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

Abstract

The invention discloses an element logging abnormal signal suppression method and system, electronic equipment and a storage medium. The method comprises the steps of obtaining a target element logging signal and a target speed curve; performing depth-time conversion on the target element logging signal based on the target speed curve to obtain a time domain signal; performing translation transformation on the time domain signal to obtain a waveform signal; performing abnormal signal suppression on the waveform signal based on modal constraint to obtain an initial suppression signal; and performing inverse transformation on the initial suppression signal to obtain a target suppression signal. According to the embodiment of the invention, a target element logging signal is converted from a depth axis to a time axis by using a target speed curve, then a waveform signal is obtained through translation conversion, then abnormal signal suppression is carried out based on the waveform signal, and finally the signal is converted back to a depth domain through inverse conversion to obtain a final signal. The method can avoid signal data missing caused by direct elimination of abnormal signals, and can be widely applied to the technical field of data processing.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method, system, electronic equipment and storage medium for suppressing abnormal signals of element logging. Background Art

[0002] Mixed sedimentary formations are characterized by alternating terrigenous clastics and carbonates. As a unique sedimentological phenomenon, mixed sedimentation has garnered widespread attention in the study of its genetic mechanisms and key controlling factors. Its distribution characteristics are of great significance to oil and gas exploration. Well logging or elemental logging data can effectively distinguish the lithologic variations between clastics and carbonates, reveal the vertical development patterns of mixed sedimentary formations, and analyze the factors controlling their developmental characteristics.

[0003] During the sedimentation process, short, sudden, high-energy events such as collapse and erosion can disrupt the original sedimentary formation signal, interfering with the extraction of useful information from well logging signals. Suppressing these interfering signals is crucial for analyzing the causes of mixed sedimentary formations. Currently, the commonly used approach is to directly remove data corrupted by abnormal events, but this results in the loss of this signal, hindering the analysis of the primary controlling factors. Summary of the Invention

[0004] The present invention aims to at least partially address the limitations of related technologies. To this end, the present invention provides a method, system, electronic device, and storage medium for suppressing abnormal signals in elemental logging, which can avoid the side effects of directly eliminating abnormal signals.

[0005] In one aspect, an embodiment of the present invention provides a method for suppressing abnormal signals in element logging, comprising the following steps:

[0006] Obtain target element logging signals and target velocity curves;

[0007] Based on the target velocity curve, the target element logging signal is deep-time converted to obtain the time domain signal;

[0008] Perform translation transformation on the time domain signal to obtain a waveform signal;

[0009] Abnormal signal suppression is performed on the waveform signal based on modal constraints to obtain an initial suppressed signal;

[0010] The initial suppression signal is inversely transformed to obtain the target suppression signal.

[0011] Optionally, before the step of performing deep-time conversion on the target element logging signal based on the target velocity curve, the method further comprises the following steps:

[0012] Preprocess the target element logging signal;

[0013] Among them, preprocessing includes signal correction and isolated point elimination.

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

[0015] Obtaining a velocity curve recorded during the drilling process as a target velocity curve;

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

[0017] The experience curve of the drilling area is used as the target speed curve.

[0018] Optionally, performing deep-time conversion on the target element logging signal based on the target velocity curve to obtain a time domain signal includes the following steps:

[0019] Construct a depth-time relationship function based on the target velocity curve;

[0020] Among them, the expression of the relational function is:

[0021]

[0022] Where, t represents time; z i represents depth; V(z) represents target velocity curve;

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

[0024] The time domain signal is obtained by regularizing the time signal corresponding to each time point of the target element logging signal.

[0025] Optionally, performing a translation transformation on the time domain signal to obtain a waveform signal includes the following steps:

[0026] All signal values in the time domain signal are averaged to obtain the average value of the time domain signal;

[0027] The time domain signal is translated using its average value to obtain a waveform signal.

[0028] Optionally, performing abnormal signal suppression on the waveform signal based on the modal constraint to obtain an initial suppressed signal includes the following steps:

[0029] Using wavelet transform to decompose waveform signal into scale signals of multiple scales;

[0030] Construct multiple pattern signals based on the main controlling factors corresponding to the abnormal data of the target element logging signal;

[0031] Constructing the objective function of modal constraint according to the scale signal and the mode signal;

[0032] Among them, the expression of the objective function is:

[0033]

[0034] Where J(N) represents the objective function; ‖·‖ is the norm symbol; a i (t) represents the i-th scale signal; 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] Optimize the objective function and obtain the target scale;

[0036] The scale signals are integrated based on the target scale to obtain the initial suppression signal.

[0037] Optionally, performing an inverse transformation on the initial suppression signal to obtain a target suppression signal comprises the following steps:

[0038] The initial suppressed signal is subjected to an inverse transformation of the translation transformation, and then the signal is transformed into the depth domain through time-depth conversion to obtain the target suppressed signal.

[0039] On the other hand, an embodiment of the present invention provides an element logging abnormal signal suppression system, comprising:

[0040] The first module is used to obtain the target element logging signal and 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 a time domain signal;

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

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

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

[0045] Optionally, the system further includes:

[0046] The sixth module is used to pre-process the target element logging signal;

[0047] Among them, preprocessing includes signal correction and isolated point elimination.

[0048] On the other hand, an embodiment of the present invention provides 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 element logging abnormal signal suppression method.

[0049] On the other hand, an embodiment of the present invention provides a computer storage medium storing a program executable by a processor. When the program is executed by the processor, it is used to implement the above-mentioned element logging abnormal signal suppression method.

[0050] The embodiment of the present invention obtains a target element logging signal and a target velocity curve; performs a deep-time conversion on the target element logging signal based on the target velocity curve to obtain a time domain signal; performs a translation transformation on the time domain signal to obtain a waveform signal; suppresses abnormal signals on the waveform signal based on modal constraints to obtain an initial suppression signal; and performs an inverse transformation on the initial suppression signal to obtain a target suppression signal. The embodiment of the present invention uses the target velocity curve to transform the target element logging signal from the depth axis to the time axis, and then obtains a waveform signal through a translation transformation. Abnormal signals are then suppressed based on the waveform signal, and finally the signal is transformed back to the depth domain through an inverse transformation to obtain the final signal. The present invention obtains a time domain waveform signal through deep-time conversion and numerical translation transformation, which is beneficial to increasing the stability of the algorithm. In addition, by introducing modal constraints, the present invention can maintain the relationship between various modes when suppressing noise, making subsequent geological interpretation more reliable. The present invention can avoid signal data loss caused by directly eliminating abnormal signals. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The accompanying drawings are used to provide a further understanding of the technical solution of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the technical solution of the present invention and do not constitute a limitation to the technical solution of the present invention.

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

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

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

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

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

[0057] Figure 6 A schematic diagram comparing the original calcium content curve and the transformed calcium content curve provided in an embodiment of the present invention;

[0058] Figure 7 A schematic diagram comparing the original calcium content curve and the calcium content curve after suppressing abnormal signals provided in an embodiment of the present invention;

[0059] Figure 8 The embodiment of the present invention provides Figure 7 Schematic diagram of the comparison of the calcium content curve superimposed on the sedimentation mode curve results;

[0060] Figure 9 A schematic structural diagram of an element logging abnormal signal suppression system provided by an embodiment of the present invention;

[0061] Figure 10 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0062] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present 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 only used to explain the present invention and are not intended to limit the present invention.

[0063] It should be noted that although the system diagrams illustrate functional module divisions and the flowcharts illustrate a logical sequence, in certain circumstances, the steps shown or described may be performed in a sequence that differs from the module divisions in the system or the sequence in the flowcharts. The terms "first / S100," "second / S200," and the like in the specification, claims, and drawings are used to distinguish similar objects and are not necessarily intended to describe a specific sequence or precedence.

[0064] References to "embodiments" in this disclosure mean that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the disclosure. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0065] It is understandable that the element logging abnormal signal suppression method provided in the embodiment of the present 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 communications, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. Optionally, the terminal is a smart phone, tablet computer, laptop computer, desktop computer, etc., but is not limited to this.

[0066] To facilitate understanding of the technical solutions of the present invention, the following are first explained regarding the technical features that may appear in the embodiments of the present invention:

[0067] like Figure 1 FIG. 1 is a schematic diagram of an implementation environment provided by an embodiment of the present invention. 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 to the network in a wireless or wired manner to complete data transmission and exchange.

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

[0069] In addition, server 101 can also be a node server in a blockchain network. Blockchain is a new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, and encryption algorithm.

[0070] The terminal 102 may be a smart phone, tablet computer, laptop computer, desktop computer, smart speaker, smart watch, etc., but is not limited thereto. The terminal 102 and the server 101 may be connected directly or indirectly via wired or wireless communication, which is not limited in this embodiment of the present invention.

[0071] Based on the example Figure 1In the implementation environment shown, an embodiment of the present invention provides a method for suppressing abnormal element logging signals. The following description will be given by taking the method for suppressing abnormal element logging signals in the server 101 as an example. It can be understood that the method for suppressing abnormal element logging signals can also be applied to the terminal 102.

[0072] Reference Figure 2 , Figure 2 The flowchart of the element logging abnormal signal suppression method applied to the server provided by the embodiment of the present invention, the execution subject of the element logging abnormal signal suppression method can be any of the aforementioned computer devices (including servers or terminals). Figure 2 , the method comprises the following steps:

[0073] S100, obtaining target element logging signals and target velocity curves;

[0074] It should be noted that, in some embodiments, obtaining the target velocity curve may include one of the following steps: obtaining a 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; and using the empirical curve of the drilling area as the target velocity curve.

[0075] For example, in some specific embodiments, velocity curves for transformation are collected. The velocity curves may be velocity curves recorded simultaneously during drilling, curves obtained by analyzing 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 isolated point elimination.

[0077] For example, in some specific implementations, preprocessing mainly includes conventional correction and removal of obvious isolated points. Taking the calcium signal of 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] Long-term instrument operation can cause 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 increase or decrease (for example, from 2000 ppm to 2200 ppm).

[0080] Solution:

[0081] Sliding average method: select a window size 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 fitting: `[2020,2070,2120,2170]` (window sliding mean calculation);

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

[0085] 2. Isolated point removal:

[0086] The calcium signal at a certain depth 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 chip 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] The values exceeding the threshold of 3500 ppm were determined to be isolated points.

[0090] Processing method:

[0091] Linear interpolation: Replace the outlier with the mean of the previous and next points (such as 2000 and 2050) to obtain the corrected value 2025ppm.

[0092] S200, performing deep-time conversion on the target element logging signal based on the target velocity curve to obtain a time domain signal;

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

[0094]

[0095] Where, t represents time; z i represents depth; V(z) represents target velocity curve;

[0096] S202, transforming the target element logging signal from the depth domain to the time axis based on the relationship function to obtain a time signal; S203, performing regularization processing on the time signal corresponding to each time point of the target element logging signal to obtain a time domain signal.

[0097] For example, in some specific embodiments, also taking calcium signals as an example, deep-time conversion of target element logging signals can be achieved as follows:

[0098] 1) First, collect the velocity curve for transformation, which is recorded as V(z).

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

[0100]

[0101] 3) In this way, any depth z can be calculated i The corresponding time is converted from the depth domain of the calcium signal to be processed to the time axis.

[0102] 4) Regularization. Although the depth axis is uniform, it may be 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, performing translation transformation on the time domain signal to obtain a waveform signal;

[0104] It should be noted that, in some embodiments, 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 embodiments, calcium signal data are generally all positive values. When transformed into the frequency domain or other similar domains for signal analysis, very small frequencies or wave numbers may appear, which is not conducive 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 previous step, and M is the average value of signal G1(t). Finally, through a translation transformation, the all-positive signal is transformed into a waveform signal G2(t) with both positive and negative values.

[0108] S400, suppressing abnormal signals on the waveform signal based on modal constraints to obtain an initial suppressed signal;

[0109] It should be noted that, in some embodiments, 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 controlling factors corresponding to the abnormal data of the target element logging signal; S403, constructing the objective function of modal constraint based on the scale signals and the mode signals; wherein the expression of the objective function is:

[0110]

[0111] Where J(N) represents the objective function; ‖·‖ is the norm symbol; a i (t) represents the i-th scale signal; 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 a target scale; S405: Integrate the scaled signals based on the target scale to obtain an initial suppressed signal. In some optional implementations, multi-scale decomposition of the waveform signal may also employ a transformation algorithm with local attribute functions, such as empirical mode decomposition and Wigner-Wiley transform.

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

[0114] Abnormal data usually appears during an abnormal event and has localized properties. First, the signal G2(t) is decomposed into signals of different scales by conventional wavelet transform, which are denoted as a1(t), a2(t), ..., a n (t), corresponding to n scales. Usually, by selecting a threshold N, the signal is reconstructed from the signals of 1…N scales to obtain the denoised signal. However, this method is highly subjective and may also destroy the underlying pattern information. In view of this, the present invention proposes a modal constraint strategy

[0115]

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

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

[0118]

[0119] S500: Perform inverse transformation on the initial suppression signal to obtain a 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 translation transformation on the initial suppressed signal, and then transforming the signal into the depth domain through time-depth conversion to obtain a target suppressed signal.

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

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

[0123] First, it should be noted that, using calcium data from elemental logging as an example, this invention proposes a technical solution for suppressing noise from abnormal events, addressing the problem of abnormal events disrupting the calcium signal from elemental logging and affecting the pattern recognition of the primary controlling factor. The key idea is to transform the preprocessed calcium signal data from the depth axis to the time axis using velocity curves from well logging or curves obtained from seismic analysis. A translation transform converts the all-positive signal into a waveform signal with both positive and negative values. Abnormal signals are then suppressed based on this waveform signal. Finally, an inverse translation transform and time-to-depth conversion are used to transform the data back to the depth domain to obtain the final signal.

[0124] The specific implementation plan is as follows:

[0125] The first step is preprocessing of elemental logging calcium signals, which mainly includes routine correction and elimination of obvious isolated points.

[0126] The second step is to transform the calcium signal from the depth domain to the time axis. Usually, the calcium signal is in the depth domain, with the intervals between sampling points being several meters or tens of meters.

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

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

[0129]

[0130] 3) In this way, any depth z can be calculated i The corresponding time is converted from the depth domain of the calcium signal to be processed to the time axis.

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

[0132] Step 3: Through a translation transformation, the all-positive signal is transformed into a waveform signal G2(t) with both positive and negative values.

[0133] Usually calcium signal data are all positive values. When transformed into the frequency domain of signal analysis or other similar domains, very small frequencies or wave numbers will appear, which is not conducive 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 the signal G1(t).

[0136] Step 4: Abnormal signal suppression.

[0137] Abnormal data in calcium signals generally appear during an abnormal event and have localized properties. First, the signal G2(t) is decomposed into signals of different scales using conventional wavelet transform, which are denoted as a1(t), a2(t), ..., a n (t), corresponding to n scales. Typically, a threshold N is selected and the signal from scales 1…N is used to reconstruct the signal to obtain the denoised signal. However, this approach is highly subjective and may also destroy the underlying pattern information. In view of this, the present invention proposes a modal constraint strategy:

[0138]

[0139] where b i (t), i = 1: m are the various modes calculated based on the main controlling factor theory.

[0140] By optimizing the above objective function, we can get the optimal Nopt. Then we can get the signal:

[0141]

[0142] Step 5: Perform an inverse transform back to the original depth domain to complete the entire suppression process and obtain the calcium signal after noise suppression caused by the abnormal event.

[0143] In some specific application scenarios, the technical effects of the present invention are described below in combination with specific example data (the following data examples are only for illustration and cannot be regarded as limiting the present invention):

[0144] like Figure 6(The left side shows the actual calcium content signal curve recorded by well logging in a certain area, and the right side shows the positive and negative waveform data after coordinate axis conversion and numerical translation conversion). Figure 6 On the left side, the depth ranges from 2500 to 3200 meters, and there are abnormal data caused by abnormal sedimentation in the section from 2600 to 2800 meters, which affects the subsequent analysis of various main controlling factors.

[0145] like Figure 7 (The left side shows the data before noise suppression, and the right side shows the data after abnormal signal suppression). Figure 7 The results after suppressing the abnormal signal show that after suppression according to the technical ideas of the present invention, in the 2600 to 2800 segment, the curve appears smoother in the general trend, and is no longer nearly straight like the original data.

[0146] like Figure 8 (The left side shows the data superimposed sedimentation model curve result before noise suppression, and the right side shows the data superimposed sedimentation model curve result after abnormal signal suppression). Figure 8 is Figure 7 The sedimentation pattern curve was superimposed on the data. It can be further seen that the calcium content signal after abnormal suppression is very close to the theoretical pattern curve, demonstrating that the technology proposed in this invention can maintain the pattern curve intact, which is beneficial for subsequent further pattern separation and interpretation of the main controlling factors.

[0147] In summary, the present invention performs coordinate and translation transformations on the original logging data to obtain waveform data with both positive and negative values, which improves algorithm stability. Furthermore, the present invention incorporates modal constraints during the denoising process, which helps maintain the integrity of the modal data of various controlling factors and prevents significant distortion.

[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 is beneficial to increasing the stability of the algorithm.

[0150] (2) By introducing pattern signals for constraints, the relationship between various patterns can be maintained while suppressing noise, making subsequent geological interpretation more reliable.

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

[0152] The first module 901 is used to obtain target element logging signals and target velocity curves;

[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 a waveform signal;

[0155] The fourth module 904 is configured to suppress abnormal signals on the waveform signal based on the modal constraint to obtain an initial suppressed signal;

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

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

[0158] The sixth module is used to pre-process the target element logging signal;

[0159] Among them, preprocessing includes signal correction and isolated point elimination.

[0160] The contents of the method embodiments of the present invention are all applicable to the system embodiments. The functions specifically implemented by the system 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.

[0161] In another aspect, an embodiment of the present invention further provides an electronic device comprising a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the aforementioned method for suppressing abnormal element logging signals. The electronic device can be any intelligent terminal, including a tablet computer and an in-vehicle computer.

[0162] It can be understood that the contents of the above method embodiments are applicable to the present device embodiments, the functions specifically implemented by the present device 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 method embodiments.

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

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

[0165] The memory 1002 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 1002 can store an 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 by the processor 1001 to execute the network node population optimization method of the embodiment of the present invention.

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

[0167] Communication interface 1004, used to implement communication interaction between this device and other devices, which 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 , which 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 , the memory 1002 , the input / output interface 1003 and the communication interface 1004 are connected to each other in communication within the device via the 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, i.e., they may be located in one location or distributed across multiple network units. Some or all of these modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0171] The contents of the method embodiments of the present invention are all applicable to the electronic device embodiments. The functions specifically implemented by the electronic device 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 method embodiments.

[0172] Another aspect of an embodiment of the present invention further provides a computer-readable storage medium, wherein the storage medium stores a program, and the program is executed by a processor to implement the above method.

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

[0174] The contents of the method embodiments of the present invention are all applicable to the computer-readable storage medium embodiments. The functions specifically 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 method embodiments.

[0175] The present 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 above method.

[0176] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the above-mentioned module, program segment, or a part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0177] It should be noted that although several modules of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to an embodiment of the present invention, the features and functions of two or more modules or units described above can be concretized in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units to be concretized.

[0178] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution 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 (which can be a CD to ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes a number of instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiments of the present invention.

[0179] In some optional embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the present invention is provided in an exemplary manner for the purpose of providing a more comprehensive understanding of the technology. The disclosed method is not limited to the operation and logic flow presented by the present invention. Optional embodiments are contemplated in which the order of the various operations is changed and the sub-operations described as a part of a larger operation are performed independently.

[0180] Furthermore, while the present invention has been described in the context of functional modules, it should be understood that, unless otherwise indicated, 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 separate physical devices or software modules. It will also be understood that a detailed discussion of the actual implementation of each module is not necessary for understanding the present invention. More specifically, given the properties, functions, and internal relationships of the various functional modules in the system disclosed in the present invention, the actual implementation of the module will be understood within the ordinary skill of an engineer. Therefore, a person skilled in the art will be able to implement the present invention as set forth in the claims using ordinary skill without undue experimentation. It will also be understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0181] If the function is implemented in the form of 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 the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program code.

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

[0183] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0184] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0185] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations 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 any one or more embodiments or examples.

[0186] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the claims and their equivalents.

[0187] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the embodiments. Those skilled in the art can make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of the present invention.

Claims

1. A method for suppressing abnormal signals in element logging, characterized in that: The following steps are involved: Obtain target element logging signals and target velocity curves; Performing deep-time conversion on the target element logging signal based on the target velocity curve to obtain a time domain signal; Performing a translation 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 suppressed signal; The initial suppression signal is inversely transformed to obtain a target suppression signal.

2. The method for suppressing abnormal signals of element logging according to claim 1, characterized in that: Before the step of performing deep-time conversion on the target element logging signal based on the target velocity curve, the method further includes the following steps: Preprocessing the target element logging signal; The preprocessing includes signal correction and isolated point elimination.

3. The method for suppressing abnormal signals of element logging according to claim 1, characterized in that: Obtaining the target speed curve includes one of the following steps: Acquire a velocity curve recorded during the drilling process as the target velocity curve; Acquiring seismic data from seismic exploration, and obtaining the target velocity curve based on analysis results of the seismic data; The experience curve of the drilling area is used as the target speed curve.

4. The method for suppressing abnormal signals of element logging according to claim 1, characterized in that: The step of performing deep-time conversion on the target element logging signal based on the target velocity curve to obtain a time domain signal comprises the following steps: Constructing a depth-time relationship function based on the target speed curve; Wherein, the expression of the relationship function is: Where, t represents time; z i represents depth; V(z) represents target velocity curve; transforming the target element logging signal from the depth domain to the time axis based on the relationship function to obtain a time signal; The time domain signal is obtained by performing regularization processing on the time signal corresponding to each time point of the target element logging signal.

5. The method for suppressing abnormal signals of element logging according to claim 1, characterized in that: The step of performing translation transformation on the time domain signal to obtain a waveform signal comprises the following steps: Averaging all signal values in the time domain signal to obtain an average value of the time domain signal; The time domain signal is subjected to a translation transformation using the average value of the time domain signal to obtain the waveform signal.

6. The method for suppressing abnormal signals of element logging according to claim 1, characterized in that: The abnormal signal suppression of the waveform signal based on the modal constraint to obtain an initial suppressed signal includes the following steps: Decomposing the waveform signal into scale signals of multiple scales by using wavelet transform; constructing a plurality of pattern signals based on the main controlling factors corresponding to the abnormal data of the target element logging signal; constructing an objective function of the modal constraint according to the scale signal and the mode signal; Wherein, the expression of the objective function is: Where J(N) represents the objective function; ‖·‖ is the norm symbol; a i (t) represents the i-th scale signal; 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; Optimizing the objective function to obtain a target scale; The scale signal is integrated based on the target scale to obtain the initial suppression signal.

7. The method for suppressing abnormal signals of element logging according to claim 1, characterized in that: The inverse transformation of the initial suppression signal to obtain the target suppression signal comprises the following steps: The initial suppressed signal is subjected to an inverse transformation of the translation transformation, and the signal is then transformed into a depth domain through time-depth conversion to obtain the target suppressed signal.

8. An element logging abnormal signal suppression system, characterized in that: include: The first module is used to obtain the target element logging signal and 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 translation transformation on the time domain signal to obtain a waveform signal; A fourth module is configured to suppress abnormal signals on the waveform signal based on modal constraints to obtain an initial suppressed signal; The fifth module is configured to perform an inverse transformation on the initial suppression signal to obtain a target suppression signal.

9. An electronic device, characterized in that: including a processor and a memory; The memory is used to store programs; The processor executes the program to implement the method according to any one of claims 1 to 7.

10. A computer storage medium storing a program executable by a processor, characterized in that: The program executable by the processor is used to implement the method according to any one of claims 1 to 7 when executed by the processor.

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