CN114578414A - Earthquake focus monitoring method, earthquake focus monitoring device, computer equipment and storage medium - Google Patents

Abstract

The invention relates to a seismic source monitoring method, a device, computer equipment and a storage medium, wherein the method comprises the following steps: acquiring seismic data acquired by monitoring points in a preset historical time period; according to the first translational component, the second translational component and each candidate azimuth angle, obtaining a lateral acceleration corresponding to each candidate azimuth angle; determining a zero lag cross-correlation coefficient between each lateral acceleration and the third rotational component; and determining the final azimuth angle from the seismic source to the monitoring point according to the candidate azimuth angle and the zero lag cross-correlation coefficient. The method can accurately determine the seismic source position of the earthquake.

Description

Earthquake source monitoring method, device, computer equipment, and storage medium

technical field

The present invention relates to the technical field of earthquake monitoring, in particular to a method, device, computer equipment and storage medium for monitoring an earthquake source.

Background technique

When an earthquake with a larger magnitude occurs, its rupture path and speed are important factors that determine its destructive ability. Based on closely fitted observations of a large number of free parameters, a finite fault inversion method is used to determine the kinematics of the rupture. However, building a kinematic model containing prior information on the fault structure is inherently non-unique and does not ensure mechanical consistency in seismic dynamics. The ever-increasing computational resources allow the development of observationally constrained dynamic rupture models to complement data-driven analyses. Such rupture scenarios provide a physically self-consistent description of complex fault slip, while their complexity limits the total number of feasible numerical experiments. The rise of large-scale, dense seismic array instruments has enabled complementary techniques for tracking earthquakes in space and time. This method requires very limited prior knowledge to image coherent high-frequency energy radiation in a simple and fast manner.

Rotation can be derived from the spatial gradient of translational motion, and the array observation method is a measure that calculates the rotational component from the translational component. This method uses a large number of translation sensors to form a sensor array. On a local uniform structure, the finite difference method is used to act on the translation component measured by a well-calibrated sensor, and the corresponding seismic rotation signal is calculated. This method requires the seismometer itself to be sensitive to rotational motion. Although the minimum number of stations required is three, it usually requires several more stations with fairly good azimuth coverage to make stable observations. In addition, the quality control of the rotational component measurement based on the array observation method is very difficult. The relative spatial gradient at the reference station can be estimated by a certain number of dense arrays (Oliveira and Bolt, 1989; Spudich et al., 1995). FrohlichandPulliam (1999) pointed out that the classical single-station method has some ambiguities compared with travel-time-based methods, such as the problem of 180° inverse azimuth error. Joint analysis of translational and rotational motion data can help overcome these shortcomings.

The most common techniques for imaging seismic properties using array data can be divided into two categories, both based on analyzing P-wave phase information. The first category of methods is based on traditional array measurements. Known as the backprojection method, seismic energy radiation is imaged by applying array beamforming techniques. Backprojection was first demonstrated successfully at the 2004 Sumatra-Andaman earthquake source (epicenter). Directional effects are used to characterize faulting mechanisms (Krüger and Ohrnberger, 2005; Ishiie et al., 2005). The second type of seismic rupture tracking method is to estimate the back azimuth (BackAzimuth, BAz) of the incident wave through a single station. In polarization analysis, the three translational components of a standard seismometer can be used to estimate the back azimuth and incidence angle of the incoming wave. Cochard et al. (2006) proposed that on a free surface, the three rotational components in the x, y, and z directions can be expressed as:

表示位移波场的时间倒数。因此旋转可以由横向运动的空间梯度推导出来。in

Represents the time reciprocal of the displacement wavefield. The rotation can thus be derived from the spatial gradient of the lateral motion.

Bayer et al. (2012) developed a single-station method for tracking moving sources through polarization analysis of local and regional P-wave arrivals.

SUMMARY OF THE INVENTION

The present application provides a seismic source monitoring method, device, computer equipment and storage medium.

A first aspect provides an earthquake source monitoring method, the method comprising:

Acquire seismic data collected at the monitoring point within a preset historical time period, the seismic data including: a first translational component and a first rotational component on a first coordinate axis that are perpendicular to each other in a three-dimensional space, and a first rotational component on the second coordinate axis. The second translational component and the second rotational component, the third translational component and the third rotational component on the third coordinate axis;

determining the candidate inverse azimuth from the source to the monitoring point;

According to the first translational component, the second translational component and each of the candidate inverse azimuth angles, obtain the lateral acceleration corresponding to each of the candidate inverse azimuth angles;

determining a zero-lag cross-correlation coefficient between each of the lateral acceleration and the third rotational component;

According to the candidate inverse azimuth angle and the zero-lag cross-correlation coefficient, the final inverse azimuth angle from the source to the monitoring point is determined.

In some embodiments, the determining a candidate inverse azimuth from the source to the monitoring point includes:

Divide the reverse azimuth from 0° to 360° into multiple parts at preset degree intervals to obtain multiple candidate reverse azimuths.

In some embodiments, determining the final inverse azimuth from the source to the monitoring point according to the candidate inverse azimuth and the zero-lag cross-correlation coefficient includes:

The candidate inverse azimuth corresponding to the largest zero-lag cross-correlation coefficient is selected as the final inverse azimuth.

In some embodiments, the determining a candidate inverse azimuth from the source to the monitoring point includes:

Obtain the estimated inverse azimuth angle according to the first rotation component with the largest amplitude and the second rotation component with the largest amplitude;

Determine whether the estimated reverse azimuth angle is less than 0, if the estimated reverse azimuth angle is not less than 0, use the estimated reverse azimuth angle as the candidate reverse azimuth angle; if the estimated reverse azimuth angle is less than 0, use Add 180° to the estimated inverse azimuth to obtain the candidate inverse azimuth.

In some embodiments, the determining a candidate inverse azimuth from the source to the monitoring point includes:

Determine whether the zero-lag cross-correlation coefficient is greater than 0. If it is greater than 0, add 180° to the candidate inverse azimuth to obtain the final inverse azimuth; if it is less than or equal to 0, then the candidate inverse azimuth is is the final reverse azimuth.

In some embodiments, obtaining the estimated inverse azimuth angle according to the first rotation component with the largest magnitude and the second rotation component with the largest magnitude includes:

Substitute the first rotation component with the largest amplitude and the second rotation component with the largest amplitude into the estimated inverse azimuth angle calculation formula to obtain the estimated inverse azimuth angle, wherein the estimated inverse azimuth angle calculation formula is:

In the formula, θ _BAz is the estimated inverse azimuth angle; Re is the first rotation component with the largest amplitude; R _{n is} the second rotation component with the largest amplitude.

In some embodiments, the seismic data further includes: the wave speed of the p-wave and the wave speed of the s-wave in the seismic wave, and the propagation time difference of the p-wave and the s-wave;

The method further includes: determining the distance from the seismic source of the seismic wave to the monitoring point according to the wave speed of the p-wave, the wave speed of the s-wave and the propagation time difference.

A second aspect provides a seismic source monitoring device, the device comprising:

a data input unit, configured to acquire seismic data collected by the monitoring point within a preset historical time period, the seismic data including: a first translational component and a first rotational component on a first coordinate axis that are perpendicular to each other in a three-dimensional space, the second translational component and the second rotational component on the second coordinate axis, the third translational component and the third rotational component on the third coordinate axis;

a candidate determination unit, configured to determine a candidate inverse azimuth angle from the source to the monitoring point;

a lateral acceleration calculation unit, configured to obtain the lateral acceleration corresponding to each of the candidate inverse azimuth angles according to the first translational component, the second translational component and each of the candidate inverse azimuth angles;

a cross-correlation coefficient calculation unit, configured to determine a zero-lag cross-correlation coefficient between each of the lateral acceleration and the third rotational component;

A result output unit, configured to determine the final inverse azimuth from the seismic source to the monitoring point according to the candidate inverse azimuth and the zero-lag cross-correlation coefficient.

A third aspect provides a computer device, including a memory and a processor, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the processor causes the processor to execute the above-mentioned Steps of a seismic source monitoring method.

A fourth aspect provides a storage medium storing computer-readable instructions, and when the computer-readable instructions are executed by one or more processors, the one or more processors perform the steps of the above-mentioned earthquake source monitoring method .

The above-mentioned seismic source monitoring method, device, computer equipment and storage medium obtain the translational components and rotational motion components in three directions of the three-dimensional space collected by the monitoring point; The inverse azimuth angle from the source to the monitoring point; the distance from the source to the monitoring point is determined according to the wave speed and propagation time difference of the p-wave and s-wave in the vibration wave. Therefore, the single-station method has no site restrictions and the cost of manual layout is greatly reduced, and the inverse azimuth information can be obtained by combining the rotation and translation components of a single station. Cross-correlation methods focus on torsional motion (rotation of the vertical axis), and polarization analysis methods focus on rocking motion (rotation of the horizontal axis). The inverse azimuth estimation based on the polarization analysis of the horizontal rotation component is not affected by the small but influential asynchrony since only one data logger is involved. The polarization analysis method is more flexible to use and can be used for P, SV, SH, Rayleigh and Love waves.

Description of drawings

1 is a flowchart of a method for monitoring an earthquake source in an embodiment of the present application;

FIG. 2 is a flow chart of the realization of seismic source tracking using the cross-correlation method of the seismic source monitoring method in an embodiment of the application;

3 is a waveform diagram of the lateral acceleration and the third rotation component of the seismic source monitoring method in an embodiment of the application when the reverse azimuth angle is 280°; the abscissa corresponds to time (unit, seconds), and the ordinate corresponds to normalization Amplitude; the solid line represents the third rotational component, and the dashed line represents the transformed lateral acceleration;

4 is the cross-correlation coefficient obtained by the cross-correlation method for each inverse azimuth angle in the time window of 270s to 300s in the seismic source monitoring method in an embodiment of the application, the abscissa corresponds to each inverse azimuth, the ordinate is the corresponding cross-correlation coefficient;

FIG. 5 is a flow chart of realizing the seismic source tracking using the polarization analysis method of the seismic source monitoring method in one embodiment of the present application;

6 is the inverse azimuth and theoretical inverse azimuth predicted by the polarization analysis method in the entire time window of the seismic source monitoring method in an embodiment of the application; wherein the black asterisk is the predicted inverse azimuth in each time window, The horizontal solid line is the theoretical reverse azimuth value; the abscissa is the sampling time (in seconds), and the ordinate is the angle of the reverse azimuth;

7 is a structural block diagram of a seismic source monitoring device in an embodiment of the application;

FIG. 8 is a block diagram of the internal structure of a computer device in one embodiment.

Detailed ways

In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention.

It can be understood that the terms "first", "second", third", etc. used in this application can be used to describe various elements herein, but these elements are not limited by these terms. These terms are only used to describe the first One element is distinguished from another.

An implementation environment diagram of the seismic source monitoring method provided in one embodiment, in which the implementation environment may include computer equipment and seismic monitoring instruments.

The computer device has an interface, for example, the interface can be an API (Application Programming Interface, that is, an application programming interface). The seismic monitor is a device that collects seismic data set at the detection point, and the computer equipment monitors the seismic source (eg, determines the inverse azimuth of the source) according to the data collected by the seismic monitor.

It should be noted that the terminal and the computer equipment may be smart phones, tablet computers, notebook computers, desktop computers, etc., but are not limited thereto. The computer equipment and the terminal can be connected through Bluetooth, USB (Universal Serial Bus, Universal Serial Bus) or other communication connection methods, which are not limited in the present invention.

As shown in FIGS. 1 to 6 , in one embodiment, a method for monitoring an earthquake source is proposed. The method for monitoring an earthquake source can be applied to the above-mentioned computer equipment, and the method can be implemented based on a python program. As shown in Figure 1, it may specifically include the following steps:

Step 101: Acquire seismic data collected by the monitoring point within a preset historical time period. The seismic data includes: a first translational component and a first rotational component on a first coordinate axis that are perpendicular to each other in a three-dimensional space, and a second coordinate axis on the second coordinate axis. The second translational component and the second rotational component of , the third translational component and the third rotational component on the third coordinate axis;

It can be understood that, in this step, the translational components and rotational components of the three coordinate axes of the three-dimensional space collected by the monitoring point are acquired, including:

A six-component seismometer is used to collect translational and rotational components in three directions in a three-dimensional space; the six-component seismometer may include: three fiber optic gyroscopes, three accelerometers, and a six-component signal processing module, wherein three optical fibers The sensitive axes of the gyroscope are orthogonal to each other, and the sensitive axes of the three accelerometers are orthogonal to each other; the sensitive axes of each fiber optic gyroscope are respectively parallel or coincident with the three sensitive axes of an accelerometer; the input end of the six-component signal processing module Connect the detection signal output end of each fiber optic gyroscope and the detection signal output end of each accelerometer, the six-component signal processing module is used to generate the modulation signal required by the fiber optic gyroscope, and the output according to the detection signal output end of the fiber optic gyroscope. Detect the signal, obtain the detected angular velocity, and perform error compensation on the detected angular velocity. The six-component signal processing module is also used to obtain the detected translation acceleration according to the detection signal output by the detection signal output end of the three-axis accelerometer, and perform error compensation on the detected translation acceleration. . Among them, a space rectangular coordinate system is established, and the space rectangular coordinate system includes a first coordinate axis (X axis), a second coordinate axis (Y axis) and a third coordinate axis (Z axis) that are orthogonal to each other, a fiber optic gyroscope and an accelerometer. The sensitive axes of are respectively on the first coordinate axis (X axis), the second coordinate axis (Y axis) and the third coordinate axis (Z axis).

Further, the six-component seismometer includes three orthogonal fiber optic gyroscopes and three orthogonal accelerometers. The fiber optic gyroscope is used to measure the angular velocity information of the object, the three-axis accelerometer is used to measure the linear motion information of the carrying body, and the six The component signal processing module can provide complete seismic wavefield information by processing the output of the fiber optic gyroscope and accelerometer.

Step 102: Determine the candidate inverse azimuth from the source to the monitoring point;

It can be understood that the candidate inverse azimuth may be determined empirically or determined based on seismic data.

In some embodiments, a cross-correlation method is used to determine the final inverse azimuth of the source. In the above-mentioned step 102 of the cross-correlation method, setting the candidate inverse azimuth according to experience may include:

Step 1021a: Divide the reverse azimuth from 0° to 360° into multiple parts at preset degree intervals to obtain multiple candidate reverse azimuths.

In some embodiments, a polarization analysis method is used to determine the final inverse azimuth of the seismic source. In the polarization analysis method, the determination of the candidate inverse azimuth according to the seismic data in the above step 102 may include:

Step 1022a, obtain the estimated inverse azimuth angle according to the first rotation component with the largest amplitude and the second rotation component with the largest amplitude;

In this step, the first rotation component with the largest amplitude and the second rotation component with the largest amplitude are substituted into the calculation formula of the estimated inverse azimuth angle to obtain the estimated inverse azimuth angle, wherein the calculation formula of the estimated inverse azimuth angle is:

In the formula, θ _BAz is the estimated inverse azimuth angle; Re is the first rotation component with the largest amplitude; R _{n is} the second rotation component with the largest amplitude.

Step 1022b: Determine whether the estimated reverse azimuth is less than 0. If the estimated reverse azimuth is not less than 0, the estimated reverse azimuth is used as the candidate reverse azimuth; if the estimated reverse azimuth is less than 0, the estimated reverse azimuth is added 180 ° to get the candidate inverse azimuth.

Step 103: Obtain the lateral acceleration corresponding to each candidate inverse azimuth according to the first translational component, the second translational component and each candidate inverse azimuth;

In this step, the first translational component, the second translational component and each candidate inverse azimuth are substituted into the lateral acceleration formula to obtain the lateral acceleration of each candidate inverse azimuth.

Among them, the lateral acceleration calculation formula is:

In the formula, A _e is the first translational component; An is the second translational component; θ _Baz is the candidate inverse azimuth angle; A _{t is} the lateral acceleration.

Step 104, determining the zero-lag cross-correlation coefficient between each lateral acceleration and the third rotational component;

In this step, each lateral acceleration and the third rotation component are substituted into the relevant calculation formula to obtain the zero-lag cross-correlation coefficient between the lateral acceleration and the third rotation component. Among them, the relevant calculation formula is

为横向加速度的平均值；

为垂向旋转分量的平均值；xcorr(A_t,R_z)为横向加速度和垂向旋转分量之间的零滞后互相关系数。In the formula, A _t is the lateral acceleration corresponding to each acquisition moment; R _z is the vertical rotation component acquired at each acquisition moment;

is the average value of lateral acceleration;

is the average value of the vertical rotation component; xcorr(A _t , R _z ) is the zero-lag cross-correlation coefficient between the lateral acceleration and the vertical rotation component.

是一段时间(一个时间窗口，如3秒)的横向加速度的平均值。It can be understood that the six-component seismometer collects seismic data at preset time intervals (eg, once every second), and the lateral acceleration (A _t ) corresponding to each acquisition moment is the first translational component collected at each acquisition moment. , the second translational component, and the inverse azimuth (candidate inverse azimuth or final inverse azimuth) are substituted into the lateral acceleration calculation formula. Average value of lateral acceleration

is the average of the lateral acceleration over a period of time (a time window, such as 3 seconds).

Step 105: Determine the final inverse azimuth from the source to the monitoring point according to the candidate inverse azimuth and the zero-lag cross-correlation coefficient.

In this step, there is a certain relationship between the zero-lag cross-correlation coefficient between the lateral acceleration and the third rotation component and the inverse square angle of the source. Therefore, according to the candidate inverse azimuth angle and the zero-lag cross-correlation coefficient, the source-to-monitoring coefficient can be determined. The final inverse azimuth of the point.

In some embodiments, the cross-correlation method is used to realize the determination of the final inverse azimuth of the source. In the cross-correlation method, in the above step 105, the final inverse azimuth from the source to the monitoring point is determined according to the candidate inverse azimuth and the zero-lag cross-correlation coefficient. Azimuth can include:

Step 1051a: Select the candidate inverse azimuth corresponding to the largest zero-lag cross-correlation coefficient as the final inverse azimuth.

In some embodiments, the polarization analysis method is used to determine the final inverse azimuth of the source. In the above step 105 in the polarization analysis method, the final inverse azimuth from the source to the monitoring point is determined according to the candidate inverse azimuth and the zero-lag cross-correlation coefficient. Corners can include:

Step 1052a: Determine whether the zero-lag cross-correlation coefficient is greater than 0. If it is greater than 0, add 180° to the candidate inverse azimuth to obtain the final inverse azimuth; if it is less than or equal to 0, the candidate inverse azimuth is the final inverse azimuth .

Further, it can be understood that to obtain the seismic data collected by the monitoring point within the preset historical time period, it may be to obtain the seismic data within the past 12 seconds at the current moment, and the monitoring point collects seismic data at a frequency of once every 1 second, and according to the The seismic data determines the final inverse azimuth from the source to the monitoring point. The above seismic data can be divided into 3 time windows (the duration of each time window is 4s), and one time window is used as the minimum unit. The seismic data in the time window determines the candidate inverse azimuth in the time window, and then determines the final inverse azimuth in each time window, and finally determines the inverse azimuth from the source to the monitoring point according to the final inverse azimuths of multiple time windows. That is, most of the predicted final inverse azimuths are distributed near the theoretical inverse azimuth. The results of individual time windows have large errors and may be affected by other external factors. The overall prediction results are in good agreement with the theoretical values. The final inverse azimuth of the time window is fitted to obtain the inverse azimuth from the source to the monitoring point. Of course, after the time window is divided, the "maximum amplitude" and "maximum zero-lag cross-correlation coefficient" in the above embodiment are the maximum within the unit time window.

As shown in Figures 2 to 4, in an application scenario, the M6.4 earthquake occurred in Dali, Yunnan Province collected by the Wuhan instrument on May 21, 2021 was used to test the feasibility of the cross-correlation method. Its theoretical inverse azimuth is 280°, and the distance between the source (epicenter) and the test station is about 1583km.

As shown in Figure 2, the above method may include:

Step A1: Acquire three translational component and rotational component seismic data from the rotary seismometer, wherein the intercepted record data length is 390s, starting from the occurrence of the earthquake. The data is then preprocessed to remove instrument responses;

Step A2: Divide the reverse azimuth from 0° to 360° into 72 parts at intervals of 5°, and for each given reverse azimuth angle, obtain the lateral acceleration by using the first horizontal component and the second horizontal component according to the lateral acceleration formula. Figure 3 is a waveform diagram of the lateral acceleration and the third rotation component when the reverse azimuth angle is 280°. It can be seen from the figure that the waveform can be well fitted;

Step A3: Divide the recorded 390s seismic data into 13 evenly according to 30s as a time window. Within each time window, loop over all candidate inverse azimuths and each time window. Substitute the obtained lateral acceleration A _t and the third rotational component R _z into the relevant calculation formula, and the zero-lag cross-correlation coefficient between the lateral acceleration and the third rotational component is obtained. Figure 4 is the cross-correlation coefficient corresponding to each reverse azimuth in the 270s to 300s time window;

Step A4: Select the inverse azimuth angle corresponding to the position of the maximum value of the cross-correlation coefficient as the final result of the time window.

When all the time window cycles are completed, the inverse azimuth prediction results of the entire time trace can be obtained.

As shown in Figures 5 and 6, in an application scenario, the methane explosion experiment conducted in Guangzhou on January 12, 2021 was used to test the feasibility of the polarization analysis method. Its theoretical reverse azimuth is 190°, and the distance between the methane source and the test station is about 305m. As shown in Figure 5, the above method may include:

Step B1, download and obtain three translational component and rotational component seismic data from the seismograph, wherein the intercepted record data length is 15s, starting from the explosion of the methane source. Then preprocess the downloaded data, such as removing the instrument response;

In step B2, the recorded 15s seismic data is evenly divided into 15 parts according to 1s as a time window. Within each time window, the first rotational component and the second rotational component of the largest magnitude are selected to calculate candidate inverse azimuths.

Step B3: In each 1s time window, convert the horizontal component into a lateral acceleration according to the calculated candidate inverse azimuth angle.

Step B4: Substitute the first translational component, the second translational component and the candidate inverse azimuth into the lateral acceleration formula to obtain the lateral acceleration of each candidate inverse azimuth.

Step B5, if the lateral acceleration is positively correlated with the third rotation component, then add 180° to the obtained inverse azimuth angle, and vice versa.

Step B6: Loop through each time window to obtain the prediction result of the inverse azimuth angle of the entire time trace, as shown in FIG. 6 . It can be seen that the predicted inverse azimuth is relatively uniformly distributed near the theoretical azimuth, but there are also some time windows that are affected by other external factors, and the result error is large.

In some embodiments, the wave speed of the p-wave and the wave speed of the s-wave and the propagation time difference of the p-wave and the s-wave in the above-mentioned seismic waves;

The above method also includes: determining the distance from the seismic source to the monitoring point according to the wave speed of the p-wave, the wave speed of the s-wave and the propagation time difference. Specifically, it is calculated according to the following formula:

where S is the distance from the source to the monitoring point; v _p is the wave speed of the p wave; and v _s is the wave speed of the s wave, respectively; t ₁ and t ₂ are the arrival times of the p and s waves obtained from the waveforms, respectively.

In one embodiment, a device for determining the phase velocity of a seismic wave is provided, and the device for determining the phase velocity of a seismic wave can be integrated into the above-mentioned computer equipment. As shown in FIG. 7 , the device can specifically include:

The data input unit 711 is configured to acquire the seismic data collected by the monitoring point in the preset historical time period, the seismic data includes: the first translational component and the first rotational component on the first coordinate axes that are perpendicular to each other in the three-dimensional space, the first The second translational component and the second rotational component on the second coordinate axis, the third translational component and the third rotational component on the third coordinate axis;

a candidate determination unit 712, configured to determine a candidate inverse azimuth angle from the source to the monitoring point;

a lateral acceleration calculation unit, configured to obtain the lateral acceleration corresponding to each candidate inverse azimuth according to the first translational component, the second translational component and each candidate inverse azimuth;

a cross-correlation coefficient calculation unit 713, configured to determine the zero-lag cross-correlation coefficient between each lateral acceleration and the third rotation component;

The result output unit 714 is configured to determine the final inverse azimuth from the seismic source to the monitoring point according to the candidate inverse azimuth and the zero-lag cross-correlation coefficient.

As shown in FIG. 8 , in one embodiment, a computer device is proposed, which may include a processor, a storage medium, a memory, and a network API interface connected through a system bus. Wherein, the storage medium of the computer equipment stores an operating system, a database and computer-readable instructions, and the database can store a control information sequence. When the computer-readable instructions are executed by the processor, the processor can realize an earthquake source monitoring method. The processor of the computer device is used to provide computing and control capabilities and support the operation of the entire computer device. Computer-readable instructions may be stored in the memory of the computer device, and when executed by the processor, the computer-readable instructions may cause the processor to perform a method for monitoring a seismic source. The network API interface of the computer device is used to communicate with the terminal connection. Those skilled in the art can understand that the structure shown in FIG. 8 is only a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer equipment to which the solution of the present application is applied. The specific computer equipment may include There are more or fewer components than shown in the figures, or some components are combined, or have a different arrangement of components.

When the processor executes the computer program, the following steps are implemented: acquiring seismic data collected at the monitoring point within a preset historical time period, where the seismic data includes: a first translational component and a first rotation on a first coordinate axis that are perpendicular to each other in a three-dimensional space components, the second translational component and the second rotational component on the second coordinate axis, the third translational component and the third rotational component on the third coordinate axis; determine the candidate inverse azimuth from the source to the monitoring point; according to the first The translational component, the second translational component, and each candidate inverse azimuth angle, to obtain the lateral acceleration corresponding to each candidate inverse azimuth angle; determine the zero-lag cross-correlation coefficient between each lateral acceleration and the third rotational component; according to the candidate inverse azimuth angle and the zero-lag cross-correlation coefficient to determine the final inverse azimuth from the source to the monitoring point.

In one embodiment, a storage medium storing computer-readable instructions is provided. When the computer-readable instructions are executed by one or more processors, the one or more processors perform the following steps: obtaining a monitoring point at Seismic data collected within a preset historical time period, the seismic data includes: a first translational component and a first rotational component on a first coordinate axis that are perpendicular to each other in a three-dimensional space, a second translational component on the second coordinate axis and The second rotational component, the third translational component and the third rotational component on the third coordinate axis; determine the candidate inverse azimuth angle from the source to the monitoring point; according to the first translational component, the second translational component and each candidate inverse azimuth According to the candidate inverse azimuth angle and the zero lag cross-correlation coefficient, the final distance from the source to the monitoring point is determined. reverse azimuth.

Those of ordinary skill in the art can understand that the realization of all or part of the processes in the methods of the above embodiments can be accomplished by instructing relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and the program is During execution, it may include the processes of the embodiments of the above-mentioned methods. The aforementioned storage medium may be a non-volatile storage medium such as a magnetic disk, an optical disk, a read-only memory (Read-Only Memory, ROM), or a random access memory (Random Access Memory, RAM).

The technical features of the above embodiments can be combined arbitrarily. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described. However, as long as there is no contradiction in the combination of these technical features It is considered to be the range described in this specification.

The above embodiments only represent several embodiments of the present invention, and the descriptions thereof are specific and detailed, but should not be construed as a limitation on the scope of the patent of the present invention. It should be pointed out that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can also be made, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention should be subject to the appended claims.

Claims (10)

1. an earthquake source monitoring method, is characterized in that, described method comprises: Acquire seismic data collected at the monitoring point within a preset historical time period, the seismic data including: a first translational component and a first rotational component on a first coordinate axis that are perpendicular to each other in a three-dimensional space, and a first rotational component on the second coordinate axis. The second translational component and the second rotational component, the third translational component and the third rotational component on the third coordinate axis; determining the candidate inverse azimuth from the source to the monitoring point; According to the first translational component, the second translational component and each of the candidate inverse azimuth angles, obtain the lateral acceleration corresponding to each of the candidate inverse azimuth angles; determining a zero-lag cross-correlation coefficient between each of the lateral acceleration and the third rotational component; According to the candidate inverse azimuth angle and the zero-lag cross-correlation coefficient, the final inverse azimuth angle from the source to the monitoring point is determined. 2. The method for monitoring an earthquake source according to claim 1, wherein the determining the candidate inverse azimuth from the source to the monitoring point comprises: Divide the reverse azimuth from 0° to 360° into multiple parts at preset degree intervals to obtain multiple candidate reverse azimuths. 3 . The seismic source monitoring method according to claim 2 , wherein the final inverse azimuth from the source to the monitoring point is determined according to the candidate inverse azimuth and the zero-lag cross-correlation coefficient. 4 . ,include: The candidate inverse azimuth corresponding to the largest zero-lag cross-correlation coefficient is selected as the final inverse azimuth. 4. The method for monitoring an earthquake source according to claim 2, wherein the determining the candidate inverse azimuth from the source to the monitoring point comprises: Obtain the estimated inverse azimuth angle according to the first rotation component with the largest amplitude and the second rotation component with the largest amplitude; Determine whether the estimated reverse azimuth angle is less than 0, if the estimated reverse azimuth angle is not less than 0, use the estimated reverse azimuth angle as the candidate reverse azimuth angle; if the estimated reverse azimuth angle is less than 0, use Add 180° to the estimated inverse azimuth to obtain the candidate inverse azimuth. 5 . The seismic source monitoring method according to claim 4 , wherein the final inverse azimuth from the source to the monitoring point is determined according to the candidate inverse azimuth and the zero-lag cross-correlation coefficient. 6 . , including, including: Determine whether the zero-lag cross-correlation coefficient is greater than 0. If it is greater than 0, add 180° to the candidate inverse azimuth to obtain the final inverse azimuth; if it is less than or equal to 0, then the candidate inverse azimuth is is the final reverse azimuth. 6. The seismic source monitoring method according to claim 4, wherein the estimated inverse azimuth is obtained according to the first rotation component with the largest amplitude and the second rotation component with the largest amplitude, comprising: Substitute the first rotation component with the largest amplitude and the second rotation component with the largest amplitude into the estimated inverse azimuth angle calculation formula to obtain the estimated inverse azimuth angle, wherein the estimated inverse azimuth angle calculation formula is:

In the formula, θ _BAz is the estimated inverse azimuth angle; Re is the first rotation component with the largest amplitude; R _{n is} the second rotation component with the largest amplitude. 7 . The seismic source monitoring method according to claim 1 , wherein the seismic data further comprises: the wave velocity of the p-wave and the wave velocity of the s-wave in the seismic wave and the propagation time difference of the p-wave and the s-wave. 8 . ; The method further includes: determining the distance from the seismic source of the seismic wave to the monitoring point according to the wave speed of the p-wave, the wave speed of the s-wave and the propagation time difference. 8. An earthquake source monitoring device, wherein the device comprises: a data input unit, configured to acquire seismic data collected by the monitoring point within a preset historical time period, the seismic data including: a first translational component and a first rotational component on a first coordinate axis that are perpendicular to each other in a three-dimensional space, the second translational component and the second rotational component on the second coordinate axis, the third translational component and the third rotational component on the third coordinate axis; a candidate determination unit, configured to determine a candidate inverse azimuth angle from the source to the monitoring point; a lateral acceleration calculation unit, configured to obtain the lateral acceleration corresponding to each of the candidate inverse azimuth angles according to the first translational component, the second translational component and each of the candidate inverse azimuth angles; a cross-correlation coefficient calculation unit, configured to determine a zero-lag cross-correlation coefficient between each of the lateral acceleration and the third rotational component; A result output unit, configured to determine the final inverse azimuth from the seismic source to the monitoring point according to the candidate inverse azimuth and the zero-lag cross-correlation coefficient. 9. A computer device comprising a memory and a processor, wherein computer-readable instructions are stored in the memory, and when the computer-readable instructions are executed by the processor, the processor is caused to perform as claimed in claims 1 to 7 The steps of the seismic source monitoring method according to any one of claims. 10. A storage medium storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform any one of claims 1 to 7 The steps of the seismic source monitoring method.

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