Microseismic event detection method and equipment

Abstract

The invention discloses a microseismic event detection method and device, and the method comprises the steps: carrying out the denoising of an original signal, and obtaining a denoised signal; coherence analysis is carried out on the denoised signals, and a similarity matrix is determined; and determining a detection result according to the similarity matrix. Because the original signal acquired by the DAS technology has high spatial density, that is, each small section of the optical fiber can acquire the signal, coherence analysis is carried out on the de-noised original signal, the signals at different positions in the optical fiber can be compared, and the similarity matrix representing the similarity between the signals is generated; signal segments with consistency at multiple positions are identified through coherence analysis by using the high spatial density of DAS data, the segments generally correspond to a microseismic event, and whether the microseismic event occurs or not can be detected, so that useful information in high spatial density data is reserved while data storage and processing burdens are reduced, and the accuracy of data processing is improved. And the detection accuracy and efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of microseismic detection, and in particular to a method and device for detecting microseismic events. Background Art

[0002] Distributed acoustic sensing (DAS) is an optical fiber-based data acquisition technology that is increasingly being used in microseismic detection technology.

[0003] DAS technology tends to generate excessive amounts of data during microseismic detection. A 2.5km-long sensing optical fiber with a gauge length of 10m collects data at a sampling frequency of 4kHz and a block length of 15s, which can generate more than 1TB of data per day. This can easily lead to prominent data storage issues when dealing with earthquake monitoring activities lasting weeks or months.

[0004] To solve the data storage problem, the existing solution is to store only target data, such as waveforms that only contain signals of potential seismic events. However, this solution does not take advantage of the high spatial density of DAS data and may increase the risk of permanent loss of useful information, thereby missing many seismic events that should have been monitored. Summary of the invention

[0005] In order to overcome the deficiencies of the prior art, an object of the present invention is to provide a microseismic event detection method and device, which can utilize the high spatial density of DAS data and avoid the storage and processing problems caused by excessive data volume.

[0006] In order to solve the above problems, the present invention is implemented according to the following scheme:

[0007] A microseismic event detection method is provided, comprising:

[0008] Perform denoising on the original signal to obtain a denoised signal;

[0009] Performing coherence analysis on the denoised signal to determine a similarity matrix;

[0010] A detection result is determined according to the similarity matrix.

[0011] Compared with the prior art, the beneficial effects of a microseismic event detection method of the present invention are as follows: since the original signal collected by the DAS technology has a high spatial density, that is, each small segment of the optical fiber can obtain the signal, by performing coherence analysis on the denoised original signal, the signals at different positions in the optical fiber can be compared, and a similarity matrix representing the similarity between these signals can be generated. Through coherence analysis, the high spatial density of DAS data is utilized to identify signal segments with consistency at multiple positions. These segments usually correspond to microseismic events, and whether they are microseismic events can be detected. This reduces the burden of data storage and processing while retaining useful information in high spatial density data, thereby improving detection accuracy and efficiency.

[0012] Optionally, the denoising the original signal to obtain the denoised signal includes:

[0013] Processing the original signal by using a low-pass filter and down-sampling to obtain a first signal;

[0014] removing a linear trend and an average trend of the first signal to obtain a second signal;

[0015] Based on the maximum amplitude of the second signal, normalize the second signal to obtain a third signal;

[0016] Processing the third signal using a bandpass filter to obtain a fourth signal;

[0017] Processing the fourth signal using a frequency-wavenumber filter to obtain the denoised signal;

[0018] Optionally, performing coherence analysis on the denoised signal to determine a similarity matrix includes:

[0019] Based on the hyperbolic trajectory, a similarity function is determined;

[0020] Determining similarity values ​​of the denoised signal at different time samples according to the similarity function;

[0021] The similarity matrix is ​​determined according to similarity values ​​of the denoised signal at different time samples.

[0022] Optionally, determining the detection result according to the similarity matrix includes:

[0023] Determining a coherent time series corresponding to each time sample according to the similarity matrix;

[0024] Determine whether the coherent time series meets the clustering conditions;

[0025] If the answer is no, it means that there is no potential microseismic event in the time sample corresponding to the coherent time series, and the coherent time series is removed;

[0026] If yes, it means that there is a potential microseismic event in the time sample corresponding to the coherent time series;

[0027] The detection results are determined based on the coherent time series of potential microseismic events.

[0028] Optionally, the coherent time series includes a plurality of sequentially ordered coherent values;

[0029] The determining whether the coherent time series meets the clustering condition includes:

[0030] Determining a detection threshold according to the coherent time series;

[0031] Determining a target coherence value in a coherence time series according to a plurality of sequentially ordered coherence values ​​and the detection threshold;

[0032] According to the target coherence value in the coherence time series and its corresponding sorting position, it is judged whether the coherence time series meets the clustering condition.

[0033] Optionally, the clustering condition includes:

[0034] The minimum number of consecutive samples is used to represent the minimum number of target coherence values ​​in the coherence time series;

[0035] The maximum number of interval samples is used to represent the maximum number of interval coherence values ​​between target coherence values ​​in the coherence time series;

[0036] According to the target coherence value in the coherent time series and its corresponding sorting position, it is judged whether the coherent time series meets the clustering condition, including:

[0037] According to the target coherence values ​​and their corresponding sorting positions in the coherence time series, determining the position distance between each pair of target coherence values, and determining the position distance as the number of interval coherence values;

[0038] When the number of target coherent values ​​in the coherent time series is greater than the minimum number of consecutive samples, and the number of interval coherent values ​​between target coherent values ​​is less than the maximum number of interval samples, the coherent time series meets the clustering condition.

[0039] Optionally, determining the detection result according to the coherent time series of potential microseismic events includes:

[0040] The target coherence value in the coherence time series where potential microseismic events exist is taken as a coherence sample;

[0041] Calculating a sample signal-to-noise ratio for the coherent samples;

[0042] Determining the detection result according to the coherent samples and their sample signal-to-noise ratios;

[0043] Optionally, determining the detection result according to the coherent samples and their sample signal-to-noise ratios includes:

[0044] The coherent samples whose signal-to-noise ratio is greater than the signal-to-noise ratio threshold are taken as target samples;

[0045] According to the target samples and their corresponding time samples, the interval time between each pair of target samples is determined;

[0046] The detection result is determined according to the interval time.

[0047] Optionally, determining the detection result according to the interval time includes:

[0048] When the interval time does not exceed the time threshold, the detection result is that a single microseismic event exists;

[0049] When the interval time exceeds the time threshold, the detection result is that there are multiple microseismic events;

[0050] The time threshold is the maximum duration of a microseismic event.

[0051] A computer device is also provided, comprising a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, at least one program, a code set or an instruction set is loaded and executed by the processor to implement the microseismic event detection method. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 The figure is a flow chart of the microseismic event detection method of the present invention. DETAILED DESCRIPTION

[0053] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0054] When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the attached claims. In the description of the present application, it should be understood that the terms "first", "second", "third", etc. are only used to distinguish similar objects, and do not have to be used to describe a specific order or sequence, nor can they be understood as indicating or implying relative importance. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to the specific circumstances.

[0055] See also Figure 1 As shown, the present invention provides a microseismic event detection method, comprising:

[0056] S1: De-noising the original signal to obtain a de-noised signal, including:

[0057] First, a low-pass filter is used to process the original signal to remove high-frequency components of the original signal to avoid aliasing effects, and then down-sampling is used to process the original signal to obtain a first signal.

[0058] Next, the linear trend and the average trend of the first signal are removed to obtain the second signal. The linear trend of the first signal can be removed by performing linear trend removal processing on the first signal according to the estimated linear trend to obtain a signal obtained by removing the linear trend of the first signal. The specific expression is as follows:

[0059] x linear_removed (t) = x(t) - (at + b)

[0060] Among them, x linear_removed (t) is the signal with the linear trend removed from the first signal at time t, and x(t) is the first signal at time t. (at+b) is the estimated linear trend, which is the linear drift that changes with time. Long-term drift or linear drift of the instrument can be eliminated according to the following formula:

[0061]

[0062] After removing the linear trend of the first signal, the average trend of the first signal is removed. Before removing the average trend, the mean of the first signal is calculated. The calculation formula is as follows:

[0063]

[0064] Among them, μ is the mean of the first signal, N is the total number of sampling points of the first signal, and x(t) is the first signal at time t. Finally, the first signal is subtracted from its mean to obtain the signal with the average trend removed. Its expression is as follows:

[0065] x mean_removed (t) = x linear_removed (t)-μ

[0066] Among them, x mean_removed (t) is the signal (second signal) obtained by removing the linear trend from the first signal at time t, x linear_removed (t) is the signal obtained by removing the linear trend of the first signal at time t, and μ is the mean of the first signal.

[0067] Next, based on the maximum amplitude of the second signal, the second signal is normalized to obtain a third signal, that is, the third signal is obtained by normalizing the second signal by the corresponding maximum amplitude of the second signal to reduce the influence of geometric expansion, receiver coupling and nonlinear effects on the signal.

[0068] Then, the third signal is processed by using a bandpass filter to remove high-frequency and low-frequency noise on the third signal and isolate the target frequency band of the events in the catalog to obtain a fourth signal.

[0069] Among them, the frequency spectrum of the third signal can be analyzed by fast Fourier transform to determine the main frequency range of the microseismic event. The Fourier transform can convert the time domain signal into the frequency domain, thereby identifying the frequency band where the energy in the signal is concentrated. The expression of the Fourier transform is as follows:

[0070]

[0071] Among them, x(f) is the frequency domain signal, x(t) is the time domain signal, and the frequency is f.

[0072] According to the existing data, microseismic signals are usually concentrated in the range of 10Hz to 500Hz; then use signal processing software to design a suitable bandpass filter, such as applying the filter function firwin in python to set the low-frequency cutoff and high-frequency cutoff values. Through the action of the bandpass filter, the high-frequency and low-frequency noise in the third signal will be weakened or completely eliminated, leaving only the signal components within the specified frequency range, that is, the retained signal components are the fourth signal.

[0073] Finally, the fourth signal is processed by a frequency-wavenumber filter to attenuate any coherent noise in the fourth signal to obtain a denoised signal.

[0074] By denoising the original signal to obtain a denoised signal, the coherent noise and high-frequency random noise waveform can be greatly attenuated, and interference that does not conform to the propagation laws of earthquake events can be effectively eliminated, making the true earthquake waveform more consistent in space, that is, the spatial coherence is enhanced, making it easier to detect microseismic events.

[0075] S2: performing coherence analysis on the denoised signal to determine a similarity matrix, including:

[0076] Firstly, based on the hyperbolic trajectory, a similarity function is determined; then, according to the similarity function, similarity values ​​of the denoised signal at different time samples are determined; finally, according to the similarity values ​​of the denoised signal at different time samples, a similarity matrix is ​​determined.

[0077] During the propagation of seismic waves, the time at which different sensors receive seismic signals depends on their distance from the earthquake source. Hyperbolic trajectories can reasonably simulate this propagation process, because the propagation speed and path of seismic waves often result in time differences in the signals recorded by sensors at different locations.

[0078] By assuming a hyperbolic trajectory and using a similarity function to calculate similarity values, the source of the seismic event and the wave propagation path can be inferred. In particular, for the linear segment of the optical fiber, the similarity function is used to evaluate the coherence of the seismic waveform along the hyperbolic trajectory. In the present invention, the expression of the hyperbolic trajectory is as follows:

[0079]

[0080] Where t is the position x along the fiber axis. i The time of the recorded trajectory, X, T, C represent the spatial offset, time offset and curvature relative to the vertex of the hyperbolic trajectory respectively. If X, T, C only consider positive values, the expression of the hyperbolic trajectory is as follows:

[0081]

[0082] The expression of the similarity function is as follows:

[0083]

[0084] Where S is the similarity value, M is the number of sensors, representing the different locations on the DAS fiber where data is recorded, N is the number of time samples in the geometric hyperbolic window, representing the time span for similarity calculation within a given window, and A is the amplitude value of the i-th sensor at the j-th time sample.

[0085] The denoised signal is selected to calculate the similarity according to different application scenarios. The similarity S is between 0 and 1, where 1 indicates complete coherence between different denoised signals and 0 indicates incoherence between different denoised signals. The present invention detects seismic events by evaluating the waveform coherence along a geometric hyperbolic window. A series of curvature values ​​and vertex positions can be used to search for curves that match the actual seismic wave propagation path and different seismic wave source positions.

[0086] For each fixed vertex X, the present invention generates a similarity matrix according to different curvatures C and time offsets T, which is used to represent the coherence of M sensors on N time samples. The similarity matrix is ​​a two-dimensional matrix, and the expression is as follows:

[0087]

[0088] Among them, the rows of the similarity matrix represent the curvature C, and the columns of the similarity matrix represent the time offset T. Finally, the similarity matrix with the highest similarity (the largest coherence value S) is selected, indicating that the waveform has the strongest coherence under these conditions and may correspond to a real earthquake event.

[0089] S3: Determine the detection result according to the similarity matrix, including:

[0090] First, according to the similarity matrix, the coherent time series corresponding to each time sample is determined. The coherent time series is obtained by summing the squares of the coherence values ​​in each column of the two-dimensional similarity matrix. The calculation formula is as follows:

[0091]

[0092] T j =[T 1 , T 2 , ..., T M ]

[0093] Among them, T j It is composed of the coherent values ​​of M sensors at the jth time sample, reflecting the degree of signal coherence at the time sample. It can be seen from the expression that the coherent time series includes multiple coherent values ​​arranged in sequence.

[0094] Next, determine whether the coherent time series meets the clustering conditions, including:

[0095] According to the coherent time series, the detection threshold is determined. The calculation method of the detection threshold is as follows: the coherent time series on each time sample are compared in size and sorted from small to large, 5% of the minimum and maximum values ​​in the coherent time series are selected and removed, and the remaining coherent time series are averaged after removal, and the average value is used as the detection threshold.

[0096] According to a plurality of sequentially ordered coherence values ​​and a detection threshold in the coherence time sequence, a target coherence value in the coherence time sequence is determined, specifically, a coherence value in the coherence time sequence that is greater than or equal to the detection threshold is determined as the target coherence value.

[0097] According to the target coherence values ​​in the coherent time series and their corresponding sorting positions, it is judged whether the coherent time series meets the clustering conditions, wherein the clustering conditions include: the minimum number of consecutive samples, which is used to represent the minimum number of target coherence values ​​in the coherent time series; the maximum number of interval samples, which is used to represent the maximum number of interval coherence values ​​between target coherence values ​​in the coherent time series.

[0098] According to the target coherence value in the coherent time series and its corresponding sorting position, it is judged whether the coherent time series meets the clustering condition, including:

[0099] First, it is necessary to determine the position distance between two target coherence values ​​according to the target coherence values ​​and their corresponding sorting positions in the coherence time series, and determine the position distance as the number of interval coherence values, that is, the number of adjacent coherence values ​​between two target coherence values;

[0100] When the number of target coherence values ​​in the coherence time series is greater than the minimum number of consecutive samples, and the number of interval coherence values ​​between the target coherence values ​​is less than the maximum number of interval samples, the coherence time series meets the clustering condition, which means that there are potential microseismic events in the time samples corresponding to the coherence time series.

[0101] When the number of target coherent values ​​in the coherent time series is less than or equal to the minimum number of consecutive samples, or the number of interval coherent values ​​between target coherent values ​​is less than or equal to the maximum number of interval samples, the coherent time series does not meet the clustering condition. This means that there is no potential microseismic event in the time sample corresponding to the coherent time series, and the coherent time series that does not meet the clustering condition is removed.

[0102] The detection result is determined according to the coherent time series with potential microseismic events, that is, the detection result is determined according to the retained coherent time series, including:

[0103] The target coherence value in the coherent time series with potential microseismic events is taken as the coherent sample; the sample signal-to-noise ratio is calculated for the coherent sample, and the calculation formula is as follows:

[0104]

[0105] Where SNR is the signal-to-noise ratio, E RMSsignal is the RMS value within the signal window, which is used to measure the strength of the signal. RMSnoisel is the RMS value of the noise window, which is used to measure the intensity of the noise. signal is the signal window length, N noise is the noise window length, ω i is the i-th target coherence value in the coherent sample.

[0106] Finally, the detection results are determined based on the coherent samples and their sample signal-to-noise ratio, including:

[0107] First, the coherent samples with a sample signal-to-noise ratio greater than the signal-to-noise ratio threshold are taken as target samples. According to the quality and noise level of the data, the signal-to-noise ratio threshold can be manually adjusted. An appropriate signal-to-noise ratio threshold can be set through repeated trials to find the best balance between real event detection and false positive rate. If there are many false positives, the signal-to-noise ratio threshold can be manually increased to filter out weaker signals and retain only strong signal events. If it is found that a real microseismic event is missed, the signal-to-noise ratio threshold can be lowered to allow more weaker signals to pass the detection. The detection results are optimized through the signal-to-noise ratio threshold to ensure the best balance between real event detection and false positive rate, thereby improving the accuracy and reliability of microseismic event detection.

[0108] Then, the interval time between each pair of target samples is determined based on the target samples and their corresponding time samples; and the detection result is determined based on the interval time, including:

[0109] When the interval time does not exceed the time threshold, it indicates that the time samples corresponding to these target coherence values ​​are closely connected in time, and the signal coherence of these time samples is high. In this case, it can be considered that these time samples correspond to the signal of the same microseismic event. Because the signal of a microseismic event is usually received by multiple sensors in a short time (i.e., it does not exceed the time threshold) during the propagation process, and these signals have strong continuity and coherence in time. Therefore, when the interval time does not exceed the time threshold, the detection result is the presence of a single microseismic event.

[0110] When the interval time exceeds the time threshold, it indicates that the time samples corresponding to these target coherence values ​​are separated in time, and the signal coherence of these time samples is high. In this case, it can be considered that these time samples correspond to the signals of multiple independent microseismic events. Because during the propagation of the signal of a microseismic event, if the time interval between two events exceeds the time threshold, it usually means that the two events are independent, rather than the continuation of the same event. Therefore, when the interval time exceeds the time threshold, the detection result is that there are multiple microseismic events.

[0111] The time threshold is the maximum duration of the microseismic event.

[0112] The present invention also provides a computer device, comprising a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, at least one program, a code set or an instruction set is loaded and executed by the processor to implement the above-mentioned microseismic event detection method.

[0113] The processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0114] The memory can be used to store the computer program or module, and the processor implements various functions of the microseismic event detection method by running or executing the computer program or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0115] The above are only preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A microseismic event detection method, characterized in that: include: Perform denoising on the original signal to obtain a denoised signal; Performing coherence analysis on the denoised signal to determine a similarity matrix; A detection result is determined according to the similarity matrix.

2. A microseismic event detection method according to claim 1, characterized in that: The denoising process is performed on the original signal to obtain the denoised signal, including: Processing the original signal by using a low-pass filter and down-sampling to obtain a first signal; removing a linear trend and an average trend of the first signal to obtain a second signal; Based on the maximum amplitude of the second signal, normalize the second signal to obtain a third signal; Processing the third signal using a bandpass filter to obtain a fourth signal; The fourth signal is processed by using a frequency-wavenumber filter to obtain the denoised signal.

3. A microseismic event detection method according to claim 1, characterized in that: The performing coherence analysis on the denoised signal to determine a similarity matrix includes: Based on the hyperbolic trajectory, a similarity function is determined; Determining similarity values ​​of the denoised signal at different time samples according to the similarity function; The similarity matrix is ​​determined according to similarity values ​​of the denoised signal at different time samples.

4. A microseismic event detection method according to claim 3, characterized in that: Determining the detection result according to the similarity matrix includes: Determining a coherent time series corresponding to each time sample according to the similarity matrix; Determine whether the coherent time series meets the clustering conditions; If the answer is no, it means that there is no potential microseismic event in the time sample corresponding to the coherent time series, and the coherent time series is removed; If yes, it means that there is a potential microseismic event in the time sample corresponding to the coherent time series; The detection results are determined based on the coherent time series of potential microseismic events.

5. A microseismic event detection method according to claim 4, characterized in that: The coherence time series includes a plurality of sequentially ordered coherence values; The determining whether the coherent time series meets the clustering condition includes: Determining a detection threshold according to the coherent time series; Determining a target coherence value in a coherence time series according to a plurality of sequentially ordered coherence values ​​and the detection threshold; According to the target coherence value in the coherence time series and its corresponding sorting position, it is judged whether the coherence time series meets the clustering condition.

6. A microseismic event detection method according to claim 5, characterized in that: The clustering conditions include: The minimum number of consecutive samples is used to represent the minimum number of target coherence values ​​in the coherence time series; The maximum number of interval samples is used to represent the maximum number of interval coherence values ​​between target coherence values ​​in the coherence time series; According to the target coherence value in the coherent time series and its corresponding sorting position, it is judged whether the coherent time series meets the clustering condition, including: According to the target coherence values ​​and their corresponding sorting positions in the coherence time series, determining the position distance between each pair of target coherence values, and determining the position distance as the number of interval coherence values; When the number of target coherent values ​​in the coherent time series is greater than the minimum number of consecutive samples, and the number of interval coherent values ​​between target coherent values ​​is less than the maximum number of interval samples, the coherent time series meets the clustering condition.

7. A microseismic event detection method according to claim 6, characterized in that: The step of determining the detection result based on the coherent time series of potential microseismic events includes: The target coherence value in the coherence time series where potential microseismic events exist is taken as a coherence sample; Calculating a sample signal-to-noise ratio for the coherent samples; The detection result is determined according to the coherent samples and their sample signal-to-noise ratios.

8. A microseismic event detection method according to claim 7, characterized in that: Determining the detection result according to the coherent samples and their sample signal-to-noise ratios includes: The coherent samples whose signal-to-noise ratio is greater than the signal-to-noise ratio threshold are taken as target samples; According to the target samples and their corresponding time samples, the interval time between each pair of target samples is determined; The detection result is determined according to the interval time.

9. A microseismic event detection method according to claim 8, characterized in that: Determining the detection result according to the interval time includes: When the interval time does not exceed the time threshold, the detection result is that a single microseismic event exists; When the interval time exceeds the time threshold, the detection result is that there are multiple microseismic events; The time threshold is the maximum duration of a microseismic event.

10. A computer device, characterized in that: The computer device includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, at least one program, a code set or an instruction set is loaded and executed by the processor to implement the microseismic event detection method as described in any one of claims 1 to 9.

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