calculated using the following formula:

[0113]

[0114] Where Q represents the quality factor, k represents the slope of the fitted line, and f represents the frequency.

[0115] By linearly fitting the first arrival time difference to the logarithmic spectrum ratio at each frequency point and obtaining the slope of the fitted line, and then calculating the quality factor at each frequency point in conjunction with the frequency of each frequency point, the quality factor at each frequency point can be effectively extracted. By considering the attenuation characteristics of different frequency components, an accurate quality factor can be provided for each frequency point, effectively demonstrating the attenuation characteristics and frequency dependence of the medium at different frequencies, and improving the accuracy of the quality factor.

[0116] S6. Perform a weighted average of the quality factors at each frequency point to obtain the final quality factor of the target layer.

[0117] In this embodiment of the invention, the step of weighted averaging of the quality factors at each frequency point to obtain the final quality factor of the target layer includes:

[0118] Obtain the weight of the quality factor at each frequency point;

[0119] The quality factor of each frequency point is combined with the weight to obtain the final quality factor of the target layer.

[0120] In detail, assume the set of frequency points is f1, f2, ..., f m The set of quality factors is Q1, Q2, ..., Q... m , where Q z Represents frequency f zThe corresponding quality factor is obtained by combining the quality factor of each frequency point with the weight, and the final quality factor of the target layer is calculated as follows:

[0121]

[0122] Among them, Q final w represents the final quality factor of the target layer. z Q represents the weight of the quality factor at the z-th frequency point. z Let m represent the quality factor at the z-th frequency point, and m represent the number of frequency points.

[0123] Seismic waves of different frequencies may be attenuated to varying degrees during propagation. By using weighted averaging, quality factors of different frequencies can be reasonably integrated according to their weights, making the final quality factor more consistent with the actual situation of the entire target layer. This avoids the bias of results from a single frequency point, thus making the final quality factor more representative and improving the accuracy of the calculated quality factor.

[0124] Example 2

[0125] like Figure 2 The diagram shown is a functional block diagram of a near-surface quality factor analysis system 100 provided for this embodiment.

[0126] The near-surface quality factor analysis system 100 of this invention can be installed in an electronic device. Depending on the functions implemented, the near-surface quality factor analysis system 100 may include a stratigraphic classification module 101, an amplitude spectrum acquisition module 102, a time difference calculation module 103, a spectral ratio calculation module 104, a linear fitting module 105, and a weighted average module 106. The module described in this invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and are stored in the memory of the electronic device.

[0127] In this embodiment, the functions of each module / unit are as follows:

[0128] The stratigraphic classification module 101 is used to divide the near-surface strata by using the first arrival time and thickness of the stratigraphic wave of each layer of the near-surface strata to obtain the near-surface stratigraphic classification, and randomly select one of the near-surface stratigraphic classifications as the target layer.

[0129] The amplitude spectrum acquisition module 102 is used to perform Fourier transform on each signal in the target layer to obtain the amplitude spectrum of each signal.

[0130] The time difference calculation module 103 is used to use the signal of the preset micro-wellhead position as the reference trace signal to calculate the first arrival time difference between each trace signal in the target layer and the reference trace signal.

[0131] The spectral ratio calculation module 104 is used to calculate the logarithmic spectral ratio of each frequency point in the amplitude spectrum based on the amplitude spectrum of each signal and the first arrival time difference;

[0132] The linear fitting module 105 is used to perform linear fitting between the first arrival time difference and the logarithmic spectrum ratio value of each frequency point to obtain the quality factor of each frequency point.

[0133] The weighted average module 106 is used to perform a weighted average of the quality factors at each frequency point to obtain the final quality factor of the target layer.

[0134] Example 3

[0135] Figure 3 This is a schematic diagram of the structure of an electronic device for near-surface quality factor analysis provided in an embodiment of this application.

[0136] Based on the above embodiments, this embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in the above embodiments.

[0137] In some embodiments of this example, a computer-readable storage medium is provided, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements the steps of the method described in the above embodiments.

[0138] In some embodiments of this example, a computer program product is provided, including a computer program, characterized in that the computer program, when executed by a processor, implements the steps of the method described in the above embodiments.

[0139] The processor may include, but is not limited to, one or more processors or microprocessors. Each processor may be implemented as an Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor, or other electronic component, for executing the methods in the above embodiments.

[0140] Computer-readable storage media can be implemented by any type of volatile or non-volatile storage device or a combination thereof, including but not limited to, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, and computer storage media (e.g., hard disks, floppy disks, solid-state drives, removable disks, CD-ROMs, DVD-ROMs, Blu-ray discs, etc.).

[0141] Computer-readable storage media may also store at least one computer-executable program, such as computer-readable instructions. Computer-readable storage media include, but are not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Computer-readable storage media may include, for example, read-only memory (ROM), hard disk, flash memory, etc. For example, a non-transitory computer-readable storage medium may be connected to a computing device such as a computer, and then, when the computing device executes the computer-readable instructions stored on the computer-readable storage medium, the various methods described above can be performed.

[0142] In addition, the computer device may include (but is not limited to) a data bus, an input / output (I / O) bus, a display, and input / output devices (e.g., keyboard, mouse, speakers, etc.).

[0143] The processor can communicate with external devices via the communication interface of the I / O bus through wired or wireless networks.

[0144] In one embodiment, the at least one computer-executable instruction may also be compiled into or comprise a software product / computer program product, wherein one or more computer-executable instructions are executed by a processor to perform the steps of the various functions and / or methods in the embodiments described herein.

[0145] In the embodiments provided in this application, it should be understood that the disclosed systems and methods can also be implemented in other ways. The system embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0146] It should be noted that, in this application, 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 a process, method, article, or apparatus. Without further limitation, an element limited 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.

[0147] Although the embodiments disclosed in this application are as described above, the above content is merely for the purpose of facilitating understanding of this application and is not intended to limit this application. Any person skilled in the art to which this application pertains may make any modifications and changes in the form and details of the implementation without departing from the spirit and scope disclosed in this application; however, the scope of patent protection of this application shall still be determined by the scope defined in the appended claims.

Claims

1. A method for analyzing near-surface quality factors, characterized in that, include: The near-surface strata are divided by using the first arrival time and thickness of the strata waves of each layer. The near-surface strata are classified, and one of the near-surface strata is randomly selected as the target layer. Perform a Fourier transform on each signal in the target layer to obtain the amplitude spectrum of each signal; Using the signal from the preset micro-logging wellhead position as the reference trace signal, the first arrival time difference between each trace signal in the target layer and the reference trace signal is calculated. The logarithmic spectral ratio of each frequency point in the amplitude spectrum is calculated based on the amplitude spectrum of each signal and the first arrival time difference. The quality factor of each frequency point is obtained by linearly fitting the first arrival time difference with the logarithmic spectrum ratio at each frequency point. The final quality factor of the target layer is obtained by weighted averaging of the quality factors at each frequency point.

2. The method according to claim 1, characterized in that, The near-surface strata are divided using the arrival times and thicknesses of the first arrival waves of each stratum, resulting in a near-surface stratum classification, including: The velocity of the ground wave in each layer of the near-surface strata is obtained by quoting the arrival time of the ground wave in each layer and the thickness of each layer. Determine whether the formation wave velocity in each layer is less than a preset velocity threshold; If a formation wave velocity is less than the velocity threshold, then determine whether the formation thickness of the near-surface formation corresponding to the formation wave velocity less than the velocity threshold is greater than a preset thickness threshold. If the formation thickness of the near-surface stratum corresponding to a formation wave velocity less than the velocity threshold is greater than the thickness threshold, then the near-surface stratum with a formation wave velocity less than the velocity threshold and a stratum thickness greater than the thickness threshold is classified as a deceleration layer. If the formation thickness of the near-surface stratum corresponding to a formation wave velocity less than the velocity threshold is less than or equal to the thickness threshold, then the near-surface stratum with a formation wave velocity less than the velocity threshold and a formation thickness less than or equal to the thickness threshold is classified as a low-velocity layer. If a formation wave velocity is greater than or equal to the velocity threshold, then the near-surface formation with a formation wave velocity greater than or equal to the velocity threshold is considered a high-velocity layer. The deceleration layer, the low-velocity layer, and the high-velocity layer are grouped into a near-surface stratum classification.

3. The method according to claim 1, characterized in that, The step of performing a Fourier transform on each signal in the target layer to obtain the amplitude spectrum of each signal includes: Each signal in the target layer is treated as a time-domain signal; Perform a Fourier transform on the time-domain signal to obtain a complex array; The amplitude information of each frequency component in the complex array is extracted to obtain the amplitude of each signal. The amplitude of each signal is plotted to obtain the amplitude spectrum of each signal.

4. The method according to claim 1, characterized in that, The calculation of the first arrival time difference between each signal in the target layer and the reference signal includes: Obtain the first arrival time of the reference track signal; The arrival time difference is obtained by subtracting the arrival time of each signal in the target layer from the arrival time of the reference signal.

5. The method according to claim 1, characterized in that, The step of calculating the logarithmic spectral ratio of each frequency point in the amplitude spectrum based on the amplitude spectrum of each signal and the first arrival time difference includes: Obtain the amplitude spectrum of the reference channel signal; The amplitude spectrum of each signal is divided by the amplitude spectrum of the reference signal to obtain the spectral ratio of each frequency point in the amplitude spectrum of each signal. Take the logarithm of the spectral ratio to obtain the logarithmic spectral ratio at each frequency point.

6. The method according to claim 1, characterized in that, The step of linearly fitting the first arrival time difference with the logarithmic spectrum ratio at each frequency point to obtain the quality factor at each frequency point includes: The slope of the fitted line is obtained by linearly fitting the first arrival time difference with the logarithmic spectral ratio at each frequency point. The quality factor for each frequency point is calculated using the slope of the fitted straight line and the frequency of each frequency point.

7. A near-surface quality factor analysis system, characterized in that, The system includes: The stratigraphic classification module is used to divide the near-surface strata by using the first arrival time and thickness of the stratigraphic wave of each layer of the near-surface strata to obtain the near-surface stratigraphic classification, and randomly select one of the near-surface stratigraphic classifications as the target layer. The amplitude spectrum acquisition module is used to perform Fourier transform on each signal in the target layer to obtain the amplitude spectrum of each signal. The time difference calculation module is used to use the signal at the preset micro-wellhead position as the reference trace signal to calculate the first arrival time difference between each trace signal in the target layer and the reference trace signal. The spectral ratio calculation module is used to calculate the logarithmic spectral ratio of each frequency point in the amplitude spectrum based on the amplitude spectrum of each signal and the first arrival time difference; The linear fitting module is used to perform linear fitting between the first arrival time difference and the logarithmic spectrum ratio at each frequency point to obtain the quality factor at each frequency point. The weighted average module is used to perform a weighted average of the quality factors at each frequency point to obtain the final quality factor of the target layer.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 6.

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