Three-component geophone azimuth correction method and device based on multi-event fusion and probability weighting, equipment, medium and product

Abstract

The application discloses a three-component geophone azimuth correction method and device based on multi-event fusion and probability weighting, equipment, medium and product, and relates to the technical field of geophysical exploration. The method is as follows: after at least two seismic signals recorded by a target three-component geophone and generated by at least two active source events are obtained, firstly, a preliminary azimuth calibration angle corresponding to each active source event is obtained, then a corresponding weight is assigned to the calibration angle according to a signal quality index calculated based on the corresponding seismic signal, then all calibration angles are fused according to all weights to obtain a final azimuth calibration angle, finally, the original horizontal component data is corrected by coordinate rotation using the final azimuth calibration angle to obtain corrected horizontal component data. Thus, by introducing a weighting mechanism based on signal quality, more accurate and reliable azimuth correction can be realized, the quality of seismic data and the reliability of subsequent interpretation are improved, and the application and promotion are facilitated.

Description

Technical Field

[0001] This invention belongs to the field of geophysical exploration technology, specifically to the technical field of sensor orientation correction in seismic data acquisition. It relates in detail to a method, device, equipment, medium, and product for three-component geophone orientation correction based on multi-event fusion and probability weighting. It can be applied to perform high-precision and stable correction of the deviation of the geophone's horizontal component installation orientation relative to the geographic coordinate system when using three-component geophones for active source earthquake, microseismic, or passive earthquake monitoring. Background Technology

[0002] Three-component seismic acquisition systems are commonly used in seismic exploration and monitoring systems. They simultaneously record ground motion vibrations in the vertical direction (usually denoted as Z) and two orthogonal horizontal directions (usually denoted as H1 and H2 or X and Y). To achieve accurate seismic wave polarization analysis, source location, anisotropy studies, and wavefield separation, it is essential to ensure that the horizontal component coordinate axes are precisely aligned with the geographic north (due north to due east) direction. However, in actual field deployment, due to terrain limitations, human error, and magnetic interference, the actual azimuth of the three-component geophone (the core component of the three-component seismic acquisition system) often deviates from the design direction, resulting in a non-negligible azimuth deviation. Without correction, this will severely affect the accuracy of seismic data interpretation.

[0003] Currently, there are two main categories of commonly used azimuth correction schemes: one relies on external equipment such as compasses, but this is cumbersome to operate and susceptible to local magnetic field interference, making it difficult to ensure consistency in large-scale deployments; the other is based on the polarization characteristics of seismic waves, utilizing the P-waves (i.e., longitudinal waves) or S-waves (i.e., transverse waves) excited by active source events (such as explosion events or controlled source events) with known source locations, which have definite theoretical polarization directions when propagating to the geophone. By comparing the difference between the measured waveform's principal energy axis direction in the horizontal component and the theoretical direction, the geophone's azimuth deviation angle is inverted. Although the aforementioned second scheme does not require additional hardware support, it has significant drawbacks: the results of a single event are easily affected by low signal-to-noise ratio, inaccurate first arrival picking, non-uniform near-surface velocity structure, complex source mechanisms, or local scattering interference, leading to unstable or even completely erroneous estimation results. Although existing technologies (such as patents CN103675916A and CN107607990A) have proposed some improved schemes, they still mostly rely on single events or simple averaging, failing to effectively resolve the contradiction between reliability and accuracy.

[0004] Therefore, there is an urgent need for a new solution that can improve the stability, accuracy, and reliability of the azimuth correction results of the three-component geophone by fusing information from multiple independent seismic events without relying on external orientation equipment. Summary of the Invention

[0005] The purpose of this invention is to provide a method, apparatus, computer device, computer-readable storage medium, and computer program product for azimuth correction of a three-component detector based on multi-event fusion and probability weighting, in order to solve the problems of unstable azimuth correction results, poor reliability, and / or limited application scenarios caused by existing azimuth correction schemes relying on a single active source event or a specific stable source.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] Firstly, a three-component detector azimuth correction method based on multi-event fusion and probability weighting is provided, including:

[0008] Acquire at least two seismic signals recorded by a target three-component geophone and generated using at least two active source events, wherein the at least two seismic signals correspond one-to-one with the at least two active source events;

[0009] For each active source event in the at least two active source events, the theoretical polarization azimuth angle and the measured polarization azimuth angle of the corresponding event at the target three-component detector are calculated respectively, and the difference between the theoretical polarization azimuth angle and the measured polarization azimuth angle is used as the corresponding preliminary azimuth calibration angle.

[0010] For each active source event, a signal quality index is calculated based on the corresponding seismic signal, and a corresponding weight is assigned to the corresponding preliminary azimuth calibration angle based on the signal quality index.

[0011] Based on all the weights, all the preliminary azimuth calibration angles are fused to obtain the final azimuth calibration angles;

[0012] The original horizontal component data of the target three-component detector is corrected by coordinate rotation using the final azimuth calibration angle to obtain the corrected horizontal component data.

[0013] Based on the above-mentioned invention, a novel scheme for three-component geophone azimuth correction is provided, which involves weighting and fusing multiple active source events based on signal quality indices. Specifically, after acquiring at least two seismic signals recorded by the target three-component geophone and generated by at least two active source events, a preliminary azimuth calibration angle is first obtained for each active source event. Then, a corresponding weight is assigned to this calibration angle based on the signal quality index calculated from the corresponding seismic signal. Next, all calibration angles are fused according to all weights to obtain the final azimuth calibration angle. Finally, the final azimuth calibration angle is applied to perform coordinate rotation correction on the original horizontal component data to obtain the corrected horizontal component data. By introducing a signal quality-based weighting mechanism, higher accuracy and more reliable azimuth correction can be achieved, improving the quality of seismic data and the reliability of subsequent interpretation. Furthermore, this scheme is applicable to various observation scenarios, including surface, shallow well, in-well, and inter-well observations, without relying on continuous stable sources or special operating modes. This significantly improves the universality and automation level of the geophone azimuth correction scheme, facilitating practical application and promotion.

[0014] In one possible design, for each of the at least two active source events, the theoretical and measured polarization azimuth angles of the corresponding event at the target three-component detector are calculated, including:

[0015] For the i-th active source event among the at least two active source events, based on the known horizontal coordinates (Rx, Ry) of the target three-component detector and the corresponding known source horizontal coordinates (Sx) i Sy i The theoretical polarization azimuth angle θ of the corresponding event at the target three-component detector is calculated according to the following formula. cal,i :

[0016] θ cal,i =arctan2(Sy i -Ry,Sx i -Rx)

[0017] In the formula, i represents a positive integer, and arctan2() represents the function used to calculate the arctangent value;

[0018] According to the first arrival time T of the seismic wave corresponding to the i-th active source event i The time window [T] is extracted from the three-component record data of the seismic signal corresponding to the i-th active source event. i -Nwin1,T i The original horizontal component data of [+Nwin2] are used to form a two-dimensional data matrix D with M sampling points based on the extracted data. i Wherein, the initial arrival time T of the seismic wave iThis refers to the earliest time when the seismic wave arrives at the target three-component geophone, where the seismic wave refers to either a longitudinal wave or a transverse wave. Nwin1 and Nwin2 represent preset offset times and have T. Ew,avg <Nwin1+Nwin2<2×T Ew,avg T Ew,avg The average period of the seismic wave is represented by M, where M represents a positive integer greater than or equal to 2.

[0019] The polarization analysis algorithm is used to extract the main vibration directions in the horizontal component, and the two-dimensional data matrix D is then analyzed based on the extraction results. i Principal component analysis was performed to calculate the covariance matrix C. i :

[0020]

[0021] In the formula, T represents the transpose symbol;

[0022] Obtain the covariance matrix C i The largest eigenvalue λ1 and the corresponding eigenvector v1 = [v 1x ,v 1y ];

[0023] According to the feature vector v1 = [v 1x ,v 1y The measured polarization azimuth angle θ of the i-th active source event at the target three-component detector is calculated. obs,i =arctan2(v 1y ,v 1x ).

[0024] In one possible design, for each active source event, a signal quality index is calculated based on the corresponding seismic signal, and a corresponding weight is assigned to the corresponding preliminary azimuth calibration angle based on the signal quality index, including:

[0025] For the i-th active source event among the at least two active source events, a signal quality index including signal-to-noise ratio and polarization linearity is calculated based on the three-component recorded data of the corresponding seismic signal, where i represents a positive integer, and the polarization linearity refers to the degree to which the particle motion of seismic P-waves or seismic S-waves in the horizontal plane exhibits linear polarization characteristics.

[0026] Based on the signal-to-noise ratio and the polarization linearity, a corresponding weight ω is assigned to the preliminary azimuth calibration angle corresponding to the i-th active source event according to the following formula. i :

[0027] ω i =σ(a×z) SNR,i+b×z L,i )

[0028] In the formula, z SNR,i z represents the normalized value of the signal-to-noise ratio. L,i The value represents the normalized value of the polarization linearity, where a and b represent preset empirical weighting coefficients, and σ() represents the Sigmoid normalization function.

[0029] In one possible design, all the preliminary azimuth calibration angles are fused according to all the stated weights to obtain the final azimuth calibration angles, including:

[0030] Each of the preliminary azimuth calibration angles is treated as a random variable with uncertainty. Assuming that the likelihood function of each preliminary azimuth calibration angle satisfies a von Mises distribution, with the distribution center at the corresponding preliminary azimuth calibration angle, and that the concentration parameter is positively correlated with the weight of the corresponding preliminary azimuth calibration angle, the likelihood function is defined as follows:

[0031]

[0032] In the formula, α represents the azimuth calibration angle and is the independent variable of the function, i represents a positive integer, and α i This represents the i-th preliminary azimuth calibration angle. Indicates the preliminary azimuth calibration angle α i The weights, e represents the base of the natural logarithm, κ i Representation and weight Positively correlated concentration parameters;

[0033] Based on the likelihood function, the joint posterior probability distribution function of the azimuth calibration angle α is obtained according to the following formula:

[0034]

[0035] In the formula, N represents the total number of events of the at least two active source events;

[0036] Calculate the maximum a posteriori estimate of the joint posterior probability distribution function, and use the calculated azimuth calibration angle as the final azimuth calibration angle.

[0037] In one possible design, after using the calculated azimuth calibration angle as the final azimuth calibration angle, the method further includes:

[0038] Calculate the standard deviation of the joint posterior probability distribution function, and use the calculated standard deviation as the uncertainty assessment result of the final azimuth calibration angle.

[0039] In one possible design, the final azimuth calibration angle is applied to perform coordinate rotation correction on the original horizontal component data of the target three-component detector to obtain the corrected horizontal component data, including:

[0040] Apply the final azimuth calibration angle α best The original horizontal component data of the target three-component detector is subjected to coordinate rotation correction according to the following formula to obtain the corrected horizontal component data:

[0041]

[0042] In the formula, [X,Y] represents the original horizontal component data, and [X′,Y′] represents the corrected horizontal component data.

[0043] Secondly, a three-component detector azimuth correction device based on multi-event fusion and probability weighting is provided, including a signal recording and acquisition unit, a preliminary azimuth calibration unit, a calibration weight allocation unit, a final azimuth fusion unit, and a coordinate rotation correction unit.

[0044] The signal recording and acquisition unit is used to acquire at least two seismic signals recorded by the target three-component geophone and generated by at least two active source events, wherein the at least two seismic signals correspond one-to-one with the at least two active source events;

[0045] The preliminary azimuth calibration unit is communicatively connected to the signal recording and acquisition unit. It is used to calculate the theoretical polarization azimuth angle and the measured polarization azimuth angle of each active source event in the at least two active source events, respectively, and to use the difference between the theoretical polarization azimuth angle and the measured polarization azimuth angle as the corresponding preliminary azimuth calibration angle.

[0046] The calibration weight allocation unit is communicatively connected to the signal recording acquisition unit and the preliminary azimuth calibration unit, respectively. It is used to calculate the signal quality index based on the corresponding seismic signal for each active source event, and to allocate the corresponding weight to the corresponding preliminary azimuth calibration angle based on the signal quality index.

[0047] The final azimuth fusion unit is communicatively connected to the preliminary azimuth calibration unit and the calibration weight allocation unit, respectively, and is used to fuse all the preliminary azimuth calibration angles according to all the weights to obtain the final azimuth calibration angles.

[0048] The coordinate rotation correction unit is communicatively connected to the final azimuth fusion unit and is used to perform coordinate rotation correction on the original horizontal component data of the target three-component detector using the final azimuth calibration angle to obtain the corrected horizontal component data.

[0049] Thirdly, the present invention provides a computer device comprising a storage module, a processing module, and a transceiver module connected in sequence for communication, wherein the storage module is used to store a computer program, the transceiver module is used to send and receive messages, and the processing module is used to read the computer program and execute the three-component detector azimuth correction method as described in the first aspect or any possible design in the first aspect.

[0050] Fourthly, the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, perform the three-component detector azimuth correction method as described in the first aspect or any possible design within the first aspect.

[0051] Fifthly, the present invention provides a computer program product, including a computer program or instructions, which, when executed by a computer, implement the three-component detector azimuth correction method as described in the first aspect or any possible design in the first aspect.

[0052] The beneficial effects of the above scheme are:

[0053] (1) This invention creatively provides a new scheme for weighting and fusing multiple active source events based on signal quality index to achieve three-component geophone azimuth correction. That is, after acquiring at least two seismic signals recorded by the target three-component geophone and generated by at least two active source events, the corresponding preliminary azimuth calibration angle is first obtained for each active source event, and the corresponding weight is assigned to the calibration angle according to the signal quality index calculated based on the corresponding seismic signal. Then, all calibration angles are fused according to all weights to obtain the final azimuth calibration angle. Finally, the final azimuth calibration angle is applied to the original horizontal component data for coordinate rotation correction to obtain the corrected horizontal component data. Thus, by introducing a weighting mechanism based on signal quality, higher accuracy and more reliable azimuth correction can be achieved, improving the quality of seismic data and the reliability of subsequent interpretation. In this way, it can be applied to various observation scenarios such as surface, shallow well, in-well and between wells without relying on continuous stable sources or special operating modes, significantly improving the universality and automation level of the geophone azimuth correction scheme.

[0054] (2) This scheme introduces a weighting mechanism based on signal quality. Unlike a simple arithmetic average, it gives higher weight to high-quality and high-confidence event results and automatically suppresses low-quality and unreliable results, ensuring that the final result is dominated by the most reliable data and significantly improving the overall accuracy.

[0055] (3) Compared with traditional methods that rely on a single event, this method effectively avoids the randomness and systematic error of a single measurement by integrating information from multiple independent events, and greatly increases the stability of the results.

[0056] (4) Compared with the traditional scheme that only outputs a fixed azimuth value, this scheme adopts a probabilistic framework, which can not only give the optimal estimate, but also output the uncertainty range of the result. This is crucial for evaluating the reliability of subsequent data processing (such as source location and anisotropy analysis), providing a more scientific basis for geological interpretation, and facilitating practical application and promotion. Attached Figure Description

[0057] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0058] Figure 1 A flowchart illustrating the three-component detector orientation correction method based on multi-event fusion and probability weighting provided in this application embodiment.

[0059] Figure 2 An example diagram illustrating the horizontal component recording of 100 active source events in Case 1, provided as an embodiment of this application.

[0060] Figure 3 An example diagram showing the joint posterior probability distribution of the azimuth calibration angle estimate for each event in Case 1, provided for embodiments of this application.

[0061] Figure 4 This is an example diagram comparing the orientation calibration angles of different methods under 100 sets of random tests in Case 1, provided as an embodiment of this application.

[0062] Figure 5 This is an example diagram illustrating the relative positional relationship between the active source distribution and the detector in the microseismic monitoring scenario of Case 2, provided as an embodiment of this application.

[0063] Figure 6 This is an example diagram of active source event recording in Case 2, provided as an embodiment of this application.

[0064] Figure 7 This is an example diagram comparing the azimuth calibration angle obtained from the active source event in Case 2 with the azimuth deviation of the actual position, as provided in the embodiments of this application.

[0065] Figure 8 An example diagram showing the result of the azimuth calibration angle of the detector in Case 2, provided as an embodiment of this application.

[0066] Figure 9A schematic diagram of the structure of a three-component detector orientation correction device based on multi-event fusion and probability weighting provided in an embodiment of this application.

[0067] Figure 10 A schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0068] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the present invention will be briefly introduced below in conjunction with the accompanying drawings and descriptions of the embodiments or the prior art. Obviously, the following description of the structure of the accompanying drawings is only some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained based on these embodiments without creative effort. It should be noted that the description of these embodiments is for the purpose of helping to understand the present invention, but does not constitute a limitation of the present invention.

[0069] It should be understood that although the terms "first" and "second", etc., may be used herein to describe various objects, these objects should not be limited by these terms. These terms are only used to distinguish one object from another. For example, the first object may be referred to as the second object, and similarly, the second object may be referred to as the first object, without departing from the scope of the exemplary embodiments of the invention.

[0070] It should be understood that the term "and / or" that may appear in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, B exists alone, or A and B exist simultaneously. Another example is A, B and / or C, which can mean that any one of A, B, and C or any combination thereof exists. The term " / and" that may appear in this document describes another relationship between related objects, indicating that two relationships can exist. For example, A / and B can mean: A exists alone or A and B exist simultaneously. In addition, the character " / " that may appear in this document generally indicates that the related objects before and after it are in an "or" relationship.

[0071] Example

[0072] like Figure 1 As shown, the three-component geophone azimuth correction method based on multi-event fusion and probability weighting provided in the first aspect of this embodiment can be executed, but is not limited to, by a computer device with certain computing resources and a communication connection to the three-component geophone, for example, by the core processor in a three-component seismic acquisition device. Figure 1 As shown, the three-component detector orientation correction method includes, but is not limited to, the following steps S1 to S5.

[0073] S1. Acquire at least two seismic signals recorded by the target three-component geophone and generated using at least two active source events, wherein the at least two seismic signals correspond one-to-one with the at least two active source events.

[0074] In step S1, the target three-component geophone is the object to be azimuth corrected. After deployment, it can conventionally record at least two seismic signals corresponding one-to-one with the at least two active source events (the source locations and excitation times of these events are known information) using existing technology. Furthermore, the data actually recorded by the target three-component geophone is specifically the three-component recording data of the at least two seismic signals, including the original horizontal component data. After conventional acquisition, this data can undergo conventional denoising and filtering processes, such as bandpass filtering or low-pass filtering.

[0075] S2. For each active source event in the at least two active source events, calculate the theoretical polarization azimuth angle and the measured polarization azimuth angle of the corresponding event at the target three-component detector, and use the difference between the theoretical polarization azimuth angle and the measured polarization azimuth angle as the corresponding preliminary azimuth calibration angle.

[0076] In step S2, considering an active source event with a known source location, the path of the seismic wave propagating from the source to the detector is determined. Therefore, based on the coordinates of the source and the detector, the projection direction of the ray on the horizontal plane between these two coordinates, i.e., the theoretical polarization azimuth angle θ, can be accurately calculated. cal (with north-clockwise as the positive reference direction); and considering that the raw horizontal component data recorded by the three-component detector can form a time series vector, the dominant vibration direction, i.e., the measured polarization azimuth angle θ, can be extracted by performing polarization analysis on this vector (e.g., based on principal component analysis (PCA), energy histogram method, and linear fitting method). obs Specifically, for each of the at least two active source events, the theoretical polarization azimuth angle and the measured polarization azimuth angle of the corresponding event at the target three-component detector are calculated, including but not limited to the following steps S21 to S25.

[0077] S21. For the i-th active source event among the at least two active source events, based on the known horizontal coordinates (Rx, Ry) of the target three-component detector and the corresponding known source horizontal coordinates (Sx... i Sy i The theoretical polarization azimuth angle θ of the corresponding event at the target three-component detector is calculated according to the following formula. cal,i :

[0078] θ cal,i=arctan2(Sy i -Ry,Sx i -Rx)

[0079] In the formula, i represents a positive integer, and arctan2() represents the function used to calculate the arctangent value.

[0080] S22. Based on the first arrival time T of the seismic wave corresponding to the i-th active source event. i The time window [T] is extracted from the three-component record data of the seismic signal corresponding to the i-th active source event. i -Nwin1,T i The original horizontal component data of [+Nwin2] are used to form a two-dimensional data matrix D with M sampling points based on the extracted data. i Wherein, the initial arrival time T of the seismic wave i This refers to the earliest time when the seismic wave arrives at the target three-component geophone, where the seismic wave refers to either a longitudinal wave or a transverse wave. Nwin1 and Nwin2 represent preset offset times and have T. Ew,avg <Nwin1+Nwin2<2×T Ew,avg T Ew,avg The average period of the seismic wave is represented by M, which is a positive integer greater than or equal to 2.

[0081] In step S22, the M sampling points are obtained by performing regular sampling on the intercepted data based on a certain sampling rate. The higher the sampling rate, the larger the value of M.

[0082] S23. The polarization analysis algorithm is used to extract the main vibration directions in the horizontal component, and the two-dimensional data matrix D is then analyzed based on the extraction results. i Principal component analysis was performed to calculate the covariance matrix C. i :

[0083]

[0084] In the formula, T represents the transpose symbol.

[0085] In step S23, the specific vibration direction extraction process and principal component analysis process can be derived conventionally with reference to existing technologies (such as existing orientation correction schemes based on single active source events), and will not be elaborated here.

[0086] S24. Obtain the covariance matrix C i The largest eigenvalue λ1 and the corresponding eigenvector v1 = [v 1x ,v 1y ].

[0087] In step S24, since the largest eigenvalue of the covariance matrix reflects the magnitude of the variance of the data in the main direction of change, and the eigenvector corresponding to this eigenvalue points to the principal axis direction of the data distribution, the eigenvector v1 = [v 1x ,v 1y This refers to the direction of the principal polarization axis, from which the measured azimuth angle can be calculated.

[0088] S25. Based on the eigenvector v1 = [v 1x ,v 1y The measured polarization azimuth angle θ of the i-th active source event at the target three-component detector is calculated. obs,i =arctan2(v 1y ,v 1x ).

[0089] In step S2, specifically θ can be... cal,i -θ obs,i The calculation result is used as the preliminary azimuth calibration angle α corresponding to the i-th active source event. i The initial azimuth calibration angle α i It mainly consists of two parts: the true azimuth correction angle of the detector. and measurement error ε i The expression is Considering the error ε of a single measurement i The randomness of the initial azimuth calibration angle is related to factors such as the signal-to-noise ratio and wavefield complexity of the specific event. Therefore, based on statistical principles, by weighting and averaging the initial azimuth calibration angles from multiple independent measurements, the influence of random error ε can be effectively suppressed, making the result closer to the true azimuth correction angle. Furthermore, since the horizontal component is a bidirectional axis (equivalent to ± direction), the azimuth angle has a periodicity of 180°. Considering the polarity consistency, the phase sign can be determined by cross-correlation in order to remove the uncertainty of 180°.

[0090] S3. For each active source event, a signal quality index is calculated based on the corresponding seismic signal, and a corresponding weight is assigned to the corresponding preliminary azimuth calibration angle based on the signal quality index.

[0091] In step S3, when weighting the preliminary azimuth calibration angles of multiple independent measurements, the key point is the allocation of weights. Furthermore, considering that signal quality is a comprehensive indicator of the reliability of a single measurement result, it is necessary to give greater weight to measurements with smaller errors (i.e., higher signal quality). High-quality signals are characterized by high signal-to-noise ratio, clear polarization direction (high polarization coherence), and high linearity of vector vibration trajectories. Therefore, by quantifying these characteristics and assigning them weights, an objective evaluation system can be constructed, thereby achieving scientific value assignment to different measurement results. Preferably, the signal quality indicators include, but are not limited to, signal-to-noise ratio and polarization linearity, where polarization linearity refers to the degree to which the particle motion of seismic P-waves or seismic S-waves exhibits linear polarization characteristics in the horizontal plane. In addition, to ensure the reliability of the measured values ​​of each preliminary azimuth calibration angle, a nonlinear fusion weight function can be constructed to quantify the credibility of the corresponding measurement results. That is, preferably, for each active source event, a signal quality index is calculated based on the corresponding seismic signal, and a corresponding weight is assigned to the corresponding preliminary azimuth calibration angle based on the signal quality index, including but not limited to the following steps S31 to S32.

[0092] S31. For the i-th active source event among the at least two active source events, calculate a signal quality index containing signal-to-noise ratio and polarization linearity based on the three-component recorded data of the corresponding seismic signal, where i represents a positive integer, and the polarization linearity refers to the degree to which the particle motion of the seismic P-wave or seismic S-wave exhibits linear polarization characteristics in the horizontal plane.

[0093] In step S31, the signal-to-noise ratio reflects the clarity of the data, thus measuring the intensity of the target seismic wave signal relative to background noise, and can be conventionally calculated based on existing algorithms. The polarization linearity assesses whether the particle motion of seismic P-waves or S-waves in the horizontal plane exhibits linear polarization characteristics, and can therefore be used to identify polarization ambiguity caused by scattered waves, surface wave contamination, or first-arrival mispicking. Furthermore, the specific calculation process for the polarization linearity can be conventionally derived based on existing techniques.

[0094] S32. Based on the signal-to-noise ratio and the polarization linearity, assign a corresponding weight ω to the preliminary azimuth calibration angle corresponding to the i-th active source event according to the following formula. i :

[0095] ω i =σ(a×z) SNR,i +b×z L,i )

[0096] In the formula, z SNR,i z represents the normalized value of the signal-to-noise ratio. L,iThe value represents the normalized value of the polarization linearity, where a and b represent preset empirical weighting coefficients, and σ() represents the Sigmoid normalization function.

[0097] In step S32, to avoid scaling imbalance and saturation problems caused by linear weighting, this step uses a Sigmoid normalization function to construct the comprehensive weights. The empirical weight coefficients a and b reflect the priority of the corresponding indicators and can be given based on the actual data. The Sigmoid normalization function is specifically... Where x represents the independent variable of the function, e represents the base of the natural logarithm, and β controls the steepness of the curve, typically taking a value of 4 to ensure the function output is a pure decimal and has the ability to suppress low-quality events nonlinearly. Furthermore, the weight ω... i The weight function can also be other than the Sigmoid normalization function, such as a weight function trained based on a machine learning model (such as a random forest or a neural network).

[0098] S4. Based on all the weights, perform a fusion process on all the preliminary azimuth calibration angles to obtain the final azimuth calibration angles.

[0099] In step S4, the fusion process can employ a conventional weighted average method or other methods. To obtain the optimal azimuth calibration angle estimate within a Bayesian framework, preferably, all preliminary azimuth calibration angles are fused according to all the weights to obtain the final azimuth calibration angle, including but not limited to the following steps S41 to S43.

[0100] S41. Each of the preliminary azimuth calibration angles is considered as a random variable with uncertainty. Assuming that the likelihood function of each preliminary azimuth calibration angle satisfies a von Mises distribution, with the distribution center being the corresponding preliminary azimuth calibration angle, and that the concentration parameter is positively correlated with the weight of the corresponding preliminary azimuth calibration angle, the likelihood function is defined as follows:

[0101]

[0102] In the formula, α represents the azimuth calibration angle and is the independent variable of the function, i represents a positive integer, and α i This represents the i-th preliminary azimuth calibration angle. Indicates the preliminary azimuth calibration angle α i The weights, e represents the base of the natural logarithm, κ i Representation and weight Positively correlated concentration parameters.

[0103] In step S41, the von Mises distribution is a continuous probability distribution model defined on a circle, also known as the cyclic normal distribution, which is mainly used to describe the probabilistic characteristics of angular random variables.

[0104] S42. Based on the likelihood function, the joint posterior probability distribution function of the azimuth calibration angle α is obtained according to the following formula:

[0105]

[0106] In the formula, N represents the total number of events of the at least two active source events.

[0107] In step S42, according to the above formula, after fusing all independent measurement data, the joint posterior probability distribution of the final azimuth calibration angle is proportional to the product of the likelihood functions of all individual measurements.

[0108] S43. Calculate the maximum a posteriori estimate of the joint posterior probability distribution function, and use the calculated azimuth calibration angle as the final azimuth calibration angle.

[0109] In step S43, the calculated azimuth calibration angle is the optimal azimuth calibration angle estimate (specifically, the peak value or expected value). To provide its confidence interval or standard deviation, thus providing a scientific basis for subsequent seismic data interpretation, preferably, after using the calculated azimuth calibration angle as the final azimuth calibration angle, the method further includes, but is not limited to: calculating the standard deviation of the joint posterior probability distribution function, and using the calculated standard deviation as the uncertainty assessment result of the final azimuth calibration angle. Furthermore, the specific calculation process of the maximum a posteriori estimate can be derived conventionally based on existing techniques.

[0110] S5. Apply the final azimuth calibration angle to perform coordinate rotation correction on the original horizontal component data of the target three-component detector to obtain the corrected horizontal component data.

[0111] In step S5, the purpose of the coordinate rotation correction is to correct the original horizontal component data from the instrument coordinate system to the geographic coordinate system. Specifically, the original horizontal component data of the target three-component detector is subjected to coordinate rotation correction using the final azimuth calibration angle to obtain the corrected horizontal component data, including but not limited to: applying the final azimuth calibration angle α. best The original horizontal component data of the target three-component detector is subjected to coordinate rotation correction according to the following formula to obtain the corrected horizontal component data:

[0112]

[0113] In the formula, [X,Y] represents the original horizontal component data, and [X′,Y′] represents the corrected horizontal component data.

[0114] Based on the above steps S1 to S5, this embodiment also provides two cases (i.e., Case 1 and Case 2) to test and verify the feasibility and effectiveness of the three-component detector orientation correction method in the application of three-component detector orientation calibration.

[0115] Case 1 simulates a three-dimensional three-component seismic acquisition at the surface. For a three-component geophone with an unknown azimuth, assuming a true correction angle of 30°, 100 spatially randomly distributed seismic sources were designed as active sources. Based on the relative position of the geophone to the seismic sources and the horizontal component azimuth deviation angle, the horizontal component records of the 100 active source events were simulated (e.g., ...). Figure 2 As shown in the figure, different levels of random noise are added to each active source event record to simulate the quality differences of the event signal. The theoretical polarization azimuth angle can be calculated using the simulated source coordinates and detector coordinates. The polarization angle, polarization linearity, and signal-to-noise ratio of the event P-wave can be calculated using polarization analysis methods. Based on the above steps S2 and S3, the preliminary azimuth calibration angle and weight of each active source event can be obtained (when constructing the comprehensive weight using the Sigmoid normalization function, the weights of signal-to-noise ratio and polarization linearity are considered to be the same, i.e., a = b = 0.5). Based on the above step S4, after fusing all independent measurement data, the joint posterior probability distribution of the azimuth calibration angle is the product of the likelihood functions of all individual measurements. Finally, the maximum posterior estimate of the posterior distribution is calculated as the optimal azimuth calibration angle estimate, such as... Figure 3 As shown (in) Figure 3 In the diagram, gray dots represent the preliminary azimuth calibration angles calculated for each event, black lines represent the joint posterior probability distribution, and red "×" marks represent the optimal azimuth calibration angle estimate corresponding to the maximum posterior estimate. Based on the variance of the von Mises distribution, the final azimuth calibration angle is 30.38° ± 0.57°. To test and verify the stability and reliability of the method in this embodiment, Case 1 also repeated 100 random tests. The processing results of different methods are demonstrated by comparing the traditional single-event method, the simple averaging method, and the method in this embodiment. Figure 4 To compare the azimuth calibration angles of different methods under different random tests, the black dots in the figure represent the azimuth correction angles of independent events, the red dots represent the results obtained based on the method of this embodiment, the blue dots represent the results obtained based on the single event method with the strongest signal-to-noise ratio in a single test, and the green dots represent the results obtained based on the simple averaging method. Through statistical analysis of 100 results, the variances of the results obtained based on the method of this embodiment, the single event method, and the simple averaging method are 0.27°, 2.08°, and 1.05°, respectively, which is sufficient to prove that the method of this embodiment has significant advantages.

[0116] Case 2 is a real-world microseismic monitoring case using a downhole array with 15 level three-component geophones. During the monitoring process, a total of 12 active source events were recorded (1 detonating cord and 11 ball-dropping slides). The azimuth correction angle of the geophones was calculated by identifying and extracting P-wave records. Figure 5 This represents the relative positional relationship between the active source distribution and the detector. Figure 6 This is an example of waveform recording for an active-source detonating cord event. Based on steps S2 and S3 of the method in this embodiment, the preliminary azimuth correction angle and weight for each event can be obtained. Figure 7 To compare the azimuth angle calculated relative to the detonating cord position based on the polarization angles recorded by 11 active sources with the azimuth angle of the actual spatial position, a significant deviation exists between the calculated azimuth angle and the actual azimuth angle due to substantial differences in the signal quality of the event records. Figure 7 As can be seen, it is difficult to accurately obtain the azimuth correction angle based on a single event. Based on step S4 of the method in this embodiment, after fusing all independent measurement data, the joint posterior probability distribution of the azimuth deviation angle is the product of the likelihood functions of all individual measurements. Finally, the maximum posterior estimate of the posterior distribution is calculated as the optimal azimuth calibration angle estimate, such as... Figure 8 As shown: Figure 8 The black "X" represents the azimuth deviation angle of the 12 events, and the red "O" represents the optimal azimuth calibration angle obtained by the method in this embodiment.

[0117] Based on the two cases above, it is evident that the method in this embodiment, by calculating the signal-to-noise ratio and polarization linearity of each event and performing a comprehensive quality assessment to allocate nonlinear weights, can achieve the goal of giving higher weights to high-quality events while effectively suppressing the impact of low-quality events. Furthermore, this method can be modified and extended to scenarios such as three-component geophone tilt correction and overall orientation calibration and data acquisition quality assessment of geophone arrays in wells.

[0118] Therefore, based on the three-component geophone azimuth correction method described in steps S1 to S5 above, a new scheme is provided to achieve three-component geophone azimuth correction by weighting and fusing multiple active source events based on signal quality indices. Specifically, after acquiring at least two seismic signals recorded by the target three-component geophone and generated by at least two active source events, a preliminary azimuth calibration angle is first obtained for each active source event. Then, a corresponding weight is assigned to this calibration angle based on the signal quality index calculated from the corresponding seismic signal. Next, all calibration angles are fused according to all weights to obtain the final azimuth calibration angle. Finally, the final azimuth calibration angle is applied to perform coordinate rotation correction on the original horizontal component data to obtain the corrected horizontal component data. Thus, by introducing a weighting mechanism based on signal quality, higher accuracy and more reliable azimuth correction can be achieved, improving the quality of seismic data and the reliability of subsequent interpretation. Furthermore, it can be applied to various observation scenarios such as surface, shallow well, in-well, and inter-well observations without relying on continuous stable sources or special operating modes, significantly improving the universality and automation level of the geophone azimuth correction scheme, facilitating practical application and promotion.

[0119] like Figure 9 As shown, the second aspect of this embodiment provides a virtual device for implementing the three-component detector azimuth correction method described in the first aspect, including a signal recording and acquisition unit, a preliminary azimuth calibration unit, a calibration weight allocation unit, a final azimuth fusion unit, and a coordinate rotation correction unit.

[0120] The signal recording and acquisition unit is used to acquire at least two seismic signals recorded by the target three-component geophone and generated by at least two active source events, wherein the at least two seismic signals correspond one-to-one with the at least two active source events;

[0121] The preliminary azimuth calibration unit is communicatively connected to the signal recording and acquisition unit. It is used to calculate the theoretical polarization azimuth angle and the measured polarization azimuth angle of each active source event in the at least two active source events, respectively, and to use the difference between the theoretical polarization azimuth angle and the measured polarization azimuth angle as the corresponding preliminary azimuth calibration angle.

[0122] The calibration weight allocation unit is communicatively connected to the signal recording acquisition unit and the preliminary azimuth calibration unit, respectively. It is used to calculate the signal quality index based on the corresponding seismic signal for each active source event, and to allocate the corresponding weight to the corresponding preliminary azimuth calibration angle based on the signal quality index.

[0123] The final azimuth fusion unit is communicatively connected to the preliminary azimuth calibration unit and the calibration weight allocation unit, respectively, and is used to fuse all the preliminary azimuth calibration angles according to all the weights to obtain the final azimuth calibration angles.

[0124] The coordinate rotation correction unit is communicatively connected to the final azimuth fusion unit and is used to perform coordinate rotation correction on the original horizontal component data of the target three-component detector using the final azimuth calibration angle to obtain the corrected horizontal component data.

[0125] The working process, working details and technical effects of the aforementioned device provided in the second aspect of this embodiment can be found in the three-component detector orientation correction method described in the first aspect, and will not be repeated here.

[0126] like Figure 10 As shown, the third aspect of this embodiment provides a computer device for executing the three-component detector azimuth correction method as described in the first aspect. The device includes a storage module, a processing module, and a transceiver module connected in sequence. The storage module stores a computer program, the transceiver module sends and receives messages, and the processing module reads the computer program and executes the three-component detector azimuth correction method as described in the first aspect. Specifically, the storage module may include, but is not limited to, random-access memory (RAM), read-only memory (ROM), flash memory, first-in-first-out (FIFO) memory, and / or first-in-last-out (FILO) memory, etc.; the processing module may, but is not limited to, use a microprocessor of the STM32F105 series. Furthermore, the computer device may also include, but is not limited to, a power supply module, a display screen, and other necessary components.

[0127] The working process, working details and technical effects of the aforementioned computer equipment provided in the third aspect of this embodiment can be found in the three-component detector orientation correction method described in the first aspect, and will not be repeated here.

[0128] This fourth aspect of the embodiment provides a computer-readable storage medium storing instructions comprising the three-component detector azimuth correction method as described in the first aspect. Specifically, the computer-readable storage medium stores instructions that, when executed on a computer, perform the three-component detector azimuth correction method as described in the first aspect. The computer-readable storage medium refers to a data storage medium, which may include, but is not limited to, floppy disks, optical disks, hard disks, flash memory, USB flash drives, and / or Memory Sticks. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0129] The working process, working details and technical effects of the aforementioned computer-readable storage medium provided in the fourth aspect of this embodiment can be found in the three-component detector orientation correction method described in the first aspect, and will not be repeated here.

[0130] This fifth aspect of the embodiment provides a computer program product, including a computer program or instructions, which, when executed by a computer, implement the three-component detector azimuth correction method as described in the first aspect. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device.

[0131] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A three-component detector azimuth correction method based on multi-event fusion and probability weighting, characterized in that, include: Acquire at least two seismic signals recorded by a target three-component geophone and generated using at least two active source events, wherein the at least two seismic signals correspond one-to-one with the at least two active source events; For each active source event in the at least two active source events, the theoretical polarization azimuth angle and the measured polarization azimuth angle of the corresponding event at the target three-component detector are calculated respectively, and the difference between the theoretical polarization azimuth angle and the measured polarization azimuth angle is used as the corresponding preliminary azimuth calibration angle. For each active source event, a signal quality index is calculated based on the corresponding seismic signal, and a corresponding weight is assigned to the corresponding preliminary azimuth calibration angle based on the signal quality index. Based on all the weights, all the preliminary azimuth calibration angles are fused to obtain the final azimuth calibration angles; The original horizontal component data of the target three-component detector is corrected by coordinate rotation using the final azimuth calibration angle to obtain the corrected horizontal component data.

2. The three-component detector azimuth correction method according to claim 1, characterized in that, For each of the at least two active source events, the theoretical and measured polarization azimuth angles of the corresponding event at the target three-component detector are calculated, including: For the i-th active source event among the at least two active source events, based on the known horizontal coordinates (Rx, Ry) of the target three-component detector and the corresponding known source horizontal coordinates (Sx) i Sy i The theoretical polarization azimuth angle θ of the corresponding event at the target three-component detector is calculated according to the following formula. cal,i : θ cal,i =arctan2(Sy i -Ry,Sx i -Rx) In the formula, i represents a positive integer, and arctan2() represents the function used to calculate the arctangent value; According to the first arrival time T of the seismic wave corresponding to the i-th active source event i The time window [T] is extracted from the three-component record data of the seismic signal corresponding to the i-th active source event. i -Nwin1,T i The original horizontal component data of [+Nwin2] is used to form a two-dimensional data matrix D with M sampling points based on the extracted data. i Wherein, the initial arrival time T of the seismic wave i This refers to the earliest time when the seismic wave arrives at the target three-component geophone, where the seismic wave refers to either a longitudinal wave or a transverse wave. Nwin1 and Nwin2 represent preset offset times and have T. Ew,avg <Nwin1+Nwin2<2×T Ew,avg T Ew,avg The average period of the seismic wave is represented by M, where M represents a positive integer greater than or equal to 2. The polarization analysis algorithm is used to extract the main vibration directions in the horizontal component, and the two-dimensional data matrix D is then analyzed based on the extraction results. i Principal component analysis was performed to calculate the covariance matrix C. i : In the formula, T represents the transpose sign; Obtain the covariance matrix C i The largest eigenvalue λ1 and the corresponding eigenvector v1 = [v 1x ,v 1y ]; According to the feature vector v1=[v 1x ,v 1y The measured polarization azimuth angle θ of the i-th active source event at the target three-component detector is calculated. obs,i =arctan2(v 1y ,v 1x ).

3. The three-component detector azimuth correction method according to claim 1, characterized in that, For each active source event, a signal quality index is calculated based on the corresponding seismic signal, and a corresponding weight is assigned to the corresponding preliminary azimuth calibration angle based on the signal quality index, including: For the i-th active source event among the at least two active source events, a signal quality index including signal-to-noise ratio and polarization linearity is calculated based on the three-component recorded data of the corresponding seismic signal, where i represents a positive integer, and the polarization linearity refers to the degree to which the particle motion of seismic P-waves or seismic S-waves in the horizontal plane exhibits linear polarization characteristics. Based on the signal-to-noise ratio and the polarization linearity, a corresponding weight ω is assigned to the preliminary azimuth calibration angle corresponding to the i-th active source event according to the following formula. i : oh i =σ(a×z SNR,i +b×z L,i ) In the formula, z SNR,i z represents the normalized value of the signal-to-noise ratio. L,i The value represents the normalized value of the polarization linearity, where a and b represent preset empirical weighting coefficients, and σ() represents the Sigmoid normalization function.

4. The three-component detector azimuth correction method according to claim 1, characterized in that, Based on all the aforementioned weights, all the preliminary azimuth calibration angles are fused to obtain the final azimuth calibration angles, including: Each of the preliminary azimuth calibration angles is treated as a random variable with uncertainty. Assuming that the likelihood function of each preliminary azimuth calibration angle satisfies a von Mises distribution, with the distribution center at the corresponding preliminary azimuth calibration angle, and that the concentration parameter is positively correlated with the weight of the corresponding preliminary azimuth calibration angle, the likelihood function is defined as follows: In the formula, α represents the azimuth calibration angle and is the independent variable of the function, i represents a positive integer, and α i This represents the i-th preliminary azimuth calibration angle. Indicates the preliminary azimuth calibration angle α i The weights, e represents the base of the natural logarithm, κ i Representation and weight Positively correlated concentration parameters; Based on the likelihood function, the joint posterior probability distribution function of the azimuth calibration angle α is obtained according to the following formula: In the formula, N represents the total number of events of the at least two active source events; Calculate the maximum a posteriori estimate of the joint posterior probability distribution function, and use the calculated azimuth calibration angle as the final azimuth calibration angle.

5. The three-component detector azimuth correction method according to claim 4, characterized in that, After using the calculated azimuth calibration angle as the final azimuth calibration angle, the method further includes: Calculate the standard deviation of the joint posterior probability distribution function, and use the calculated standard deviation as the uncertainty assessment result of the final azimuth calibration angle.

6. The three-component detector azimuth correction method according to claim 1, characterized in that, The original horizontal component data of the target three-component detector is corrected by coordinate rotation using the final azimuth calibration angle to obtain the corrected horizontal component data, including: Apply the final azimuth calibration angle α best The original horizontal component data of the target three-component detector is subjected to coordinate rotation correction according to the following formula to obtain the corrected horizontal component data: In the formula, [X,Y] represents the original horizontal component data, and [X′,Y′] represents the corrected horizontal component data.

7. A three-component detector azimuth correction device based on multi-event fusion and probability weighting, characterized in that, It includes a signal recording and acquisition unit, a preliminary azimuth calibration unit, a calibration weight allocation unit, a final azimuth fusion unit, and a coordinate rotation correction unit; The signal recording and acquisition unit is used to acquire at least two seismic signals recorded by the target three-component geophone and generated by at least two active source events, wherein the at least two seismic signals correspond one-to-one with the at least two active source events; The preliminary azimuth calibration unit is communicatively connected to the signal recording and acquisition unit. It is used to calculate the theoretical polarization azimuth angle and the measured polarization azimuth angle of each active source event in the at least two active source events, respectively, and to use the difference between the theoretical polarization azimuth angle and the measured polarization azimuth angle as the corresponding preliminary azimuth calibration angle. The calibration weight allocation unit is communicatively connected to the signal recording acquisition unit and the preliminary azimuth calibration unit, respectively. It is used to calculate the signal quality index based on the corresponding seismic signal for each active source event, and to allocate the corresponding weight to the corresponding preliminary azimuth calibration angle based on the signal quality index. The final azimuth fusion unit is communicatively connected to the preliminary azimuth calibration unit and the calibration weight allocation unit, respectively, and is used to fuse all the preliminary azimuth calibration angles according to all the weights to obtain the final azimuth calibration angles. The coordinate rotation correction unit is communicatively connected to the final azimuth fusion unit and is used to perform coordinate rotation correction on the original horizontal component data of the target three-component detector using the final azimuth calibration angle to obtain the corrected horizontal component data.

8. A computer device, characterized in that, It includes a storage module, a processing module, and a transceiver module that are sequentially connected in communication. The storage module is used to store a computer program, the transceiver module is used to send and receive messages, and the processing module is used to read the computer program and execute the three-component detector azimuth correction method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that... The computer-readable storage medium stores instructions that, when executed on a computer, perform the three-component detector azimuth correction method as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or the instructions are executed by the computer, they implement the three-component detector orientation correction method as described in any one of claims 1 to 6.

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