A method and system for aftershock monitoring and early warning at the earthquake rescue site

Abstract

The present invention relates to the technical field of earthquake monitoring, and specifically relates to a method and system for aftershock monitoring and early warning at the earthquake rescue site. The method includes: obtaining in real time the aftershock signal sequences at each acquisition location in the target area within a set time period; determining the arrival time of the P wave at the acquisition location where the first triggering condition occurs, and sending an early warning signal to the rescue personnel. That is, the solution of the present invention can perform aftershock monitoring and early warning in a timely and accurate manner.

Description

Technical Field

[0001] The present invention relates to the technical field of earthquake monitoring. More specifically, the present invention relates to a method and system for aftershock monitoring and early warning at the earthquake rescue site. Background Art

[0002] During the earthquake post-disaster rescue process, the monitoring and early warning of aftershocks are key measures to ensure the safety of rescue workers and disaster area residents. Therefore, it is necessary to continuously monitor aftershocks to avoid possible secondary disasters under the action of aftershocks.

[0003] Among them, the picking of the first arrival of P-waves (longitudinal waves) is the most basic and important step in microseismic identification, earthquake source location, and earthquake source mechanism interpretation. The common P-wave picking methods are as follows:

[0004] (1) Short Term Average / Long Term Average (STA / LTA) method: It uses the ratio of the short-term average value to the long-term average value to reflect the change in signal amplitude, and takes the time when the ratio is greater than a certain set threshold as the arrival time of the P-wave. The STA / LTA method has the characteristics of simple algorithm, fast calculation speed, and diverse average value characteristic functions. However, it is difficult to pick up low signal-to-noise ratio signals, and when the threshold is large, the first arrival of the P-wave may not be picked up or the picking error may be increased; when the threshold is small, it may be triggered by noise prematurely.

[0005] Based on the above problem of threshold setting, in the related art, such as in the 38th volume, No. 4 of Progress in Geophysics in 2023, an article titled "Picking the First Arrival of Natural Seismic Waves by STA / LTA Method and Its Improvement Analysis" was published. The article mentioned that by establishing the connection between the threshold and the background noise, two new methods for picking the first arrival of seismic waves based on the reference threshold were proposed, which can overcome the disadvantage that the fixed threshold cannot be applied to all seismic records, and the picking accuracy and precision are improved compared with the traditional STA / LTA method.

[0006] When determining the threshold in the above scheme, the known noise is introduced to correct the initial threshold to obtain the final threshold. However, due to the weak P-wave signal intensity of aftershocks and the different environmental noise intensities at different locations, when picking up the P-wave using the final threshold, there will still be problems such as missing the picking of the P-wave and untimely early warning.

[0007] (2) Artificial neural network method: The artificial neural network method uses the peak amplitude, the root mean square amplitude ratio within the time window, the envelope slope of the peak and its previous and subsequent peaks, and the ratio of noise to signal, etc. as the input factors of the neural network. The artificial neural network method is a relatively comprehensive P-wave first arrival picking method, which has strong adaptability to microseismic waveforms. However, its parameter calculation workload is large, the learning speed is slow, and the implementation is complex. It may have the problem of being unable to pick up the first arrival of the P-wave in time.

[0008] (3) Autoregressive model: The microseismic signal is divided into two local statistical time periods, and the autocorrelation minimum point is taken as the arrival time of the P wave. This algorithm has a relatively high picking accuracy, but it is unstable in picking low signal-to-noise ratio and tail oscillation signals.

[0009] Therefore, it is particularly important to be able to monitor and give early warnings of aftershocks in a timely and accurate manner. SUMMARY OF THE INVENTION

[0010] The object of the present invention is to provide a method and system for monitoring and early warning of aftershocks at the earthquake rescue site, so as to solve the problem that aftershocks cannot be monitored in a timely and accurate manner in the prior art; for this purpose, the present invention provides solutions in the following two aspects.

[0011] In the first aspect, a method for monitoring and early warning of aftershocks at the earthquake rescue site provided by the present invention includes:

[0012] Real-time acquisition of the aftershock signal sequence within a set time period at each acquisition position in the target area;

[0013] Determining the arrival time of the P wave at the acquisition position where the first trigger condition is met, and sending a warning signal to the rescue personnel; the condition is that the ratio of the short-term and long-term window average values of the aftershock signals at the current moment in each set time period is greater than the corresponding short-term and long-term threshold values; the short-term and long-term threshold values are the sum of the products of the ratio of the short-term and long-term window average values at the P wave arrival moment in all matching sequences at the corresponding acquisition position and the weights;

[0014] The process of obtaining the matching sequence is as follows: obtaining a plurality of historical aftershock sequences;

[0015] Obtaining the cross-spectral density curves of each historical aftershock sequence and the aftershock signal sequence; classifying all cross-spectral density curves to obtain a plurality of clusters; taking the reciprocal of the frequency of the maximum peak point in the average cross-spectral density curve of each obtained cluster as the joint period of the corresponding cluster;

[0016] Intercepting the time series segment including the current moment from the aftershock signal sequence according to the joint period; performing sliding window on each historical aftershock sequence with the joint period as the window to obtain local sequences;

[0017] Calculating the average matching degree between the time series segment under any joint period and all local sequences in the corresponding historical aftershock sequences, and taking the historical aftershock sequence with the maximum value in the average matching degree as the matching sequence;

[0018] The weight is the value after normalizing the maximum value.

[0019] In the above solution, by analyzing the cross-spectrum density curves of the aftershock signal sequences at different sampling positions and each historical aftershock sequence, the frequency-domain relationship between the two sequences can be measured, and then the joint period can be obtained. According to the length of the joint period, the average matching degree between the time series segment and all local time series is calculated, and the corresponding matching sequence can be obtained more precisely and accurately. Then, using the ratio of the long-term and short-term window averages of the P-wave arrival times of all matching sequences, the long-term and short-term thresholds at the current moment corresponding to the sampling position are estimated; by judging the ratio at the current moment corresponding to each sampling position through the long-term and short-term thresholds corresponding to each sampling position, the P-wave arrival time of the first triggered sampling position can be picked up more timely and accurately, improving the accuracy of aftershock monitoring.

[0020] Optionally, the average matching degree is the average of all matching degrees under the corresponding joint period; the matching degree is:

[0021] ;

[0022] Wherein, is the joint period the matching degree between the time series segment corresponding to and the local sequence at the a-th sliding window in the j-th historical aftershock sequence, is the standard deviation of the vibration amplitude of the time series segment corresponding to the joint period , is the standard deviation of the vibration amplitude of the local sequence at the a-th sliding window in the j-th historical aftershock sequence corresponding to the joint period , is the t-th vibration amplitude in the time series segment corresponding to the joint period , is the t-th vibration amplitude in the local sequence at the a-th sliding window in the j-th historical aftershock sequence corresponding to the joint period , is the length of the i-th joint period.

[0023] Calculating the matching degree based on the amplitude fluctuation difference and amplitude difference between the time series segment and the local sequence can obtain an accurate matching degree.

[0024] Optionally, the average matching degree is the average of the Pearson correlation coefficients between the time series segment and all local sequences in the corresponding historical aftershock sequence calculated separately under any joint period.

[0025] The matching degree calculated by the above solution can accurately measure the similarity between the two sequences.

[0026] Optionally, the ratio of the long-term and short-term window averages of the aftershock signal at the current moment is:

[0027] ;

[0028] wherein, is the ratio of the short-term and long-term window average values corresponding to the -th sampling point, S and L are respectively the numbers of aftershock signals in the short-term window and the long-term window, , are respectively the vibration amplitudes of the -th and -th aftershock signals,

[0029] Optionally, the cross-spectrum density curve is obtained by performing Fourier transform on any historical aftershock sequence and the aftershock signal sequence.

[0030] The cross-spectrum density curve obtained in the above solution can measure the degree of mutual coupling between the corresponding two sequences at different frequencies.

[0031] Optionally, the weight is: ;

[0032] is the weight of the -th joint period, is the corresponding maximum value under the joint period

[0033] The above normalization method can simplify the calculation.

[0034] Optionally, the acquisition positions are the positions where six seismographs are arranged uniformly in a triangle.

[0035] In a second aspect, a system for aftershock monitoring and early warning at the earthquake rescue site includes:

[0036] a processor;

[0037] a memory storing computer instructions for aftershock monitoring and early warning at the earthquake rescue site, and when the computer instructions are run by the processor, the system executes the above method for aftershock monitoring and early warning at the earthquake rescue site.

[0038] The beneficial effects of the present invention are:

[0039] The solution of the present invention first determines the matching sequences of the aftershock signal sequences at different sampling positions, and then estimates the short-term and long-term thresholds corresponding to the sampling positions by using the arrival times of P waves in all the matching sequences, so as to monitor the aftershocks at each sampling position in a timely and accurate manner; that is, by determining the short-term and long-term thresholds at different sampling positions, the influence of noise at different sampling positions is reduced, and the reliability of aftershock monitoring is improved. Description of the Drawings

[0040] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features, and advantages of the exemplary embodiments of the present invention will become readily understandable. In the drawings, several embodiments of the present invention are shown by way of illustration and not limitation, and like or corresponding reference numerals denote like or corresponding parts, wherein:

[0041] Figure 1 A flowchart of the steps of a method for aftershock monitoring and warning at the earthquake rescue site in this embodiment is schematically shown;

[0042] Figure 2 A schematic diagram of the seismograph distribution when collecting aftershock signals is schematically shown;

[0043] Figure 3 A block diagram of the structure of a system for aftershock monitoring and warning at the earthquake rescue site in this embodiment is schematically shown. Detailed implementation manners

[0044] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present invention.

[0045] Specifically, taking the earthquake rescue site in a certain area as an example, a method for aftershock monitoring and warning at the earthquake rescue site in this embodiment will be introduced, as Figure 1 shown, including the following steps:

[0046] Step S1, obtaining in real time the aftershock signals to be picked up in the target area.

[0047] In one embodiment, seismographs are used to collect real-time aftershock signals. Specifically, in the earthquake rescue site, a plurality of seismographs are evenly arranged in a triangular shape at various positions. Specifically, as Figure 2 shown, taking 6 seismographs as an example, the aftershock signals at the positions where the seismographs are located at different sampling times are collected by each seismograph. Among them, the aftershock signals characterize the propagation of seismic waves.

[0048] In this embodiment, the number of aftershock signals in the aftershock signal sequence can be 400 - 600, and the sampling frequency of the aftershock signals is 600 Hz, where the serial number corresponding to the number of samples corresponds to the sampling time.

[0049] In this embodiment, the aftershock signals corresponding to all times within a set time period at each collection position are used to form an aftershock signal sequence, and the end time within the set time period is the current time.

[0050] Step S2: Calculate the ratio of the short-term and long-term window average values of the aftershock signals at each acquisition location at the current moment, and determine the arrival time of the P wave at the acquisition location where the first trigger condition is met.

[0051] The above condition is that the ratio of the short-term and long-term window average values of the aftershock signals at each acquisition location at the current moment is greater than the short-term and long-term threshold values corresponding to the acquisition location.

[0052] In this embodiment, according to the condition, the ratio of each acquisition location is judged. When the first acquisition location meets the condition, the moment when the ratio of this acquisition location is located is used as the arrival time of the P wave of the aftershock.

[0053] Taking any sampling location as the target sampling location, the ratio of the short-term and long-term window average values of the target sampling location is calculated as:

[0054] ; where is the ratio of the short-term and long-term window average values corresponding to the moment of the th sampling point. S and L are respectively the numbers of aftershock signals in the short-term window and the long-term window. 、 are respectively the vibration amplitudes of the 、 th and

[0055] th aftershock signals.

[0056] Step S21: Obtain multiple historical aftershock data of different regions in the historical data.

[0057] In one embodiment, the historical aftershock data of different regions where earthquakes have occurred in the historical data are obtained through big data; each historical aftershock data includes an aftershock waveform diagram, the arrival time of the P wave, and the ratio of the short-term and long-term window average values at the arrival time of the P wave. Among them, the calculation method of the ratio of the short-term and long-term window average values is the same as the above-mentioned ratio of the short-term and long-term window average values, and will not be elaborated here.

[0058] Among them, the abscissa of the aftershock waveform diagram is time, and the ordinate is the vibration amplitude; the vibration amplitudes corresponding to each moment in the aftershock waveform diagram form a historical aftershock sequence.

[0059] It should be noted that since the aftershock signal sequence is the data of the set time period obtained in real time; while the historical aftershock sequence is obtained through historical data, theoretically the length of the historical aftershock sequence is much longer than the aftershock signal sequence.

[0060] Step S22: Obtain the cross-spectral density curve between the historical aftershock waveform diagram in each historical aftershock data and the aftershock signal.

[0061] In one embodiment, taking the aftershock signal sequence at any sampling position as an example, the time-domain signals in any historical aftershock sequence and the aftershock signal sequence are transformed into frequency-domain signals by using Fourier transform. Then, the frequency-domain signals of the two sequences are subjected to a multiplication operation. Finally, the frequency-domain signals are transformed back into time-domain signals through inverse Fourier transform to obtain the cross-spectral density, and thus the cross-spectral density curve can be obtained. Since the specific process of obtaining the cross-spectral density is a prior art, it will not be elaborated here too much.

[0062] Among them, the cross-spectral density (CSD) is a frequency-domain analysis method used to describe the mutual relationship between two signals and can measure the degree of mutual coupling between two signals at different frequencies.

[0063] In this embodiment, by obtaining the cross-spectral density of the signals at different times in the aftershock signal sequence and each historical aftershock sequence, the mutual relationship between the two signals can be obtained.

[0064] Step S23: Classify all the cross-spectral density curves to obtain multiple clusters; calculate the joint period of each cluster; calculate the matching degree between the time series segment under each joint period and any local sequence in the corresponding historical aftershock sequence to obtain the average matching degree of all local sequences, and select the historical aftershock sequence corresponding to the maximum value in the average matching degrees as the matching sequence.

[0065] In one embodiment, the K-means clustering algorithm is used to classify all the cross-spectral density curves; specifically, by using the K-means clustering algorithm to cluster all the cross-spectral density curves, multiple clusters are obtained. Exemplarily, if there are clusters.

[0066] When clustering, the CH can be used to evaluate the clustering effects of different values, and the value with the best clustering effect is selected as the final clustering result.

[0067] After obtaining K clusters, it is also necessary to average the multiple cross-spectral density curves included in each cluster to obtain the average cross-spectral density curve corresponding to each cluster. Specifically, the average cross-spectral density curve is obtained by averaging the cross-spectral densities at the same time in multiple cross-spectral density curves, and then the average cross-spectral density at all times is obtained, and thus the average cross-spectral density curve can be obtained. Among them, each cluster corresponds to an average cross-spectral density curve.

[0068] In this embodiment, by obtaining the average cross-spectral density curves of each cluster, the reciprocal of the frequency of the maximum peak point in each average cross-spectral density curve is used as the joint period of each historical aftershock sequence and the aftershock signal sequence in the corresponding cluster.

[0069] Taking the i-th cluster as an example, the joint period is: ; is the frequency corresponding to the maximum peak point in the average cross-spectral density curve of the i-th cluster.

[0070] It should be noted that each cluster corresponds to a joint period, and the number of clusters is equal to the number of joint periods; the joint periods of different cross-spectral density curves (one cross-spectral density curve corresponds to a combination of a historical aftershock sequence and an aftershock signal sequence) in the same cluster are equal.

[0071] After obtaining the joint periods of each cluster, it is also necessary to obtain time series segments and local sequences of corresponding lengths from the aftershock signal sequence and each historical aftershock sequence respectively according to the joint periods.

[0072] Specifically, the time series segment is a segment of data intercepted from the aftershock signal sequence according to the joint period and containing the current moment. Exemplarily, when the aftershock signal sequence is , taking as the starting point, intercept the aftershock signals at multiple moments before the corresponding moment (the current moment) to form a time series segment. The number of multiple moments is equal to the length of the joint period. Among them, n is the number of aftershock signals in the aftershock signal sequence, represents the n-th vibration amplitude.

[0073] Among them, the local time series is obtained by sliding a window on each historical aftershock sequence; the length of the window of the sliding window is the length of the joint period. Exemplarily, when any historical aftershock sequence is , taking as the starting point of the window, slide the window on this historical aftershock sequence in turn to obtain multiple local sequences; among them, represents the n0-th vibration amplitude in the historical aftershock sequence, and n0 is the number of aftershock signals in the historical aftershock sequence.

[0074] The step size of the above sliding window can be set to 1, and of course, it can also be determined according to the actual situation.

[0075] In this embodiment, after obtaining all the local sequences in the time series segment and the corresponding any historical aftershock sequence under each joint period, the matching degree between the time series segment and each local sequence in the corresponding any historical aftershock sequence under any joint period is also calculated.

[0076] In one embodiment, the matching degree is:

[0077] ;

[0078] Among them, is the joint period The matching degree between the corresponding time series segment and the local sequence at the a-th window sliding of the j-th historical aftershock sequence, is the combined period The standard deviation of the vibration amplitude of the corresponding time series segment, which is used to represent the combined period The volatility of the corresponding time series segment, is the combined period The standard deviation of the vibration amplitude of the local sequence at the a-th window sliding of the j-th historical aftershock sequence corresponding to it, which is used to represent the combined period The volatility of the corresponding local sequence, represents the combined period The volatility difference between the corresponding time series segment and the local sequence; is the combined period The t-th vibration amplitude in the corresponding time series segment, is the combined period The t-th vibration amplitude in the local sequence at the a-th window sliding of the j-th historical aftershock sequence corresponding to it, represents the combined period The average value of the amplitude difference between the corresponding time series segment and the local sequence at the a-th window sliding of the j-th historical aftershock sequence, is the length of the i-th combined period.

[0079] In another embodiment, the matching degree can also be the Pearson correlation coefficient between the time series segment and each local sequence. Specifically, the matching degree is:

[0080] ;

[0081] wherein, is the combined period The matching degree between the corresponding time series segment and the local sequence at the a-th window sliding of the j-th historical aftershock sequence, is the combined period The t-th vibration amplitude in the corresponding time series segment, is the combined period The t-th vibration amplitude in the local sequence at the a-th window sliding of the j-th historical aftershock sequence corresponding to it, is the combined period The mean value of the corresponding time series segment, is the combined period The average value of the local sequence at the a-th window sliding of the j-th historical aftershock sequence corresponding to it.

[0082] In this embodiment, the average matching degree between the time series segment in any combined period and all local sequences in any corresponding historical aftershock sequence is calculated as an index to measure whether the aftershock signal sequence matches any historical aftershock sequence, and the historical aftershock sequence corresponding to the maximum value in the average matching degrees is selected as the matching sequence of the aftershock signal sequence. Thus, the matching sequence for each combined period can be obtained.

[0083] The reason for calculating the matching degree is as follows: Since the P-wave signal intensity of aftershocks is weak and the environmental noise intensity varies at different locations, the pickup deviation of different aftershock signal sequences in different periods is different. Therefore, using a fixed threshold to pick up P-waves easily leads to the phenomenon of untimely P-wave pickup. Therefore, in this embodiment, according to the volatility of the aftershock signal sequence corresponding to each cluster and the historical aftershock sequence in the combined period, the matching degree between sequences in each combined period is calculated to screen out the matching sequences similar to the aftershock signal sequence, providing accurate data for estimating the long and short time thresholds at different adoption positions in the subsequent process.

[0084] The purpose of obtaining the combined period is as follows: To identify the common periodic patterns of the aftershock signal sequence and the historical aftershock sequence, and to more precisely find the historical aftershock sequence with a high matching degree with the aftershock signal sequence under the common periodic pattern, so as to obtain a more real and accurate matching sequence.

[0085] Step S24: The sum of the products of the ratio of the long and short time window average values of the P-wave arrival times in all matching sequences at the target sampling position and the weights is used as the long and short time threshold.

[0086] The long and short time threshold is: ; where is the long and short time threshold at the current moment, is the weight of the i-th combined period, is the ratio of the long and short time window average values of the P-wave arrival times in the matching sequence in the i-th combined period, is the number of combined periods.

[0087] The above weight is the maximum matching degree value after normalizing the maximum value in the average matching degrees.

[0088] Among them, normalizing the maximum value in the average matching degrees can adopt the maximum-minimum normalization method; it can also adopt the following method: ; is the weight of the i-th combined period, is the maximum value corresponding to the combined period , K is the number of combined periods, represents the sum of the maximum matching degrees under all combined periods.

[0089] In this embodiment, by calculating the short-term and long-term thresholds at different sampling positions, the arrival time of the P wave can be monitored more accurately. At the same time, as time changes, the aftershock signal sequences at each sampling position also change, and at this time, the short-term and long-term thresholds also change adaptively. That is, by determining the adaptively changing short-term and long-term thresholds, the monitoring of the arrival time of the P wave can be made more accurate.

[0090] Step S3: Based on the arrival time of the P wave, send a warning signal to the rescue personnel.

[0091] In this embodiment, according to the arrival time of the P wave of the acquired aftershock signal, the warning signal is transmitted to the alarm device carried by the rescue personnel to remind the rescue personnel to evacuate in time. At the same time, the warning signal is also transmitted to the rescue command office to direct the rescue personnel to evacuate.

[0092] The solution of the present invention can monitor aftershocks in a timely and accurate manner, improving the reliability of aftershock monitoring.

[0093] At the same time, compared with the prior art solution of first denoising the seismic signal based on a traditional denoising algorithm (which may remove some real P-wave signals during denoising and may also retain some strong noise signals) and then using a fixed short-term and long-term threshold to pick up the P wave, the solution of the present invention can pick up the arrival time of the P wave more timely and accurately, improving the accuracy of aftershock monitoring.

[0094] The present invention also provides a system for aftershock monitoring and warning at the earthquake rescue site. As Figure 3 shown, the system includes a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, a method for aftershock monitoring and warning at the earthquake rescue site according to the above of the present invention is implemented.

[0095] The system also includes other components well known to those skilled in the art such as a communication bus and a communication interface. Their settings and functions are known in the art, so they will not be described in detail here.

[0096] In the present invention, the foregoing memory may be any tangible medium that contains or stores a program, which can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, the computer-readable storage medium may be any suitable magnetic storage medium or magneto-optical storage medium, such as resistive random access memory (RRAM), dynamic random access memory (DRAM), static random access memory (SRAM), enhanced dynamic random access memory (EDRAM), high-bandwidth memory (HBM), hybrid memory cube (HMC), etc., or any other medium that can be used to store the required information and can be accessed by an application, module, or both. Any such computer storage medium may be part of the device or accessible or connectable to the device. Any application or module described in the present invention may be implemented by computer-readable / executable instructions stored or otherwise held by such a computer-readable medium.

[0097] In the description of this specification, "a plurality of" means at least two, for example, two, three, or more, etc., unless otherwise specifically defined.

[0098] Although this specification has shown and described multiple embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will think of many changes, alterations, and alternative ways without departing from the spirit and scope of the present invention. It should be understood that various alternatives to the embodiments of the present invention described herein may be employed in the practice of the present invention.

Claims

1. A method for aftershock monitoring and early warning at an earthquake rescue site, characterized in that: include: Real-time acquisition of aftershock signal sequences within a set period of time at each acquisition location in the target area; Determine the arrival time of the P wave at the acquisition location of the first trigger condition and send an early warning signal to the rescue personnel; The condition is that the ratio of the long-short time window average value of the aftershock signal at the current time in each set time period is greater than the corresponding long-short time threshold; the long-short time threshold is the sum of the product of the ratio of the long-short time window average value of the P wave arrival time in all matching sequences at the corresponding acquisition position and the weight; The matching sequence acquisition process is as follows: acquiring multiple historical aftershock sequences; Obtaining the cross-spectral density curves of each historical aftershock sequence and the aftershock signal sequence; classifying all cross-spectral density curves to obtain multiple clusters; taking the inverse of the frequency of the maximum peak point in the average cross-spectral density curve of each cluster obtained as the joint period of the corresponding cluster; According to the combined period, a time segment including the current time is intercepted from the aftershock signal sequence; a sliding window is performed on each historical aftershock sequence using the combined period as a window to obtain a local sequence; Calculate the mean matching degree of all local sequences between the time series segment and the corresponding historical aftershock sequences under any joint period, and take the historical aftershock sequence with the maximum value in the mean matching degree as the matching sequence; The weight is a normalized value of the maximum value.

2. A method for aftershock monitoring and early warning at an earthquake rescue site according to claim 1, characterized in that: The matching degree mean is the mean of all matching degrees in the corresponding joint period; the matching degree is: ； in, For joint cycle The matching degree between the corresponding time segment and the local sequence at the a-th sliding window in the j-th historical aftershock sequence, It is a joint cycle The standard deviation of the vibration amplitude of the corresponding time segment, It is a joint cycle The standard deviation of the vibration amplitude of the local sequence at the a-th sliding window in the corresponding j-th historical aftershock sequence, It is a joint cycle The tth vibration amplitude in the corresponding time segment, It is a joint cycle The corresponding t-th vibration amplitude in the local sequence at the a-th sliding window in the j-th historical aftershock sequence, is the length of the i-th joint period.

3. The method for aftershock monitoring and early warning at an earthquake rescue site according to claim 1, characterized in that: The matching degree mean is the mean of the Pearson correlation coefficients of the time series segments under any joint period and all local sequences in the corresponding historical aftershock sequences calculated respectively.

4. A method for aftershock monitoring and early warning at an earthquake rescue site according to claim 2 or 3, characterized in that: The ratio of the long and short time window average values ​​of the aftershock signal at the current moment is: ; in, For the The ratio of the average values ​​of the long and short time windows at the corresponding time of the sampling points, S and L are the number of aftershock signals in the short time window and the long time window respectively. , are the vibration amplitudes of the mth and kth aftershock signals, respectively. , are the mean vibration amplitudes of aftershock signals in the short-time window and long-time window, respectively.

5. The method for aftershock monitoring and early warning at an earthquake rescue site according to claim 1, characterized in that: The cross-spectral density curve is obtained by performing Fourier transform on any historical aftershock sequence and aftershock signal sequence.

6. A method for aftershock monitoring and early warning at an earthquake rescue site according to claim 4, characterized in that: The weights are: ; is the weight of the ith joint period, In the joint cycle The corresponding maximum value is K, which is the number of joint cycles.

7. The method for aftershock monitoring and early warning at an earthquake rescue site according to claim 1, characterized in that: The acquisition positions are the locations of six seismographs evenly arranged in a triangle.

8. A system for aftershock monitoring and early warning at an earthquake rescue site, characterized in that: include: processor; A memory storing computer instructions for aftershock monitoring and early warning at an earthquake rescue site. When the computer instructions are executed by the processor, the system executes a method for aftershock monitoring and early warning at an earthquake rescue site according to any one of claims 1 to 7.

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