Microseismic positioning method, device and equipment based on artificial bee colony algorithm, and storage medium

Abstract

The invention provides a micro-seismic positioning method and device based on an artificial bee colony algorithm, equipment and a storage medium. Relates to the technical field of dry micro-seismic data processing. The method comprises the following steps: acquiring micro-seismic data, identifying the micro-seismic data by adopting a long-short time window ratio method to obtain micro-seismic data of a micro-seismic event, and performing noise reduction processing and first arrival pickup to obtain an arrival time sequence of the micro-seismic event; taking the cosine similarity as a target function, taking the coordinates of each sensor as initial parameters, randomly selecting a plurality of initial points, starting inversion based on a set iteration number, calculating a time difference sequence from each initial point to each sensor as an arrival time sequence of an inversion waveform, and calculating the arrival time sequence of the inversion waveform; and if the cosine similarity of the arrival time sequence of the inversion waveform is higher than the value before iteration, replacing the arrival time sequence of the previous inversion waveform with the arrival time sequence of the current inversion waveform, otherwise, continuing to search until the set number of iterations is reached, and outputting the optimal seismic source point. According to the invention, the accuracy of micro-seismic positioning can be obviously improved.

Description

Technical Field

[0001] This application relates to the field of microseismic data processing technology, and in particular to a microseismic location method, device, equipment and storage medium based on artificial bee colony algorithm. Background Technology

[0002] In microseismic monitoring technology, the identification of first arrival waves, data denoising and rupture location are interconnected. Errors in any one of these steps can have a significant impact on subsequent analysis. Therefore, how to reduce errors and improve accuracy has always been a major challenge.

[0003] With the evolution of technology, methods for locating microseismic sources are constantly being innovated. Early techniques proposed a classical method that linearized a system of nonlinear equations and solved them using the least squares principle; however, this method involved significant computational costs in solving partial derivatives and inverse matrices. Later, analytical solutions for P-wave velocities were developed to locate the source. Based on this idea, various joint inversion methods emerged, further developing source location technology. For downhole seismic monitoring, a double-difference seismic imaging method combining inverse azimuth information of microseismic events and arrival time differences was proposed. This method achieves location while simultaneously obtaining the velocity structure of the target reservoir, revealing a high correlation between microseismic events and low-velocity anomalies. In the field of artificial intelligence, methods for identifying arrival time delays have been developed using convolutional neural networks and deep learning techniques to determine the source location of microseismic events in underground mines. Another technique proposes a deep learning method that can locate microseismic events and perform velocity model updates in real time, efficiently, and accurately. This method treats these two tasks as multidimensional nonlinear regression problems and designs a multi-layer two-dimensional convolutional neural network to perform the inversion. In addition, in recent years, optimization algorithms such as genetic algorithms, differential evolution, multichannel coherent migration, and simulated annealing have also been applied to the localization of microseismic events. Through data training and iteration, the errors in seismic wave velocity and stratum travel time can be reduced, thereby further improving the accuracy of inversion and localization.

[0004] However, although good progress has been made in the field of microseismic location, there are still some shortcomings in terms of location accuracy and real-time performance under complex geological conditions. This often leads to challenges for existing methods in dealing with noise and multipath propagation. Therefore, how to improve the accuracy of microseismic location is a technical problem that urgently needs to be solved. Summary of the Invention

[0005] This application provides a microseismic location method, device, equipment, and storage medium based on the artificial bee colony algorithm. Addressing the problem of locating the source of landslide microseismic signals, the application employs the artificial bee colony algorithm to construct a rock fracture location inversion algorithm model. Using this model, the spatial location and energy of landslide rock fractures are inverted and calculated to obtain the spatiotemporal distribution pattern of the landslide rock fractures. The rationality and accuracy of the proposed method are verified through comparison with traditional algorithms and simulations, providing a basis for the stability assessment of rock landslides.

[0006] Firstly, this application provides a microseismic location method based on an artificial bee colony algorithm, comprising:

[0007] Microseismic data is acquired and identified using a long-short time window ratio method to obtain microseismic data of microseismic events; wherein the microseismic data is acquired by at least two sensors;

[0008] The microseismic data of the microseismic event is denoised to obtain the denoised signal of the microseismic event;

[0009] First arrival picking is performed on the denoised signal of the microseismic event to obtain the arrival time sequence of the microseismic event;

[0010] Using cosine similarity as the objective function and the coordinates of each sensor as the initial parameter, multiple initial points are randomly selected, and inversion begins based on a set number of iterations. The time difference sequence from each initial point to each sensor is calculated as the arrival time sequence of the inverted waveform. If the cosine similarity of the arrival time sequence of the inverted waveform is higher than the value before the iteration, the arrival time sequence of the current inverted waveform is used to replace the arrival time sequence of the previous inverted waveform. If the cosine similarity of the arrival time sequence of the inverted waveform is not higher than the value before the iteration, the search continues until the set number of iterations is reached. The initial point corresponding to the arrival time sequence of the final inverted waveform is taken as the optimal source point.

[0011] In one possible design, the microseismic data is identified using the long-short time window ratio method, and the methods for obtaining microseismic data of microseismic events include:

[0012] The window threshold for microseismic data is calculated using the following formula:

[0013]

[0014] In the formula, R represents the window threshold, STA(i) and LTA(i) represent the i-th short window and long window, respectively, and W lta and W sta Let A(i) represent the lengths of the long and short time windows, respectively, and let A(i) represent the amplitude of the i-th signal in the microseismic data.

[0015] Based on a set threshold, when the window threshold of the microseismic data exceeds the set threshold, the microseismic data is identified as microseismic data of a microseismic event.

[0016] In one possible design, the arrival sequence of the microseismic event is obtained by first-arrival picking based on the denoised signal of the microseismic event, including:

[0017] Based on the denoised signal of the microseismic event, AIC(k) is calculated using the following formula:

[0018] AIC(k)=k*lg{var(x[1,k])}-(Lk-1)*lg{var(x[k+1],L)}

[0019] In the formula, AIC(k) represents the AIC value of the sample point at position k, k represents the sample in the time window, x represents the denoised signal of the microseismic event, L represents the sequence length of the denoised signal of the microseismic event, and var represents the variance;

[0020] The characteristic function of the microseismic data is set as follows:

[0021] CF(i) = X(i) 2 -X(i-1)X(i+1)

[0022] In the formula, X is the raw data from the detector; CF(i) represents the characteristic function of the microseismic signal, X(i) represents the value of the i-th sampling point in the raw data, and X(i-1) is the value of the previous sampling point;

[0023] By utilizing the correlation of data over a continuous time period, the abrupt change point of the data sequence is determined to be the first arrival point of the microwave.

[0024] In one possible design, cosine similarity is used as the objective function, and the coordinates of each sensor are used as initial parameters. Inversion begins based on a set number of iterations. The time difference sequence from each initial point to each sensor is calculated as the arrival sequence of the inverted waveform. If the cosine similarity of the arrival sequence of the inverted waveform is higher than the value before the iteration, the arrival sequence of the current inverted waveform replaces the arrival sequence of the previous inverted waveform. If the cosine similarity of the arrival sequence of the inverted waveform is not higher than the value before the iteration, the search continues until the set number of iterations is reached. The initial point corresponding to the final arrival sequence of the inverted waveform is taken as the optimal source point, including:

[0025] Set the number of hired bees to N and the maximum number of iterations to Limit. Represent each possible solution set as x. i =(x i1 ,x i2 ,x i3 …,x id), where the subscripts i = 1, 2, ..., N represent the location of the nectar source, and x i1 ,x i2 ,x i3 …,x id This represents the possible solutions for each dimension, with each possible solution being the time sequence of the inverted waveform; each hired bee randomly generates and updates the nectar source;

[0026] v ij =x ij +γ i (x ij -x δj )

[0027] In the formula, v ij x represents the value of the updated honey source at the j-th latitude. ij γ represents the component of the current nectar source in the j-th dimension. i Let x represent a random number in the range [-1, 1]. δj Let δ represent the component of another randomly selected nectar source in the j-th dimension, where δ is a random number in {1,2,…N};

[0028] The new nectar source locations are evaluated using a fitness function, which is determined based on an objective function.

[0029]

[0030] In the formula, F(x) i ) represents x i fitness, f i The objective function is represented by `abs`, which represents the function to remove the absolute value.

[0031] After completing their harvesting, the hired bees will share the location of the nectar source with the observation bees, who will then select the location using a probability equation.

[0032] When no optimal solution is found and all observer bees have completed their search, the mercenary bee will abandon the nectar source and become a scout bee to search for a new nectar source. The new honey source is randomly generated.

[0033] Return to the hired bee search process and start repeating the loop until the stopping condition is met. Select the optimal nectar source as the optimal solution and obtain the best seismic source point.

[0034] In one possible design, the objective function is expressed as:

[0035]

[0036] In the formula, A represents the arrival time sequence of the microseismic event; B represents the arrival time sequence of the inverted waveform; and C represents the cosine similarity.

[0037] In one possible design, the fitness function is expressed as:

[0038]

[0039] In the formula, F(x) i ) represents x i fitness, f i denoted as the objective function, and abs represents the function that takes the absolute value.

[0040] In one possible design, each hired bee randomly generates and updates its nectar source using the following formula;

[0041] v ij =x ij +γ i (x ij -x δj )

[0042] In the formula, v ij x represents the value of the updated honey source at the j-th latitude. ij γ represents the component of the current nectar source in the j-th dimension. i Let x represent a random number in the range [-1, 1]. δj Let δ represent the component of another randomly selected nectar source in the j-th dimension, where δ is a random number in {1,2,…N}.

[0043] Bees are observed to select nectar source locations using the following probability equation:

[0044]

[0045] In the formula, P i Let F(x) represent the probability equation. n ) represents the fitness of possible solutions in the nth dimension;

[0046] New honey sources are randomly generated according to the following formula:

[0047]

[0048] In the formula, Let x represent the i-th solution of the newly generated honey source in the j-th dimension, and rand represent a random number uniformly distributed within the interval. maxj x represents the upper limit of the allowed values ​​in the j-th dimension. minj This represents the lower limit of the allowed values ​​in the j-th dimension.

[0049] Secondly, this application provides a microseismic positioning device based on an artificial bee colony algorithm, comprising:

[0050] The microseismic data identification module is configured to acquire microseismic data and identify the microseismic data using a long-short time window ratio method to obtain microseismic data of microseismic events; wherein the microseismic data is acquired by at least two sensors;

[0051] The microseismic data denoising module is configured to perform denoising processing on the microseismic data of the microseismic event to obtain the denoised signal of the microseismic event.

[0052] The microseismic data acquisition module is configured to acquire the first arrival based on the denoised signal of the microseismic event to obtain the arrival time sequence of the microseismic event;

[0053] The microseismic data localization module is configured to use cosine similarity as the objective function, the coordinates of each sensor as the initial parameter, randomly select multiple initial points, and start inversion based on a set number of iterations. It calculates the time difference sequence from each initial point to each sensor as the arrival time sequence of the inverted waveform. If the cosine similarity of the arrival time sequence of the inverted waveform is higher than the value before the iteration, the arrival time sequence of the current inverted waveform is used to replace the arrival time sequence of the previous inverted waveform. If the cosine similarity of the arrival time sequence of the inverted waveform is not higher than the value before the iteration, the search continues until the set number of iterations is reached. The initial point corresponding to the arrival time sequence of the final inverted waveform is taken as the optimal source point.

[0054] Thirdly, embodiments of this application provide an electronic device, including: at least one processor and a memory; the memory stores computer execution instructions; the at least one processor executes the computer execution instructions stored in the memory, causing the at least one processor to perform the microseismic location method based on the artificial bee colony algorithm as described in the first aspect and various possible designs of the first aspect.

[0055] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions. When a processor executes the computer-executable instructions, it implements the micro-seismic location method based on the artificial bee colony algorithm as described in the first aspect and various possible designs of the first aspect.

[0056] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the microseismic location method based on the artificial bee colony algorithm as described in the first aspect and various possible designs of the first aspect.

[0057] The beneficial effects of this application are as follows:

[0058] This application employs an artificial bee colony inversion algorithm to locate microseismic signals on rocky bank slopes. Utilizing a layered velocity model and cosine similarity function, the simulated location results demonstrate higher accuracy compared to existing simulated annealing inversion methods. Furthermore, based on the landslide location results, the distribution pattern of landslide source locations is explained. By comparing the source location of the landslide's microseismic monitoring data with the original geological data, a high correlation is found between the two, further demonstrating the rationality of the proposed method. Attached Figure Description

[0059] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0060] Figure 1 A flowchart illustrating a microseismic location method based on an artificial bee colony algorithm provided in this application embodiment;

[0061] Figure 2 Example diagram of microseismic events provided in the embodiments of this application;

[0062] Figure 3 A flowchart for noise reduction processing of microseismic data of microseismic events provided in an embodiment of this application;

[0063] Figure 4 This is an example diagram of the initial arrival pickup event provided in an embodiment of this application;

[0064] Figure 5 Elevation map of the landslide monitoring area provided in the embodiments of this application;

[0065] Figure 6 A schematic diagram of the layered velocity model provided in the embodiments of this application;

[0066] Figure 7 This is a schematic diagram of the location of the displacement monitoring equipment for the Jiuxianping landslide provided in an embodiment of this application;

[0067] Figure 8 This is a schematic diagram showing the locations of the equipment points and the seismic source points provided in the embodiments of this application;

[0068] Figure 9 This is a schematic diagram of the location point search provided in an embodiment of this application;

[0069] Figure 10 The epicenter location map for May 23 to July 10 provided for the embodiments of this application;

[0070] Figure 11 Earthquake source location map from July 10 to September 10, 2024, provided for embodiments of this application;

[0071] Figure 12The epicenter location map for September 23, 2024 to December 10, 2024, provided for the embodiments of this application;

[0072] Figure 13 This is a schematic diagram of a microseismic positioning device based on the artificial bee colony algorithm, provided in an embodiment of this application.

[0073] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0074] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0075] The collection, storage, use, processing, transmission, provision, and disclosure of information such as financial data, user data, or medical image data involved in the technical solution of this application all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0076] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0077] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0078] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0079] Example 1:

[0080] This application provides a microseismic location method based on an artificial bee colony algorithm. For example... Figure 1As shown, the microseismic location method based on the artificial bee colony algorithm includes the following steps S10-S40.

[0081] S10: Acquire microseismic data and identify the microseismic data using the long-short time window ratio method to obtain microseismic data of microseismic events; wherein, the microseismic data is acquired by at least two sensors.

[0082] In this embodiment, a bank slope microseismic monitoring system is used to monitor microseismic data. This system is a high-precision monitoring method for monitoring bank slope stability and identifying potential landslide risks. The system acquires microseismic signals by deploying sensors and analyzes and issues early warnings for microseismic events through data acquisition, transmission, and reception. The entire process includes three key stages: sensor deployment, acquisition, and transmission. The rational design and optimization of each stage directly affect the stability and data quality of the monitoring system. These three stages are described in detail below.

[0083] The first step in microseismic monitoring is the selection and deployment of sensors, which directly affects data quality and source location accuracy. Commonly used sensors include accelerometers, velocities, and detectors; appropriate sensitivity and frequency bands can be selected based on monitoring requirements. Sensors can be deployed on the ground surface or in boreholes. Borehole deployment can reduce environmental noise and improve the signal-to-noise ratio of microseismic signals. Simultaneously, sensors require calibration and adjustment after installation to ensure the accuracy of measurement data, and regular maintenance is necessary to prevent drift.

[0084] The array configuration determines the detection capability and location accuracy of microseismic events. Common methods include linear arrays, grid arrays, and array deployments. When deploying, it is necessary to comprehensively consider terrain conditions, the size of the monitoring area, the distribution characteristics of seismic sources, and the target frequency band to ensure that the sensor spacing can cover the entire monitoring area while meeting the requirements for seismic source location. A reasonable array configuration can effectively reduce blind spots, improve data quality, and provide high-precision data support for subsequent microseismic event identification and early warning.

[0085] Data acquisition is the core of microseismic monitoring, determining the integrity and availability of the signal. Acquisition equipment must possess characteristics such as high sampling rate, low noise, and high dynamic range to ensure the capture of sufficiently fine seismic waveforms. Before acquisition, the signal is typically pre-amplified to enhance weak vibrations and filtered to remove environmental noise. Furthermore, the acquisition system requires high-precision time synchronization, often employing GPS timing or a highly stable clock to ensure time consistency of data from different sensors, providing accurate time information for seismic source location.

[0086] Data acquisition methods can be divided into continuous acquisition and triggered acquisition. Continuous acquisition is suitable for all-time monitoring, but it has a high storage pressure; triggered acquisition records valid events based on set thresholds, improving storage efficiency. The acquired data is usually stored in field devices and periodically transmitted to the monitoring center. To ensure data quality, the system also needs to have real-time monitoring and anomaly detection functions to promptly detect sensor failures or data anomalies and avoid monitoring blind spots.

[0087] Data transmission is a crucial link connecting on-site data acquisition and remote analysis in microseismic monitoring systems, requiring assurance of data real-time performance, integrity, and stability. Transmission methods primarily include wired and wireless. Wired methods, such as fiber optic communication, offer advantages like high bandwidth and low latency, but are more expensive to deploy and suitable for long-term, fixed monitoring. Wireless methods, including Wi-Fi, 4G / 5G, and satellite communication, are suitable for remote areas or complex terrain, but may be affected by signal interference, necessitating optimized transmission protocols to improve data stability.

[0088] To reduce bandwidth consumption and storage pressure, data is typically compressed before transmission, such as through wavelet transform or data redundancy removal. Some systems employ edge computing technology, performing preliminary data analysis at on-site terminals and transmitting only key event information to improve transmission efficiency. Furthermore, data transmission systems must possess functions such as reconnection after disconnection, error correction, and data backup to ensure stable operation even in harsh environments, guaranteeing the continuity and reliability of monitoring data.

[0089] Because the sampling frequency is 1000Hz and the data is acquired in real time, the amount of data is relatively large. Traditional manual identification and sample extraction would consume a lot of time. Therefore, in this embodiment, the long-short window ratio method is used to identify microseismic data and obtain microseismic data of microseismic events. The long-short window ratio method sets a long time window and a short time window. The windows move with the time series, and the signal amplitude within the long time window and the short time window are averaged simultaneously. When the ratio of the two exceeds a set threshold, it is determined that an event has occurred.

[0090] In some embodiments, the window threshold for microseismic data is calculated using the following formula:

[0091]

[0092] In the formula, R represents the window threshold, STA(i) and LTA(i) represent the i-th short window and long window, respectively, and W lta and W sta Let A(i) represent the lengths of the long and short time windows, respectively, and let A(i) represent the amplitude of the i-th signal in the microseismic data.

[0093] Based on a set threshold, when the window threshold of the microseismic data exceeds the set threshold, the microseismic data is identified as microseismic data of a microseismic event.

[0094] A long time window measures the intensity of environmental noise, while a short time window reflects sensitivity to event signals. Therefore, the long time window should not be set too long, and the short time window should not be set too short. The threshold value also reflects sensitivity to events; the lower the threshold, the more sensitive the event identification, but this can lead to false identification. Therefore, the surrounding environment should be considered when setting the threshold. In an exemplary embodiment, the long and short time windows are set to lengths of 30 seconds and 0.5 seconds, respectively, and the threshold is set to 2. Figure 2 The figure shown is an example of a microseismic event provided in the embodiments of this application. It shows the same event detected by different sensors. A total of 153 microseismic events were detected from May 23, 2024 to December 10, 2024, and all sensors received the data.

[0095] S20: Perform noise reduction processing on the microseismic data of the microseismic event to obtain the noise-reduced signal of the microseismic event.

[0096] In some embodiments, such as Figure 3 The diagram shown is a flowchart of the noise reduction process for microseismic data of microseismic events provided in an embodiment of this application. The process of noise reduction for microseismic data of microseismic events to obtain a noise-reduced signal specifically includes the following steps:

[0097] Step S201: Perform CEEMDAN decomposition on the signal to obtain m intrinsic mode components, arranged from high frequency to low frequency as M = {imf1, imf2, imf3, ..., imf...} k ,...,imf m}(k<m);

[0098] Step S202: Calculate the energy entropy of each component, and find the right-symmetric point of the local minimum, which is the boundary point imf of the signal set M. k Discarding the low-frequency components and retaining the main high-frequency signal, denoted as Q = {imf1, imf2, imf3, ..., imf...} k};

[0099] Step S203: Retain the main high-frequency signal component Q, and reconstruct the aggregate x. i (t) is used as the input to the SSA denoising algorithm, and the embedding window length is selected as W. l =20.

[0100] Step S204: Transfer signal x i (t) is centered, and the signal hysteresis is arranged to form a Hankel matrix, denoted as H. L×H .

[0101] Step S205: Perform singular value decomposition on the trajectory matrix, and convert H... L×HDecompose into U∑V T Calculate the trajectory covariance matrix K = HH in the form of T The eigenvalues ​​of K are obtained by eigenvalue decomposition [λ1,λ2,...,λ]. L ] and the corresponding feature vectors [U1, U2, ..., U L ].

[0102] Step S206: Divide the L components of the trajectory matrix into c disjoint groups, i.e., H = H l1 +...+H lc Calculate the weight of each component in the original sequence segment according to the previous formula, and select components to recombine each component based on the magnitude of the singular values.

[0103] Step S207: Calculate the cross-correlation coefficients of each component waveform, select components with coefficients greater than 0.8 to reconstruct the signal, and perform secondary filtering. The formula for calculating the cross-correlation coefficients of each component waveform is:

[0104]

[0105] In the formula:

[0106] x(t) is the amplitude of the first waveform;

[0107] y(t) is the amplitude of the second waveform;

[0108] and These are the average amplitudes of the two waveforms over the time series.

[0109] The reconstructed components are determined by calculating the cross-correlation coefficients of the waveforms after singular spectral decomposition. When S xy The closer the value is to 1, the higher the similarity between the two waveforms. The threshold is set to 0.8 to reconstruct the signal and finally obtain the denoised signal.

[0110] S30: First arrival picking is performed on the denoised signal based on microseismic events to obtain the arrival time sequence of microseismic events.

[0111] In some embodiments, the arrival sequence of microseismic events is acquired based on the Akaike Information Criterion (AIC). AIC is typically used to determine the boundary between two distinct stationary sequences of seismic waves. Autoregressive (AR) techniques used for phase arrival detection assume that the seismic record exists in two distinct steady states before and after the arrival of the phase. For seismic data, AIC is defined as:

[0112] AIC(k)=k*lg{var(x[1,k])}-(Lk-1)*lg{var(x[k+1],L)}

[0113] In the formula, AIC(k) represents, k represents the sample in the time window, x represents the denoised signal of the microseismic event, L represents the sequence length of the denoised signal of the microseismic event, and var represents.

[0114] The local minimum of the AIC corresponds to each microseismic event. If the time window is appropriate, the AIC selector can accurately find the wave arrival time. The time corresponding to the local minimum in the AIC is the time of the first arrival of the microseismic wave. The AIC process was explained using data from a three-component geophone.

[0115] In this embodiment, the characteristic function (CF) of microseismic data is defined as:

[0116] CF(i) = X(i) 2 -X(i-1)X(i+1)

[0117] In the formula, X is the raw data from the detector; CF(i) represents X(i), X(i-1), and X(i-1) represent X and X(i-1), respectively.

[0118] In microseismic event identification, the abrupt changes in the signal are first enhanced using a feature function (CF) to help pinpoint the time interval where the event may occur. The CF function is constructed based on local structural changes in the original waveform data, highlighting the inflection points in the signal where background noise transitions to seismic waves. The result is typically a noticeable peak near the event's origin. Utilizing this property, the CF curve of the microseismic signal can be calculated first, and by detecting the peak location, the possible time range of the seismic event can be preliminarily determined.

[0119] After obtaining the possible event intervals, the Akaike Information Criterion (AIC) method is further employed to perform sliding segment analysis on the signal. Specifically, at each time point, the signal is divided into two segments, the variance of each segment is calculated, and an AIC cost function is constructed. The minimum value of the AIC curve typically corresponds to the location where the signal's statistical characteristics abruptly change, i.e., the boundary between background noise and the vibration waveform. By finding the local minimum value of the AIC curve within a window near the CF abrupt change location, the arrival time of the event can be accurately determined. This "coarse-to-fine" strategy combines the abrupt change enhancement capability of CF with the statistical discriminative capability of AIC, achieving high-precision microseismic event identification.

[0120] By utilizing the correlation of data over a continuous time period, the abrupt change point of the data sequence is determined to be the first arrival point of the microwave.

[0121] like Figure 4As shown, the AIC (Akaike Information Criterion) method is used to identify the first arrival of a microseismic event. The spikes represent energy release, a typical characteristic of microseismic events. The extreme points calculated using the AIC method are the first arrival points of the P-wave. Simultaneously, by aligning the times of the six microseismometers, the arrival sequence of the event is obtained.

[0122] S40: Using cosine similarity as the objective function and the coordinates of each sensor as the initial parameters, multiple initial points are randomly selected, and inversion begins based on the set number of iterations. The time difference sequence from each initial point to each sensor is calculated as the arrival time sequence of the inverted waveform. If the cosine similarity of the arrival time sequence of the inverted waveform is higher than the value before the iteration, the arrival time sequence of the current inverted waveform is used to replace the arrival time sequence of the previous inverted waveform. If the cosine similarity of the arrival time sequence of the inverted waveform is not higher than the value before the iteration, the search continues until the set number of iterations is reached. The initial point corresponding to the arrival time sequence of the final inverted waveform is taken as the optimal source point.

[0123] Step S40 is the process of inverting the arrival sequence of microseismic events obtained from processing the microseismic data in steps S10-S30 based on the artificial bee colony algorithm to determine the optimal seismic source point.

[0124] The fundamental principle of the Artificial Bee Colony (ABC) algorithm is that bees perform various activities according to their assigned roles, finding the optimal solution to a problem by sharing and exchanging information. This algorithm employs a heuristic search strategy in both local and global searches. It boasts advantages such as strong optimal solution discovery capability, positive feedback, fast convergence speed, strong robustness, and ease of integration with other methods. The artificial bee colony in the ABC algorithm includes hired bees, observer bees, and scout bees, which exhibit three basic behaviors: searching for nectar sources, utilizing nectar sources, and abandoning nectar sources. The location of a nectar source represents a potential solution to the problem, and the amount of honey in the nectar source corresponds to the fitness of the relevant solution. In the algorithm, hired bees are responsible for the global search, observer bees for the local search, and hired bees that are using up nectar sources become scout bees, searching for another nectar source. The main processing steps are as follows:

[0125] Step S401: After initialization, set the control parameters and solution range to their default values, set the number of hired bees to N, and set the maximum number of iterations to Limit. Assume each possible solution x... i =(x i1 ,x i2 ,x i3 …,x id ) is a d-dimensional vector, where d is the number of optimization parameters, and i = 1, 2, ..., N. The location of the nectar source represents a possible solution in the algorithm; therefore, each hired bee randomly generates and updates the nectar source through the equation.

[0126] vij =x ij +γ i (x ij -x δj )

[0127] In the formula, v ij x represents the value of the updated honey source at the j-th latitude. ij γ represents the component of the current nectar source in the j-th dimension. i Let x represent a random number in the range [-1, 1]. δj Let δ represent the component of another randomly selected nectar source in the j-th dimension, where δ is a random number in {1,2,…N};

[0128] As the search time increases, the neighborhood range gradually shrinks, which helps to reduce computation time and improve the accuracy of the algorithm.

[0129] Step S402: Evaluate the obtained new nectar source locations using a fitness function. After comparing with the old locations, only the better locations are retained. The fitness function is expressed as follows:

[0130]

[0131] In the formula, F(x) i ) represents x i fitness, f i denoted as the objective function, and abs represents the function that takes the absolute value.

[0132] Step S403: After completing their foraging, the hired bees will share the location of the nectar source with the observation bees. The observation bees determine this location using the following probability equation P. i Select the honey source location:

[0133]

[0134] In the formula, P i Let F(x) represent the probability equation. n ) represents the fitness of possible solutions in the nth dimension;

[0135] Observe the bees searching for new nectar sources around the nectar source and select it using the methods described in the steps.

[0136] Step S404: When no optimal solution is found and all observer bees have completed their search, the hired bees will abandon the nectar source and become scout bees to search for new nectar sources. New honey sources are randomly generated according to the following formula:

[0137]

[0138] In the formula, Let x represent the i-th solution of the newly generated honey source in the j-th dimension, and rand represent a random number uniformly distributed within the interval. maxj x represents the upper limit of the allowed values ​​in the j-th dimension. minj This represents the lower limit of the allowed values ​​in the j-th dimension.

[0139] Step S405: Return to the hired bee search process and begin repeating the loop until the stopping condition is met. Select the best nectar source as the optimal solution for the model.

[0140] In some embodiments, when this microseismic location method based on the artificial bee colony algorithm is applied to landslide monitoring, step S40 can be implemented through the following steps:

[0141] S410, Coordinate system establishment and elevation extraction.

[0142] First, the coordinate system of the study area needs to be determined, requiring the elevation data on this map to be converted into an elevation matrix. Geographic Information System (GIS) software is used to export the elevation matrix data. Then, Python is used to plot the landslides, creating a 3D topographic map using Python libraries (such as Matplotlib). Figure 5 As shown, the elevation map of the landslide monitoring area can more intuitively display the landslide area. The visualization of the 3D topographic map not only clarifies the landslide occurrence area but also provides fundamental data support for simulating the propagation of seismic waves in the region.

[0143] S420. Establish a layered velocity model for landslides.

[0144] After acquiring elevation data and constructing a 3D terrain, a layered velocity model needs to be established to more realistically simulate the propagation of seismic waves in underground structures. For example... Figure 6As shown, a layered velocity model is a model used to describe the variation of velocity in the subsurface medium with depth. In seismology, such models can be used to understand the propagation velocity of seismic waves in different strata. Typically, this model divides the Earth's crust into several layers with different seismic wave velocities; the velocity in each layer can be constant or gradually varying with depth. DEM (Digital Elevation Model) data is commonly used to represent the elevation information of the Earth's surface. When analyzing microseismic records, the velocity structure below the surface is simulated by shifting the DEM data downwards. This means that each elevation point not only represents the actual height of the surface but can also estimate the structure and thickness of the layers below by extending downwards. Using this model, the propagation path of seismic waves generated by microseismic events in the subsurface structure can be analyzed. This includes the propagation velocity and direction of the waveform; by simulating seismic wave propagation, the tangent points between the source and each stratum can be determined. In a layered velocity model, the different velocities in each layer affect the travel time calculation. The velocity of the seismic wave in each layer determines its velocity through that layer, thus affecting the total time of arrival. The establishment of this layered velocity model provides crucial support for subsequent analysis of wave propagation paths in microseismic events, making travel time calculations more physically realistic.

[0145] S430. Establishing the objective function.

[0146] After establishing the velocity model, the next crucial step is source location. In microseismic monitoring, seismic waveform data received from different seismic stations are first processed into time series. Each series represents the amplitude of the seismic wave at different time points. To estimate the source location, cosine similarity between the time series is calculated. Cosine similarity assesses the similarity between two series by measuring the cosine of the angle between them, with values ​​ranging from -1 to 1. By comparing the series recorded at different stations, it is determined which waveform records show a high degree of similarity, reflecting that they may originate from the same source. This objective function provides an evaluation criterion for the subsequent search algorithm.

[0147]

[0148] In the formula, A represents the arrival time sequence of the microseismic event; B represents the arrival time sequence of the inverted waveform; and C represents the cosine similarity.

[0149] S440, artificial bee colony inversion positioning.

[0150] With a clearly defined objective function, the artificial bee colony algorithm is used to invert and search for the earthquake source location. This algorithm is a swarm intelligence optimization algorithm that simulates the foraging behavior of bees and is suitable for global optimal search in high-dimensional parameter spaces.

[0151] Based on the preceding steps S401-S406, cosine similarity is used as the objective function to invert and search for the location of the seismic source. Sensor coordinates are used as initial parameters, and cosine similarity is calculated for X, Y, and Z coordinates. 100 initial points are randomly selected within a specified range to begin the inversion. The perturbation range for the X and Y axes is (0, 150), and the perturbation range for the Z axis is (0, 400). The maximum number of iterations is 10. The time difference sequence from each point to each sensor is calculated. If the cosine similarity of the sequence is higher than the value before the iteration, it is replaced; otherwise, the search continues. The minimum value calculated by C is taken as the optimal seismic source.

[0152] Based on the similarity of arrival time sequences from various sensors, combined with the geographical location of each site and estimates of seismic wave propagation velocity, an artificial bee colony localization algorithm is used to invert the epicenter location. The direction of the epicenter is determined using the arrival time data with the highest similarity, and then the approximate location of the epicenter is calculated based on the differences in seismic wave arrival times at different sites. This algorithm, by combining waveform data similarity information with three-dimensional spatial location, can effectively invert the most probable epicenter point, providing a scientific basis for subsequent geological hazard analysis and risk assessment.

[0153] From establishing the coordinate system and extracting elevation data (S410), to velocity modeling of the underground structure in the landslide area (S420), then to establishing the cosine similarity objective function (S430), and finally using the artificial bee colony algorithm to complete the source location inversion (S440), this process forms a complete and closed-loop microseismic event analysis system. By efficiently combining geospatial data, geophysical models, and intelligent optimization algorithms, accurate source inversion results and propagation mechanism analysis can be provided for geological hazards such as landslides.

[0154] Example 2:

[0155] This application provides an application example of the microseismic location method based on the artificial bee colony algorithm as described in Example 1. The method is applied to the Jiuxianping landslide. The Jiuxianping landslide is located in a tectonic denudation low mountain and hilly terrain, mainly characterized by steep cliffs, slopes, gullies, and terraces. The front of the landslide features steep cliffs and slopes, with the Yangtze River flowing to its toe. The overall topography is higher on the NW side and lower on the SE side (Yangtze River). The overall elevation ranges from 95 to 545 meters, with the highest point at the top of the Jiuxianping landslide on the NW side at 545 meters and the lowest point at the SE side (Yangtze River), resulting in a maximum relative elevation difference of 450 meters. The overall slope of the landslide area is steep at the back and gentle at the front, with a slope angle of 0° to 19°.

[0156] The microseismic data used to implement the method proposed in this application are derived from deployed bank slope microseismic monitoring systems and existing GNSS monitoring networks.

[0157] As shown in Table 1, the microseismic equipment used in the bank slope microseismic monitoring system is an IGU-16HR three-component seismograph with a sensor frequency of 1000 Hz. Choosing the appropriate equipment type is crucial for accurately capturing ground motion. The storage type is DLD format. With three channels, the device can simultaneously record seismic signals in three directions (horizontal X, horizontal Y, and vertical Z), providing more comprehensive ground motion information. The sampling rate is set to 1 ms, meaning sampling once per millisecond; a high sampling rate allows for more detailed recording of subtle vibration changes. The low-cut filter is set to "DC removed," meaning the DC component in the signal is filtered out. All channels are set to a gain of 24 dB. The gain setting affects the signal amplification; appropriate gain ensures the signal is as clear as possible without overloading. These parameter settings ensure that the device accurately records microseismic activity with high data quality, supporting subsequent analysis and evaluation.

[0158] Table 1 Microseismic Equipment Parameters

[0159]

[0160]

[0161] Before conducting field monitoring with the microseismometer, site testing of the new microseismic equipment was completed. This testing covered the processes of equipment installation, functional verification, data acquisition, and analysis. The equipment was successfully installed and passed field simulation testing, and preliminary data has been analyzed and confirmed to be valid on-site, ensuring that the equipment performance meets the expected requirements.

[0162] The equipment array needs to meet three main requirements. First, it must cover key areas. The equipment should be placed in key areas of the Jiuxianping landslide, including the rear, middle, and front edges of the landslide, so as to capture ground vibration information at different locations. Second, the terrain must be taken into consideration. Different terrain undulations directly affect the accuracy of monitoring data. The deployment location should consider key monitoring areas and should be located as far away from roads as possible, as vehicle noise has a significant impact on monitoring. Finally, the equipment should be easy to maintain and retrieve data.

[0163] As shown in Table 2, the microseismic monitoring of the Jiuxianping landslide was conducted using six microseismic devices that provided full coverage of the main landslide body and were located far from major traffic arteries. Among them, monitoring points E and F were located in an open area at the leading edge of the landslide, which enabled them to monitor microseismic signals effectively.

[0164] Table 2 Equipment Location Coordinates

[0165] Points longitude latitude Elevation A 108.78354824 30.93486331 264 B 108.78510863 30.93773383 307 C 108.78660760 30.93985286 299 D 108.78642138 30.93619260 251 E 108.78839177 30.93378061 177 F 108.78932727 30.93691187 209

[0166] The rainfall at the Jiuxianping landslide site is concentrated between May and October, accounting for 79% of the annual precipitation, while the main period of water level change is from May to November. Therefore, the monitoring period is mainly from May to December 2024, with data collected and new equipment replaced in three time periods: the first period is from May to July 2024; the second period is from July to September 2024; and the third period is from September to December 2024.

[0167] The existing GNSS monitoring network basically meets the needs of monitoring rear deposits and ancient landslides. GNSS monitoring points are divided into different cross sections to monitor each landslide area, such as... Figure 7 As shown, section 4-4' was used to monitor the Hejiabao landslide, with four GNSS surface displacement monitoring points deployed at elevations of 334m, 300m, 245m, and 195m. Sections 1-1', 2-2', and 3-3' were used to monitor the rear accumulation landslide and the Jiuxianpinggu landslide. Three GNSS surface displacement monitoring points were deployed on the 1-1' monitoring profile, with elevations of 375m, 265m, and 205m. Five GNSS surface displacement monitoring points were deployed on the 2-2' monitoring profile, with elevations of 470m, 430m, 340m, 290m, and 205m. Three GNSS surface displacement monitoring points were deployed on the 3-3' monitoring profile, with elevations of 297m, 245m, and 190m. Two GNSS surface displacement monitoring points were deployed on the 5-5' section for monitoring the Lingyuan landslide, with elevations of 365m and 325m. A total of 18 GNSS monitoring points have been deployed for surface displacement monitoring. One rainfall monitoring point was deployed near the central part of the Jiuxianpinggu landslide.

[0168] The method proposed in this application was verified and analyzed based on the data obtained in the above manner. A uniform geological model was simulated with a model size of 1000m×1000m×1000m. It was assumed that the random source point was located at (547,126,823), and 10 receivers were arranged at the top corner and the edge line. The wave velocity was assumed to be 3500m / s. The arrival time of the seismic wave from the source point to each device was calculated, and the location of the source point was inverted using the artificial bee colony positioning algorithm based on the calculation.

[0169] like Figure 8 The spatial layout of the microseismic monitoring equipment is shown. The three-dimensional coordinate system in the figure is based on the X, Y, and Z axes, representing the horizontal and vertical directions of the equipment installation, respectively. Each blue dot marks the location of a monitoring point, labeled "Equipment Point 1" to "Equipment Point 10." These equipment points are arranged at different coordinate positions, covering a large area to achieve comprehensive monitoring of microseismic activity within that area. The even and widespread distribution of each equipment point ensures accurate acquisition of vibration data across the spatial range.

[0170] The red triangles in the diagram represent the location of the "source point," indicating the starting point or source of the microseismic signal. Through sensors deployed at various equipment points, the system can capture and record microseismic fluctuation data transmitted from the source point in real time, providing fundamental data support for subsequent vibration analysis and monitoring. The light blue area in the diagram represents the coverage area of ​​the entire monitoring area; this is the spatial region that the microseismic monitoring equipment can effectively detect. This simulation helps to visually demonstrate the spatial distribution and data acquisition capabilities of the microseismic monitoring system, showcasing the comprehensiveness and efficiency of the monitoring equipment.

[0171] The initial arrival time series is inverted using the cosine similarity function as the objective function. To obtain more accurate calculation results, the number of iterations is set to 300, and the iteration path is as follows: Figure 9 After 20 iterations, its accuracy reached (546.86, 126.47, 821.90), and after 100 iterations, its positioning result was (546.99, 126.00, 822.99). Yin Qifeng's simulated annealing method, after 132 iterations, had a maximum error of 4.47m, showing a significant advantage over Yin Qifeng's method. Figure 9 As can be seen, the method proposed in this application begins searching for the optimal solution after a random initial value, and its convergence speed is relatively fast. After multiple trials, different initial values ​​will affect the inversion path, but accurate inversion results can still be obtained. Compared with traditional linear inversion methods, the method proposed in this application can obtain the global optimal solution in inversion localization without depending on the choice of initial value.

[0172] Table 3 Detector Coordinates and Time of Arrival

[0173]

[0174]

[0175] Based on Table 3, the method of this application is applied to calculate the location of the seismic source point. Figure 9 This paper illustrates the process by which the proposed method determines the hypocenter in three-dimensional space through iterative search. The initial point (marked in green) represents the preliminary estimated hypocenter location, with coordinates (482.51, 22.30, 877.11). Through global exploration using a bee colony algorithm, the algorithm continuously adjusts the search path, gradually approaching the true hypocenter. The purple trajectory represents the algorithm's search process, showing the path adjustments during multiple iterations and how the bee colony balances local and global searches in the search space. Finally, after multiple iterations, the algorithm converges to the endpoint (marked in red), which is the final calculated hypocenter location, with coordinates (547.00, 126.00, 823.00).

[0176] This convergence process demonstrates that the proposed method, by effectively utilizing global and local search strategies, can accurately estimate the source location of microseismic signals. In microseismic monitoring, accurate source location is crucial for subsequent source analysis and seismic propagation studies. The bee colony algorithm not only overcomes the local optima problem that may exist in traditional methods but also provides efficient source location results in complex geographical and seismic environments. Therefore, the application of the artificial bee colony algorithm in microseismic monitoring effectively improves the accuracy and efficiency of source location, providing reliable technical support for the monitoring and analysis of seismic activity.

[0177] In addition, the method of this application and the simulated annealing localization algorithm were compared. Ten localization points were randomly selected as source points within the simulation range. The cosine similarity function was selected as the objective function for inversion localization. The calculation results of artificial bee colony algorithm (ABC) and simulated annealing (SA) are shown in Table 4 and Table 5.

[0178] Table 4. Simulation Comparison of Positioning Methods

[0179]

[0180]

[0181] Table 5 Comparison of Positioning Method Errors

[0182]

[0183] Comparing the two methods, it can be seen that the method of this application has a significant advantage. The number of iterations selected in the calculation process is determined according to the calculation time of both methods. The number of iterations with the same calculation time is used to calculate the error of the two algorithms. The average error percentage of the method of this application is 0.0014%, and the maximum error percentage is 0.0032%. However, the average error percentage of simulated annealing is 6.57%, and the maximum error percentage is 16.65%.

[0184] For applications in landslides, the calculation range is relatively small and requires high accuracy. Therefore, the method in this application has a significant advantage in the application of landslide microseismic location.

[0185] The Jiuxianping landslide is an ancient landslide. Its rear slope subsided by 3 to 4 meters after a torrential rain in 1982, forming a dam. Between 2003 and 2004, torrential rain caused approximately 1 meter of subsidence in the central section of the road, ultimately leading to its abandonment. Within the landslide, there have been instances of sudden groundwater seepage and abrupt changes in the water level of the dam. Obvious striations are visible at the interface between the mudstone and sandstone at the front, indicating sliding activity. The leading edge of the landslide exhibits distinct bulges and significant deformation characteristics. In 2009, heavy rainfall caused significant deformation and cracks at the rear edge of the landslide, and cracks also appeared in the ground of houses in the area.

[0186] In recent years, the impoundment of the Three Gorges Reservoir and rainfall have significantly impacted the landslide, particularly causing severe deformation and damage in the middle and rear sections. This deformation mainly occurs where the terrain slope transitions from gentle to steep, i.e., the middle of the landslide. The leading edge of the landslide exhibits an arched shape, and the area near the riverbank is a steep cliff, frequently experiencing localized collapses. Laterally, the deformation on the right side of the landslide is more pronounced than on the left. With the operation of the Three Gorges Reservoir, the Jiuxianping landslide remains in a state of overall creep deformation.

[0187] Therefore, stability analysis of the Jiuxianping landslide based on microseismic events requires inversion calculation of the earthquake source location to further analyze the slope stability. Previous research has shown that the artificial bee colony algorithm has significant advantages in earthquake source location. We will now analyze data from May 23, 2024 to December 10, 2024 to locate typical microseismic events.

[0188] Figures 10 to 12 The image shows the distribution of microseismic sources in the Jiuxianping landslide area from May 23 to December 10. The locations of the sources are marked with red dots, and their distribution is mainly concentrated in higher terrain areas, especially in the center and upper right of the image. The distribution of source locations is closely related to changes in terrain elevation, indicating that microseismic activity in the landslide area is mainly concentrated in steeper or unstable zones.

[0189] In summary, this application's embodiments detail the process and simulation experiments of the proposed method, compare the error analysis of the simulated annealing-based seismic source location method, and further analyze the accuracy of source location for all microseismic events during the monitoring period of the Jiuxianping landslide from May 23, 2024 to December 10, 2024, by comparing geological data. The analysis of the above process leads to the following conclusions:

[0190] (1) Based on the artificial bee colony algorithm, this application can improve the accuracy and reliability of earthquake source location by virtue of its global search capability, adaptability to complex objective functions, simple implementation, high computational efficiency and good convergence performance.

[0191] (2) Compared with the simulated annealing source inversion and location method, the method of this application has a significant advantage. The number of iterations selected in the calculation process is determined according to the calculation time of both methods. The number of iterations with the same calculation time is used to calculate the error of the two algorithms. The average error percentage of the method of this application is 0.0014%, and the maximum error percentage is 0.0032%. However, the average error percentage of simulated annealing is 6.57%, and the maximum error percentage is 16.65%.

[0192] (3) By locating the seismic source of the Jiuxianping landslide using microseismic monitoring data and comparing it with the original geological data, it was found that the left boundary of the Jiuxianping landslide was significantly deformed, the road cracks continued to widen, causing slight road surface heave, and the cemetery wall, crematorium workshop and surrounding area showed obvious deformation. This data showed a high correlation with the seismic source location results, further proving the rationality of the method.

[0193] Example 3:

[0194] Figure 13 This is a schematic diagram of the structure of a microseismic location device based on the artificial bee colony algorithm provided in an embodiment of this application. This application also provides a microseismic location device based on the artificial bee colony algorithm. Figure 13 As shown, the microseismic location device based on the artificial bee colony algorithm includes:

[0195] The microseismic data identification module 1301 is configured to acquire microseismic data and identify the microseismic data using a long-short time window ratio method to obtain microseismic data of microseismic events; wherein the microseismic data is acquired by at least two sensors;

[0196] The microseismic data denoising module 1302 is configured to perform denoising processing on the microseismic data of the microseismic event to obtain the denoised signal of the microseismic event.

[0197] The microseismic data acquisition module 1303 is configured to acquire the first arrival based on the denoised signal of the microseismic event to obtain the arrival time sequence of the microseismic event;

[0198] The microseismic data localization module 1304 is configured to use cosine similarity as the objective function, the coordinates of each sensor as the initial parameter, randomly select multiple initial points, and start inversion based on a set number of iterations. It calculates the time difference sequence from each initial point to each sensor as the arrival time sequence of the inverted waveform. If the cosine similarity of the arrival time sequence of the inverted waveform is higher than the value before the iteration, the arrival time sequence of the current inverted waveform is used to replace the arrival time sequence of the previous inverted waveform. If the cosine similarity of the arrival time sequence of the inverted waveform is not higher than the value before the iteration, the search continues until the set number of iterations is reached. The initial point corresponding to the arrival time sequence of the final inverted waveform is taken as the optimal source point.

[0199] The microseismic location device based on the artificial bee colony algorithm provided in this application embodiment can be used to execute the technical solution of the microseismic location method based on the artificial bee colony algorithm in the above embodiment. Its implementation principle and technical effect are similar, and will not be repeated here.

[0200] This application provides an electronic device. The electronic device may include a processor and a memory, wherein the processor and the memory can communicate; exemplarily, the processor and the memory communicate via a communication bus.

[0201] The processor executes computer execution instructions stored in memory, causing the processor to perform the scheme in the above embodiments. The processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0202] Communication buses can be Peripheral Component Interconnect (PCI) buses or Extended Industry Standard Architecture (EISA) buses, etc. System buses can be divided into address buses, data buses, control buses, etc. For ease of representation, only one thick line is used in the diagram, but this does not indicate that there is only one bus or one type of bus. Transceivers are used to enable communication between database access devices and other computers (e.g., clients, read-write libraries, and read-only libraries). Memory may include random access memory (RAM) and may also include non-volatile memory.

[0203] The electronic device provided in this application embodiment can be the terminal device described in the above embodiments.

[0204] This application also provides a computer-readable storage medium storing computer instructions. When the computer instructions are executed on a computer, the computer performs the technical solution of the microseismic location method based on the artificial bee colony algorithm described in the above embodiments.

[0205] This application also provides a computer program product, which includes a computer program stored in a computer-readable storage medium. At least one processor can read the computer program from the computer-readable storage medium. When the at least one processor executes the computer program, it can implement the technical solution of the microseismic location method based on the artificial bee colony algorithm in the above embodiments.

[0206] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or modules, and may be electrical, mechanical, or other forms.

[0207] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to implement the solution of this embodiment according to actual needs.

[0208] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The unit composed of the above modules can be implemented in hardware or in the form of hardware plus software functional units.

[0209] The integrated modules described above, implemented as software functional modules, can be stored in a computer-readable storage medium. These software functional modules, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods of the various embodiments of this application.

[0210] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly manifested as being executed by a hardware processor, or executed by a combination of hardware and software modules within the processor.

[0211] The memory may include high-speed RAM, and may also include non-volatile storage (NVM), such as at least one disk storage device, and may also be a USB flash drive, external hard drive, read-only memory, disk or optical disc, etc.

[0212] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, the bus in this application is not limited to a single bus or a single type of bus.

[0213] The aforementioned storage medium can be implemented from any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The storage medium can be any available medium accessible to general-purpose or special-purpose computers.

[0214] An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Alternatively, the storage medium can be an integral part of the processor. The processor and storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and storage medium can exist as discrete components in an electronic control unit or main control device.

[0215] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0216] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A microseismic location method based on artificial bee colony algorithm, characterized in that, include: Microseismic data is acquired and identified using a long-short time window ratio method to obtain microseismic data of microseismic events; wherein the microseismic data is acquired by at least two sensors; The microseismic data of the microseismic event is denoised to obtain the denoised signal of the microseismic event; First arrival picking is performed on the denoised signal of the microseismic event to obtain the arrival time sequence of the microseismic event; Using cosine similarity as the objective function and the coordinates of each sensor as the initial parameter, multiple initial points are randomly selected, and inversion begins based on a set number of iterations. The time difference sequence from each initial point to each sensor is calculated as the arrival time sequence of the inverted waveform. If the cosine similarity of the arrival time sequence of the inverted waveform is higher than the value before the iteration, the arrival time sequence of the current inverted waveform is used to replace the arrival time sequence of the previous inverted waveform. If the cosine similarity of the arrival time sequence of the inverted waveform is not higher than the value before the iteration, the search continues until the set number of iterations is reached. The initial point corresponding to the arrival time sequence of the final inverted waveform is taken as the optimal source point.

2. The method according to claim 1, characterized in that, The microseismic data of microseismic events can be obtained by identifying the microseismic data using the long-short time window ratio method, including: The window threshold for microseismic data is calculated using the following formula: In the formula, R represents the window threshold, STA(i) and LTA(i) represent the i-th short window and long window, respectively, and W lta and W sta Let A(i) represent the lengths of the long and short time windows, respectively, and let A(i) represent the amplitude of the i-th signal in the microseismic data. Based on a set threshold, when the window threshold of the microseismic data exceeds the set threshold, the microseismic data is identified as microseismic data of a microseismic event.

3. The method according to claim 1, characterized in that, First arrival picking is performed on the denoised signal of the microseismic event to obtain the arrival time sequence of the microseismic event, including: Based on the denoised signal of the microseismic event, AIC(k) is calculated using the following formula: AIC(k)=k*lg{var(x[1,k])]-(Lk-1)*lg{var(x[k+1],L)} In the formula, AIC(k) represents the AIC value of the sample point at position k, k represents the sample in the time window, x represents the denoised signal of the microseismic event, L represents the sequence length of the denoised signal of the microseismic event, and var represents the variance; The characteristic function of the microseismic data is defined as follows: CF(i)=X(i) 2 -X(i-1)X(i+1) In the formula, X is the raw data from the detector; CF(i) represents the characteristic function of the microseismic signal, X(i) represents the value of the i-th sampling point in the raw data, and X(i-1) is the value of the previous sampling point; By utilizing the correlation of data over a continuous time period, the abrupt change point of the data sequence is determined to be the first arrival point of the microwave.

4. The method according to any one of claims 1 to 3, characterized in that, Using cosine similarity as the objective function and the coordinates of each sensor as initial parameters, inversion begins based on a set number of iterations. The time difference sequence from each initial point to each sensor is calculated as the arrival time sequence of the inverted waveform. If the cosine similarity of the arrival time sequence of the inverted waveform is higher than the value before the iteration, the arrival time sequence of the current inverted waveform replaces the arrival time sequence of the previous inverted waveform. If the cosine similarity of the arrival time sequence of the inverted waveform is not higher than the value before the iteration, the search continues until the set number of iterations is reached. The initial point corresponding to the final arrival time sequence of the inverted waveform is taken as the optimal source point, including: Set the number of hired bees to N and the maximum number of iterations to Limit. Represent each possible solution set as x. i =(x i1 ,x i2 ,x i3 …,x id ), where the subscripts i = 1, 2, ..., N represent the location of the nectar source, x i1 ,x i2 ,x i3 …,x id This represents the possible solutions for each dimension, with each possible solution being the time sequence of the inverted waveform; each hired bee randomly generates and updates the nectar source; v ij =x ij +γ i (x ij -x δj ) In the formula, v ij x represents the value of the updated honey source at the j-th latitude. ij γ represents the component of the current honey source in the j-th dimension. i Let x represent a random number in the range [-1, 1]. δj Let δ represent the component of another randomly selected nectar source in the j-th dimension, where δ is a random number in {1,2,…N}; The new nectar source locations are evaluated using a fitness function, which is determined based on an objective function. In the formula, F(x) i ) represents x i fitness, f i The objective function is represented by abs, which represents the function that takes the absolute value. After completing their harvesting, the hired bees will share the location of the nectar source with the observation bees, who will then select the location using a probability equation. When no optimal solution is found and all observer bees have completed their search, the mercenary bee will abandon the nectar source and become a scout bee to search for a new nectar source. The new honey source is randomly generated. Return to the hired bee search process and start repeating the loop until the stopping condition is met. Select the optimal nectar source as the optimal solution and obtain the best seismic source point.

5. The method according to claim 4, characterized in that, The objective function is expressed as: In the formula, A represents the arrival time sequence of the microseismic event; B represents the arrival time sequence of the inverted waveform; and C represents the cosine similarity.

6. The method according to claim 4, characterized in that, The fitness function is expressed as follows: In the formula, F(x) i ) represents x i fitness, f i denoted as the objective function, and abs represents the function that takes the absolute value.

7. The method according to claim 4, characterized in that, Each hired bee randomly generates and updates its nectar source using the following formula; v ij =x ij +γ i (x ij -x δj ) In the formula, v ij x represents the value of the updated honey source at the j-th latitude. ij γ represents the component of the current honey source in the j-th dimension. i Let x represent a random number in the range [-1, 1]. δj Let δ represent the component of another randomly selected nectar source in the j-th dimension, where δ is a random number in {1,2,…N}; Bees are observed to select nectar source locations using the following probability equation: In the formula, P i Let F(x) represent the probability equation. n ) represents the fitness of possible solutions in the nth dimension; New honey sources are randomly generated according to the following formula: In the formula, Let x represent the i-th solution of the newly generated honey source in the j-th dimension, and rand represent a random number uniformly distributed within the interval. maxj x represents the upper limit of the allowed values ​​in the j-th dimension. minj This represents the lower limit of the allowed values ​​in the j-th dimension.

8. A microseismic positioning device based on artificial bee colony algorithm, characterized in that, include: The microseismic data identification module is configured to acquire microseismic data and identify the microseismic data using a long-short time window ratio method to obtain microseismic data of microseismic events; wherein the microseismic data is acquired by at least two sensors; The microseismic data denoising module is configured to perform denoising processing on the microseismic data of the microseismic event to obtain the denoised signal of the microseismic event. The microseismic data acquisition module is configured to acquire the first arrival based on the denoised signal of the microseismic event to obtain the arrival time sequence of the microseismic event; The microseismic data localization module is configured to use cosine similarity as the objective function, the coordinates of each sensor as the initial parameter, randomly select multiple initial points, and start inversion based on a set number of iterations. It calculates the time difference sequence from each initial point to each sensor as the arrival time sequence of the inverted waveform. If the cosine similarity of the arrival time sequence of the inverted waveform is higher than the value before the iteration, the arrival time sequence of the current inverted waveform is used to replace the arrival time sequence of the previous inverted waveform. If the cosine similarity of the arrival time sequence of the inverted waveform is not higher than the value before the iteration, the search continues until the set number of iterations is reached. The initial point corresponding to the arrival time sequence of the final inverted waveform is taken as the optimal source point.

9. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-7.

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