Repeated earthquake identification method based on earthquake observation data of single station

Abstract

The invention discloses a repeated earthquake identification method based on single-station seismological observation data, and belongs to the technical field of repeated earthquake identification, and the method comprises the steps: obtaining a template earthquake, stations and continuous waveform data; calculating a multi-section cross correlation coefficient by using a multi-section cross correlation method, and screening potential repeated seismic events; calculating the magnitude difference between the template earthquake event and the potential repeated earthquake event, and screening out the potential repeated earthquake event which is small in magnitude difference with the template earthquake event; the hypothetical stress drop value is used to calculate the radius of the seismic source fracture area of the template seismic event and the potential repeated seismic event; estimating the seismic source spacing by using the PS travel time difference to the same station by using the repeated seismic event pair, and calculating the seismic source spacing; and calculating the overlapping degree of the seismic source fracture area by using the seismic source spacing and screening repeated earthquakes. According to the method, under the condition that only single-station seismological observation data are available, the waveform similarity judgment method and the physical judgment method are combined at the same time, and the reliability and accuracy of repeated seismic recognition are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of repeated earthquake identification, and in particular to a repeated earthquake identification method based on seismic observation data of a single station. Background Art

[0002] Repeating earthquakes refer to a group of earthquakes that occur at the same location on a fault, have almost the same magnitude and recurrence interval, and have highly similar waveforms and focal mechanisms. They are currently widely used in many fields, such as studying the incubation process and precursor characteristics of earthquakes and landslides, earthquake prediction, the time-varying nature of the Earth's inner core, improving positioning accuracy, estimating the slip rate of deep faults, monitoring underground medium changes, and measuring deep-sea temperature changes.

[0003] The numerous applications of repeating earthquakes make accurate identification of repeating earthquakes particularly important. There are currently three methods for identifying repeating earthquakes. The first is the waveform similarity judgment method. This method is also the most popular method, relying on the matched filtering method. It essentially measures the similarity of the waveforms between a pair of earthquakes through the corresponding mutual correlation coefficients; the second is the physical judgment method. This method uses accurate earthquake positioning and source rupture scale calculations to make judgments based on the overlap of the target earthquake rupture area and the magnitude difference of the earthquake, but usually requires a dense network of stations; the third method is the mixed condition judgment method. In addition to requiring a certain degree of waveform similarity, this method also requires additional conditions, such as earthquake recurrence interval, SP time difference, magnitude difference, etc.

[0004] According to incomplete statistics, in previous studies on repeating earthquakes, due to the small number of stations or low positioning accuracy, more than 60% of the studies chose to use only waveform similarity to identify repeating earthquakes, of which about 40% of the studies used only data from a single station. However, some scholars have proved that because waveform similarity is affected by many factors, it is impossible to reliably identify repeating earthquakes using waveform similarity alone. Therefore, in extreme cases where only seismic waveform data from a single station is available, relying solely on waveform similarity to identify repeating earthquakes makes the identification results very unreliable. The reliability of repeating earthquake identification results is crucial to related research, and incorrectly identified repeating earthquakes may lead to incorrect research results and interpretations. Summary of the invention

[0005] The purpose of the present invention is to provide a method for identifying repeated earthquakes based on seismic observation data from a single station. When only seismic observation data from a single station is available, the method combines a waveform similarity judgment method and a physical judgment method to improve the reliability and accuracy of repeated earthquake identification.

[0006] To achieve the above object, the present invention provides a method for identifying repeated earthquakes based on seismic observation data of a single station, the steps comprising:

[0007] S1. Acquire data, including template earthquake data, station data, and continuous waveform data of the same station during the study period;

[0008] S2, calculating multi-segment cross-correlation coefficients based on the acquired data, using a multi-segment cross-correlation method to calculate multi-segment cross-correlation coefficients between template earthquake events and continuous waveforms, and screening potential repeated earthquake events;

[0009] S3, calculating the magnitude difference, calculating the magnitude difference between the template earthquake event and the potential repeated earthquake event screened out in step S2 according to the amplitude ratio of the S wave between the template earthquake event and the potential repeated earthquake event, and screening out the potential repeated earthquake events with a small magnitude difference with the template earthquake event;

[0010] S4, calculating the earthquake source rupture radius, using the assumed stress drop value to calculate the earthquake source rupture area radius of the template earthquake event and the potential repeated earthquake event screened in step S3;

[0011] S5, calculating the focal distance, estimating the focal distance using the PS travel time difference of repeated earthquake events to the same station, and calculating the focal distance between the template earthquake event and the potential repeated earthquake event screened in step S3;

[0012] S6. Use the focal distance to calculate the overlap of the focal rupture areas, and screen out repeated earthquakes that meet the conditions based on the overlap of the focal rupture areas.

[0013] Preferably, the template seismic data in step S1 includes three-component waveform data, longitude and latitude, and source parameters, the station data includes longitude and latitude, depth, and azimuth, and the continuous waveform data is a three-component continuous waveform.

[0014] Preferably, the multi-segment cross-correlation method is used in step S2 to calculate the multi-segment cross-correlation coefficient between the template earthquake event and the continuous waveform, including: dividing the template earthquake into multiple segments, moving each segment window along the continuous waveform, performing cross-correlation calculation on each segment window and its corresponding waveform, and calculating the average value of the cross-correlation coefficient of all windows, and using the average value as the multi-segment cross-correlation coefficient between the template earthquake event and the continuous waveform.

[0015] Preferably, the cross-correlation calculation formula for each window segment and its corresponding waveform is:

[0016]

[0017] The formula for finding the average value of the cross-correlation coefficients of all windows is:

[0018]

[0019] Where L represents the length of each window, L = n / N seg , N segIndicates the number of segments that the plate earthquake is divided into on average, m = 1, 2, ..., N seg , a m , b m represents the amplitude of each point of the two events, represents the average amplitude of two events in each window.

[0020] Preferably, in step S3, the epicenter distances of the repeated earthquakes are the same, and the magnitude difference is expressed as:

[0021] ΔM L =log(A1 / A2)

[0022] Where A is the amplitude of the earthquake's S wave.

[0023] Preferably, in step S4, the rupture area is set to be disc-shaped, and based on the disc-shaped rupture radius, the radius of the source rupture area of ​​the template earthquake event and the potential repeated earthquake event is calculated using the assumed stress drop value, and the formula is:

[0024]

[0025] Where M0 represents the seismic moment and Δσ represents the stress drop.

[0026] Preferably, the step S5 of calculating the earthquake source spacing includes:

[0027] The inter-event distance between the template earthquake event and the potential repeating earthquake event is calculated as:

[0028]

[0029] In the formula, ||d es || and ||d gs || are the distances between the template earthquake e and the potential repeating earthquake g screened out in step S3 and station s;

[0030] Assume that the seismic velocities of P wave and S wave in the uniform half space are V P and V S , the focal distance between the template earthquake e and the potential repeating earthquake g is:

[0031]

[0032] In the formula, K V =V P V S (V S -V P ), t S ,t p are the travel times of the P and S waves of the template earthquake event e and the potential repeater earthquake g to station s, respectively.

[0033] Preferably, the overlap of the earthquake source rupture area in step S6 is calculated by the ratio of the interval between the template earthquake event and the potential repeated earthquake event and the earthquake source rupture radius of the large magnitude earthquake, and the formula is:

[0034]

[0035] In the formula, r e and r g They represent the source rupture radius of the template earthquake e and the potential repeating earthquake g respectively, and Overap represents the overlap of the source rupture of the template earthquake e and the potential repeating earthquake g.

[0036] Therefore, the present invention adopts the above-mentioned repeated earthquake identification method based on single station seismic observation data, which has the following beneficial effects:

[0037] (1) The method of the present invention combines the characteristics of high similarity of repeated earthquake waveforms with the physical characteristics of small magnitude difference and high overlap of earthquake source rupture areas, and uses the physical characteristics to further constrain the waveform similarity recognition results, thereby improving the reliability of repeated earthquake recognition;

[0038] (2) The multi-segment cross-correlation method is used to calculate waveform similarity. The multi-segment cross-correlation method equally incorporates the contribution of each segment of the waveform, making it more sensitive to the tiny waveform differences caused by different locations of earthquakes. The obtained multi-segment cross-correlation coefficient can more reliably represent the similarity of waveforms.

[0039] (3) The method of the present invention is not only applicable to extreme cases where data from a single station is available, but can also be applied to areas with densely populated stations, and has wide applicability.

[0040] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A schematic diagram of an embodiment of a repeated earthquake identification method based on seismic observation data of a single station according to the present invention;

[0042] Figure 2 It is a flow chart of an embodiment of a repeated earthquake identification method based on seismic observation data of a single station according to the present invention;

[0043] Figure 3 This is the waveform information of the MOGE station in Nevada area in an embodiment of the method for identifying repeated earthquakes based on seismic observation data of a single station of the present invention;

[0044] Figure 4 This is an embodiment of a repeated earthquake identification method based on single-station seismic observation data of the present invention, which shows a pair of repeated earthquakes identified in the Nevada region. DETAILED DESCRIPTION

[0045] Example

[0046] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0047] Reference Figure 1-2 The present invention provides a method for identifying repeated earthquakes based on seismic observation data of a single station, the steps comprising:

[0048] S1. Acquire data, including template earthquake data, station data, and continuous waveform data of the same station during the study period.

[0049] In this embodiment, the three-component waveform data, longitude and latitude, and source parameter data of the S station of the template earthquake e in the study area (Nevada area) are obtained. The source parameter data includes depth, magnitude M Le , earthquake occurrence time, P wave arrival time and S wave arrival time, etc., obtain the longitude, depth and azimuth data of the S station and the three-component continuous waveform data of the same station during the study period, and preprocess the acquired data.

[0050] S2. Calculate multi-segment cross-correlation coefficients based on the acquired data, use a multi-segment cross-correlation method to calculate the multi-segment cross-correlation coefficients of the template earthquake event and the continuous waveform, and screen potential repeated earthquake events.

[0051] The waveform similarity can be measured by calculating the mutual correlation coefficient of two waveforms. The mutual correlation coefficient can reflect the similarity of the waveforms. The larger the mutual correlation coefficient, the higher the waveform similarity, and vice versa. The multi-segment cross-correlation method equally incorporates the contribution of each segment of the waveform, making it more sensitive to the tiny waveform differences caused by different locations of earthquakes. The resulting multi-segment cross-correlation coefficient can more reliably characterize the similarity of waveforms.

[0052] In this embodiment, the multi-segment cross-correlation method is used to calculate the multi-segment cross-correlation coefficient between the template earthquake event and the continuous waveform, which includes: dividing the template earthquake e average (including n samples) into N segments (L = n / N seg ), by moving each window segment along the continuous waveform, performing cross-correlation calculation on each window segment and its corresponding waveform, obtaining the average value of the cross-correlation coefficient of all windows, and using it as the multi-segment cross-correlation coefficient between the template earthquake event and the continuous waveform.

[0053] The cross-correlation calculation formula for each window and its corresponding waveform is:

[0054]

[0055] The formula for finding the average value of the cross-correlation coefficients of all windows is:

[0056]

[0057] Where L represents the length of each window, L = n / N seg , N seg Indicates the number of segments that the plate earthquake is divided into on average, m = 1, 2, ..., N seg , a m , b m represents the amplitude of each point of the two events, represents the average amplitude of two events in each window.

[0058] In this embodiment, cross-correlation calculation is performed on all continuous waveforms to obtain potential repetitive earthquakes f greater than a multi-segment cross-correlation coefficient threshold of 0.9, such as Figure 3 shown.

[0059] S3, calculate the magnitude difference, calculate the magnitude difference between the template earthquake event and the potential repeated earthquake event screened out in step S2 according to the amplitude ratio of the S wave, and screen out potential repeated earthquake events with a small magnitude difference with the template earthquake event.

[0060] In this embodiment, the magnitude of the local earthquake is expressed as M L Said, the formula is:

[0061] M L =log(A)+alog(d)+br+c

[0062] Where A is the amplitude of the earthquake's S wave, d is the focal distance, and a, b, and c are constants representing geometric diffusion, attenuation, and baseline, respectively.

[0063] For repeated earthquakes, the source distance r is the same, and the magnitude difference △M L It is expressed as:

[0064] △M L =log(A1 / A2)

[0065] Therefore, the local magnitude of the potential repeating earthquake is obtained as M Lf :

[0066] M Lf =M Le +△M L

[0067] Where M Le Indicates the magnitude of the template earthquake.

[0068] The magnitude differences between all potential repeating earthquakes f and template earthquakes e are calculated to obtain potential earthquakes g (g≤f) with a magnitude difference threshold of 0.3 and their nearby earthquake magnitudes.

[0069] S4. Calculate the earthquake source rupture radius, and use the assumed stress drop value to calculate the earthquake source rupture area radius of the template earthquake event and the potential repeated earthquake event screened out in step S3.

[0070] In this embodiment, the rupture area is set to be disc-shaped. Based on the disc-shaped rupture radius, the radius of the source rupture area of ​​the template earthquake event and the potential repeated earthquake event is calculated using the assumed stress drop value. The formula is:

[0071]

[0072] Where M0 represents the seismic moment, which is determined by the local magnitude M of the potential earthquake g. Lf Directly converted, the source rupture radius r of the template earthquake e can be calculated e The source rupture radius r of the potential repeating earthquake g g , △σ represents stress drop.

[0073] S5. Calculate the focal distance, estimate the focal distance using the PS travel time difference of repeated earthquake events to the same station, and calculate the focal distance between the template earthquake event and the potential repeated earthquake event screened out in step S3.

[0074] In this embodiment, calculating the distance between earthquake sources includes:

[0075] When the source spacing of repeating earthquakes is much smaller than their distance to station S, the inter-event distance between the template earthquake event and the potential repeating earthquake event is expressed as:

[0076]

[0077] In the formula, ‖d gs ‖ and ‖d gs ‖ are the distances from the template earthquake e and the potential repeater earthquake g to the station s, respectively.

[0078] Assume that the seismic velocities of P wave and S wave in the uniform half space are V P and V S , the focal distance between the template earthquake e and the potential repeating earthquake g is:

[0079]

[0080] In the formula, K V =V P V S (V S -V P), t S ,t p are the travel times of the P and S waves of the template earthquake event e and the potential repeater earthquake g to station s, respectively.

[0081] S6. Use the focal distance to calculate the overlap of the focal rupture areas, and screen out repeated earthquakes that meet the conditions based on the overlap of the focal rupture areas.

[0082] In this embodiment, the overlap of the earthquake source rupture area is calculated by the ratio of the earthquake source distance between the template earthquake event e and the potential repeated earthquake event g to the earthquake source rupture radius of the larger magnitude earthquake, and the formula is:

[0083]

[0084] In the formula, r e and r g They represent the source rupture radius of the template earthquake e and the potential repeating earthquake g respectively, and Overap represents the overlap of the source rupture of the template earthquake e and the potential repeating earthquake g.

[0085] In this embodiment, by calculating the source rupture overlap Overlap of the template earthquake e and the potential repeating earthquake g, the final repeating earthquake h (h≤g) less than the source rupture overlap threshold of 0.8 is obtained, as shown in FIG. Figure 4 shown.

[0086] Therefore, the present invention adopts the above-mentioned repeated earthquake identification method based on single-station seismic observation data, combines the waveform similarity judgment method and the physical judgment method, and uses the multi-segment cross-correlation method to calculate the multi-segment cross-correlation coefficient. According to the characteristics of high similarity of repeated earthquake waveforms and the physical characteristics of small magnitude difference and high overlap of earthquake source rupture areas, the physical characteristics are used to further constrain the waveform similarity recognition results, thereby improving the reliability of repeated earthquake identification.

[0087] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for identifying repeated earthquakes based on seismic observation data from a single station, characterized in that the steps include: S1. Acquire data, including template earthquake data, station data, and continuous waveform data of the same station during the study period; S2, calculating multi-segment cross-correlation coefficients based on the acquired data, using a multi-segment cross-correlation method to calculate multi-segment cross-correlation coefficients between template earthquake events and continuous waveforms, and screening potential repeated earthquake events; S3, calculating the magnitude difference, calculating the magnitude difference between the template earthquake event and the potential repeated earthquake event screened out in step S2 according to the amplitude ratio of the S wave between the template earthquake event and the potential repeated earthquake event, and screening out the potential repeated earthquake events with a small magnitude difference with the template earthquake event; S4, calculating the earthquake source rupture radius, using the assumed stress drop value to calculate the earthquake source rupture area radius of the template earthquake event and the potential repeated earthquake event screened in step S3; S5, calculating the focal distance, estimating the focal distance using the PS travel time difference of repeated earthquake events to the same station, and calculating the focal distance between the template earthquake event and the potential repeated earthquake event screened in step S3; S6. Use the focal distance to calculate the overlap of the focal rupture areas, and screen out repeated earthquakes that meet the conditions based on the overlap of the focal rupture areas.

2. The method for identifying repeated earthquakes based on seismic observation data from a single station according to claim 1, characterized in that: The template seismic data in step S1 includes three-component waveform data, longitude and latitude, and source parameters; the station data includes longitude and latitude, depth, and azimuth; and the continuous waveform data is a three-component continuous waveform.

3. The method for identifying repeated earthquakes based on single-station earthquake observation data according to claim 1, characterized in that: The method of using a multi-segment cross-correlation method to calculate the multi-segment cross-correlation coefficient between the template earthquake event and the continuous waveform in step S2 includes: dividing the template earthquake into multiple segments, moving each segment window along the continuous waveform, performing cross-correlation calculation on each segment window and its corresponding waveform, calculating the average cross-correlation coefficient of all windows, and using the average cross-correlation coefficient as the multi-segment cross-correlation coefficient between the template earthquake event and the continuous waveform.

4. The method for identifying repeated earthquakes based on single-station seismic observation data according to claim 3, characterized in that: The cross-correlation calculation formula for each window and its corresponding waveform is: The formula for finding the average value of the cross-correlation coefficients of all windows is: Where L represents the length of each window, L = n / N seg , N seg Indicates the number of segments that the plate earthquake is divided into on average, m = 1, 2, ..., N seg , a m , b m represents the amplitude of each point of the two events, represents the average amplitude of two events in each window.

5. The method for identifying repeated earthquakes based on single-station seismic observation data according to claim 1, characterized in that: In step S3, the epicenter distances of repeated earthquakes are the same, and the magnitude difference is expressed as: ΔM L =log(A1 / A2) Where A is the amplitude of the earthquake's S wave.

6. The method for identifying repeated earthquakes based on seismic observation data from a single station according to claim 1, characterized in that: In step S4, the rupture area is set to be disc-shaped. Based on the disc-shaped rupture radius, the radius of the source rupture area of ​​the template earthquake event and the potential repeated earthquake event is calculated using the assumed stress drop value. The formula is: Where M0 represents the seismic moment and Δσ represents the stress drop.

7. The method for identifying repeated earthquakes based on single-station earthquake observation data according to claim 1, characterized in that: The calculation of the focal distance in step S5 includes: The inter-event distance between the template earthquake event and the potential repeating earthquake event is calculated as: In the formula, ||d es || and ||d gs || are the distances between the template earthquake e and the potential repeating earthquake g screened out in step S3 and station s; Assume that the seismic velocities of P wave and S wave in the uniform half space are V P and V S , the focal distance between the template earthquake e and the potential repeating earthquake g is: In the formula, K V =V P V S (V S -V P ), t S ,t p are the travel times of the P and S waves of the template earthquake event e and the potential repeater earthquake g to station s, respectively.

8. The method for identifying repeated earthquakes based on seismic observation data from a single station according to claim 1, characterized in that: The overlap of the earthquake source rupture area in step S6 is calculated by the ratio of the interval between the template earthquake event and the potential repeated earthquake event and the earthquake source rupture radius of the large magnitude earthquake, and the formula is: In the formula, r e and r g They represent the source rupture radius of the template earthquake e and the potential repeating earthquake g respectively, and Overap represents the overlap of the source rupture of the template earthquake e and the potential repeating earthquake g.

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