Logging signal processing method, system and product

Abstract

The embodiment of the invention provides a well logging signal processing method and system and a product. The method comprises the steps that a well logging signal is acquired; inputting the logging signal into a first decomposition model to obtain first-scale low-frequency information; inputting the logging signal and the first-scale low-frequency information into a second decomposition model to obtain first-scale high-frequency information; inputting the first-scale low-frequency information into a first decomposition model to obtain second-scale low-frequency information; inputting the first scale low-frequency information and the second scale low-frequency information into a second decomposition model to obtain second scale high-frequency information; the first decomposition model is used for decomposing the logging signal with a preset decomposition operator based on a Minkowski one-dimensional sum-difference algorithm, and the preset decomposition operator is used for determining the decomposition scale of the logging signal; and the second decomposition model is used for obtaining a difference function of the two input values. According to the embodiment of the invention, multi-scale decomposition is carried out on the logging signal based on the Minkowski sum-difference algorithm and the preset decomposition operator, and efficient and accurate decomposition of the logging signal can be realized.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of geophysical exploration technology, and in particular to a method for processing well logging signals, a system for processing well logging signals, a computer-readable storage medium, an electronic device, and a well logging instrument. Background Art

[0002] Well logging, also known as geophysical logging, is a method of measuring geophysical parameters by using the geophysical properties of rock formations, such as electrochemical properties, electrical conductivity, acoustic properties, and radioactivity. For example, well logging is required during oil drilling to obtain various petroleum geological and engineering technical data, such as the effective thickness, porosity, oil and gas saturation, and permeability of the oil layer. Well logging methods have been widely used in drilling holes for oil, coal, metal and non-metallic minerals, hydrogeology, and engineering geology, and have become one of the indispensable geophysical exploration methods. Well logging can be completed by a sensor device, and the signal measured by the sensor device is called a logging signal. For example, the logging signal can be an acoustic signal. Due to the complexity and diversity of geophysics, the logging signal is often a superposition of multiple signals measured from the feedback of the target to be measured. It takes a lot of calculations to separate the target signal from the background signal from the logging signal. In addition, the signal obtained during the entire logging process is not stable, and the accurate decomposition of the logging signal is a difficult problem for technicians. Therefore, how to efficiently and accurately decompose the logging signal has always been a top priority for technicians. Summary of the invention

[0003] The embodiments of the present application provide a method for processing logging signals, a system for processing logging signals, a computer-readable storage medium, an electronic device, and a logging instrument, which are intended to decompose logging signals based on a Minkowski sum-difference algorithm and a preset decomposition operator, thereby obtaining multi-scale signal information and achieving efficient and accurate decomposition of logging signals.

[0004] In one aspect, an embodiment of the present application provides a method for processing a logging signal, comprising:

[0005] Acquire logging signals;

[0006] Inputting the logging signal into a first decomposition model to obtain first-scale low-frequency information;

[0007] Inputting the logging signal and the first-scale low-frequency information into a second decomposition model to obtain first-scale high-frequency information;

[0008] Inputting the first-scale low-frequency information into the first decomposition model to obtain second-scale low-frequency information;

[0009] Inputting the first-scale low-frequency information and the second-scale low-frequency information into the second decomposition model to obtain second-scale high-frequency information;

[0010] The first decomposition model is used to decompose the logging signal with a preset decomposition operator based on a Minkowski one-dimensional sum-difference algorithm, and the preset decomposition operator is used to determine a scale for decomposing the logging signal;

[0011] The second decomposition model is used to obtain a difference function of two input values.

[0012] Optionally, the logging signal includes a one-dimensional time domain signal; and the first decomposition model includes:

[0013] An expansion model, for performing a one-dimensional sum operation in the time domain on the input value and the preset decomposition operator based on a Minkowski sum-difference algorithm;

[0014] A contraction model, used for performing a one-dimensional difference operation in the time domain on the input value and the preset decomposition operator based on a Minkowski sum-difference algorithm;

[0015] The mean model computes the mean function of at least two input values.

[0016] Optionally, the step of inputting the logging signal into a first decomposition model to obtain first-scale low-frequency information includes:

[0017] Inputting the logging signal into the expansion model to obtain a first expansion function;

[0018] Inputting the logging signal into the contraction model to obtain a first contraction function;

[0019] Inputting the first expansion function into the contraction model to obtain first decomposition information of the logging signal;

[0020] Inputting a first contraction function into the expansion model to obtain second decomposition information of the logging signal;

[0021] The first decomposition information and the second decomposition information are input into the mean model to obtain a mean function of the first decomposition information and the second decomposition information as the first-scale low-frequency information.

[0022] Optionally, the step of inputting the logging signal and the first-scale low-frequency information into a second decomposition model to obtain the first-scale high-frequency information includes:

[0023] Inputting the logging signal and the first-scale low-frequency information into a second decomposition model to obtain a first difference function between the logging signal and the first-scale low-frequency information as the first-scale high-frequency information;

[0024] The step of inputting the first-scale low-frequency information and the second-scale low-frequency information into the second decomposition model to obtain the second-scale high-frequency information comprises:

[0025] The first-scale low-frequency information and the second-scale low-frequency information are input into the second decomposition model to obtain a second difference function between the first-scale low-frequency information and the second-scale low-frequency information as the second-scale high-frequency information.

[0026] Optionally, the preset decomposition operator includes: a one-dimensional waveform function and a geometric center of the one-dimensional waveform function;

[0027] Wherein, the geometric center of the one-dimensional waveform function is within the range of the preset decomposition operator.

[0028] Optionally, it also includes:

[0029] Obtaining geophysical parameters of the target to be measured according to the first-scale high-frequency information, the second-scale low-frequency information, and the frequency information included in the second-scale high-frequency information;

[0030] The first-scale high-frequency information, the second-scale low-frequency information, and the second-scale high-frequency information contain different frequency information.

[0031] In another aspect, the embodiment of the present application further provides a system for processing well logging signals, comprising:

[0032] A signal acquisition unit, used for acquiring logging signals;

[0033] A first decomposition unit, used for inputting the logging signal into a first decomposition model to obtain first-scale low-frequency information;

[0034] A second decomposition unit is used to input the logging signal and the first-scale low-frequency information into a second decomposition model to obtain the first-scale high-frequency information;

[0035] A third decomposition unit, configured to input the first-scale low-frequency information into the first decomposition model to obtain second-scale low-frequency information;

[0036] A fourth decomposition unit, inputting the first-scale low-frequency information and the second-scale low-frequency information into the second decomposition model to obtain second-scale high-frequency information;

[0037] The first decomposition model is used to decompose the logging signal with a preset decomposition operator based on a Minkowski one-dimensional sum-difference algorithm, and the preset decomposition operator is used to determine a scale for decomposing the logging signal;

[0038] The second decomposition model is used to obtain a difference function of two input values.

[0039] On the other hand, an embodiment of the present application further provides a computer-readable storage medium storing a computer program, which implements the steps in the method in the above embodiment when executed by a processor.

[0040] On the other hand, an embodiment of the present application further provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps in the method in the above embodiment when executed.

[0041] In another aspect, the present application also provides a well logging tool, including:

[0042] The electronic device in the above embodiment;

[0043] The sensor device is used to generate a logging signal and transmit it to the electronic device.

[0044] Compared with the prior art, the advantages of the embodiments of the present application are:

[0045] (1) The embodiment of the present application applies the Minkowski sum-difference algorithm, which is commonly used in two-dimensional or high-dimensional dimensional processing, to the processing of one-dimensional time-domain logging signals, thereby realizing the dimensionality reduction processing of the Minkowski sum-difference algorithm.

[0046] (2) Compared with the time-frequency analysis algorithm in the related art that requires a large number of Fourier transform calculations, the embodiment of the present application can use Minkowski one-dimensional sum and difference calculations, and can use sum and difference operations with smaller computational complexity in more concise steps, avoiding product operations with more steps and larger computational complexity, and efficiently and accurately realizing multi-scale separation of logging signals, reducing calculation time, improving calculation efficiency, and saving computing resources.

[0047] (3) The embodiment of the present application introduces a preset decomposition operator for determining the scale for decomposing the logging signal. Starting from the waveform details of the time domain signal, the Minkowski preset decomposition operator with different step sizes can be used to screen the waveform, and the waveform is divided into sub-signals of different scales according to the step size of the operator, thereby completing the fast and efficient multi-scale decomposition of the complex logging signal, which can effectively overcome the problem of bandwidth overlap between signals and separate the target signal from the background signal. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The accompanying drawings are for reference and illustration only and are not intended to limit the scope of protection of this application. The technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0049] Figure 1 A flowchart showing a method for processing a logging signal in an embodiment provided by the present application is shown;

[0050] Figure 2 A flowchart of steps for obtaining low-frequency information of a first scale in an embodiment provided by the present application is shown;

[0051] Figure 3 A schematic diagram of sum and difference operation signals of a first decomposition model in an embodiment provided by the present application is shown;

[0052] Figure 4 A signal decomposition schematic diagram of a first decomposition model in an embodiment provided by the present application is shown;

[0053] Figure 5 A schematic diagram of signal decomposition of first scale information in an embodiment provided by the present application is shown;

[0054] Figure 6 A schematic diagram of a point set of a preset decomposition operator in an embodiment provided by the present application is shown;

[0055] Figure 7 A schematic diagram of signal decomposition of multi-scale information in an embodiment provided by the present application is shown;

[0056] Figure 8 A structural block diagram of a logging signal processing system in an embodiment provided by the present application is shown. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0058] Geophysical logging technology has become one of the fastest-growing and most widely used geophysical methods, involving fields such as petroleum geology and engineering geology. It has played a huge role in predicting abnormal reservoir pressure, identifying fluids, identifying sedimentary microfacies, solving the problem of uniform control of pore area after fracturing of oil and gas wells, dividing reservoir space types, and dividing complex lithology. At present, the main methods for analyzing logging signals include frequency domain filtering based on Fourier transform theory, short-time Fourier transform, wavelet analysis, Hilbert-Huang transform, bilinear time-frequency distribution, and other methods. Frequency domain filtering developed based on Fourier transform is to linearly decompose the logging signal based on trigonometric functions, and use several single-frequency signals to express the frequency information contained in the signal. When it is known that the target signal contains fixed frequency components, a bandpass and band-notch filtering mode can be established to filter and realize the "picking" process. In order to adapt to the non-stationarity of logging signals, time-frequency analysis methods were introduced. This type of method selects time domain signals by opening time windows, and then performs Fourier transform on each signal segment to obtain the frequency spectrum of the signal about the time window time, referred to as the time-frequency spectrum. It not only takes into account the assumption that the time domain signal is stable within a certain period of time, but also expands the application scope of the Fourier transform method. Similarly, the core parameter of bilinear time-frequency analysis is the step length. The step length parameter is required in the relevant calculation process to determine the length of the signal time shift, which is the same as the idea of ​​the short-time Fourier transform method. Wavelet transform has made significant improvements in the scalability of the time window. By changing the time-varying nature of the time window, it can detect the frequency characteristics of local small signals in the time domain. At the same time, for the entire signal, multi-scale decomposition is achieved, giving different time-frequency components of the signal.

[0059] Taking the wavelet method as an example, the related technology uses the wavelet method to decompose the logging signal, but in fact, it is easy to have high-frequency components distributed in multiple scales, and similar frequency components appear repeatedly. Although the wavelet decomposition obtains the large-scale shape of the logging curve, it can be used to fit the trend of the curve, but there is an error with the real curve. For example, within a specific depth range, there will be unreal waveforms. Therefore, the current related technology uses the wavelet method to decompose the logging signal, but it cannot achieve effective separation of the logging signal.

[0060] In summary, the current decomposition of logging signals still has the following problems:

[0061] (1) The signals obtained in the actual logging process are non-stationary. The frequency components contained in each point of the time domain signal are different. It is impossible to directly calculate a single frequency signal by integrating and summing the time domain. Therefore, the decomposition methods currently used, such as Fourier transform, are not ideal.

[0062] (2) The short-time Fourier transform method and the bilinear time-frequency analysis method can only obtain the spectrum of the signal at one time scale when the time window or step size is fixed, and the operation flexibility is poor. When the bandwidth of the target signal and the background signal overlap, the wavelet analysis multi-scale algorithm cannot effectively separate them, and the algorithm redundancy is serious when the waveform is complex. This is the same as the Hilbert-Huang transform. When the Hilbert-Huang transform faces logging signals with complex waveforms, more application experience is needed.

[0063] (3) A large number of multiplication operations are required from the time domain to the frequency domain. When massive amounts of data need to be processed in real time, the computer must have very high computing performance, which also places higher requirements on the computational efficiency of the algorithm.

[0064] In view of this, the embodiments of the present application propose a method for processing well logging signals, a system for processing well logging signals, a computer-readable storage medium, an electronic device, and a well logging instrument, which are intended to be based on the Minkowski one-dimensional sum and difference algorithm, use a preset decomposition operator to determine the scale for decomposing the well logging signal, and then decompose the well logging signal, so as to obtain multi-scale signal information, realize efficient and accurate decomposition of the well logging signal, divide the waveform into sub-signals of different scales according to the step size of the preset decomposition operator, and thus effectively separate the target signal from the background signal.

[0065] The embodiments of the present application are described below with reference to the accompanying drawings.

[0066] Embodiment 1

[0067] Reference Figure 1 , Figure 1 A flowchart of the steps of a method for processing a logging signal in an embodiment provided by the present application is shown.

[0068] like Figure 1 As shown, the embodiment of the present application provides a method for processing a logging signal, comprising:

[0069] Step S301, obtaining a logging signal.

[0070] The logging signal may be a multi-scale superimposed acoustic wave signal, which may be obtained by measuring the target to be measured.

[0071] The target to be measured may be a geophysical target to be measured. Specifically, the target to be measured may be a well body after the well has reached the designed well depth or after the casing of the oil well has been completed.

[0072] Step S302: input the logging signal into a first decomposition model to obtain first-scale low-frequency information.

[0073] Among them, the first decomposition model is based on Minkowski sum and difference operations, wherein the Minkowski sum and difference operations in the embodiments of the present application are suitable for one-dimensional operations, and can be obtained by performing dimensionality reduction processing on the Minkowski sum and difference operations in the related technology.

[0074] Among them, the Minkowski sum and difference operation is an operation relationship established based on the intersection and union operation of point sets in Euclidean space, and the operation result is mainly obtained through the addition and subtraction operation of the point set, that is, the first scale low-frequency information is obtained through addition and subtraction operations.

[0075] Step S303: input the logging signal and the first-scale low-frequency information into a second decomposition model to obtain the first-scale high-frequency information.

[0076] Step S304: input the first-scale low-frequency information into the first decomposition model to obtain second-scale low-frequency information.

[0077] Step S305: input the first-scale low-frequency information and the second-scale low-frequency information into the second decomposition model to obtain second-scale high-frequency information.

[0078] The first decomposition model is used to decompose the logging signal with a preset decomposition operator based on the Minkowski one-dimensional sum-difference algorithm, and the preset decomposition operator is used to determine the scale for decomposing the logging signal.

[0079] The second decomposition model is used to obtain a difference function of two input values.

[0080] Through the above embodiments, the present application proposes a method for processing logging signals based on Minkowski sum and difference. The method can establish basic operations based on the intersection and union operations of point sets in Euclidean space. The core algorithm only involves addition and subtraction operations, and has high computational efficiency. The first decomposition model in the embodiment of the present application is based on the Minkowski two-dimensional and above sum and difference operation formulas to deduce a one-dimensional signal multi-scale decomposition algorithm. The preset decomposition operator has a simple waveform structure and can be flexibly designed according to specific needs. Compared with the wavelet analysis method for logging signal processing in the related art, the embodiment of the present application can achieve efficient decomposition of one-dimensional multi-scale logging signals with fewer decomposition steps and lower computational complexity, effectively solving the problem of bandwidth overlap between the target signal and the background signal.

[0081] Embodiment 2

[0082] The present application also provides a method for processing a well logging signal, including:

[0083] Step S301, obtaining a logging signal.

[0084] Step S302: input the logging signal into a first decomposition model to obtain first-scale low-frequency information.

[0085] Step S303: input the logging signal and the first-scale low-frequency information into a second decomposition model to obtain the first-scale high-frequency information.

[0086] Step S304: input the first-scale low-frequency information into the first decomposition model to obtain second-scale low-frequency information.

[0087] Step S305: input the first-scale low-frequency information and the second-scale low-frequency information into the second decomposition model to obtain second-scale high-frequency information.

[0088] The first decomposition model is used to decompose the logging signal with a preset decomposition operator based on the Minkowski one-dimensional sum-difference algorithm, and the preset decomposition operator is used to determine the scale for decomposing the logging signal.

[0089] The second decomposition model is used to obtain a difference function of two input values.

[0090] In an embodiment of the present application, the logging signal includes a one-dimensional time domain signal; the first decomposition model includes:

[0091] The expansion model is used to perform a one-dimensional sum operation in the time domain on the input value and the preset decomposition operator based on the Minkowski sum-difference algorithm.

[0092] In an optional example, the expansion model can be performed by performing a one-dimensional sum operation in the time domain through formula (1):

[0093]

[0094] Among them, Expand[P(t),Q(t)] is the expansion function, P(t) is the input value, Q(t) is the preset decomposition operator, and Q m (t) is the function value of the preset decomposition operator at the geometric center Qm, P i (t) is the function value of the input value at time i.

[0095] The input value of the expansion model may be a one-dimensional time domain signal such as a logging signal, a first contraction function or a first scale low-frequency information.

[0096] Specifically, when the input value of the expansion model is a logging signal, Expand[P(t), Q(t)] may be the first expansion function.

[0097] When the input value of the expansion model is the first contraction function, Expand[P(t), Q(t)] may be the second decomposition information.

[0098] The input value of the expansion model can be a superposition of multi-scale signals. Exemplarily, P(t) can be composed of the superposition of two signals g(t) and f(t). Further exemplary, the g(t) signal bandwidth can be 3 to 30 Hz, and the f(t) bandwidth can be 3 to 125 Hz.

[0099] Reference Figure 6 , Figure 6 FIG. 1 shows a schematic diagram of a point set of a preset decomposition operator in an embodiment provided by the present application. Figure 6 As shown, inheriting the design ideas of two-dimensional and high-dimensional Minkowski sum and difference operations, Q(t) can be a signal with a short delay and a simple waveform, which can specifically include the waveform Q(t) and the geometric center Qm. In the two-dimensional case, Qm is located at the geometric center of Q(t). In the one-dimensional case, simple functions of the waveform can be used as operators, Qm is within the range of Q(t), and all points in Q(t) can be used as the center Qm.

[0100] The contraction model is used to perform a one-dimensional difference operation in the time domain on the input value and the preset decomposition operator based on the Minkowski sum-difference algorithm.

[0101] In an optional example, the shrinkage model can be used to perform a one-dimensional difference operation in the time domain through formula (2):

[0102]

[0103] Among them, Contract[P(t),Q(t) is the contraction function, P(t) is the input value, Q(t) is the preset decomposition operator, and Q m (t) is the function value of the preset decomposition operator at the geometric center Qm, P i (t) is the function value of the input value at time i.

[0104] The input value of the contraction function may be a well logging signal or a one-dimensional time domain signal such as the first expansion function.

[0105] Specifically, when the input value of the contraction model is a logging signal, Contract[P(t),Q(t)] may be the first contraction function.

[0106] When the input value of the contraction model is the first expansion function, Contract[P(t),Q(t)] may be the first decomposition information.

[0107] Reference Figure 3 , Figure 3 A schematic diagram of sum and difference operation signals of a first decomposition model in an embodiment provided by the present application is shown.

[0108] like Figure 3As shown, the input value of the shrinkage model can be the superposition of multi-scale signals.

[0109] like Figure 3 (a) and Figure 3 As shown in (b), exemplarily, P(t) can be composed of the superposition of two signals g(t) and f(t). Further exemplarily, the bandwidth of the g(t) signal can be 3 to 30 Hz, and the bandwidth of the f(t) signal can be 3 to 125 Hz.

[0110] Accordingly, if Figure 3 (c) and Figure 3 As shown in (d), the first decomposition model can expand the input time domain signal through the expansion model to obtain the expansion function signal Expand(t), and shrink the input signal through the contraction model to obtain the contraction function signal Contract(t).

[0111] Reference Figure 6 , Figure 6 A schematic diagram of a point set of a preset decomposition operator in an embodiment provided by the present application is shown.

[0112] like Figure 6 As shown, inheriting the design ideas of two-dimensional and high-dimensional Minkowski sum and difference operations, Q(t) can be a signal with a short delay and a simple waveform, which can specifically include the waveform Q(t) and the geometric center Qm. In the two-dimensional case, Qm is located at the geometric center of Q(t). In the one-dimensional case, simple functions of the waveform can be used as operators, Qm is within the range of Q(t), and all points in Q(t) can be used as the center Qm.

[0113] The mean model computes the mean function of at least two input values.

[0114] In an optional example, the mean model can calculate the mean function in the time domain by formula (3):

[0115] P a1 (t)={ExC[P(t),Q(t)]+CoE[P(t),Q(t)]} / 2 (3)

[0116] Among them, P a1 (t) is the mean function, ExC[P(t),Q(t)] is the first input value, and CoE[P(t),Q(t)] is the second input value.

[0117] When the first input value and the second input value are the first decomposition information and the second decomposition information respectively, the mean function P a1 (t) may be the first scale low frequency information.

[0118] Reference Figure 2 , Figure 2 A flowchart of the steps of obtaining first-scale low-frequency information in an embodiment provided by the present application is shown.

[0119] like Figure 2 As shown, in order to achieve accurate separation of the logging signal, repeated expansion and contraction are required. To this end, in an embodiment of the present application, the step of inputting the logging signal into the first decomposition model to obtain the first scale low-frequency information includes:

[0120] Step S3021: input the logging signal into the expansion model to obtain a first expansion function.

[0121] Wherein, step S3021 can be completed by inputting the logging signal into equation (1), that is, the logging signal is used as the input value of the expansion model.

[0122] Step S3022: input the logging signal into the contraction model to obtain a first contraction function.

[0123] Wherein, step S3021 can be completed by inputting the logging signal into formula (2), that is, the logging signal is used as the input value of the contraction model.

[0124] Step S3023: input the first expansion function into the contraction model to obtain first decomposition information of the logging signal.

[0125] Specifically, the first expansion function is input into the contraction model, and a one-dimensional difference operation in the time domain can be performed through equation (4):

[0126] ExC[P(t),Q(t)]=Contract[Expand(P(t),Q(t)),Q(t)] (4)

[0127] Among them, ExC[P(t),Q(t)] is the first decomposition information, Expand(P(t),Q(t)) is the first expansion function, and Q(t) is the preset decomposition operator.

[0128] Specifically, when the input value of the contraction model is the first expansion function, Expand[P(t), Q(t)] may be the second contraction function, which serves as the first decomposition information of the logging signal.

[0129] Step S3024: input the first contraction function into the expansion model to obtain second decomposition information of the logging signal.

[0130] Specifically, the first contraction function is input into the expansion model, and a one-dimensional sum operation in the time domain can be performed through formula (5):

[0131] CoE[P(t),Q(t)]=Expand[Contract(P(t),Q(t)),Q(t)] (5)

[0132] Among them, CoE[P(t),Q(t)] is the second decomposition information, Contract(P(t),Q(t)) is the first contraction function, and Q(t) is the preset decomposition operator.

[0133] Reference Figure 4 , Figure 4 A signal decomposition diagram of a first decomposition model in an embodiment provided by the present application is shown.

[0134] like Figure 4 As shown, the first decomposition information ExC(t) and the second decomposition information CoE(t) can be further decomposition of the logging signal P(t). The first decomposition information ExC(t) obtains the upper half of the low-frequency information of the logging signal P(t), and the second decomposition information CoE(t) obtains the lower half of the low-frequency information of the logging signal P(t).

[0135] Step S3025: input the first decomposition information and the second decomposition information into the mean model to obtain the mean function of the first decomposition information and the second decomposition information as the first-scale low-frequency information.

[0136] Specifically, the first decomposition information and the second decomposition information can be used as the first input value and the second input value in formula (3) respectively, input into the mean model, and the mean function of the first decomposition information and the second decomposition information in the time domain is calculated as the first-scale low-frequency information.

[0137] Through the above embodiments, the present application proposes a method for processing logging signals based on Minkowski sum and difference. The method can establish basic operations based on the intersection and union operations of point sets in Euclidean space. The core algorithm only involves addition and subtraction operations, and has high computational efficiency. The first decomposition model in the embodiment of the present application is based on the Minkowski two-dimensional and above sum and difference operation formulas to deduce a one-dimensional signal multi-scale decomposition algorithm. The preset decomposition operator has a simple waveform structure and can be flexibly designed according to specific needs. Compared with the wavelet analysis method for logging signal processing in the related art, the embodiment of the present application can achieve efficient decomposition of one-dimensional multi-scale logging signals with fewer decomposition steps and lower computational complexity, effectively solving the problem of bandwidth overlap between the target signal and the background signal.

[0138] Embodiment 3

[0139] The present application also provides a method for processing a well logging signal, including:

[0140] Step S301, obtaining a logging signal.

[0141] Step S302: input the logging signal into a first decomposition model to obtain first-scale low-frequency information.

[0142] Step S303: input the logging signal and the first-scale low-frequency information into a second decomposition model to obtain the first-scale high-frequency information.

[0143] Step S304: input the first-scale low-frequency information into the first decomposition model to obtain second-scale low-frequency information.

[0144] Step S305: input the first-scale low-frequency information and the second-scale low-frequency information into the second decomposition model to obtain second-scale high-frequency information.

[0145] The first decomposition model is used to decompose the logging signal with a preset decomposition operator based on the Minkowski one-dimensional sum-difference algorithm, and the preset decomposition operator is used to determine the scale for decomposing the logging signal.

[0146] The second decomposition model is used to obtain a difference function of two input values.

[0147] In an embodiment of the present application, the step of inputting the logging signal and the first-scale low-frequency information into the second decomposition model to obtain the first-scale high-frequency information includes:

[0148] Inputting the logging signal and the first-scale low-frequency information into a second decomposition model to obtain a first difference function between the logging signal and the first-scale low-frequency information as the first-scale high-frequency information;

[0149] The step of inputting the first-scale low-frequency information and the second-scale low-frequency information into the second decomposition model to obtain the second-scale high-frequency information comprises:

[0150] The first-scale low-frequency information and the second-scale low-frequency information are input into the second decomposition model to obtain a second difference function between the first-scale low-frequency information and the second-scale low-frequency information as the second-scale high-frequency information.

[0151] In an optional example, the second decomposition model can perform time domain difference calculation through formula (6):

[0152] P d1 (t) = P(t) - P a1 (t) (6)

[0153] Among them, P a1(t) represents the first scale low-frequency information of signal P(t) under the signal scale determined by the current preset decomposition operator. d1 (t) is the first-scale high-frequency information of the signal P(t) at the signal scale determined by the current preset decomposition operator.

[0154] In an example case, P(t) may be a well logging signal, P a1 (t) can be the first scale low frequency information.

[0155] In another example, P(t) may be the first scale low-frequency information, P a1 (t) can be the first scale low frequency information.

[0156] Reference Figure 5 , Figure 5 A schematic diagram of signal decomposition of first scale information in an embodiment provided by the present application is shown.

[0157] like Figure 5 As shown, the first-scale low-frequency information and the second-scale high-frequency information may be waveforms obtained by separating the logging signal at the first scale, and may be referred to as first-scale information.

[0158] Signal P(t) and P a1 (t) is subjected to difference operation to obtain the high-frequency part of the first scale. In formula (6), P d1 (t) represents the high-frequency information of the first scale.

[0159] By analogy, P a1 (t) is input into formula (5) as the new P(t), and the new P a1 (t) can be used as the second scale low-frequency information P a2 (t), and then the new P a1 (t) and the new P(t) are input into formula (6), and the second scale high frequency information P can be obtained. d2 (t). The signal decomposition at a higher scale can be achieved by analogy using equations (5) and (6).

[0160] Reference Figure 7 , Figure 7 A schematic diagram of signal decomposition of multi-scale information in an embodiment provided by the present application is shown.

[0161] like Figure 7 As shown, through the above embodiment, at least three sub-signals can be obtained, the first scale high frequency information P d1 (t), the second scale low frequency information P a2 (t) and the second scale high frequency information P d2(t), thus, three separated signals can be obtained by inputting the operation of the first decomposition model twice, which greatly improves the operation efficiency of the logging signal decomposition.

[0162] Through the above embodiments, the present application proposes a method for processing logging signals based on Minkowski sum and difference. The method can establish basic operations based on the intersection and union operations of point sets in Euclidean space. The core algorithm only involves addition and subtraction operations, and has high computational efficiency. The first decomposition model in the embodiment of the present application is based on the Minkowski two-dimensional and above sum and difference operation formulas to deduce a one-dimensional signal multi-scale decomposition algorithm. The preset decomposition operator has a simple waveform structure and can be flexibly designed according to specific needs. Compared with the wavelet analysis method for logging signal processing in the related art, the embodiment of the present application can achieve efficient decomposition of one-dimensional multi-scale logging signals with fewer decomposition steps and lower computational complexity, effectively solving the problem of bandwidth overlap between the target signal and the background signal.

[0163] Embodiment 4

[0164] The present application also provides a method for processing a well logging signal, including:

[0165] Step S301, obtaining a logging signal.

[0166] Step S302: input the logging signal into a first decomposition model to obtain first-scale low-frequency information.

[0167] Step S303: input the logging signal and the first-scale low-frequency information into a second decomposition model to obtain the first-scale high-frequency information.

[0168] Step S304: input the first-scale low-frequency information into the first decomposition model to obtain second-scale low-frequency information.

[0169] Step S305: input the first-scale low-frequency information and the second-scale low-frequency information into the second decomposition model to obtain second-scale high-frequency information.

[0170] The first decomposition model is used to decompose the logging signal with a preset decomposition operator based on the Minkowski one-dimensional sum-difference algorithm, and the preset decomposition operator is used to determine the scale for decomposing the logging signal.

[0171] The second decomposition model is used to obtain a difference function of two input values.

[0172] The preset decomposition operator includes: a one-dimensional waveform function and a geometric center of the one-dimensional waveform function;

[0173] Wherein, the geometric center of the one-dimensional waveform function is within the range of the preset decomposition operator.

[0174] In the embodiment of the present application, the method for processing the logging signal further includes:

[0175] The geophysical parameters of the target to be measured are obtained according to the first-scale high-frequency information, the second-scale low-frequency information and the frequency information included in the second-scale high-frequency information.

[0176] The first-scale high-frequency information, the second-scale low-frequency information, and the second-scale high-frequency information contain different frequency information.

[0177] Through the above embodiments, the present application proposes a method for processing logging signals based on Minkowski sum and difference. The method can establish basic operations based on the intersection and union operations of point sets in Euclidean space. The core algorithm only involves addition and subtraction operations, and has high computational efficiency. The first decomposition model in the embodiment of the present application is based on the Minkowski two-dimensional and above sum and difference operation formulas to deduce a one-dimensional signal multi-scale decomposition algorithm. The preset decomposition operator has a simple waveform structure and can be flexibly designed according to specific needs. Compared with the wavelet analysis method for logging signal processing in the related art, the embodiment of the present application can achieve efficient decomposition of one-dimensional multi-scale logging signals with fewer decomposition steps and lower computational complexity, effectively solving the problem of bandwidth overlap between the target signal and the background signal.

[0178] Embodiment 5

[0179] The present application also provides a method for processing a well logging signal, including:

[0180] Step S301, obtaining a logging signal.

[0181] Step S302: input the logging signal into a first decomposition model to obtain first-scale low-frequency information.

[0182] Step S303: input the logging signal and the first-scale low-frequency information into a second decomposition model to obtain the first-scale high-frequency information.

[0183] Step S304: input the first-scale low-frequency information into the first decomposition model to obtain second-scale low-frequency information.

[0184] Step S305: input the first-scale low-frequency information and the second-scale low-frequency information into the second decomposition model to obtain second-scale high-frequency information.

[0185] The first decomposition model is used to decompose the logging signal with a preset decomposition operator based on the Minkowski one-dimensional sum-difference algorithm, and the preset decomposition operator is used to determine the scale for decomposing the logging signal.

[0186] The second decomposition model is used to obtain a difference function of two input values.

[0187] In an embodiment of the present application, the logging signal includes a one-dimensional time domain signal; the first decomposition model includes:

[0188] An expansion model, for performing a one-dimensional sum operation in the time domain on the input value and the preset decomposition operator based on a Minkowski sum-difference algorithm;

[0189] A contraction model, used for performing a one-dimensional difference operation in the time domain on the input value and the preset decomposition operator based on a Minkowski sum-difference algorithm;

[0190] The mean model computes the mean function of at least two input values.

[0191] In an embodiment of the present application, the step of inputting the logging signal into the first decomposition model to obtain the first scale low-frequency information includes:

[0192] Inputting the logging signal into the expansion model to obtain a first expansion function;

[0193] Inputting the logging signal into the contraction model to obtain a first contraction function;

[0194] Inputting the first expansion function into the contraction model to obtain first decomposition information of the logging signal;

[0195] Inputting a first contraction function into the expansion model to obtain second decomposition information of the logging signal;

[0196] The first decomposition information and the second decomposition information are input into the mean model to obtain a mean function of the first decomposition information and the second decomposition information as the first-scale low-frequency information.

[0197] In an embodiment of the present application, the step of inputting the logging signal and the first-scale low-frequency information into the second decomposition model to obtain the first-scale high-frequency information includes:

[0198] Inputting the logging signal and the first-scale low-frequency information into a second decomposition model to obtain a first difference function between the logging signal and the first-scale low-frequency information as the first-scale high-frequency information;

[0199] The step of inputting the first-scale low-frequency information and the second-scale low-frequency information into the second decomposition model to obtain the second-scale high-frequency information comprises:

[0200] The first-scale low-frequency information and the second-scale low-frequency information are input into the second decomposition model to obtain a second difference function between the first-scale low-frequency information and the second-scale low-frequency information as the second-scale high-frequency information.

[0201] In an embodiment of the present application, the preset decomposition operator includes: a one-dimensional waveform function and a geometric center of the one-dimensional waveform function;

[0202] Wherein, the geometric center of the one-dimensional waveform function is within the range of the preset decomposition operator.

[0203] In the embodiment of the present application, it also includes:

[0204] Obtaining geophysical parameters of the target to be measured according to the first-scale high-frequency information, the second-scale low-frequency information, and the frequency information included in the second-scale high-frequency information;

[0205] The first-scale high-frequency information, the second-scale low-frequency information, and the second-scale high-frequency information contain different frequency information.

[0206] Through the above embodiments, the present application proposes a method for processing logging signals based on Minkowski sum and difference. The method can establish basic operations based on the intersection and union operations of point sets in Euclidean space. The core algorithm only involves addition and subtraction operations, and has high computational efficiency. The first decomposition model in the embodiment of the present application is based on the Minkowski two-dimensional and above sum and difference operation formulas to deduce a one-dimensional signal multi-scale decomposition algorithm. The preset decomposition operator has a simple waveform structure and can be flexibly designed according to specific needs. Compared with the wavelet analysis method for logging signal processing in the related art, the embodiment of the present application can achieve efficient decomposition of one-dimensional multi-scale logging signals with fewer decomposition steps and lower computational complexity, effectively solving the problem of bandwidth overlap between the target signal and the background signal.

[0207] Embodiment 6

[0208] The present application also provides a method for processing a well logging signal, including:

[0209] Acquire logging signals;

[0210] Inputting the logging signal into a first decomposition model to obtain first-scale low-frequency information;

[0211] Inputting the logging signal and the first-scale low-frequency information into a second decomposition model to obtain first-scale high-frequency information;

[0212] Inputting the first-scale low-frequency information into the first decomposition model to obtain second-scale low-frequency information;

[0213] Inputting the first-scale low-frequency information and the second-scale low-frequency information into the second decomposition model to obtain second-scale high-frequency information;

[0214] The first decomposition model is used to decompose the logging signal with a preset decomposition operator based on the Minkowski one-dimensional sum-difference algorithm, and the preset decomposition operator is used to determine the scale for decomposing the logging signal.

[0215] The second decomposition model is used to obtain a difference function of two input values.

[0216] Furthermore, in order to perform higher-scale signal separation on the logging signal, the following steps can be performed:

[0217] Step S801: input the second-scale low-frequency information into the first decomposition model to obtain third-scale low-frequency information.

[0218] Step S802: input the third-scale low-frequency information into the second decomposition model to obtain the third-scale high-frequency information.

[0219] Step S803: input the third-scale low-frequency information into the first decomposition model to obtain fourth-scale low-frequency information.

[0220] Step S804: input the fourth-scale low-frequency information into the second decomposition model to obtain the third-scale high-frequency information.

[0221] And so on.

[0222] That is, by inputting the low-frequency information of the preset level scale into the first decomposition model, the low-frequency information of the higher level scale can be obtained, and by performing a difference operation between the low-frequency information of the higher level scale and the low-frequency information of the preset level scale, the high-frequency information of the higher level scale can be obtained, thereby realizing a higher-scale decomposition of the logging signal.

[0223] Through the above embodiments, the present application proposes a method for processing logging signals based on Minkowski sum and difference. The method can establish basic operations based on the intersection and union operations of point sets in Euclidean space. The core algorithm only involves addition and subtraction operations, and has high computational efficiency. The first decomposition model in the embodiment of the present application is based on the Minkowski two-dimensional and above sum and difference operation formulas to deduce a one-dimensional signal multi-scale decomposition algorithm. The preset decomposition operator has a simple waveform structure and can be flexibly designed according to specific needs. Compared with the wavelet analysis method for logging signal processing in the related art, the embodiment of the present application can achieve efficient decomposition of one-dimensional multi-scale logging signals with fewer decomposition steps and lower computational complexity, effectively solving the problem of bandwidth overlap between the target signal and the background signal.

[0224] Embodiment 7

[0225] Reference Figure 8 , Figure 8 FIG. 2 shows a structural block diagram of a logging signal processing system in an embodiment provided by the present application. Figure 8 As shown, based on the same inventive concept, the embodiment of the present application also provides a processing system for logging signals, including:

[0226] The signal acquisition unit 901 is used to acquire the logging signal.

[0227] The first decomposition unit 902 is used to input the logging signal into a first decomposition model to obtain first-scale low-frequency information.

[0228] The second decomposition unit 903 is used to input the logging signal and the first-scale low-frequency information into a second decomposition model to obtain the first-scale high-frequency information.

[0229] The third decomposition unit 904 is used to input the first-scale low-frequency information into the first decomposition model to obtain second-scale low-frequency information.

[0230] The fourth decomposition unit 905 inputs the first-scale low-frequency information and the second-scale low-frequency information into the second decomposition model to obtain second-scale high-frequency information.

[0231] The first decomposition model is used to decompose the logging signal with a preset decomposition operator based on the Minkowski one-dimensional sum-difference algorithm, and the preset decomposition operator is used to determine the scale for decomposing the logging signal.

[0232] The second decomposition model is used to obtain a difference function of two input values.

[0233] Through the above embodiments, the present application proposes a processing system for logging signals based on Minkowski sum and difference. The system can establish basic operations based on the intersection and union operations of point sets in Euclidean space. The core algorithm only involves addition and subtraction operations, and has high computational efficiency. The first decomposition model in the embodiment of the present application is based on the Minkowski two-dimensional and above sum and difference operation formulas to deduce a one-dimensional signal multi-scale decomposition algorithm. The preset decomposition operator has a simple waveform structure and can be flexibly designed according to specific needs. Compared with the wavelet analysis method for logging signal processing in the related art, the embodiment of the present application can achieve efficient decomposition of one-dimensional multi-scale logging signals with fewer decomposition steps and lower computational complexity, effectively solving the problem of bandwidth overlap between the target signal and the background signal.

[0234] Based on the same inventive concept, another embodiment of the present application provides a railway traction power supply system, including:

[0235] On the other hand, an embodiment of the present application further provides a computer-readable storage medium storing a computer program, which implements the steps in the method in the above embodiment when executed by a processor.

[0236] On the other hand, an embodiment of the present application further provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps in the method in the above embodiment when executed.

[0237] In another aspect, the present application also provides a well logging tool, including:

[0238] The electronic device in the above embodiment;

[0239] The sensor device is used to generate a logging signal and transmit it to the electronic device.

[0240] On the other hand, an embodiment of the present application further provides a computer program, which, when executed by a processor, implements the steps in the method in the above embodiment.

[0241] As for the device embodiments or product embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0242] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0243] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, devices, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented 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.

[0244] The embodiments of the present application are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0245] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0246] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0247] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present application.

[0248] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

Claims

1. A method for processing well logging signals, characterized in that: include: Acquire logging signals; Inputting the logging signal into a first decomposition model to obtain first-scale low-frequency information; Inputting the logging signal and the first-scale low-frequency information into a second decomposition model to obtain first-scale high-frequency information; Inputting the first-scale low-frequency information into the first decomposition model to obtain second-scale low-frequency information; Inputting the first-scale low-frequency information and the second-scale low-frequency information into the second decomposition model to obtain second-scale high-frequency information; The first decomposition model is used to decompose the logging signal with a preset decomposition operator based on a Minkowski one-dimensional sum-difference algorithm, and the preset decomposition operator is used to determine a scale for decomposing the logging signal; The second decomposition model is used to obtain a difference function of two input values.

2. The method for processing logging signals according to claim 1, characterized in that: The logging signal includes a one-dimensional time domain signal; the first decomposition model includes: An expansion model, for performing a one-dimensional sum operation in the time domain on the input value and the preset decomposition operator based on a Minkowski sum-difference algorithm; A contraction model, used for performing a one-dimensional difference operation in the time domain on the input value and the preset decomposition operator based on a Minkowski sum-difference algorithm; The mean model computes the mean function of at least two input values.

3. The method for processing logging signals according to claim 2, characterized in that: The step of inputting the logging signal into the first decomposition model to obtain the first scale low-frequency information comprises: Inputting the logging signal into the expansion model to obtain a first expansion function; Inputting the logging signal into the contraction model to obtain a first contraction function; Inputting the first expansion function into the contraction model to obtain first decomposition information of the logging signal; Inputting a first contraction function into the expansion model to obtain second decomposition information of the logging signal; The first decomposition information and the second decomposition information are input into the mean model to obtain a mean function of the first decomposition information and the second decomposition information as the first-scale low-frequency information.

4. The method for processing logging signals according to claim 1, characterized in that: The step of inputting the logging signal and the first-scale low-frequency information into a second decomposition model to obtain the first-scale high-frequency information comprises: Inputting the logging signal and the first-scale low-frequency information into a second decomposition model to obtain a first difference function between the logging signal and the first-scale low-frequency information as the first-scale high-frequency information; The step of inputting the first-scale low-frequency information and the second-scale low-frequency information into the second decomposition model to obtain the second-scale high-frequency information comprises: The first-scale low-frequency information and the second-scale low-frequency information are input into the second decomposition model to obtain a second difference function between the first-scale low-frequency information and the second-scale low-frequency information as the second-scale high-frequency information.

5. The method for processing logging signals according to claim 1, characterized in that: The preset decomposition operator includes: a one-dimensional waveform function and a geometric center of the one-dimensional waveform function; Wherein, the geometric center of the one-dimensional waveform function is within the range of the preset decomposition operator.

6. The method for processing logging signals according to claim 1, characterized in that: Also includes: Obtaining geophysical parameters of the target to be measured according to the first-scale high-frequency information, the second-scale low-frequency information, and the frequency information included in the second-scale high-frequency information; The first-scale high-frequency information, the second-scale low-frequency information, and the second-scale high-frequency information contain different frequency information.

7. A logging signal processing system, characterized in that: include: A signal acquisition unit, used for acquiring logging signals; A first decomposition unit, used for inputting the logging signal into a first decomposition model to obtain first-scale low-frequency information; A second decomposition unit is used to input the logging signal and the first-scale low-frequency information into a second decomposition model to obtain the first-scale high-frequency information; A third decomposition unit, configured to input the first-scale low-frequency information into the first decomposition model to obtain second-scale low-frequency information; A fourth decomposition unit, inputting the first-scale low-frequency information and the second-scale low-frequency information into the second decomposition model to obtain second-scale high-frequency information; The first decomposition model is used to decompose the logging signal with a preset decomposition operator based on a Minkowski one-dimensional sum-difference algorithm, and the preset decomposition operator is used to determine a scale for decomposing the logging signal; The second decomposition model is used to obtain a difference function of two input values.

8. A computer-readable storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a processor, the steps in the method according to any one of claims 1 to 6 are implemented.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps in the method according to any one of claims 1 to 6 when executing the program.

10. A well logging tool, characterized in that: include: The electronic device as claimed in claim 9; The sensor device is used to generate a logging signal and transmit it to the electronic device.

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