Elemental logging abnormal signal suppression method and system, electronic equipment and storage medium

Abstract

The application discloses an element logging abnormal signal suppression method and system, electronic equipment and a storage medium. The method comprises the following steps: obtaining a target element logging signal and a target velocity curve; performing deep-time conversion on the target element logging signal based on the target velocity curve to obtain a time domain signal; performing a shift transformation on the time domain signal to obtain a waveform signal; performing abnormal signal suppression on the waveform signal based on modal constraints to obtain an initial suppression signal; and performing inverse transformation on the initial suppression signal to obtain a target suppression signal. In the embodiment of the application, the target element logging signal is converted from a depth axis to a time axis by using the target velocity curve, and then a waveform signal is obtained through a shift transformation. Then, abnormal signal suppression is performed based on the waveform signal. Finally, the signal is converted back to the depth domain through inverse transformation to obtain the final signal. The application can avoid signal data loss caused by directly removing abnormal signals, and can be widely applied in the technical field of data processing.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and in particular to an element logging abnormal signal suppression method and system, an electronic device, and a storage medium. BACKGROUND

[0002] Mixed sedimentary layer system is a kind of sedimentary type in which terrigenous clastic and carbonate rock are alternately interbedded. As a special genetic sedimentary phenomenon, mixed sedimentation has attracted widespread attention in terms of genetic mechanism and main controlling factors, and its distribution characteristics are of great significance to oil and gas exploration. Logging or element logging data can better distinguish the lithological changes of clastic rock and carbonate rock, reflect the development law of mixed sedimentary layer system in the vertical direction, and analyze the controlling factors of its development characteristics.

[0003] During the deposition process, the event deposition process caused by short, sudden and high-energy events will destroy the original sedimentary stratum signal, such as collapse, erosion, etc., which will interfere with the extraction of useful information from the logging signal. How to suppress these interference signals becomes an important part of the genetic analysis of mixed sedimentary layer system. At present, the commonly used method is to directly exclude the data destroyed by abnormal events, but this will lose the signal of this section and affect the analysis of the main controlling factors. SUMMARY

[0004] The present application aims to at least partially solve the problems of the related art. To this end, the present application provides an element logging abnormal signal suppression method and system, an electronic device, and a storage medium, which can avoid the side effects of directly excluding abnormal signals.

[0005] In one aspect, the present application provides an element logging abnormal signal suppression method, including the following steps:

[0006] Obtaining a target element logging signal and a target velocity curve;

[0007] Converting the target element logging signal into a time domain signal based on the target velocity curve;

[0008] Performing a shift transformation on the time domain signal to obtain a waveform signal;

[0009] Suppressing abnormal signals in the waveform signal based on modal constraints to obtain an initial suppression signal;

[0010] Performing an inverse transformation on the initial suppression signal to obtain a target suppression signal.

[0011] Optionally, before the step of converting the target element logging signal into a time domain signal based on the target velocity curve, the method further includes the following steps:

[0012] Preprocessing the target element logging signal;

[0013] The preprocessing includes signal correction and outlier removal.

[0014] Optionally, obtaining the target velocity curve includes at least one of the following steps:

[0015] The velocity curve recorded during the drilling process is used as the target velocity curve;

[0016] Obtain seismic data from seismic exploration and obtain the target velocity curve based on the analysis results of the seismic data;

[0017] The empirical curve of the drilling area is used as the target velocity curve.

[0018] Optionally, the target element logging signal is subjected to depth-time conversion based on the target velocity curve to obtain a time-domain signal, including the following steps:

[0019] Construct a function relating depth and time based on the target velocity curve;

[0020] The expression for the relational function is:

[0021]

[0022] In the formula, t represents time; z i V(z) represents depth; V(z) represents the target velocity curve.

[0023] Based on the relational function, the target element logging signal is transformed from the depth domain to the time axis to obtain the time signal;

[0024] The time signals corresponding to each time point of the target element logging signal are regularized to obtain the time domain signal.

[0025] Optionally, a translation transformation is performed on the time-domain signal to obtain a waveform signal, including the following steps:

[0026] The average value of the time-domain signal is obtained by averaging all signal values ​​in the time-domain signal.

[0027] The waveform signal is obtained by shifting the time-domain signal using its average value.

[0028] Optionally, the waveform signal is suppressed based on modal constraints to obtain an initial suppressed signal, including the following steps:

[0029] Wavelet transform is used to decompose waveform signals into scale signals of multiple scales;

[0030] Multiple pattern signals are constructed based on the main controlling factors corresponding to the abnormal data of the target element logging signals;

[0031] Construct the objective function for modal constraints based on scale signals and pattern signals;

[0032] The objective function is expressed as follows:

[0033]

[0034] In the formula, J(N) represents the objective function; ||·|| is the norm symbol; a i (t) represents the signal at the i-th scale; N represents the scale threshold, and N is the optimization variable of the objective function; b i (t) represents the i-th mode signal; m represents the number of mode signals;

[0035] The objective function is optimized to obtain the target scale;

[0036] The initial suppression signal is obtained by integrating the scale signals based on the target scale.

[0037] Optionally, the initial suppression signal is inversely transformed to obtain the target suppression signal, including the following steps:

[0038] The initial suppression signal is subjected to an inverse translational transformation, and then the signal is transformed to the depth domain through time-depth transformation to obtain the target suppression signal.

[0039] On the other hand, embodiments of the present invention provide an element logging anomaly signal suppression system, comprising:

[0040] The first module is used to acquire the target element logging signal and the target velocity curve;

[0041] The second module is used to perform deep-time conversion on the target element logging signal based on the target velocity curve to obtain the time domain signal;

[0042] The third module is used to perform translation transformation on the time-domain signal to obtain the waveform signal;

[0043] The fourth module is used to suppress abnormal signals in waveform signals based on modal constraints to obtain the initial suppressed signal;

[0044] The fifth module is used to perform an inverse transformation on the initial suppression signal to obtain the target suppression signal.

[0045] Optionally, the system also includes:

[0046] The sixth module is used to preprocess the logging signals of the target elements;

[0047] The preprocessing includes signal correction and outlier removal.

[0048] On the other hand, embodiments of the present invention provide an electronic device, including: a processor and a memory; the memory is used to store a program; the processor executes the program to implement the above-mentioned method for suppressing abnormal signals in element logging.

[0049] On the other hand, embodiments of the present invention provide a computer storage medium storing a processor-executable program, which, when executed by a processor, is used to implement the above-described method for suppressing abnormal signals in element logging.

[0050] This invention acquires target element logging signals and target velocity curves; performs depth-time transformation on the target element logging signals based on the target velocity curves to obtain time-domain signals; performs translation transformation on the time-domain signals to obtain waveform signals; performs anomaly suppression on the waveform signals based on modal constraints to obtain initial suppressed signals; and performs inverse transformation on the initial suppressed signals to obtain the target suppressed signals. This invention utilizes the target velocity curve to transform the target element logging signals from the depth axis to the time axis, then obtains waveform signals through a translation transformation, performs anomaly suppression based on these waveform signals, and finally transforms the signals back to the depth domain through an inverse transformation to obtain the final signals. This invention obtains time-domain waveform signals through depth-time transformation and numerical translation transformation, which helps increase the stability of the algorithm; furthermore, by introducing modal constraints, this invention can maintain the relationship between various modes while suppressing noise, making subsequent geological interpretation more reliable. This invention can avoid signal data loss caused by directly removing anomalies. Attached Figure Description

[0051] The accompanying drawings are provided to further understand the technical solutions of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the technical solutions of the present invention, and do not constitute a limitation on the technical solutions of the present invention.

[0052] Figure 1 This is a schematic diagram of an implementation environment for the method of suppressing abnormal signals in element logging provided in this embodiment of the invention;

[0053] Figure 2 This is a flowchart illustrating a method for suppressing abnormal signals in element logging provided in an embodiment of the present invention;

[0054] Figure 3 A schematic diagram illustrating the unfolding process of step S200 provided in an embodiment of the present invention;

[0055] Figure 4 A schematic diagram illustrating the unfolding process of step S300 provided in an embodiment of the present invention;

[0056] Figure 5 A schematic diagram illustrating the unfolding process of step S400 provided in an embodiment of the present invention;

[0057] Figure 6 A comparative schematic diagram of the original calcium content curve and the transformed calcium content curve provided for embodiments of the present invention;

[0058] Figure 7 A comparative schematic diagram of the original calcium content curve and the calcium content curve after suppressing abnormal signals, provided for embodiments of the present invention;

[0059] Figure 8 The embodiments of the present invention are based on Figure 7 A schematic diagram comparing the results of calcium content curves overlaid with those of deposition mode curves;

[0060] Figure 9 This is a schematic diagram of the structure of an element logging anomaly signal suppression system provided in an embodiment of the present invention;

[0061] Figure 10 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0063] It should be noted that although functional modules are divided in the system diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the system or the order in the flowchart. The terms "first / S100," "second / S200," etc., in the specification, claims, and the aforementioned figures are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0064] In this invention, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this invention can be combined with other embodiments.

[0065] It is understood that the element logging anomaly signal suppression method provided in this embodiment of the invention can be applied to any computer device with data processing and computing capabilities, and this computer device can be various terminals or servers. When the computer device in the embodiment is a server, the server is an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. Optionally, the terminal can be a smartphone, tablet, laptop, or desktop computer, but it is not limited to these.

[0066] To facilitate understanding of the technical solution of this invention, the technical features and proper nouns that may appear in the embodiments of this invention will first be explained:

[0067] like Figure 1 The diagram shown is a schematic representation of an implementation environment provided by an embodiment of the present invention. (Refer to...) Figure 1 The implementation environment includes at least one terminal 102 and a server 101. The terminal 102 and the server 101 can be connected via a network, either wirelessly or via a wired connection, to complete data transmission and exchange.

[0068] Server 101 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0069] Additionally, server 101 can also be a node server in a blockchain network. Blockchain is a novel application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms.

[0070] Terminal 102 can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, etc., but is not limited to these. Terminal 102 and server 101 can be directly or indirectly connected via wired or wireless communication, and this embodiment of the invention does not impose any limitations.

[0071] Exemplary based on Figure 1The implementation environment shown in this embodiment of the invention provides a method for suppressing abnormal element logging signals. The following description uses the application of this method in server 101 as an example. It can be understood that this method can also be applied in terminal 102.

[0072] Reference Figure 2 , Figure 2 This is a flowchart illustrating a method for suppressing element logging anomaly signals applied to a server, provided in an embodiment of the present invention. The executing entity of this method can be any of the aforementioned computer devices (including a server or terminal). (Refer to...) Figure 2 The method includes the following steps:

[0073] S100: Acquire the target element logging signal and target velocity curve;

[0074] It should be noted that, in some embodiments, obtaining the target velocity curve may include one of the following steps: obtaining the velocity curve recorded during the drilling process as the target velocity curve; obtaining seismic data from seismic exploration and obtaining the target velocity curve based on the analysis results of the seismic data; or using the empirical curve of the drilling area as the target velocity curve.

[0075] For example, in some specific embodiments, velocity curves are collected for transformation. These velocity curves can be velocity curves recorded simultaneously during drilling, curves obtained from seismic data during seismic exploration, or empirical curves of the drilling area.

[0076] In some embodiments, the method may further include the following steps: preprocessing the target element logging signal; wherein the preprocessing includes signal correction and outlier removal.

[0077] For example, in some specific implementations, preprocessing mainly includes conventional correction and removal of obvious isolated points. Taking the calcium signal from elemental logging as an example, the specific process and practical application scenarios of signal correction and isolated point removal are explained step by step:

[0078] 1. Signal correction:

[0079] Prolonged instrument operation can lead to baseline drift. For example, in continuous logging, the calcium signal in the same formation (such as a limestone layer) should be stable, but instrument drift may cause the measured value to gradually become higher or lower (e.g., from 2000 ppm to 2200 ppm).

[0080] Solution:

[0081] Moving average method: Select a window of 5 data points and calculate the local mean of each point as the baseline;

[0082] Original data: `[2000,2050,2100,2150,2200]`;

[0083] Baseline fit: `[2020,2070,2120,2170]` (mean calculated by sliding window);

[0084] Baseline subtraction: Subtract the baseline signal from the original signal to obtain the corrected data.

[0085] II. Outlier Removal:

[0086] The calcium signal at a certain depth point suddenly changed to 3500 ppm (the mean of adjacent points was 2000 ppm, and the standard deviation was 150 ppm), which may be caused by rock debris contamination or instrument noise.

[0087] Solution:

[0088] Calculate the mean (μ = 2000) and standard deviation (σ = 150), and set the threshold μ ± 3σ → [1550, 2450].

[0089] A value exceeding the threshold of 3500 ppm was identified as an isolated point.

[0090] Handling method:

[0091] Linear interpolation: Replace outliers with the mean of the preceding and following points (e.g., 2000 and 2050) to obtain a corrected value of 2025ppm.

[0092] S200. Based on the target velocity curve, perform deep-time conversion on the target element logging signal to obtain the time domain signal;

[0093] It should be noted that in some embodiments, such as Figure 3 As shown, step S200 may include the following steps: S201, constructing a relationship function between depth and time based on the target velocity curve; wherein, the expression of the relationship function is:

[0094]

[0095] In the formula, t represents time; z i V(z) represents depth; V(z) represents the target velocity curve.

[0096] S202. Based on the relational function, transform the target element logging signal from the depth domain to the time axis to obtain the time signal; S203. Based on the time signal corresponding to each time point of the target element logging signal, perform regularization processing to obtain the time domain signal.

[0097] For example, in some specific implementations, taking calcium signals as an example, the depth-time conversion of the logging signal for the target element can be achieved as follows:

[0098] 1) First, collect the velocity curves used for transformation, which are denoted as V(z).

[0099] 2) Establish the relationship between depth domain time, and the calculation formula is as follows:

[0100]

[0101] 3) This allows us to calculate any depth z. i The corresponding time, thus transforming the depth domain of the calcium signal to be processed onto the time axis.

[0102] 4) Regularization. Although the depth axis is uniform, it may become non-uniform after transformation to the time domain, requiring regularization. Specifically, signal regularization can be performed using methods such as linear interpolation, cubic spline interpolation, inverse distance weighted interpolation, non-uniform Fourier transform, and least squares fitting regularization.

[0103] S300: Perform a translation transformation on the time-domain signal to obtain a waveform signal;

[0104] It should be noted that in some embodiments, such as Figure 4 As shown, step S300 may include the following steps: S301, averaging all signal values ​​in the time domain signal to obtain the average value of the time domain signal; S302, using the average value of the time domain signal to perform a translation transformation on the time domain signal to obtain a waveform signal.

[0105] For example, in some specific implementations, calcium signal data are typically all positive values. When transformed to the frequency domain or other similar domains for signal analysis, very small frequencies or wavenumbers appear, which is detrimental to the stability of the algorithm. The transformation is performed using the following formula:

[0106] G2(t)=G1(t-M

[0107] Here, G1(t) is the signal obtained in the preceding steps, and M is the average value of signal G1(t). Finally, through a translation transformation, the signal, which consists entirely of positive values, is transformed into a waveform signal G2(t) with both positive and negative values.

[0108] S400. Based on modal constraints, abnormal signals are suppressed in the waveform signal to obtain the initial suppressed signal;

[0109] It should be noted that in some embodiments, such as Figure 5 As shown, step S400 may include the following steps: S401, decomposing the waveform signal into scale signals of multiple scales using wavelet transform; S402, constructing multiple mode signals based on the main control factors corresponding to the abnormal data of the target element logging signal; S403, constructing a modal constraint objective function based on the scale signals and mode signals; wherein, the expression of the objective function is:

[0110]

[0111] In the formula, J(N) represents the objective function; ||·|| is the norm symbol; a i (t) represents the signal at the i-th scale; N represents the scale threshold, and N is the optimization variable of the objective function; b i (t) represents the i-th mode signal; m represents the number of mode signals;

[0112] S404. Optimize the objective function to obtain the target scale; S405. Integrate the scale signals based on the target scale to obtain the initial suppression signal. In some optional implementations, multi-scale decomposition of the waveform signal can also employ transformation algorithms with local property capabilities, such as empirical mode decomposition transform and Wigner-Willi transform.

[0113] For example, in some specific implementations, taking the calcium signal as an example, abnormal signal suppression can be achieved as follows:

[0114] Outlier data typically appears during a specific anomalous event and exhibits localized characteristics. First, the signal G2(t) is decomposed into signals of different scales using a conventional wavelet transform, denoted as a1(t), a2(t), ..., a... n (t), corresponding to n scales. Typically, a threshold N is selected, and the signal is reconstructed from signals at scales 1 to N to obtain the denoised signal. However, this method is highly subjective and may destroy the underlying mode information. Therefore, this invention proposes a modal constraint strategy.

[0115]

[0116] Where b i (t), i = 1: m are the various models calculated based on the theory of controlling factors. Specifically, the theoretical curves corresponding to each model can be calculated based on the era information of the logging area and combined with the climate change cycle controlling factor model. This can be achieved through conventional calculations in the field of logging information processing.

[0117] By optimizing the above objective function, the optimal Nopt (i.e., the target scale) is obtained. Then, the signal (i.e., the initial suppression signal) is obtained:

[0118]

[0119] S500: Perform an inverse transformation on the initial suppression signal to obtain the target suppression signal.

[0120] It should be noted that in some embodiments, step S500 may include the following steps: performing an inverse transformation of the initial suppression signal by translation transformation, and then transforming the signal to the depth domain by time-depth transformation to obtain the target suppression signal.

[0121] For example, in some specific implementations, the signal is finally converted back to the original depth domain by inverse transformation to complete the entire suppression process and obtain the calcium signal after noise suppression caused by the abnormal event.

[0122] To explain in detail the principle of the technical solution of the present invention, the overall process of the present invention will be described below with reference to some specific embodiments. It is easy to understand that the following is an explanation of the technical principle of the present invention and should not be regarded as a limitation of the present invention.

[0123] First, it should be noted that, taking calcium as an example, this invention, based on calcium element data from elemental logging, addresses the problem of abnormal events disrupting the identification of the main controlling factor pattern in calcium signals. This invention proposes a technical solution for suppressing noise from abnormal events. The main idea is to use velocity curves from logging or curves obtained from seismic analysis to transform the preprocessed calcium signal data from the depth axis to the time axis. Through a translation transformation, the all-positive signal is transformed into a waveform signal with both positive and negative values. Then, abnormal signal suppression is performed based on this waveform signal. Finally, through inverse translation transformation and time-depth conversion transformation, the signal is returned to the depth domain to obtain the final signal.

[0124] The specific implementation plan is as follows:

[0125] The first step is preprocessing of the calcium signal from elemental logging. This mainly includes routine correction and removal of obvious isolated points.

[0126] The second step is to perform a coordinate transformation on the calcium signal, changing it from the depth domain to the time axis. Typically, the coordinate axis of the calcium signal is in the depth domain, and the interval between sampling points is several meters or tens of meters.

[0127] 1) First, collect the velocity curves used for transformation. The velocity curves can be those recorded simultaneously during drilling, those obtained from seismic data analysis during seismic exploration, or empirical curves from the drilling area. This velocity curve is denoted as V(z).

[0128] 2) Establish the relationship between depth domain time, and the calculation formula is as follows:

[0129]

[0130] 3) This allows us to calculate any depth z. i The corresponding time, thus transforming the depth domain of the calcium signal to be processed onto the time axis.

[0131] 4) Regularization. Although the depth axis is uniform, it may become non-uniform after transformation to the time domain, requiring regularization. The signal obtained in this step is denoted as G1(t).

[0132] Step 3: Transform the all-positive signal into a waveform signal G2(t) with both positive and negative values ​​through a translation transformation.

[0133] Typically, calcium signal data consists entirely of positive values. When transformed to the frequency domain or other similar domains for signal analysis, very small frequencies or wavenumbers appear, which is detrimental to the stability of the algorithm. The transformation is performed using the following formula:

[0134] G2(t)=G1(t-M

[0135] Where M is the average value of signal G1(t).

[0136] Step 4: Suppressing abnormal signals.

[0137] Anomalous data in calcium signals typically occur during a specific anomalous event and exhibit localized characteristics. First, the signal G2(t) is decomposed into signals of different scales using conventional wavelet transform, denoted as a1(t), a2(t), ..., a... n (t), corresponding to n scales. Typically, a threshold N is selected, and the signal is reconstructed from signals at scales 1 to N to obtain the denoised signal. However, this method is highly subjective and may destroy the underlying mode information. Therefore, this invention proposes a modal constraint strategy:

[0138]

[0139] Where b i (t),i=1:m are the various models calculated based on the theory of controlling factors.

[0140] By optimizing the above objective function, the optimal Nopt is obtained. Then, the signal is obtained:

[0141]

[0142] Step 5: Perform an inverse transform to return to the original depth domain, completing the entire suppression process and obtaining the calcium signal after suppressing the noise caused by the abnormal event.

[0143] In some specific application scenarios, the technical effects of the present invention will be further explained below with specific example data (the following data examples are for illustrative purposes only and should not be regarded as a limitation of the present invention):

[0144] like Figure 6(The left side shows the calcium content signal curve actually recorded by well logging in a certain area, and the right side shows the waveform data with positive and negative values ​​after coordinate axis transformation and numerical translation.) See the reference. Figure 6 On the left, the depth ranges from 2500 to 3200 meters, with abnormal data caused by abnormal sedimentation in the 2600 to 2800 meter range, affecting various controlling factors in subsequent analysis.

[0145] like Figure 7 (The left side shows the data before noise suppression, and the right side shows the data after abnormal signal suppression) As shown, from... Figure 7 The results after suppressing the abnormal signal show that, after suppression according to the technical approach of this invention, the curve in the 2600 to 2800 range is smoother in the general trend, and is no longer as flat as the original data.

[0146] like Figure 8 (The left side shows the result of data superimposed on the depositional pattern curve before noise suppression, and the right side shows the result of data superimposed on the depositional pattern curve after anomalous signal suppression.) Figure 8 Is Figure 7 The deposition model curves were overlaid on the data. It can be further seen that the calcium content signal after abnormal suppression is very close to the theoretical model curve, indicating that the technique proposed in this invention can preserve the model curve, which is beneficial for further model separation and explanation of the controlling factors.

[0147] In summary, this invention obtains waveform data with both positive and negative values ​​by performing coordinate and translation transformations on the original logging data, which is beneficial to the stability of the algorithm. Furthermore, the addition of modal constraints during the denoising process helps maintain the integrity of the modes of various controlling factors, preventing large distortions.

[0148] Compared with the prior art, the present invention has at least the following beneficial effects:

[0149] (1) By performing coordinate axis transformation and numerical translation transformation on the calcium signal, the calcium signal data is transformed into a time domain waveform signal with positive and negative values, which helps to increase the stability of the algorithm.

[0150] (2) By introducing model signals for constraint, the relationship between various models can be maintained while suppressing noise, making subsequent geological interpretations more reliable.

[0151] On the other hand, such as Figure 9 As shown, this embodiment of the invention provides an element logging anomaly signal suppression system 900, which may include:

[0152] The first module 901 is used to acquire the target element logging signal and the target velocity curve;

[0153] The second module 902 is used to perform deep-time conversion on the target element logging signal based on the target velocity curve to obtain a time domain signal;

[0154] The third module 903 is used to perform translation transformation on the time-domain signal to obtain the waveform signal;

[0155] The fourth module 904 is used to suppress abnormal signals of waveform signals based on modal constraints to obtain the initial suppressed signal;

[0156] The fifth module 905 is used to perform an inverse transformation on the initial suppression signal to obtain the target suppression signal.

[0157] In some embodiments, the system may further include:

[0158] The sixth module is used to preprocess the logging signals of the target elements;

[0159] The preprocessing includes signal correction and outlier removal.

[0160] The content of the method embodiments of the present invention is applicable to the system embodiments. The specific functions implemented in the system embodiments are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above methods.

[0161] On the other hand, embodiments of the present invention also provide an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-mentioned method for suppressing abnormal signals in element logging. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0162] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0163] like Figure 10 As shown, Figure 10 The hardware structure of an electronic device 1000 according to another embodiment is illustrated. The electronic device 1000 includes:

[0164] The processor 1001 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (aSIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present invention.

[0165] The memory 1002 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RaM). The memory 1002 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1002 and is called and executed by the processor 1001 to execute the network node population optimization method of the embodiments of this invention.

[0166] Input / output interface 1003 is used to implement information input and output;

[0167] The communication interface 1004 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0168] Bus 1005 transmits information between various components of the device (e.g., processor 1001, memory 1002, input / output interface 1003, and communication interface 1004);

[0169] The processor 1001, memory 1002, input / output interface 1003 and communication interface 1004 are connected to each other within the device via bus 1005.

[0170] The electronic device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0171] The content of the method embodiments of the present invention is applicable to the embodiments of the present electronic device. The specific functions implemented by the embodiments of the present electronic device are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above methods.

[0172] Another aspect of this invention provides a computer-readable storage medium storing a program that is executed by a processor to implement the aforementioned method.

[0173] It should be noted that the computer-readable medium shown in the embodiments of the present invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD to ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, wherein computer-readable program code is carried. Such transmitted data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0174] The content of the method embodiments of the present invention is applicable to the computer-readable storage medium embodiments. The specific functions implemented by the computer-readable storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above methods.

[0175] This invention also discloses a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium and execute the computer instructions, causing the computer device to perform the aforementioned method.

[0176] 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 the present invention. 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 indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated 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 or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0177] It should be noted that although several modules for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of the present invention, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0178] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, portable hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, touch terminal, or network device, etc.) to execute the method according to the embodiments of the present invention.

[0179] In some alternative embodiments, the functions / operations mentioned in the block diagrams may not occur in the order shown in the operation diagrams. For example, depending on the functions / operations involved, two consecutively shown blocks may actually be executed substantially simultaneously, or the blocks may sometimes be executed in reverse order. Furthermore, the embodiments presented and described in the flowcharts of this invention are provided by way of example to provide a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented in this invention. Alternative embodiments are contemplated, in which the order of various operations is changed and sub-operations described as part of a larger operation are executed independently.

[0180] Furthermore, although the invention has been described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the invention. Rather, given the properties, functions, and internal relationships of the various functional modules in the system disclosed herein, the actual implementation of the module will be understood within the scope of conventional skill of an engineer. Therefore, those skilled in the art can implement the invention as set forth in the claims using ordinary techniques without excessive experimentation. It is also understood that the specific concepts disclosed are merely illustrative and not intended to limit the scope of the invention, which is determined by the full scope of the appended claims and their equivalents.

[0181] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0182] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, system, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, system, or device). For the purposes of this specification, "computer-readable medium" can mean any system that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, system, or device.

[0183] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0184] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0185] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0186] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

[0187] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of the present invention.

Claims

1. A method for suppressing abnormal signals in element logging, characterized in that, Includes the following steps: Acquire the target element's logging signal and target velocity curve; Based on the target velocity curve, the logging signal of the target element is subjected to deep-time conversion to obtain a time-domain signal; The time-domain signal is shifted and transformed to obtain a waveform signal; The waveform signal is suppressed based on modal constraints to obtain an initial suppressed signal. The step of suppressing abnormal signals in the waveform signal based on modal constraints to obtain an initial suppressed signal includes the following steps: The waveform signal is decomposed into scale signals of multiple scales using wavelet transform; Multiple pattern signals are constructed based on the main controlling factors corresponding to the abnormal data of the target element logging signals; Construct the objective function of the modal constraint based on the scale signal and the pattern signal; The expression for the objective function is: In the formula, Represent the objective function; Norm symbol; Represents the i-th scale signal; Indicates the scale threshold. These are the optimization variables of the objective function; This represents the j-th mode signal; Indicates the number of mode signals; The objective function is optimized to obtain the target scale; The initial suppression signal is obtained by integrating the scale signals based on the target scale. The initial suppression signal is inversely transformed to obtain the target suppression signal.

2. The method for suppressing elemental logging anomaly signals according to claim 1, characterized in that, Before the step of performing depth-time conversion on the target element logging signal based on the target velocity curve, the method further includes the following steps: The logging signals of the target elements are preprocessed; The preprocessing includes signal correction and outlier removal.

3. The method for suppressing elemental logging anomaly signals according to claim 1, characterized in that, Obtaining the target velocity curve includes one of the following steps: The velocity curve recorded during the drilling process is used as the target velocity curve. Seismic data from seismic exploration is acquired, and the target velocity curve is obtained based on the analysis results of the seismic data; The empirical curve of the drilling area is used as the target velocity curve.

4. The method for suppressing elemental logging anomaly signals according to claim 1, characterized in that, The process of performing depth-time conversion on the target element logging signal based on the target velocity curve to obtain a time-domain signal includes the following steps: Construct a function relating depth and time based on the target velocity curve; The expression for the relational function is as follows: In the formula, Indicates time; Indicates depth; Represents the target velocity curve; Based on the aforementioned relationship function, the logging signal of the target element is transformed from the depth domain to the time axis to obtain a time signal; The time-domain signal is obtained by performing regularization processing on the time signals corresponding to each time point of the target element logging signal.

5. The method for suppressing elemental logging anomaly signals according to claim 1, characterized in that, The step of performing a translation transformation on the time-domain signal to obtain a waveform signal includes the following steps: The average value of the time-domain signal is obtained by averaging all signal values ​​in the time-domain signal. The waveform signal is obtained by shifting the time-domain signal using the average value of the time-domain signal.

6. The method for suppressing elemental logging anomaly signals according to claim 1, characterized in that, The process of inversely transforming the initial suppression signal to obtain the target suppression signal includes the following steps: The initial suppression signal is subjected to the inverse transformation of the translation transformation, and then the signal is transformed to the depth domain through time-depth transformation to obtain the target suppression signal.

7. A system for suppressing abnormal signals in element logging, characterized in that, include: The first module is used to acquire the target element logging signal and the target velocity curve; The second module is used to perform deep-time conversion on the target element logging signal based on the target velocity curve to obtain a time-domain signal. The third module is used to perform a translation transformation on the time-domain signal to obtain a waveform signal; The fourth module is used to suppress abnormal signals of the waveform signal based on modal constraints to obtain an initial suppressed signal; The step of suppressing abnormal signals in the waveform signal based on modal constraints to obtain an initial suppressed signal includes the following steps: The waveform signal is decomposed into scale signals of multiple scales using wavelet transform; Multiple pattern signals are constructed based on the main controlling factors corresponding to the abnormal data of the target element logging signals; Construct the objective function of the modal constraint based on the scale signal and the pattern signal; The expression for the objective function is: In the formula, Represent the objective function; Norm symbol; Represents the i-th scale signal; Indicates the scale threshold. These are the optimization variables of the objective function; This represents the j-th mode signal; Indicates the number of mode signals; The objective function is optimized to obtain the target scale; The initial suppression signal is obtained by integrating the scale signals based on the target scale. The fifth module is used to perform an inverse transformation on the initial suppression signal to obtain the target suppression signal.

8. An electronic device, characterized in that, Including the processor and memory; The memory is used to store programs; The processor executes the program to implement the method as described in any one of claims 1 to 6.

9. A computer storage medium storing a processor-executable program, characterized in that, The processor-executable program, when executed by the processor, is used to implement the method as described in any one of claims 1 to 6.

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