Multi-group cross-correlation micro-seismic source positioning method based on dynamic mass weighting

Abstract

The invention provides a multi-group cross-correlation micro-seismic source positioning method based on dynamic quality weighting, and the method comprises the steps: constructing the dynamic quality weight of a signal based on an energy stability index, a signal-to-noise ratio index and a spatial coherence index of the signal of each receiver in a noise environment; grouping to obtain a signal of a dynamic grouping number G through a two-dimensional feature clustering method combining the three-dimensional coordinates of the receivers and the dynamic quality weights; carrying out time reversal on each group of signals, then carrying out wave field back propagation, applying a perfect matching layer to the boundary of the wave field to absorb a condition to suppress noise, obtaining wave field energy in each group, and carrying out geometric mean operation integration to obtain imaging data of each group of signals; after imaging data are integrated, space-time filtering is carried out, and an effective seismic source position is detected by combining a connected domain detection algorithm. Therefore, the method of fusing the dynamic quality weight of the signal, the dynamic grouping strategy and the perfect matching layer absorption condition geometric average imaging realizes the high-precision positioning of the micro seismic source, and solves the problems of noise sensitivity and weak seismic source leak detection.

Description

Technical Field

[0001] The present invention relates to the technical field of microseismic monitoring, and in particular to a method and device for locating multiple groups of cross-correlated microseismic sources based on dynamic mass weighting. Background Art

[0002] In related technologies, when processing low signal-to-noise ratio data and complex scenarios, the receiver signal grouping method is fixed and not dynamically adjusted based on signal quality. Low-quality receiver signals may introduce noise, affecting imaging accuracy. Furthermore, in multi-source scenarios, traditional signal cross-correlation imaging, due to fixed weights, causes the energy of strong sources to mask weaker sources, making it difficult to identify multiple sources with large energy differences. Furthermore, it has poor adaptability to complex scene noise, and false focal points are prone to appearing in low signal-to-noise ratio data, significantly increasing positioning errors. This leads to noise sensitivity, missed detection of weak sources, and an inability to accurately locate microseismic sources. Summary of the Invention

[0003] The present invention aims to solve one of the technical problems in the related art at least to a certain extent.

[0004] To this end, the first purpose of the present invention is to propose a multi-group cross-correlation microseismic source positioning method based on dynamic quality weighting, which integrates the dynamic quality weight of the signal, the dynamic grouping strategy and the geometric mean imaging method of the perfect matching layer absorption condition to achieve high-precision positioning of the microseismic source and solve the problems of noise sensitivity and missed detection of weak sources.

[0005] The second object of the present invention is to provide a multi-group cross-correlation microseismic source positioning device based on dynamic mass weighting.

[0006] A third object of the present invention is to provide an electronic device.

[0007] A fourth object of the present invention is to provide a non-transitory computer-readable storage medium storing computer instructions.

[0008] To achieve the above-mentioned purpose, a first embodiment of the present invention proposes a method for locating microseismic sources based on multiple groups of cross-correlations based on dynamic mass weighting, the method comprising:

[0009] Based on the energy stability index, signal-to-noise ratio index, and spatial coherence index of the signal of each receiver in a noisy environment, a dynamic quality weight of the signal is constructed. The energy stability index is characterized by the time domain fluctuation characteristics of the signal waveform, the signal-to-noise ratio index is obtained by using a sliding window technique to separate the signal and noise energy, and the spatial coherence is the average of the waveform correlation coefficients between the signals of each receiver and multiple adjacent receivers.

[0010] Grouping the signals into G dynamic groups using a two-dimensional feature clustering method that combines the three-dimensional coordinates of each receiver with the dynamic quality weight, where the standard deviation of the dynamic quality weight within each group in G is less than a set threshold;

[0011] After time reversing each group of signals in the dynamic group number G, the finite difference method of the acoustic wave equation is used to perform wavefield backpropagation. A perfectly matched layer absorption condition is applied at the wavefield boundary to suppress noise, thereby obtaining the internal wavefield energy of each group. The internal wavefield energy of each group is then integrated through geometric averaging to obtain the imaging data of each signal group.

[0012] The imaging data of each signal group are integrated by a quality-weighted fusion strategy, and then space-time filtering is performed. Then, the connected domain detection algorithm is used to detect the focal area in the target imaging result after space-time filtering, which is used as the effective source location.

[0013] To achieve the above-mentioned purpose, a second embodiment of the present invention proposes a multi-group cross-correlation microseismic source positioning device based on dynamic mass weighting, the device comprising:

[0014] A construction module is used to construct a dynamic quality weight of the signal based on the energy stability index, signal-to-noise ratio index, and spatial coherence index of the signal of each receiver in a noisy environment, wherein the energy stability index is characterized by the time domain fluctuation characteristics of the signal waveform, the signal-to-noise ratio index is obtained by separating the signal and noise energy using a sliding window technique, and the spatial coherence is the average of the waveform correlation coefficients between the signals of each receiver and multiple adjacent receivers;

[0015] a grouping module configured to group signals into a dynamic group number G using a two-dimensional feature clustering method combining the three-dimensional coordinates of each receiver with the dynamic quality weight, wherein a standard deviation of the dynamic quality weight within each group in the dynamic group number G is less than a set threshold;

[0016] The operation module is used to perform time reversal on each group of signals in the dynamic group number G, perform wavefield backpropagation using the finite difference method of the acoustic wave equation, and apply the perfectly matched layer absorption condition at the wavefield boundary to suppress noise to obtain the internal wavefield energy of each group. The internal wavefield energy of each group is then integrated through the geometric mean operation to obtain the imaging data of each group of signals;

[0017] The detection module is used to integrate the imaging data of each group of signals using a quality-weighted fusion strategy and then perform spatiotemporal filtering. It then combines the connected domain detection algorithm to detect the focal area in the target imaging results after spatiotemporal filtering as the effective source location.

[0018] To achieve the above-mentioned purpose, the third aspect embodiment of the present invention proposes an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method described in the first aspect.

[0019] In order to achieve the above-mentioned objectives, an embodiment of the fourth aspect of the present invention proposes a non-transitory computer-readable storage medium storing computer instructions, where the computer instructions are used to enable the computer to execute the method described in the first aspect.

[0020] The embodiments of the present invention provide a method, device, electronic device, and storage medium for locating multiple groups of mutually correlated microseismic sources based on dynamic quality weighting. The method, device, electronic device, and storage medium construct a dynamic quality weight of the signal based on the energy stability index, signal-to-noise ratio index, and spatial coherence index of the signal of each receiver in a noisy environment. The method grouping the signals into G dynamic groups is performed by combining the three-dimensional coordinates of each receiver with a two-dimensional feature clustering method of the dynamic quality weight. Each group of signals is time-reversed and then wavefield backpropagation is performed. A perfect matching layer absorption condition is applied at the wavefield boundary to suppress noise, thereby obtaining the wavefield energy within each group. The imaging data of each group of signals is then integrated through geometric averaging. After integrating the imaging data of each group of signals, spatiotemporal filtering is performed, and then the effective source position is detected in combination with a connected domain detection algorithm. Thus, the method of integrating the dynamic quality weight of the signal, the dynamic grouping strategy, and the geometric averaging imaging method of the perfect matching layer absorption condition can achieve high-precision positioning of microseismic sources and solve the problems of noise sensitivity and missed detection of weak sources.

[0021] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0023] Figure 1 A schematic flow chart of a method for locating microseismic sources based on dynamic mass weighting using multiple groups of cross-correlations provided by an embodiment of the present invention;

[0024] Figure 2 An example diagram of a P-wave velocity model provided by an embodiment of the present invention;

[0025] Figure 3 A comparison chart of microseismic source location results provided by an embodiment of the present invention;

[0026] Figure 4A flowchart of an execution of a multi-group cross-correlation microseismic source positioning method based on dynamic mass weighting provided by an embodiment of the present invention;

[0027] Figure 5 A schematic structural diagram of a multi-group cross-correlation microseismic source positioning device based on dynamic mass weighting provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0028] The following describes embodiments of the present invention in detail, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and are not to be construed as limiting the present invention.

[0029] It should be noted that the acquisition, storage, use, and processing of data in the technical solution of the present invention comply with the relevant provisions of relevant laws and regulations.

[0030] The following describes a method and apparatus for locating microseismic sources using multiple groups of cross-correlations based on dynamic mass weighting according to an embodiment of the present invention with reference to the accompanying drawings.

[0031] Figure 1 A schematic flow chart of a method for locating microseismic sources using multiple groups of cross-correlations based on dynamic mass weighting provided by an embodiment of the present invention.

[0032] like Figure 1 As shown, the method includes the following steps:

[0033] Step 101: Based on the energy stability index, signal-to-noise ratio index, and spatial coherence index of the signal of each receiver in a noisy environment, a dynamic quality weight of the signal is constructed, wherein the energy stability index is characterized by the time domain fluctuation characteristics of the signal waveform, the signal-to-noise ratio index is obtained by separating the signal and noise energy using a sliding window technology, and the spatial coherence is the average of the waveform correlation coefficients between each receiver and multiple adjacent receiver signals. Therefore, in order to address the problem of significant differences in receiver signal quality in complex noise environments, a three-dimensional quantification system including energy stability, signal-to-noise ratio (SNR), and spatial coherence indicators is constructed to reduce the problem of differences in receiver signal quality.

[0034] In some possible implementations, the energy stability index is E s , std(·), d r (t) is the time domain signal of the receiver, std(·) and mean(·) represent the standard deviation and mean operations respectively, E s The value range is [0, 1]; the signal-to-noise ratio index is SNR r , n r (t) is the noise signal; the spatial coherence index is C r , K is the set of neighboring receivers, corr(·) is the cross-correlation function; the dynamic quality weight is w r , w r ∈[0, 1].

[0035] Among them, the energy stability index reflects the signal stability through the waveform time domain fluctuation characteristics, E s A higher value indicates a more stable signal waveform; the signal-to-noise ratio index quantifies the degree of dominance of signal energy relative to noise; the spatial coherence index reflects the consistency of the signal in spatial distribution.

[0036] Optionally, for a dynamic quality weight of w r , you can set the threshold to 0.3 (w r <0.3) to identify low-quality signal sources (w r <0.3), providing a data screening basis for subsequent dynamic grouping strategies.

[0037] Step 102 : Group the signals to obtain signals of a dynamic group number G by combining the three-dimensional coordinates of each receiver with a two-dimensional feature clustering method of the dynamic quality weight, wherein the standard deviation of the dynamic quality weight within each group in the dynamic group number G is less than a set threshold.

[0038] In some possible implementations, a signal is grouped to obtain a dynamic group number G of signals by combining a two-dimensional feature clustering method based on the three-dimensional coordinates of each receiver and the dynamic quality weight, wherein the standard deviation of the dynamic quality weight within each group in the dynamic group number G is less than a set threshold, including: combining the three-dimensional coordinates X of each receiver r =(x r ,y r ,z r ) and dynamic quality weight w r , construct the feature vector (X r , w r ); the eigenvector uses a two-dimensional feature clustering method to minimize the objective function Dynamically divide the signals of each receiver in the receiver array to obtain the signals of dynamic group number G, where d space is the Euclidean distance, μ g is the spatial centroid of the g-th group, The weighted average weight of the signals within each group is calculated. The standard deviation of the dynamic quality weights within each group in the dynamic group number G is less than the set threshold. This solves the problem of fixed grouping's lack of adaptability to signal quality differences.

[0039] Optionally, the two-dimensional feature clustering method may include but is not limited to a K-means++ algorithm, which is not specifically limited in this embodiment.

[0040] It can be understood that by constraining the standard deviation of the dynamic quality weights within each group in the dynamic group number G to be less than a set threshold (e.g., 0.2), uniform signal quality within the group and a reasonable spatial distribution are ensured. The dynamic group number G ranges from [N / 5, N / 2] (where N is the total number of receivers). The minimum dynamic group number can be set to 3 to avoid overfitting and achieve adaptive partitioning of the receiver array.

[0041] In step 103, after time reversing each group of signals in the dynamic group number G, the finite difference method of the acoustic wave equation is used to perform wavefield backpropagation, and a perfectly matched layer absorption condition is applied at the wavefield boundary to suppress noise, so as to obtain the wavefield energy within each group. The wavefield energy within each group is then integrated through a geometric mean operation to obtain the imaging data of each group of signals.

[0042] In some possible implementations, after time reversing each group of signals in the dynamic group number G, wavefield backpropagation is performed using the finite difference method of the acoustic wave equation, and noise is suppressed by applying a perfectly matched layer absorption condition at the wavefield boundary to obtain each group of internal wavefield energy, and then the internal wavefield energy of each group is integrated by a geometric mean operation to obtain imaging data of each group of signals. The method further includes: after time reversing each group of signals in the dynamic group number G, wavefield backpropagation is performed using the finite difference method of the acoustic wave equation to obtain initial each group of internal wavefield energy; the perfectly matched layer absorption condition includes a time window constraint and a spatial aperture constraint, and the time window constraint and the spatial aperture constraint are respectively applied at the wavefield boundary to suppress long-period reflection noise and eliminate edge receiver noise of each initial group of internal wavefield energy to obtain each group of internal wavefield energy; and the internal wavefield energy of each group is integrated by a geometric mean operation to obtain imaging data I of each group of signals. g (x), n g is the number of receivers in each group, rect is the rectangular window function, W g (x, t) is the propagation model of the wave field backpropagation, t is time, and Δt is the focusing time window set according to the main frequency of the signal to improve adaptability to complex noise environments.

[0043] Specifically, for each group of signals D g After time reversal (t), the wave field is propagated back using the finite difference method of the acoustic wave equation (space grid 10m×10m, time step 0.001s), and a perfectly matched layer (PML) absorption condition is applied to the boundary to suppress noise. The propagation model of the wave field is W g (x,t), F -1 is the inverse Fourier transform, Dg (ω) is the spectrum of each signal group, G * Represents the complex conjugate of the Green's function. For example, the wave field of the low-mass signal source in the low-mass group is multiplied by an attenuation factor of 0.6 to reduce the noise contribution.

[0044] The dual constraints in time and space domains are introduced to optimize the imaging process (perfectly matched layer absorption conditions are applied to the wavefield boundary to suppress noise): the time window constraint sets the focusing time window Δt = 1 / (2f0) based on the signal main frequency f0, retaining only the wavefield energy within the excitation time t0 + Δt, effectively suppressing long-period reflection noise. The spatial aperture constraint multiplies the wavefield by the distance attenuation factor e for receivers that are more than 1 / 2 of the aperture from the center of the array. -d100 (d is the distance from the receiver to the center), reducing the interference of edge receiver noise on imaging.

[0045] Finally, the wave field energy within the group is integrated through geometric mean operation to obtain the imaging data I of each group of signals. g (x),

[0046] Step 104: A quality-weighted fusion strategy is used to integrate the imaging data of each signal group and then perform spatiotemporal filtering. Then, a connected domain detection algorithm is used to detect the focal area in the target imaging result after spatiotemporal filtering as the effective source position.

[0047] In some possible implementations, a quality-weighted fusion strategy is used to integrate the imaging data of each group of signals and then perform spatiotemporal filtering. Then, a connected domain detection algorithm is used to detect the focal area in the target imaging result after spatiotemporal filtering to serve as the effective source location, including: using a quality-weighted fusion strategy to integrate the imaging data of each group of signals to obtain an initial imaging result I final (x), Initial imaging results I final (x) Performing spatiotemporal filtering to obtain target imaging results, wherein spatiotemporal filtering includes time domain and space domain filtering. In the time domain, set point median filtering is used to remove isolated noise peaks, and in the space domain, Gaussian filtering is used to smooth the energy gradient. Combined with the connected domain detection algorithm, the focus area with an area larger than the set value in the target imaging result is detected as the effective source position, balancing the energy differences of multiple sources and enhancing the ability to identify weak sources.

[0048] in, The weighted average weight of the signals in each group is used to highlight the contribution of high-quality groups (such as w r The weight of groups with values ​​greater than 0.7 is increased by 30%. rect is a rectangular window function used to extract valid wavefield signals within the time window.

[0049] The energy distribution is further optimized through joint filtering in the time and space domains: a 5-point median filter is used in the time domain to remove isolated noise spikes, and a 3×3 Gaussian filter (σ=1.5) is used in the spatial domain to smooth the energy gradient. Finally, combined with the connected domain detection algorithm, only the focal area with an area larger than the 3×3 grid is retained as the effective source position, achieving accurate positioning of multiple sources. Figure 2 is the longitudinal wave velocity model (Vp, km / s), where the black five-pointed star represents the location of the underground microseismic source, the horizontal axis is the actual horizontal distance (Distance, km), and the vertical axis is the actual depth (Depth, km). Figure 3 A comparison diagram of microseismic source location results provided by an embodiment of the present invention (for Figure 2 The comparison diagram of microseismic source location results includes the traditional geometric mean imaging result a and the dynamic quality weighted multi-group cross-correlation imaging result b example diagram, as shown in Figure 4 shown.

[0050] The embodiment of the present invention is based on a method for locating microseismic sources with multiple groups of cross-correlated signals based on dynamic quality weighting. The method constructs the dynamic quality weight of the signal based on the energy stability index, signal-to-noise ratio index and spatial coherence index of the signal of each receiver in a noisy environment. The method groups the signals into G dynamic groups by combining the three-dimensional coordinates of each receiver with the two-dimensional feature clustering method of the dynamic quality weight. After time reversing each group of signals, the wave field is reversed, and the noise is suppressed by applying the perfect matching layer absorption condition at the wave field boundary to obtain the wave field energy within each group. The imaging data of each group of signals is then integrated by geometric mean operation to obtain the imaging data of each group of signals. After integrating the imaging data of each group of signals, spatiotemporal filtering is performed, and the effective source position is detected by combining the connected domain detection algorithm. Thus, the method of integrating the dynamic quality weight of the signal, the dynamic grouping strategy and the geometric mean imaging method of the perfect matching layer absorption condition can realize high-precision positioning of microseismic sources and solve the problems of noise sensitivity and missed detection of weak sources.

[0051] The present invention also proposes an execution flow chart of a method for locating microseismic sources based on dynamic mass weighting of multiple groups of cross-correlations, as shown in FIG. Figure 4 As shown, the execution process includes receiving records, quality assessment (E s , SNR r 、C r ), weight calculation w r , K-means++ clustering (space + weight features), dynamic grouping G groups (each group w r Standard deviation <0.2), weighted constrained reverse time propagation (time window constraint and spatial aperture constraint), geometric mean imaging, quality weighted integration, spatiotemporal filtering, and output of microseismic source locations. Specifically:

[0052] Receiving record: Receive the signal of each receiver in a noisy environment.

[0053] Quality Assessment (E s , SNR r 、C r ):Calculate the energy stability index E based on the signal of each receiver in the noise environment s , signal-to-noise ratio index and spatial coherence index SNR r , construct the dynamic quality weight C of the signal r .

[0054] Weight calculation w r :According to the energy stability index E s , signal-to-noise ratio index and spatial coherence index SNR r , construct the dynamic quality weight C of the signal r , construct the dynamic quality weight w of the signal r .

[0055] K-means++ clustering (spatial + weight features): By combining the three-dimensional coordinates X of each receiver r =(x r ,y r ,z r ) and dynamic quality weight w r , construct the feature vector (X r , w r ), the eigenvector uses a two-dimensional feature clustering method to minimize the objective function Dynamically divide the signals of each receiver in the receiver array.

[0056] Dynamic grouping G group (each group w r Standard deviation <0.2): A signal of the dynamic group number G is obtained, and the standard deviation of the dynamic quality weight within each group in the dynamic group number G is less than the set threshold.

[0057] Weighted constrained time-reverse propagation (with time window and spatial aperture constraints): After time reversing each signal group in the dynamic group number G, the finite difference method of the acoustic wave equation is used for wavefield reverse propagation to obtain the initial internal wavefield energy of each group. Time window constraints and spatial aperture constraints are applied at the wavefield boundaries to suppress long-period reflection noise and remove edge receiver noise, thus obtaining the internal wavefield energy of each group.

[0058] Geometric mean imaging: The energy of each internal wave field is integrated through geometric mean operation to obtain the imaging data of each group of signals.

[0059] Quality-weighted integration: The imaging data of each signal group are integrated using a quality-weighted fusion strategy to obtain the initial imaging results.

[0060] Spatiotemporal filtering: Perform spatiotemporal filtering on the initial imaging results to obtain the target imaging results.

[0061] Output microseismic source location: Combined with the connected domain detection algorithm, the focus area with an area larger than the set value in the target imaging result is detected and output as the effective source location.

[0062] In order to implement the above embodiment, the present invention further proposes a multi-group cross-correlation microseismic source positioning device based on dynamic mass weighting.

[0063] Figure 5 A schematic structural diagram of a multi-group cross-correlation microseismic source positioning device based on dynamic mass weighting provided by an embodiment of the present invention.

[0064] like Figure 5 As shown, the multi-group mutual correlation microseismic source positioning device 50 based on dynamic mass weighting includes: a construction module 51, a grouping module 52, a calculation module 53, and a detection module 54.

[0065] A construction module 51 is configured to construct a dynamic quality weight of the signal based on an energy stability index, a signal-to-noise ratio index, and a spatial coherence index of the signal of each receiver in a noisy environment, wherein the energy stability index is characterized by the time-domain fluctuation characteristics of the signal waveform, the signal-to-noise ratio index is obtained by separating the signal and noise energy using a sliding window technique, and the spatial coherence index is the average of the waveform correlation coefficients between the signals of each receiver and multiple adjacent receivers;

[0066] a grouping module 52 for grouping signals to obtain signals of a dynamic group number G by using a two-dimensional feature clustering method combining the three-dimensional coordinates of each receiver with the dynamic quality weight, wherein a standard deviation of the dynamic quality weight within each group in the dynamic group number G is less than a set threshold;

[0067] The operation module 53 is used to perform time reversal on each group of signals in the dynamic group number G, perform wavefield backpropagation using the finite difference method of the acoustic wave equation, apply a perfectly matched layer absorption condition at the wavefield boundary to suppress noise, so as to obtain the wavefield energy within each group, and then integrate the wavefield energy within each group through a geometric mean operation to obtain imaging data for each group of signals;

[0068] The detection module 54 is used to integrate the imaging data of each group of signals using a quality-weighted fusion strategy and then perform spatiotemporal filtering. It then combines the connected domain detection algorithm to detect the focal area in the target imaging result after spatiotemporal filtering as the effective source position.

[0069] Furthermore, in a possible implementation of the embodiment of the present invention, wherein:

[0070] The energy stability index is E s , std(·), d r (t) is the time domain signal of the receiver, std(·) and mean(·) represent the standard deviation and mean operations respectively, Es The value range is [0, 1];

[0071] The signal-to-noise ratio indicator is SNR r , n r (t) is the noise signal;

[0072] The spatial coherence index is C r , K is the set of neighboring receivers, corr(·) is the cross-correlation function;

[0073] The dynamic quality weight is w r , w r ∈[0, 1].

[0074] Furthermore, in a possible implementation of the embodiment of the present invention, the grouping module 52 is specifically configured to:

[0075] By combining the three-dimensional coordinates X of each receiver r =(x r ,y r ,z r ) and dynamic quality weight w r , construct the feature vector (X r , w r );

[0076] The feature vector minimizes the objective function using a two-dimensional feature clustering method Dynamically divide the signals of each receiver in the receiver array to obtain the signals of dynamic group number G, where d space is the Euclidean distance, μ g is the spatial centroid of the g-th group, is the weighted average weight of the signals within each group;

[0077] Among them, the standard deviation of the dynamic quality weight in each group in the dynamic group number G is less than the set threshold.

[0078] Furthermore, in a possible implementation of the embodiment of the present invention, the operation module 53 is specifically configured to:

[0079] After time reversing each group of signals in the dynamic group number G, the finite difference method of acoustic wave equation is used to perform wave field back propagation to obtain the initial wave field energy in each group;

[0080] The perfectly matched layer absorption condition includes a time window constraint and a spatial aperture constraint, which are respectively applied at the wavefield boundary to suppress long-period reflection noise and remove edge receiver noise from each initial group of internal wavefield energies, thereby obtaining each group of internal wavefield energies;

[0081] The energy of each internal wave field is integrated by geometric mean operation to obtain the imaging data I of each signal group. g (x), n g is the number of receivers in each group, rect is the rectangular window function, W g (x, t) is the propagation model of the wave field back propagation, t is the time, and Δt is the focusing time window set according to the signal main frequency.

[0082] Furthermore, in a possible implementation of the embodiment of the present invention, the detection module 54 is specifically configured to:

[0083] The imaging data of each signal group are integrated using a quality-weighted fusion strategy to obtain the initial imaging result I final (x),

[0084] Initial imaging results I final (x) performing spatiotemporal filtering to obtain target imaging results, wherein the spatiotemporal filtering includes filtering in the time domain and in the space domain. In the time domain, a set point median filter is used to remove isolated noise peaks, and in the space domain, a Gaussian filter is used to smooth the energy gradient;

[0085] Combined with the connected domain detection algorithm, the focus area with an area larger than the set value in the target imaging result is detected as the effective source position.

[0086] The embodiment of the present invention is a multi-group cross-correlated microseismic source positioning device based on dynamic quality weighting. Based on the energy stability index, signal-to-noise ratio index and spatial coherence index of the signal of each receiver in a noisy environment, the dynamic quality weight of the signal is constructed; by combining the three-dimensional coordinates of each receiver with the two-dimensional feature clustering method of the dynamic quality weight, the signals of the dynamic group number G are grouped; after time reversing each group of signals, the wave field is reversed, and the noise is suppressed by applying the perfect matching layer absorption condition at the wave field boundary to obtain the wave field energy within each group, and then the imaging data of each group of signals is obtained by geometric mean operation integration; after integrating the imaging data of each group of signals, spatiotemporal filtering is performed, and then the effective source position is detected by combining the connected domain detection algorithm. Thus, the dynamic quality weight of the signal, the dynamic grouping strategy and the geometric mean imaging method of the perfect matching layer absorption condition are integrated to achieve high-precision positioning of microseismic sources and solve the problems of noise sensitivity and missed detection of weak sources.

[0087] In order to implement the above embodiment, the present invention further provides an electronic device, including:

[0088] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the aforementioned method.

[0089] In order to implement the above embodiment, the present invention further proposes a non-transitory computer-readable storage medium storing computer instructions, where the computer instructions are used to enable the computer to execute the above method.

[0090] In the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0091] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0092] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0093] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced 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, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. 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 device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and a portable compact disc read-only memory (CDROM). Furthermore, 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 and then editing, interpreting or processing it in another suitable manner if necessary, and then storing it in a computer memory.

[0094] 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.

[0095] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0096] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing module, or each unit may exist physically separately, or two or more units may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or in the form of software functional modules. If the integrated modules are implemented in the form of software functional modules and sold or used as independent products, they may also be stored in a computer-readable storage medium.

[0097] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and are not to be construed as limiting the present invention. Persons skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for locating microseismic sources based on dynamic mass weighting of multiple groups of cross-correlations, characterized in that: The method comprises: Based on the energy stability index, signal-to-noise ratio index, and spatial coherence index of the signal of each receiver in a noisy environment, a dynamic quality weight of the signal is constructed. The energy stability index is characterized by the time domain fluctuation characteristics of the signal waveform, the signal-to-noise ratio index is obtained by using a sliding window technique to separate the signal and noise energy, and the spatial coherence is the average of the waveform correlation coefficients between the signals of each receiver and multiple adjacent receivers. Grouping the signals into G dynamic groups using a two-dimensional feature clustering method that combines the three-dimensional coordinates of each receiver with the dynamic quality weight, where the standard deviation of the dynamic quality weight within each group in G is less than a set threshold; After time reversing each group of signals in the dynamic group number G, the finite difference method of the acoustic wave equation is used to perform wavefield backpropagation. A perfectly matched layer absorption condition is applied at the wavefield boundary to suppress noise, thereby obtaining the internal wavefield energy of each group. The internal wavefield energy of each group is then integrated through geometric averaging to obtain the imaging data of each signal group. The imaging data of each signal group are integrated by a quality-weighted fusion strategy, and then space-time filtering is performed. Then, the connected domain detection algorithm is used to detect the focal area in the target imaging result after space-time filtering, which is used as the effective source location.

2. The method according to claim 1, characterized in that in: The energy stability index is E s , d r (t) is the time domain signal of the receiver, std(·) and mean(·) represent the standard deviation and mean operations respectively, E s The value range is [0, 1]; The signal-to-noise ratio indicator is SNR r , n r (t) is the noise signal; The spatial coherence index is C r , K is the set of neighboring receivers, corr(·) is the cross-correlation function; The dynamic quality weight is w r , w r ∈[0, 1].

3. The method according to claim 2, characterized in that The signal grouping is performed by combining the three-dimensional coordinates of each receiver with the two-dimensional feature clustering method of the dynamic quality weight to obtain a dynamic group number G of signals, wherein the standard deviation of the dynamic quality weight within each group in the dynamic group number G is less than a set threshold, including: By combining the three-dimensional coordinates X of each receiver r =(x r ,y r ,z r ) and dynamic quality weight w r , construct the feature vector (X r , w r ); The feature vector minimizes the objective function using a two-dimensional feature clustering method Dynamically divide the signals of each receiver in the receiver array to obtain the signals of dynamic group number G, where d space is the Euclidean distance, μ g is the spatial centroid of the g-th group, is the weighted average weight of the signals within each group; Among them, the standard deviation of the dynamic quality weight in each group in the dynamic group number G is less than the set threshold.

4. The method according to claim 1, wherein After time reversing each group of signals in the dynamic group number G, wavefield backpropagation is performed using the finite difference method of the acoustic wave equation, and a perfectly matched layer absorption condition is applied at the wavefield boundary to suppress noise to obtain the internal wavefield energy of each group. The internal wavefield energy of each group is then integrated through a geometric mean operation to obtain imaging data of each group of signals, and further includes: After time reversing each group of signals in the dynamic group number G, the finite difference method of acoustic wave equation is used to perform wave field back propagation to obtain the initial wave field energy in each group; The perfectly matched layer absorption condition includes a time window constraint and a spatial aperture constraint, which are respectively applied at the wavefield boundary to suppress long-period reflection noise and remove edge receiver noise from each initial group of internal wavefield energies, thereby obtaining each group of internal wavefield energies; The energy of each internal wave field is integrated by geometric mean operation to obtain the imaging data of each signal group. n g is the number of receivers in each group, rect is the rectangular window function, W g (x, t) is the propagation model of the wave field back propagation, t is the time, and Δt is the focusing time window set according to the signal main frequency.

5. The method according to claim 4, characterized in that The quality-weighted fusion strategy is used to integrate the imaging data of each group of signals and then perform spatiotemporal filtering. Then, the connected domain detection algorithm is used to detect the focal area in the target imaging result after spatiotemporal filtering as the effective source location, including: The imaging data of each signal group are integrated using a quality-weighted fusion strategy to obtain the initial imaging result I final (x), Initial imaging results I final (x) performing spatiotemporal filtering to obtain target imaging results, wherein the spatiotemporal filtering includes filtering in the time domain and in the space domain. In the time domain, a set point median filter is used to remove isolated noise peaks, and in the space domain, a Gaussian filter is used to smooth the energy gradient; Combined with the connected domain detection algorithm, the focus area with an area larger than the set value in the target imaging result is detected as the effective source position.

6. A multi-group cross-correlation microseismic source positioning device based on dynamic mass weighting, characterized in that: The device comprises: A construction module is used to construct a dynamic quality weight of the signal based on the energy stability index, signal-to-noise ratio index, and spatial coherence index of the signal of each receiver in a noisy environment, wherein the energy stability index is characterized by the time domain fluctuation characteristics of the signal waveform, the signal-to-noise ratio index is obtained by separating the signal and noise energy using a sliding window technique, and the spatial coherence is the average of the waveform correlation coefficients between the signals of each receiver and multiple adjacent receivers; a grouping module configured to group signals into a dynamic group number G using a two-dimensional feature clustering method combining the three-dimensional coordinates of each receiver with the dynamic quality weight, wherein a standard deviation of the dynamic quality weight within each group in the dynamic group number G is less than a set threshold; The operation module is used to perform time reversal on each group of signals in the dynamic group number G, perform wavefield backpropagation using the finite difference method of the acoustic wave equation, and apply the perfectly matched layer absorption condition at the wavefield boundary to suppress noise to obtain the internal wavefield energy of each group. The internal wavefield energy of each group is then integrated through the geometric mean operation to obtain the imaging data of each group of signals; The detection module is used to integrate the imaging data of each group of signals using a quality-weighted fusion strategy and then perform spatiotemporal filtering. It then combines the connected domain detection algorithm to detect the focal area in the target imaging results after spatiotemporal filtering as the effective source location.

7. The device according to claim 6, characterized in that in: The energy stability index is E s , d r (t) is the time domain signal of the receiver, std(·) and mean(·) represent the standard deviation and mean operations respectively, E s The value range is [0, 1]; The signal-to-noise ratio indicator is SNR r , n r (t) is the noise signal; The spatial coherence index is C r , K is the set of neighboring receivers, corr(·) is the cross-correlation function; The dynamic quality weight is w r , w r ∈[0, 1].

8. The device according to claim 7, characterized in that The grouping module is specifically used to: By combining the three-dimensional coordinates X of each receiver r =(x r ,y r ,z r ) and dynamic quality weight w r , construct the feature vector (X r , w r ); The feature vector minimizes the objective function using a two-dimensional feature clustering method Dynamically divide the signals of each receiver in the receiver array to obtain the signals of dynamic group number G, where d space is the Euclidean distance, μ g is the spatial centroid of the g-th group, is the weighted average weight of the signals within each group; Among them, the standard deviation of the dynamic quality weight in each group in the dynamic group number G is less than the set threshold.

9. The device according to claim 8, characterized in that The operation module is specifically used for: After time reversing each group of signals in the dynamic group number G, the finite difference method of acoustic wave equation is used to perform wave field back propagation to obtain the initial wave field energy in each group; The perfectly matched layer absorption condition includes a time window constraint and a spatial aperture constraint, which are respectively applied at the wavefield boundary to suppress long-period reflection noise and remove edge receiver noise from each initial group of internal wavefield energies, thereby obtaining each group of internal wavefield energies; The energy of each internal wave field is integrated by geometric mean operation to obtain the imaging data of each signal group. n g is the number of receivers in each group, rect is the rectangular window function, W g (x, t) is the propagation model of the wave field back propagation, t is the time, and Δt is the focusing time window set according to the signal main frequency.

10. The device according to claim 9, characterized in that The detection module is specifically used to: The imaging data of each signal group are integrated using a quality-weighted fusion strategy to obtain the initial imaging result I final (x), Initial imaging results I final (x) performing spatiotemporal filtering to obtain target imaging results, wherein the spatiotemporal filtering includes filtering in the time domain and in the space domain. In the time domain, a set point median filter is used to remove isolated noise peaks, and in the space domain, a Gaussian filter is used to smooth the energy gradient; Combined with the connected domain detection algorithm, the focus area with an area larger than the set value in the target imaging result is detected as the effective source position.

11. An electronic device, characterized in that: include: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 5.

12. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 5.

Images