A microseismic event first arrival picking method and system based on dynamic time warping

Abstract

The application discloses a microseismic event first arrival picking method and system based on dynamic time warping in the field of geophysical exploration technology, which is used for picking the first arrival of the seismic phase of a microseismic event according to a waveform to be processed, and comprises the following steps: step one, detecting the waveform to be processed and carrying out denoising processing; step two, time difference calibration of the waveform to be processed; step three, superposition processing of the waveform to be processed; step four, first arrival picking of the waveform to be processed; step five, relative to-time comparison; step six, verification of the position of a detector; and step seven, calculation parameter adjustment. The application can effectively improve the accuracy of the first arrival picking of the seismic phase of microseismic, reference oscillation waves are emitted by oscillation generators at multiple fixed positions, the position of the detector is verified based on an arrival time difference positioning algorithm, the waveform to be processed is superposed after time difference calibration, and the first arrival is picked up from the superposition channel of the waveform to be processed by a long-short time energy ratio method, so that the influence of information error between individual channels is reduced, and the reliability of the microseismic phase identification and the first arrival picking is improved.

Description

Technical Field

[0001] This invention belongs to the field of geophysical exploration technology, specifically a method and system for picking first arrivals of microseismic events based on dynamic time warping. Background Technology

[0002] Microseismic monitoring technology, as a widely studied geophysical technique, is an important means to understand the distribution patterns of hydraulic fracturing fractures and evaluate the effectiveness of fracturing. With the rapid development of information technology in recent years, this technology has been widely applied in underground engineering fields such as oil and gas field development, mine safety production, tunnel construction, and geological disaster monitoring. The identification and first arrival picking of microseismic events are key steps in microseismic monitoring data processing. The accurate identification of microseismic events directly affects the accuracy of first arrival picking, and its error determines the accuracy of subsequent source location. Therefore, improving the accuracy of microseismic event identification and the precision of first arrival picking is of crucial and significant importance.

[0003] Traditional microseismic phase identification and first arrival picking rely primarily on manual methods, which are extremely labor-intensive and susceptible to human error due to inconsistent picking standards and subjective experience. Therefore, methods for automatically identifying and picking microseismic event phases and first arrivals have been proposed. Common first arrival picking methods can be categorized into single-channel feature methods, multi-channel cross-correlation methods, and template matching methods. Single-channel feature methods include the long-short-time mean ratio (STA / LTA) method, the AIC method, polarization analysis, the PAI-S / K method, and the fractal dimension method. These single-channel feature algorithms pick phases and first arrivals based on the spectral characteristics of a single-channel waveform. They are less sensitive to low signal-to-noise ratio events and struggle to reduce the impact of individual inter-channel information on the overall microseismic event picking results.

[0004] In actual production, multiple geophones that detect microseismic information may be affected by earthquakes, causing positional changes. The positional accuracy of the geophones directly affects the calculation parameters and relative time accuracy of the waveform to be processed, and thus affects the accuracy of microseismic identification and first arrival picking results.

[0005] Therefore, it is necessary to propose a method and system for picking the first arrival of microseismic events based on dynamic time warping, which can perform spatial positioning verification of multiple detectors and can pick the first arrival of the waveform to be processed by superimposing the trace seismic phase based on a highly computationally efficient single-channel feature algorithm, thereby reducing the impact of individual inter-channel information errors and detector displacement errors. Summary of the Invention

[0006] To address the aforementioned issues, the present invention aims to provide a method and system for first arrival picking of microseismic events based on dynamic time warping. The method involves superimposing waveforms after time difference calibration, using the long-short time energy ratio method to pick the first arrival of the superimposed waveform channels, comparing the relative time differences between channels to determine the position changes of the geophones, emitting reference oscillation waves through multiple fixed-position oscillators, verifying the geophone positions based on a time difference of arrival (TDOA) positioning algorithm, and updating the relative spacing and position parameters of the geophones to reduce the impact of information errors between individual channels, thereby improving the accuracy of microseismic phase identification and first arrival picking.

[0007] To achieve the above objectives, the technical solution of the present invention is as follows: a method for picking up the first arrival of microseismic events based on dynamic time warping, used to pick up the first arrival of the phase of a microseismic event according to the waveform to be processed, including step one: detecting the waveform to be processed by each detector provided in the detection area and performing noise reduction processing;

[0008] Step 2: Time difference calibration of the waveform to be processed. Use the cross-correlation function to obtain the inter-channel time difference of the waveform to be processed. Based on the relationship between the inter-channel time difference and the relative arrival time, establish a linear equation system between the inter-channel time difference and the relative arrival time of each channel and solve it to perform time difference calibration of the arrival time of each channel.

[0009] Step 3: Superposition processing of the waveforms to be processed. The horizontal superposition method is used for superposition processing. According to the time delay theorem of Fourier transform, the amplitude spectrum function and phase spectrum function of the superimposed channel are obtained.

[0010] Step 4: Initial arrival acquisition of the waveform to be processed. The long-short time energy ratio method is used to acquire the initial arrival of the waveform to be processed, and then the relative arrival time t in Step 3 is used as the basis for the acquisition. i Perform reverse time difference calibration to obtain the actual first arrival and arrival times T for each channel record. i ;

[0011] Step 5: Relative arrival time comparison. Based on the relative arrival time ti of each channel obtained in Step 3, compare it with the relative arrival time of each channel obtained from the first arrival of the previous phase. Mark the detector corresponding to the waveform with a relative arrival time error exceeding ±5% as displacement.

[0012] Step 6: Detector position verification. Reference waveforms are emitted at several fixed locations using an oscillator. After detection by detectors marked with displacement, the detector positions are verified based on the time difference of arrival (TDOA) positioning algorithm.

[0013] Step 7: Calculate parameter adjustments. After verifying the new detector positions, change the preset detector spacing and position parameters, repeat step two of the first-arrival phase acquisition, and optimize the calculation accuracy for the next first-arrival phase acquisition. The basic principle of the scheme is: by preprocessing the waveform to be processed, calibrating the dynamic time difference, and superimposing the waveform, the influence of individual inter-channel information errors is minimized. At the same time, the long-short time energy ratio method is used to simplify the calculation complexity and improve the accuracy and efficiency of the first-arrival phase acquisition. Using the reference waveform emitted by the oscillator, the spatial positioning verification of multiple detectors is performed based on the time difference of arrival positioning algorithm, and the detector position parameters are updated to ensure the accuracy of subsequent first-arrival phase acquisition parameters.

[0014] The beneficial effects of the basic scheme are: 1. Preprocessing of the waveform to be processed reduces the impact of noise on the waveform through noise reduction, making the waveform clearer and facilitating subsequent analysis and acquisition.

[0015] 2. The time difference calibration process utilizes a cross-correlation function to calculate the inter-channel time difference and solves it by establishing a system of linear equations, achieving precise calibration of the arrival time of each channel. This reduces errors caused by inter-channel time differences and improves the accuracy of waveform analysis.

[0016] 3. Waveform superposition processing: The horizontal superposition method reduces the impact of inter-channel information errors, enhances the signal-to-noise ratio, and obtains the amplitude spectrum function and phase spectrum function of the superimposed channel, providing more accurate information for subsequent first arrival picking.

[0017] 4. The application of the long-time-short-time energy ratio method simplifies the calculation complexity and improves the accuracy and efficiency of picking up the first arrival of the seismic phase.

[0018] 5. The position of the geophone can be accurately verified and calibrated using the reference waveform emitted by the oscillator and the time difference of arrival (TDOA) positioning algorithm. This ensures that the geophone position parameters used in subsequent first-arrival pickup processes are accurate, thereby improving the accuracy of the first-arrival pickup results.

[0019] 6. This method is not only applicable to first arrival picking of microseismic events, but can also be extended to the analysis of other types of seismic events. Its dynamic time warping characteristic enables the method to adapt to waveform data of different time scales and complexities, improving the method's versatility and practicality.

[0020] Furthermore, the denoising process of the waveform to be processed in step one includes frequency domain filter-based denoising, Fourier transform and inverse transform of the waveform to be processed, and static correction and deconvolution denoising.

[0021] The beneficial effects of the basic scheme are: 1. Frequency domain filters can effectively remove high-frequency noise and interference from the waveform to be processed. By converting the waveform from the spatial domain to the frequency domain through Fourier transform, filtering is performed on specific frequency components, and finally, the waveform is restored to the spatial domain through inverse Fourier transform. This preserves the effective signal components in the waveform while removing irrelevant background noise. This helps enhance the characteristics of the waveform, making the first arrival detection of microseismic events more accurate and reliable.

[0022] 2. Static correction is a process of fine-tuning the position of waveform data, while deconvolution is a method used to remove multiples and interference waves from seismic data. After denoising, noise components in the waveform data are effectively removed, enabling static correction and deconvolution algorithms to more accurately identify and adjust the position of the waveform data, thereby further improving the stacking effect and the accuracy of data analysis.

[0023] Furthermore, the cross-correlation function between the two waveform channels to be processed in step two is defined as:

[0024]

[0025] In the formula, P is the number of sampling points, and x i (p) and x j (p) represents two data points. When c i,j When (k) is at its maximum, it is denoted as c. i,j (max), at which point x i (p) and x j The waveforms of (p) have the highest similarity, and the x of the two signals are... i (p) and x j (p) time difference Δt i,j Considered as the time difference between the first arrival and arrival times of the two routes;

[0026] Inter-track time difference Δt i,j and the initial arrival time t of the two records i and t j The relationship between them is:

[0027] t i -t j =Δt i，j .

[0028] Furthermore, the five linear equations relating the inter-channel time difference and the relative arrival times of each channel in step two are as follows:

[0029]

[0030] For M channels of records, the above formula requires M(M-1) / 2 cross-correlation operations to obtain M(M-1) / 2 sets of inter-channel time difference information. The formula can be simplified to the following form:

[0031] At = Δt

[0032] In the simplified formula, A is the sparse coefficient matrix, Δt is the measured data vector, and t is the relative arrival vector to be determined. The least squares solution of the simplified formula is:

[0033] t=(A T A) -1 A T Δt.

[0034] The beneficial effects of the basic scheme are: 1. The inter-channel time difference information calculated using the cross-correlation function can accurately reflect the time differences of signals received by different detectors. By constructing and solving a system of linear equations, accurate calibration of the arrival time of each channel signal can be achieved, thereby improving the accuracy of time synchronization.

[0035] 2. By transforming the time difference calibration problem into a system of linear equations, efficient numerical methods (such as the least squares method) can be used to solve it. This significantly improves the speed and efficiency of data processing compared to traditional track-by-track alignment and calibration methods.

[0036] 3. Precise time difference calibration is an important prerequisite for waveform superposition processing. By calibrating the arrival time of each signal, the consistency of time between the signals during superposition can be ensured, thereby improving the waveform superposition effect and signal-to-noise ratio.

[0037] 4. After time difference calibration, the arrival times of each signal are more accurate, which helps to more accurately identify the start time of microseismic events in subsequent first-arrival picking steps. Improving the accuracy of first-arrival picking is of great significance for the location and parameter estimation of seismic events.

[0038] 5. By constructing and solving a system of linear equations, the correlation information between multiple channels can be fully utilized, improving the robustness and stability of the system. Even if some channels have anomalies or missing data, compensation and calibration can still be performed using data from other channels, ensuring the accuracy of the overall processing results.

[0039] 6. Accurate time difference calibration and first arrival picking are fundamental to the analysis of complex seismic events. This method can provide accurate and reliable data support for subsequent seismic wave propagation path analysis and source parameter estimation.

[0040] Furthermore, the superimposed amplitude spectrum function obtained in step three is as follows:

[0041]

[0042] In the formula, S(ω) represents the j-th waveform to be processed after time difference calibration. j The spectrum of (t), A j(ω) represents the amplitude spectrum of the j-th waveform to be processed.

[0043] Furthermore, the superimposed channel phase spectrum function obtained in step three is as follows:

[0044]

[0045] In the formula, φ(ω) represents the j-th waveform to be processed after time difference calibration. j The phase spectrum of (t), Φ j (ω) represents the phase spectrum of the j-th waveform to be processed.

[0046] The beneficial effects of the basic scheme are: 1. The superimposed amplitude spectrum function A(ω) effectively reduces the interference of random noise and improves the signal-to-noise ratio by averaging the amplitude spectrum of the multi-channel waveform. This helps to more accurately identify and analyze the characteristics of the seismic signal in subsequent processing.

[0047] 2. The phase spectrum function Φ(ω) of the superimposed traces preserves the main phase characteristics of the waveforms by averaging the phase spectrum of multiple traces. Phase information is an important component of seismic signal processing and is crucial for the location of seismic events and parameter estimation. Superimposition processing can further highlight these phase characteristics and improve the accuracy of the analysis.

[0048] 3. The amplitude and phase spectrum functions after superposition processing provide clearer spectral information, which helps to more accurately identify and analyze the components and characteristics of seismic signals in the frequency domain. This has a positive promoting effect on subsequent processing steps such as seismic event classification, identification, and parameter estimation.

[0049] 4. Overlay processing is a highly efficient signal processing method. By averaging data from multiple waveforms, it can significantly reduce the computational load and complexity in subsequent processing steps. This helps improve processing efficiency, shorten analysis time, and provide strong support for real-time monitoring and rapid response to seismic events.

[0050] 5. Overlay processing, by averaging data from multiple waveforms, can effectively reduce the impact of anomalies or errors in a single waveform on the overall processing results. This enhances the stability and reliability of the method, and improves the accuracy and credibility of the processing results.

[0051] 6. The superimposed amplitude and phase spectrum functions provide crucial foundational data for subsequent seismic signal processing and analysis. This data can be used for various aspects, including seismic event location, parameter estimation, waveform classification and identification, providing strong support for the construction of earthquake monitoring and early warning systems.

[0052] Furthermore, in step four, the ratio of the short-window kurtosis average value to the long-window kurtosis average value of the superimposed waveform trace to be processed is calculated, and the first arrival pick of the superimposed waveform trace to be processed is performed based on the ratio. The calculation formula is as follows:

[0053]

[0054] In the formula, x(n) is the superimposed trace, M is the long time window length, and N is the short time window length. The maximum value of R is usually approximated as the first arrival point of the seismic wave.

[0055] After obtaining the initial arrival time T0 of the superimposed channels, combine it with the relative arrival time t recorded for each channel in step three. i Reverse time difference calibration is performed to obtain the actual first arrival and arrival times T of each channel record. i The calculation formula is:

[0056] T i =T0+t i .

[0057] The beneficial effects of the basic scheme are: 1. Kurtosis is a statistical measure that reflects the shape of data distribution and is highly sensitive to noise and outliers. By calculating the kurtosis ratio, the influence of background noise and interference signals can be suppressed more effectively, improving the accuracy of signal recognition.

[0058] 2. This method avoids the complex waveform matching and feature extraction process, and directly performs initial arrival picking by calculating the kurtosis ratio, which greatly simplifies the processing flow and improves processing efficiency.

[0059] 3. By calculating the ratio of the average short-term window kurtosis to the average long-term window kurtosis of the stacked traces, the first arrival point of seismic waves can be identified more effectively, overcoming the drawback of single-trace feature algorithms being significantly affected by errors between individual traces. This method can also overcome the errors and uncertainties present in traditional first arrival picking methods, improving the accuracy and reliability of first arrival picking.

[0060] Furthermore, in step six, the number of fixed locations for the oscillation generator should be at least three.

[0061] The beneficial effects of the basic scheme are: 1. At least three fixed-location oscillators can form a basic spatial positioning network. By measuring the time difference between the arrival of the reference waveforms emitted by these oscillators at each detector, the position of the detector can be accurately calculated using a time difference of arrival (TDOA) positioning algorithm. In three-dimensional space, at least three non-collinear points are required to determine the position of a point. Therefore, at least three oscillators are the minimum requirement to ensure the accuracy of detector spatial positioning.

[0062] 2. When using at least three oscillators, if the data from one or two oscillators fails (e.g., malfunction, signal interference), the data from the remaining oscillators can still be used for detector positioning. This increases the redundancy of the positioning system and improves its reliability and stability.

[0063] 3. Accurate detector position information is fundamental for subsequent processing steps (such as time difference calibration, waveform superposition, and first arrival pickup). Errors in detector position will directly affect the accuracy of these subsequent processing steps. Therefore, using at least three oscillators to ensure detector position accuracy can indirectly improve the accuracy and efficiency of the entire processing flow.

[0064] 4. In complex terrain and geological conditions, the propagation path of seismic waves may be affected by various factors (such as topographic relief and stratigraphic changes). Using at least three oscillators can create a wider spatial coverage and reduce positioning errors caused by changes in terrain and geological conditions. This helps ensure accurate detector location information even in complex environments.

[0065] Furthermore, the arrival time difference positioning algorithm in step six calculates the relative position between the detector and each oscillator to perform spatial positioning of the detector based on the time difference between the time when the detector detects the first peak of the reference waveform and the time when the oscillator emits the first peak of the reference waveform.

[0066] The advantages of the basic scheme are: 1. This algorithm does not depend on specific hardware or equipment layout. As long as the time difference can be accurately measured, it can perform positioning under different terrain and geological conditions. This makes the algorithm highly flexible and adaptable, and can be applied to various complex environments.

[0067] 2. Compared to other positioning methods, such as GPS positioning or landmark-based positioning, this algorithm does not require additional equipment or complex operating procedures. It only requires setting up an oscillator at a fixed location and recording the time point when the detector detects the wave peak to perform calculations and positioning.

[0068] 3. This algorithm, based on time difference measurements, can perform data processing and location calculations in real-time or near real-time. This helps to quickly obtain the location information of the geophone, providing timely data support for subsequent microseismic event analysis.

[0069] A microseismic event first arrival picking system based on dynamic time warping includes several detectors, several oscillation generators, a processing module, and a display and reporting module;

[0070] Detectors are used to detect the reference waveform and the waveform to be processed, and the number of detectors is at least 3;

[0071] The oscillation generator is used to simultaneously transmit reference oscillation waves at different fixed locations to verify the position of the detector.

[0072] The processing module is used to calculate the spatial positioning information of the detector based on the reference waveform and the waveform data to be processed detected by the detector, and to perform initial arrival picking calculation on the waveform to be processed.

[0073] The display report module is used to display data visualization results and report changes in detector position based on the calculation results of the processing module.

[0074] The beneficial effects of the basic scheme are: 1. By employing a dynamic time warping algorithm in the processing module, the system can accurately calculate the relative time difference between the waveforms to be processed, thereby accurately determining the first arrival of the superimposed waveform. Time difference calibration effectively solves the alignment difference problem existing in the traditional first arrival picking method, significantly improving the accuracy of first arrival picking.

[0075] 2. The system employs at least three detectors for detection, enabling the acquisition of more comprehensive waveform data and further improving the accuracy of initial arrival pickup. The collaborative operation of multiple detectors also allows for cross-validation of waveform data, reducing errors and uncertainties.

[0076] 3. By simultaneously transmitting reference oscillation waves at different fixed locations, the oscillation generator can verify the accuracy of the detector's position. This helps ensure that the detector maintains the correct spatial layout during monitoring, thereby improving the reliability and stability of the entire system.

[0077] 4. This system automates the initial acquisition process, enabling rapid acquisition, processing, and analysis of waveform data without manual intervention. This significantly reduces the workload of staff and improves work efficiency.

[0078] 5. The report display module can present the calculation results of the processing module in a data visualization format and automatically generate detailed reports. This allows staff to intuitively understand the monitoring results, promptly identify problems, and take appropriate measures.

[0079] 6. This system has broad application prospects in the field of petroleum exploration. By accurately acquiring the first arrival and arrival times of microseismic events, it can provide reliable data support for source location and focal mechanism inversion, thereby helping petroleum companies to more accurately understand the distribution and characteristics of underground reservoirs and improve exploration success rates. Besides petroleum exploration, this system can also be applied to other related fields such as earthquake monitoring and geological disaster early warning. By monitoring and analyzing seismic waveform data in real time, it can provide timely and accurate earthquake early warning information to relevant departments, effectively reducing losses caused by earthquake disasters. Attached Figure Description

[0080] Figure 1 This is a schematic diagram of the microseismic event first arrival picking method based on dynamic time warping in an embodiment of the present invention.

[0081] Figure 2 This is a schematic diagram showing the positional relationship between the detector and the oscillator in an embodiment of the present invention.

[0082] Figure 3 This is a waveform diagram of the time difference calibration result of the microseismic event first arrival picking method based on dynamic time warping in an embodiment of the present invention.

[0083] Figure 4 This is a waveform diagram of the first arrival picking results of superimposed trace phases in an embodiment of the present invention based on the dynamic time warping method for picking first arrivals of microseismic events.

[0084] Figure 5 This is a waveform diagram of the actual first arrival results of each trace of the microseismic event first arrival picking method based on dynamic time warping in an embodiment of the present invention.

[0085] Figure 6 This is a schematic diagram of a microseismic event first arrival picking system based on dynamic time warping in an embodiment of the present invention.

[0086] The reference numerals in the accompanying drawings include: ★, oscillator position; ▼, detector position; ●, initial arrival time of the oscillation phase. Detailed Implementation

[0087] The following detailed description illustrates the specific implementation method:

[0088] Example 1

[0089] The basics are as follows: Figure 1 , Figure 2 , Figure 3 , Figure 4 and Figure 5 As shown: A method for picking the first arrival of microseismic events based on dynamic time warping is used to pick the first arrival of the phase of microseismic events according to the waveform to be processed. The method includes step one: detecting the waveform to be processed by each detector in the detection area and performing noise reduction processing. The noise reduction processing includes filtering noise reduction based on frequency domain filter, performing Fourier transform and inverse transform on the waveform to be processed, and performing static correction and deconvolution noise reduction processing.

[0090] Step 2: Time difference calibration of the waveform to be processed. The inter-channel time difference of the waveform to be processed is calculated using the cross-correlation function. Based on the relationship between the inter-channel time difference and the relative arrival time, a system of linear equations relating the inter-channel time difference to the relative arrival time of each channel is established and solved to calibrate the time difference of each channel's arrival time. The cross-correlation function of the inter-channel signals of the waveform to be processed is defined as:

[0091]

[0092] In the formula, P is the number of sampling points, and x i (p) and x j (p) represents two data points. When c i,j When (k) is at its maximum, it is denoted as c. i,j (max), at which point x i (p) and x j The waveforms of (p) have the highest similarity, and the x of the two signals are... i (p) and x j (p) time difference Δt i,j Considered as the time difference between the first arrival and arrival times of the two routes;

[0093] Inter-track time difference Δt i,j and the initial arrival time t of the two records i and t j The relationship between them is:

[0094] t i -t j =Δt i，j ,

[0095] The linear equations relating inter-track time difference to relative arrival times of each track, and the solution process, are as follows, taking 5 track records as an example:

[0096]

[0097] For M channels of records, the above formula requires M(M-1) / 2 cross-correlation operations to obtain M(M-1) / 2 sets of inter-channel time difference information. The formula can be simplified to the following form:

[0098] At = Δt

[0099] In the simplified formula, A is the sparse coefficient matrix, Δt is the measured data vector, and t is the relative arrival vector to be determined. The least squares solution of the simplified formula is:

[0100] t=(A T A) -1 A T Δt;

[0101] Step 3: Waveform superposition processing. The horizontal superposition method is used. According to the Fourier transform time delay theorem, the amplitude spectrum function and phase spectrum function of the superimposed channel are obtained as follows:

[0102]

[0103] In the formula, S(ω) represents the j-th waveform to be processed after time difference calibration. j The spectrum of (t), A j (ω) represents the amplitude spectrum of the j-th waveform to be processed.

[0104]

[0105] In the formula, φ(ω) represents the j-th waveform to be processed after time difference calibration. j The phase spectrum of (t), Φ j (ω) represents the phase spectrum of the j-th waveform to be processed;

[0106] Step 4: First arrival acquisition of the waveform to be processed. The first arrival of the waveform to be processed is acquired using the long-short time energy ratio method. Then, reverse time difference calibration is performed based on the relative arrival time ti in Step 3 to obtain the actual first arrival time Ti of each channel record. The calculation formula is as follows:

[0107]

[0108] In the formula, x(n) is the superimposed trace, M is the long time window length, and N is the short time window length. The maximum value of R is usually approximated as the first arrival point of the seismic wave.

[0109] After obtaining the initial arrival time T0 of the superimposed channels, combine it with the relative arrival time t recorded for each channel in step three. i Reverse time difference calibration is performed to obtain the actual first arrival and arrival times T of each channel record. i The calculation formula is:

[0110] T i =T0+t i .

[0111] Example 2

[0112] The difference from the above embodiments is that, as shown in the appendix Figure 1 , Figure 2 As shown: The microseismic event first arrival picking method based on dynamic time warping also includes step five: relative arrival time comparison. The relative arrival time ti of each trace obtained in step three is compared with the relative arrival time of each trace obtained in the previous phase first arrival picking. The detectors corresponding to the waveforms with relative arrival time errors exceeding ±5% are marked as displacements.

[0113] Step Six: Detector Position Verification. Reference waveforms are emitted using oscillators at several fixed locations. After detection by detectors marked with displacement, the detector positions are verified based on a time difference of arrival (TDOA) positioning algorithm. The number of fixed locations for the oscillators is set to at least three. The TDOA positioning algorithm calculates the relative position between the detector and each oscillator based on the time difference between the time the detector detects the first peak of the reference waveform and the time the oscillator emits the first peak of the reference waveform.

[0114] Step 7: Calculate parameter adjustment. After verifying the new position of the detector, change the preset detector spacing and position parameters, repeat step 2 of phase first arrival acquisition, and optimize the calculation accuracy of the next phase first arrival acquisition.

[0115] Example 3

[0116] The difference from the above embodiments is that, as shown in the appendix Figure 1 , Figure 2 and Figure 6 As shown: A microseismic event first arrival picking system based on dynamic time warping includes several detectors, several oscillation generators, a processing module and a display and reporting module;

[0117] Detectors are used to detect the reference waveform and the waveform to be processed, and the number of detectors is at least 3;

[0118] The oscillation generator is used to simultaneously transmit reference oscillation waves at different fixed locations to verify the position of the detector.

[0119] The processing module is used to calculate the spatial positioning information of the detector based on the reference waveform and the waveform data to be processed detected by the detector, and to perform initial arrival picking calculation on the waveform to be processed.

[0120] The display report module is used to display data visualization results and report changes in detector position based on the calculation results of the processing module.

[0121] The specific implementation process is as follows: During the initial arrival pickup of microseismic phases, the microseismic oscillations are first detected by each geophone and each channel of waveform to be processed is output. The waveform data of each channel is preprocessed in the processing module to remove noise. Then, the processing module uses the cross-correlation function to obtain the inter-channel time difference of the waveform to be processed. After solving the linear equation system of inter-channel time difference and relative arrival time of each channel, the arrival time of each channel is calibrated. The waveforms to be processed after time difference calibration are horizontally superimposed to improve the signal-to-noise ratio and obtain the amplitude spectrum function and phase spectrum function of the superimposed waveforms as a reference for subsequent calculations. Finally, the initial arrival of the single superimposed waveform is picked up after calculation by the long-short time energy ratio method. The pickup time is calibrated by reverse time difference to obtain the initial arrival time of each channel of waveform to be measured, avoiding the influence of individual inter-channel errors and improving the accuracy and reliability of initial arrival pickup. The acquired results are displayed and reported visually to staff through the reporting module. These clear and intuitive results enhance staff's understanding of microseismic events, including location, parameter estimation, waveform classification, and identification, providing strong data support for the construction of earthquake monitoring and early warning systems. By comparing the relative arrival times of each trace with those obtained from the initial arrival of the previous phase, waveforms with relative arrival errors exceeding ±5% are marked as having displacement by the corresponding geophones, and geophone position verification is then initiated.

[0122] During the verification of the detector position, the oscillator generators emit reference oscillation waves at their respective fixed positions. After the detector detects the reference waveform, the reference waveform data is transmitted to the processing module, including the reference waveform spectrum data and time data. The processing module uses the time difference of arrival positioning algorithm to calculate the spatial position of the detector. If the actual position of the detector is different from the preset position, the data is reported to the staff through the display report module, the detector position parameters are automatically corrected, and step two is restarted to optimize and calibrate the initial arrival pickup effect of the oscillation phase.

[0123] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0124] The above descriptions are merely embodiments of the present invention. Commonly known structures and characteristics are not described in detail here. Those skilled in the art are aware of all common technical knowledge in the field prior to the application date or priority date, are aware of all existing technologies in that field, and have the ability to apply conventional experimental methods prior to that date. Those skilled in the art can, under the guidance of this application, improve and implement this solution in combination with their own capabilities. Some typical known structures or methods should not be obstacles for those skilled in the art to implement this application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the structure of the present invention. These should also be considered within the scope of protection of the present invention, and will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A method for picking the first arrival of microseismic events based on dynamic time warping, used to pick the first arrival of the phase of a microseismic event according to the waveform to be processed, characterized in that, include: Step 1: Detect the waveform to be processed using the detectors located within the detection area and perform noise reduction processing; Step 2: Time difference calibration of the waveform to be processed. Use the cross-correlation function to obtain the inter-channel time difference of the waveform to be processed. Based on the relationship between the inter-channel time difference and the relative arrival time, establish a linear equation system between the inter-channel time difference and the relative arrival time of each channel and solve it to perform time difference calibration of the arrival time of each channel. Step 3: Superposition processing of the waveforms to be processed. The horizontal superposition method is used for superposition processing. According to the time delay theorem of Fourier transform, the amplitude spectrum function and phase spectrum function of the superimposed channel are obtained. Step 4: First arrival pickup of the waveform to be processed. The first arrival of the waveform to be processed is picked up using the long-short time energy ratio method. Then, the reverse time difference calibration is performed according to the relative arrival time ti in Step 3 to obtain the actual first arrival time Ti of each channel record. Step 5: Relative arrival time comparison. Based on the relative arrival time ti of each channel obtained in Step 3, compare it with the relative arrival time of each channel obtained from the first arrival of the previous phase. Mark the detector corresponding to the waveform with a relative arrival time error exceeding ±5% as displacement. Step 6: Detector position verification. Reference waveforms are emitted at several fixed locations using an oscillator. After detection by detectors marked with displacement, the detector positions are verified based on the time difference of arrival (TDOA) positioning algorithm. Step 7: Calculate parameter adjustment. After verifying the new position of the detector, change the preset detector spacing and position parameters, repeat step 2 of phase first arrival acquisition, and optimize the calculation accuracy of the next phase first arrival acquisition.

2. The method for picking first arrivals of microseismic events based on dynamic time warping according to claim 1, characterized in that: The first step involves denoising the waveform, including frequency domain filter-based denoising, Fourier transform and inverse transform of the waveform, and static correction and deconvolution denoising.

3. The method for picking first arrivals of microseismic events based on dynamic time warping according to claim 2, characterized in that: In step two, the cross-correlation function between the two waveform channels to be processed is defined as: ； In the formula, P is the number of sampling points, and x i (p) and x j (p) represents two data points. When c i,j When (k) is at its maximum, it is denoted as c. i,j (max), at this time x i (p) and x j The waveforms of (p) have the highest similarity, and the x of the two signals are... i (p) and x j (p) time difference Δt i,j Considered as the time difference between the first arrival and arrival times of the two routes; Inter-track time difference Δt i,j and the initial arrival time t of the two records i and t j The relationship between them is: 。 4. The method for picking first arrivals of microseismic events based on dynamic time warping according to claim 3, characterized in that: The five linear equations relating the inter-channel time difference and the relative arrival times of each channel in step two are as follows: ； For M channels of records, the above formula requires M(M-1) / 2 cross-correlation operations to obtain M(M-1) / 2 sets of inter-channel time difference information. The formula can be simplified to the following form: ； In the simplified formula, A is the sparse coefficient matrix, Δt is the measured data vector, and t is the relative arrival vector to be determined. The least squares solution of the simplified formula is: 。 5. The method for picking first arrivals of microseismic events based on dynamic time warping according to claim 4, characterized in that: The superimposed amplitude spectrum function obtained in step three is as follows: ； In the formula, S(ω) represents the j-th waveform to be processed after time difference calibration. j The spectrum of (t), A j (ω) represents the amplitude spectrum of the j-th waveform to be processed.

6. The method for picking first arrivals of microseismic events based on dynamic time warping according to claim 5, characterized in that: The superimposed phase spectrum function obtained in step three is as follows: ； In the formula, φ(ω) represents the j-th waveform to be processed after time difference calibration. j The phase spectrum of (t), Φ j (ω) represents the phase spectrum of the j-th waveform to be processed.

7. The method for picking first arrivals of microseismic events based on dynamic time warping according to claim 6, characterized in that: In step four, the ratio of the short-window kurtosis average value to the long-window kurtosis average value of the superimposed waveform traces is calculated, and the first arrival pick of the superimposed waveform traces is performed based on this ratio. The calculation formula is as follows: ； In the formula, x(n) is the superimposed trace, M is the long time window length, and N is the short time window length. The maximum value of R is usually approximated as the first arrival point of the seismic wave. After obtaining the initial arrival time T0 of the superimposed channels, combine it with the relative arrival time t recorded for each channel in step three. i Reverse time difference calibration is performed to obtain the actual first arrival and arrival times T of each channel record. i The calculation formula is: 。 8. The method for picking first arrivals of microseismic events based on dynamic time warping according to claim 7, characterized in that: In step six, the number of fixed locations for the oscillation generator should be at least three.

9. The method for picking first arrivals of microseismic events based on dynamic time warping according to claim 8, characterized in that: The time difference of arrival (TDOA) positioning algorithm in step six calculates the relative position between the detector and each oscillator to achieve spatial positioning of the detector based on the time difference between the time the detector detects the first peak of the reference waveform and the time the oscillator emits the first peak of the reference waveform.

10. A system for implementing the microseismic event first arrival picking method based on dynamic time warping as described in any one of claims 1-9, characterized in that: It includes several detectors, several oscillators, a processing module, and a display and reporting module; Detectors are used to detect the reference waveform and the waveform to be processed, and the number of detectors is at least 3; The oscillation generator is used to simultaneously transmit reference oscillation waves at different fixed locations to verify the position of the detector. The processing module is used to calculate the spatial positioning information of the detector based on the reference waveform and the waveform data to be processed detected by the detector, and to perform initial arrival picking calculation on the waveform to be processed. The display report module is used to display data visualization results and report changes in detector position based on the calculation results of the processing module.

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