Microseismic event detection method and device
By denoising and performing coherence analysis on DAS data, a similarity matrix is generated, which solves the problem of excessive data in DAS technology and achieves efficient and accurate microseismic event detection.
Patent Information
- Authority / Receiving Office
- WO · WO
- Patent Type
- Applications
- Current Assignee / Owner
- GUANGZHOU METRO GRP CO LTD
- Filing Date
- 2025-03-11
- Publication Date
- 2026-06-18
AI Technical Summary
DAS technology generates excessive data in microseismic detection, leading to storage and processing problems, and existing methods may lose useful information and miss seismic events.
By denoising and performing coherence analysis on DAS data, a similarity matrix is generated, and microseismic events are identified using the high spatial density of fiber optic signals.
This reduces the burden of data storage and processing while retaining useful information, thus improving the accuracy and efficiency of microseismic event detection.
Smart Images
Figure CN2025081822_18062026_PF_FP_ABST
Abstract
Description
A method and device for detecting microseismic events Technical Field
[0001] This invention relates to the field of microseismic detection technology, and in particular to a method and device for detecting microseismic events. Background Technology
[0002] Distributed acoustic sensing (DAS) is a fiber optic-based data acquisition technology that is increasingly being used in microseismic detection.
[0003] DAS technology tends to generate excessive amounts of data during microseismic detection. A 2.5km gauge length and 10m sensing fiber, with a sampling frequency of 4kHz and a data acquisition block of 15s, can generate more than 1TB of data per day. This can lead to significant data storage problems when dealing with earthquake monitoring activities that last for weeks or months.
[0004] To address the data storage problem, existing solutions involve storing only target data, such as waveforms containing only 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 permanently losing useful information, thus missing many seismic events that should have been monitored. Summary of the Invention
[0005] In order to overcome the shortcomings of the prior art, the present invention aims to provide a microseismic event detection method and device, which can utilize the high spatial density of DAS data and avoid storage and processing problems caused by excessive data volume.
[0006] To solve the above problems, the present invention is implemented according to the following solution:
[0007] A method for detecting microseismic events is provided, including:
[0008] The original signal is denoised to obtain a denoised signal.
[0009] Perform coherence analysis on the denoised signal to determine the similarity matrix;
[0010] The detection result is determined based on the similarity matrix.
[0011] Compared with existing technologies, the beneficial effects of the microseismic event detection method of the present invention are as follows: Since the original signal acquired by DAS technology has a high spatial density, that is, each small segment of the optical fiber can acquire a signal, by performing coherence analysis on the denoised original signal, the signals at different locations in the optical fiber can be compared, and a similarity matrix representing the similarity between these signals can be generated. By utilizing the high spatial density of DAS data through coherence analysis, signal segments with consistency at multiple locations can be identified. These segments usually correspond to microseismic events, thus detecting whether a microseismic event is occurring. This reduces the burden of data storage and processing while retaining useful information in the high spatial density data, improving the accuracy and efficiency of detection.
[0012] Optionally, the denoising process on the original signal to obtain a denoised signal includes:
[0013] The original signal is processed using a low-pass filter and downsampling to obtain a first signal;
[0014] Remove the linear trend and average trend from the first signal to obtain the second signal;
[0015] Based on the maximum amplitude of the second signal, the second signal is normalized to obtain the third signal;
[0016] The third signal is processed using a bandpass filter to obtain the fourth signal;
[0017] The fourth signal is processed using a frequency-wavenumber filter to obtain the denoised signal;
[0018] Optionally, the step of performing coherence analysis on the denoised signal to determine the similarity matrix includes:
[0019] Based on the hyperbolic trajectory, determine the similarity function;
[0020] Based on the similarity function, the similarity values of the denoised signal at different time samples are determined;
[0021] The similarity matrix is determined based on the similarity values of the denoised signal at different time samples.
[0022] Optionally, determining the detection result based on the similarity matrix includes:
[0023] Based on the similarity matrix, determine the coherent time series corresponding to each time sample;
[0024] Determine whether a coherent time series satisfies the clustering criteria;
[0025] If no, it means that there are no potential microseismic events in the time sample corresponding to the coherent time series, and the coherent time series is removed.
[0026] If yes, it indicates that there are potential microseismic events 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 multiple sequentially ordered coherent values;
[0029] The determination of whether a coherent time series satisfies the clustering conditions includes:
[0030] The detection threshold is determined based on the coherent time series.
[0031] The target coherence value in the coherent time series is determined based on multiple sequentially ordered coherence values and the detection threshold.
[0032] Based on the target coherence value in the coherent time series and its corresponding sorting position, determine whether the coherent time series meets the clustering conditions.
[0033] Optionally, the clustering conditions include:
[0034] Minimum number of consecutive samples, used to represent the minimum number of target coherent values in a coherent time series;
[0035] Maximum interval sample number is used to represent the maximum number of interval coherent values between target coherent values in a coherent time series.
[0036] Based on the target coherence value and its corresponding sorting position in the coherent time series, determine whether the coherent time series meets the clustering conditions, including:
[0037] Based on the target coherence values and their corresponding sorting positions in the coherent time series, the positional distance between each pair of target coherence values is determined, and the positional distance is determined as the number of interval coherence values;
[0038] The coherent time series satisfies the clustering condition 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 the target coherent values is less than the maximum number of interval samples.
[0039] Optionally, determining the detection result based on the coherent time series of potential microseismic events includes:
[0040] The target coherence values in the coherent time series containing potential microseismic events are used as coherent samples;
[0041] Calculate the signal-to-noise ratio of the coherent samples;
[0042] The detection result is determined based on the coherent samples and their signal-to-noise ratios.
[0043] Optionally, determining the detection result based on the coherent samples and their signal-to-noise ratio includes:
[0044] Coherent samples with a signal-to-noise ratio greater than the signal-to-noise ratio threshold are used as target samples;
[0045] Based on the target samples and their corresponding time samples, determine the time interval between each pair of target samples;
[0046] The detection result is determined based on the specified interval.
[0047] Optionally, determining the detection result based on 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 exceeds the time threshold, the detection result indicates the presence of multiple microseismic events.
[0050] The time threshold is the maximum duration of the microseismic event.
[0051] A computer device is also provided, including a processor and a memory, wherein the memory stores at least one instruction, at least one program, code set, or instruction set, and the at least one instruction, at least one program, code set, or instruction set is loaded and executed by the processor to implement the microseismic event detection method. Attached Figure Description
[0052] Figure 1 is a flowchart of the microseismic event detection method of the present invention. Detailed Implementation
[0053] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.
[0054] In the following description, when referring to the accompanying 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 this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims. In the description of this application, it should be understood that the terms "first," "second," "third," etc., are used only to distinguish similar objects and are not necessarily used to describe a specific order or sequence, nor should they be construed as indicating or implying relative importance. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.
[0055] Referring to Figure 1, the present invention provides a microseismic event detection method, comprising:
[0056] S1: Denoise the original signal to obtain a denoised signal, including:
[0057] First, a low-pass filter is used to process the original signal to remove high-frequency components and avoid aliasing. Then, downsampling is used to process the original signal to obtain the first signal.
[0058] Next, the linear trend and average trend of the first signal are removed to obtain the second signal. The linear trend of the first signal can be removed by first removing the linear trend based on the estimated linear trend, resulting in the signal with the linear trend removed. The specific expression is as follows: xlinear_removed(t)=x(t)-(at+b)
[0059] Where xlinear_removed(t) is the signal at time t after removing the linear trend from the first signal, and x(t) is the first signal at time t. (at+b) is the estimated linear trend, which is the linear drift that varies with time. Long-term drift or linear drift of the instrument can be eliminated according to the following formula:
[0060] 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 using the following formula:
[0061] Where 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 mean of the first signal is subtracted from the first signal to obtain the signal of the first signal after removing the average trend, and its expression is as follows: X mean_removed (t)=xlinear_removed(t)-μ
[0062] Where xmean_removed(t) is the first signal at time t after removing the linear trend (the second signal), xlinear_removed(t) is the first signal at time t after removing the linear trend, and μ is the mean of the first signal.
[0063] Next, based on the maximum amplitude of the second signal, the second signal is normalized to obtain the third signal. That is, the third signal is obtained by normalizing the second signal based on the maximum amplitude of the second signal, so as to reduce the influence of geometric spread, receiver coupling and nonlinear effects on the signal.
[0064] Then, a bandpass filter is used to process the third signal to remove high-frequency and low-frequency noise and isolate the target frequency band of the events in the catalog to obtain the fourth signal.
[0065] The main frequency range of the microseismic event can be determined by analyzing the spectrum of the third signal using the Fast Fourier Transform (FFT). The FFT can convert a time-domain signal to the frequency domain, thereby identifying the frequency bands where energy is concentrated in the signal. The expression for the FFT is as follows:
[0066] Where x(f) is the frequency domain signal, x(t) is the time domain signal, and f is the frequency.
[0067] Based on existing data, microseismic signals are typically concentrated in the range of 10Hz to 500Hz. Then, a suitable bandpass filter is designed using signal processing software, such as the filter function firwin in Python, to set 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, which are the retained signal components, i.e., the fourth signal.
[0068] Finally, a frequency-wavenumber filter is used to process the fourth signal to attenuate any coherent noise in the fourth signal and obtain a denoised signal.
[0069] By denoising the original signal to obtain the denoised signal, coherent noise and high-frequency random noise waveforms can be significantly attenuated, effectively eliminating interference that does not conform to the propagation law of seismic events, making the true seismic waveform more consistent in space, that is, enhancing spatial coherence, thus making it easier to detect microseismic events.
[0070] S2: Perform coherence analysis on the denoised signal to determine the similarity matrix, including:
[0071] First, a similarity function is determined based on the hyperbolic trajectory; then, the similarity value of the denoised signal at different time samples is determined based on the similarity function; finally, the similarity matrix is determined based on the similarity value of the denoised signal at different time samples.
[0072] During the propagation of seismic waves, the time it takes for different sensors to receive seismic signals depends on their distance from the epicenter. Hyperbolic trajectories can reasonably simulate this propagation process because the propagation speed and path of seismic waves often cause time differences in the signals recorded by sensors at different locations.
[0073] By assuming a hyperbolic trajectory and calculating similarity values using a similarity function, the source and wave propagation path of an earthquake event can be inferred. Particularly for the linear segment of an optical fiber, the coherence of the earthquake waveform along the hyperbolic trajectory is evaluated using a similarity function. In this invention, the expression for the hyperbolic trajectory is as follows:
[0074] Where t is the position x along the fiber axis i The recorded trajectory's time, where X, T, and C represent the spatial offset, time offset, and curvature relative to the hyperbola's vertex, respectively. If only positive values of X, T, and C are considered, the hyperbola's trajectory is expressed as follows:
[0075] The expression for the similarity function is as follows:
[0076] Where S is the similarity value, M is the number of sensors, representing the different locations of the data recorded on the DAS fiber, 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.
[0077] The denoised signal is selected to calculate similarity based on different application scenarios. The similarity S is between 0 and 1, where 1 represents complete coherence between different denoised signals and 0 represents incoherence between different denoised signals. This invention detects seismic events by evaluating the waveform coherence along a geometric hyperbolic window. It can search through a series of curvature values and vertex positions to find curves that match the actual seismic wave propagation path and different seismic wave source locations.
[0078] This invention generates a similarity matrix for each fixed vertex X based on different curvatures C and time offsets T, which represents the coherence of M sensors across N time samples. This matrix is a two-dimensional matrix, expressed as follows:
[0079] In this similarity matrix, the rows represent curvature C, and the columns represent time offset T. The similarity matrix with the highest similarity (maximum coherence value S) is selected, indicating that the waveform has the strongest coherence under these conditions and may correspond to a real earthquake event.
[0080] S3: Determine the detection results based on the similarity matrix, including:
[0081] First, based on 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: T j = [T1, T2, ..., T M ]
[0082] Among them, T j The coherent time series consists of coherent values from M sensors at the j-th time sample, reflecting the degree of signal coherence at that time sample. As can be seen from the expression, the coherent time series includes multiple sequentially ordered coherent values.
[0083] Next, determine whether the coherent time series meets the clustering conditions, including:
[0084] Based on the coherent time series, the detection threshold is determined. The detection threshold is calculated as follows: the coherent time series on each time sample are compared in size and sorted from smallest to largest. 5% of the minimum and maximum values in the coherent time series are removed. The remaining coherent time series are averaged, and this average value is used as the detection threshold.
[0085] Based on multiple sequentially ordered coherence values in the coherent time series and the detection threshold, the target coherence value in the coherent time series is determined. Specifically, the coherence values in the coherent time series that are greater than or equal to the detection threshold are determined as the target coherence values.
[0086] Based on the target coherence values and their corresponding sorting positions in the coherent time series, it is determined whether the coherent time series meets the clustering conditions. The clustering conditions include: minimum number of consecutive samples, which represents the minimum number of target coherence values in the coherent time series; and maximum number of interval samples, which represents the maximum number of interval coherence values between the target coherence values in the coherent time series.
[0087] Based on the target coherence value and its corresponding sorting position in the coherent time series, determine whether the coherent time series meets the clustering conditions, including:
[0088] First, based on the target coherence values in the coherent time series and their corresponding sorting positions, it is necessary to determine the positional distance between each pair of target coherence values. The positional distance is then determined as the number of interval coherence values, that is, the number of adjacent coherence values between each pair of target coherence values.
[0089] When the number of target coherent values in a coherent time series is greater than the minimum number of consecutive samples, and the number of interval coherent values between the target coherent values is less than the maximum number of interval samples, the coherent time series satisfies the clustering condition. In this case, it indicates that there are potential microseismic events in the time samples corresponding to the coherent time series.
[0090] When the number of target coherent values in a 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 conditions. This indicates that there are no potential microseismic events in the time samples corresponding to the coherent time series, and the coherent time series that do not meet the clustering conditions are removed.
[0091] The detection results are determined based on the coherent time series containing potential microseismic events, i.e., based on the preserved coherent time series, including:
[0092] The target coherence values in the coherent time series containing potential microseismic events are used as coherent samples; the signal-to-noise ratio of the coherent samples is calculated using the following formula:
[0093] Where SNR is the signal-to-noise ratio, E RMSsignal E represents the root mean square value within the signal window, used to measure the signal strength. RMSnoisel N represents the root mean square value of the noise window, used to measure the intensity of the noise. signal N is the length of the signal window. noise ω is the length of the noise window. i Let be the i-th target coherence value in the coherent sample.
[0094] Finally, based on the coherent samples and their signal-to-noise ratios, the detection results are determined, including:
[0095] First, coherent samples with a signal-to-noise ratio (SNR) greater than a threshold are selected as target samples. Based on data quality and noise levels, the SNR threshold can be manually adjusted. Through repeated experimentation, a suitable SNR threshold can be established to find the optimal balance between real event detection and false positive rate. If there are many false positives, the SNR threshold can be manually increased to filter out weaker signals, retaining only strong signal events. If real microseismic events are missed, the SNR threshold can be decreased to allow more weak signals to pass through detection. Optimizing the detection results through the SNR threshold ensures an optimal balance between real event detection and false positive rate, thereby improving the accuracy and reliability of microseismic event detection.
[0096] Next, based on the target samples and their corresponding time samples, the interval time between each pair of target samples is determined; based on the interval time, the detection results are determined, including:
[0097] 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 signals of these time samples have high coherence. In this case, it can be considered that these time samples correspond to the signal of the same microseismic event. This is because the signal of a microseismic event is usually received by multiple sensors within a short period of time (i.e., within the time threshold) during propagation, and these signals have strong temporal continuity and coherence. Therefore, when the interval time does not exceed the time threshold, the detection result is that a single microseismic event exists.
[0098] When the interval exceeds the time threshold, it indicates that the time samples corresponding to these target coherence values are temporally separated, and the signals of these time samples have high coherence. In this case, it can be considered that these time samples correspond to signals of multiple independent microseismic events. Because during the propagation of microseismic event signals, if the time interval between two events exceeds the time threshold, it usually means that the two events are independent, rather than a continuation of the same event. Therefore, when the interval exceeds the time threshold, the detection result is that multiple microseismic events exist.
[0099] The time threshold is the maximum duration of the microseismic event.
[0100] The present invention also provides a computer device, including a processor and a memory. The memory stores at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, at least one program, code set, or instruction set is loaded and executed by the processor to implement the above-described microseismic event detection method.
[0101] The processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.
[0102] The memory can be used to store the computer program or module. 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 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function, etc.; the data storage area may store data created based on the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.
[0103] The above are merely preferred embodiments of this application and are not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.
Claims
1. A method for detecting microseismic events, characterized in that, include: The original signal is denoised to obtain a denoised signal. Perform coherence analysis on the denoised signal to determine the similarity matrix; The detection result is determined based on the similarity matrix.
2. [Amended according to Rule 26, 19.05.2025] A microseismic event detection method according to claim 1, characterized in that, The denoising process of the original signal to obtain a denoised signal includes: The original signal is processed using a low-pass filter and downsampling to obtain a first signal; Remove the linear trend and average trend from the first signal to obtain the second signal; Based on the maximum amplitude of the second signal, the second signal is normalized to obtain the third signal; The third signal is processed using a bandpass filter to obtain the fourth signal; The fourth signal is processed using a frequency-wavenumber filter to obtain the denoised signal.
3. The microseismic event detection method according to claim 1, characterized in that, The step of performing coherence analysis on the denoised signal to determine the similarity matrix includes: Based on the hyperbolic trajectory, determine the similarity function; Based on the similarity function, the similarity values of the denoised signal at different time samples are determined; The similarity matrix is determined based on the similarity values of the denoised signal at different time samples.
4. The microseismic event detection method according to claim 3, characterized in that, Determining the detection result based on the similarity matrix includes: Based on the similarity matrix, determine the coherent time series corresponding to each time sample; Determine whether a coherent time series satisfies the clustering criteria; If no, it means that there are no potential microseismic events in the time sample corresponding to the coherent time series, and the coherent time series is removed. If yes, it indicates that there are potential microseismic events 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. The microseismic event detection method according to claim 4, characterized in that, The coherent time series includes multiple coherent values ordered sequentially; The determination of whether a coherent time series satisfies the clustering conditions includes: The detection threshold is determined based on the coherent time series. The target coherence value in the coherent time series is determined based on multiple sequentially ordered coherence values and the detection threshold. Based on the target coherence value in the coherent time series and its corresponding sorting position, determine whether the coherent time series meets the clustering conditions.
6. The microseismic event detection method according to claim 5, characterized in that, The clustering conditions include: Minimum number of consecutive samples, used to represent the minimum number of target coherent values in a coherent time series; Maximum interval sample number is used to represent the maximum number of interval coherent values between target coherent values in a coherent time series. Based on the target coherence value and its corresponding sorting position in the coherent time series, determine whether the coherent time series meets the clustering conditions, including: Based on the target coherence values and their corresponding sorting positions in the coherent time series, the positional distance between each pair of target coherence values is determined, and the positional distance is determined as the number of interval coherence values; The coherent time series satisfies the clustering condition 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 the target coherent values is less than the maximum number of interval samples.
7. The microseismic event detection method according to claim 6, characterized in that, The determination of detection results based on coherent time series of potential microseismic events includes: The target coherence values in the coherent time series containing potential microseismic events are used as coherent samples; Calculate the signal-to-noise ratio of the coherent samples; The detection result is determined based on the coherent samples and their signal-to-noise ratios.
8. The microseismic event detection method according to claim 7, characterized in that, Determining the detection result based on the coherent samples and their signal-to-noise ratio includes: Coherent samples with a signal-to-noise ratio greater than the signal-to-noise ratio threshold are used as target samples; Based on the target samples and their corresponding time samples, determine the time interval between each pair of target samples; The detection result is determined based on the specified interval.
9. A microseismic event detection method according to claim 8, characterized in that, Determining the detection result based on the interval includes: When the interval time does not exceed the time threshold, the detection result is that a single microseismic event exists; When the interval exceeds the time threshold, the detection result indicates the presence of multiple microseismic events. The time threshold is the maximum duration of the microseismic event.
10. A computer device, characterized in that, The computer device includes a processor and a memory, the memory storing at least one instruction, at least one program, code set, or instruction set, the at least one instruction, at least one program, code set, or instruction set being loaded and executed by the processor to implement the microseismic event detection method as described in any one of claims 1 to 9.