CN112014883A - Log-Cosh function-based microseismic source positioning method, system and device and readable storage medium - Google Patents
Log-Cosh function-based microseismic source positioning method, system and device and readable storage medium Download PDFInfo
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Abstract
The invention discloses a method, a system and a device for positioning a microseismic source based on a Log-Cosh function and a readable storage medium, wherein the method comprises the following steps: s1: acquiring a microseismic signal sent by a seismic source based on a sensor in a monitoring area; s2: the method comprises the steps of utilizing the position of a sensor, the P wave first arrival time picked up by a microseismic signal received by the sensor and a plurality of random sampling points in a monitoring area, and carrying out microseismic source positioning based on a microseismic source positioning target function to obtain a seismic source position, wherein the microseismic source positioning target function is constructed based on a Log-Cosh function. According to the method, the Log-Cosh function is adopted to replace commonly used L1 norms and L2 norms to realize seismic source positioning, the problems that the L1 norm positioning accuracy is low and the L2 norm is easily affected by large picking errors are solved, and therefore the seismic source positioning accuracy is improved.
Description
Technical Field
The invention belongs to the technical field of microseismic monitoring and positioning, and particularly relates to a microseismic seismic source positioning method, system and device based on a Log-Cosh function and a readable storage medium.
Background
The microseismic monitoring technology is a geophysical technology for evaluating the stability of rock mass by receiving elastic waves released when the rock mass is fractured through a sensor. In recent years, due to the importance of the country on safety, the microseismic monitoring technology is gradually applied to various projects, technical support is provided for the safety production of the projects, and economic benefits and social benefits are improved. The microseismic monitoring technology comprises the following steps: the method comprises the following steps of sensor network optimization arrangement, micro-seismic monitoring data acquisition and processing, micro-seismic event positioning, rock mass stability evaluation and the like. The seismic source positioning method is one of important factors influencing the positioning accuracy of the microseismic event, and has important significance for researching basic problems such as mine rock mechanics and the like.
At present, a travel time-based ray tracing and positioning method is commonly used, which is mostly developed from a Geiger method, and the main idea is to establish a residual function between theoretical time and observed time, then search coordinates in a monitoring area and iterate to enable the residual function to be minimum, wherein the coordinates are event positions. For example, Waldhauser and Ellsworth propose a double-difference positioning method, which determines the position of an event through the residual error between the observed time difference of the event and the theoretically calculated time difference, and then iteratively finds a least square solution; zhang et al propose a double-difference seismic tomography method based on absolute and relative arrival time, the method can reduce the system error, have better velocity model and positioning effect; zhou et al propose a microseismic event positioning method combining P-S wave arrival time difference and P wave arrival time difference, which can better restrict the length of a ray, thereby enabling the positioning position to be more stable and accurate. Most of time-travel-based ray tracking positioning methods use L1 and L2 norms to establish a seismic source positioning target function, but the L1 norm has low positioning accuracy when the error is small, and the L2 norm is influenced by large pickup error, so that the positioning result is unstable, and the seismic source positioning effect is influenced.
Disclosure of Invention
The invention aims to provide a microseismic seismic source positioning method, a microseismic seismic source positioning system, a microseismic seismic source positioning device and a readable storage medium based on a Log-Cosh function, wherein the Log-Cosh function is adopted to replace commonly used L1 norm and L2 norm to realize seismic source positioning, and the problems that the L1 norm positioning precision is low and the L2 norm is easily influenced by large picking errors are solved, so that the seismic source positioning precision is improved.
On one hand, the invention provides a microseismic seismic source positioning method based on Log-Cosh function, which comprises the following steps:
s1: acquiring a microseismic signal sent by a seismic source based on a sensor in a monitoring area;
s2: the method comprises the steps of utilizing the position of a sensor, the P wave first arrival time picked up by a microseismic signal received by the sensor and a plurality of random sampling points in a monitoring area to carry out microseismic source positioning based on a microseismic source positioning target function to obtain the position of a seismic source, wherein the microseismic source positioning target function is constructed based on a Log-Cosh function.
The Log-Cosh function is similar to the L2 norm when the error is small, and is similar to the L1 norm when the error is large, so that the method can solve the problems that the L1 norm has large error when the error is small, the L2 norm is easily influenced by large picking errors and the like, and the seismic source positioning target function based on the Log-Cosh function has higher positioning accuracy and better stability.
Further preferably, the microseismic source positioning target function constructed based on the Log-Cosh function is as follows:
min f(x0,y0,z0,t0)
in the formula Ii(i ═ 1,2,3 …, n) is the linear distance between each station to the event, i.e. the source location (x)0,y0,z0) And ith sensor position (x)i,yi,zi) N is the number of sensors participating in the positioning calculation, tiP-wave first arrival time v picked up for microseismic signal of ith sensorpIs the P wave propagation velocity, t0K is the integral scaling factor for the time of the seismic source, and the value range of the integral scaling factor k is [50,150]Cosh () refers to a hyperbolic cosine function;
each sampling point is regarded as an initial seismic source position, the initial seismic source position corresponding to the sampling point is adopted in an iteration mode, and the target function f (x) is positioned based on the micro-seismic source0,y0,z0,t0) F (x) is obtained by calculation0,y0,z0,t0) Corresponding to a minimum value of (x)0,y0,z0,t0). The specific iterative process may have various implementations, and the present invention is not particularly limited in this regard.
Further preferably, N is randomly set in the monitoring area1Sampling points based on the N in step S21Carrying out microseismic seismic source positioning on the P wave first arrival time and the microseismic seismic source positioning target function picked up by the sampling points and the sensors to obtain N1A seismic source location point and based on said N1Obtaining the seismic source position N from the seismic source positioning points1Is a positive integer.
Further preferably, the acquisition process of the source position is as follows:
obtaining an initial positioning result based on the plurality of random sampling points and the microseismic seismic source positioning objective function;
then, removing the sensor data corresponding to the propagation time greater than the threshold value by using the initial positioning result;
and finally, carrying out microseismic source positioning by using the removed data and the microseismic source positioning objective function to obtain the seismic source position.
According to the method, a plurality of sampling points are randomly generated, and the initial positioning result is determined according to the high-density positioning result, so that the influence of an initial iteration center can be reduced, and the stability of seismic source positioning is improved; meanwhile, P-wave first arrival data of a sensor with a longer propagation distance are removed, P-wave first arrival pickup error influence caused by high and low speed areas of the mine and waveform attenuation can be reduced, and the denoised P-wave first arrival data set is used for carrying out positioning again, so that high-precision positioning of a mine seismic source can be realized.
Further preferably, a seismic source positioning point is obtained by corresponding each random sampling point, and then an initial positioning result is determined according to the data field potential value of each seismic source positioning point.
Further preferably, the initial positioning result is a coordinate mean of N seismic source positioning points with the largest data field potential value among all seismic source positioning points.
Go toStep preferably, N is randomly set in the monitoring area1A sampling point, N1Has a value range of [1500,2500 ]](ii) a The value range of N is [30, 80]]The value range of the threshold is [0.15, 0.20]]s。
Further preferably, the process of obtaining the seismic source position by using the removed data and the microseismic seismic source positioning objective function to perform microseismic seismic source positioning comprises the following steps:
based on the sampling point, the position of the residual sensor after data removal and the P wave first arrival time picked up by the microseismic signal received by the sensor, and based on the microseismic source positioning target function, the microseismic source positioning is carried out again to obtain a plurality of seismic source positioning points;
and finally, determining the position of the seismic source based on the data field potential value and the plurality of seismic source positioning points.
In a second aspect, the present invention provides a system based on the method, including:
microseismic signal acquisition module: the system is used for acquiring microseismic signals sent by a seismic source;
a positioning module: and the method is used for positioning the micro-seismic source based on the micro-seismic source positioning target function by utilizing the position of the sensor, the P wave first arrival time picked up by the micro-seismic signal received by the sensor and the sampling points in the monitoring area to obtain the position of the seismic source.
In a third aspect, the present invention provides an apparatus comprising a processor and a memory, the processor storing a computer program, the processor invoking the computer program to perform: and acquiring data, and positioning a micro seismic source by using the position of a sensor in the data, the P wave first arrival time picked up by a micro seismic signal received by the sensor and a plurality of random sampling points in a monitoring area and based on a micro seismic source positioning objective function to obtain the position of the seismic source.
In a fourth aspect, the present invention provides a readable storage medium storing a computer program for execution by a processor to:
and acquiring data, and positioning a micro seismic source by using the position of a sensor in the data, the P wave first arrival time picked up by a micro seismic signal received by the sensor and a plurality of random sampling points in a monitoring area and based on a micro seismic source positioning objective function to obtain the position of the seismic source.
Advantageous effects
The invention provides a microseismic seismic source positioning method based on a Log-Cosh function, which adopts the Log-Cosh function to replace commonly used L1 and L2 norms to realize seismic source positioning, and because the Log-Cosh function is close to the L2 norm when the error is small and is close to the L1 norm when the error is large, the problems that the L1 norm positioning precision is low and the L2 norm is easily influenced by large picking errors can be solved, thereby improving the seismic source positioning precision and stability.
Drawings
FIG. 1 is a schematic flow chart of the method provided by the embodiment of the invention;
FIG. 2 is a diagram of a sensor generating theoretical data and a location of an event to be tested, provided by an embodiment of the present invention;
FIG. 3 is a plot of distance traveled versus time fitness for noisy microseismic events as provided by an embodiment of the present invention;
FIG. 4 is a graph of the relationship between the error of three functions and the value of the objective function provided by the embodiment of the present invention;
FIG. 5 is a diagram of randomly generated initial point locations provided by an embodiment of the present invention;
FIG. 6 is a block diagram of positioning error for three different methods provided by embodiments of the present invention;
fig. 7 shows positioning results of 2000 sampling points for determining whether to eliminate P-wave first-arrival data of a sensor with a longer propagation distance according to an embodiment of the present invention, (a) the long-distance P-wave first-arrival data is not removed, and (b) the long-distance P-wave first-arrival data is removed.
Detailed Description
The invention provides a microseismic seismic source positioning method, a microseismic seismic source positioning system, a microseismic seismic source positioning device and a readable storage medium based on a Log-Cosh function, wherein the microseismic seismic source positioning method, the microseismic seismic source positioning system, the microseismic seismic source positioning device and the readable storage medium adopt the Log-Cosh function to replace the commonly used L1 norm and L2 norm to realize seismic source positioning, and the problems are solved. The invention will now be further described with reference to examples, which are given by way of illustration of the method as applied in mines.
Example 1:
various search algorithms exist in the art for optimally solving the seismic source positioning objective function, and the common methods include a grid search method, a simplex method, a genetic algorithm, a particle swarm algorithm, a Bayes method and the like. Song Weiqi et al propose a Bayes theory differential evolution inversion method for microseismic data, which utilizes differential vectors among individuals in a population to disturb individuals, fully utilizes population distribution characteristics and improves the searching capability of an algorithm; buland combines a QR algorithm with a Geiger method and introduces step damping to expand a convergence domain, Nelson and Vidale provide a grid search algorithm which can be used for positioning in any complex three-dimensional speed structure area, and Prugger and Gendzwil introduce a simplex algorithm into microseismic positioning and obtain a better positioning result. In the calculation process of the algorithm, the calculation amount of the grid search algorithm is huge, the selection of the initial solution of the iterative method is extremely important, the initial solution is easy to fall into a local extreme value when the selection is not proper, and the positioning precision of the seismic source is more dependent on the accuracy of the initial solution.
In addition, due to the complex mine environment, the P wave velocity exists in a high-velocity region and a low-velocity region, the high-velocity region enables the P wave to be faster relative to a uniform velocity model, and the P wave propagation in the low-velocity region is slower. In addition, a wave front healing effect exists in the wave form propagation process, and the effect is difficult to reflect by the traditional ray time-lapse method. When the P wave propagation distance is long, the error between the observed time and the theoretical propagation time calculated according to the uniform wave velocity is large, and the method has large influence on the seismic source positioning. In addition, the waveform energy of the seismic wave is attenuated continuously when the seismic wave propagates in the medium, and the attenuation of the seismic wave is gradually increased along with the increase of the propagation distance. When the sensor is far away from the event, the primary motion amplitude of the P wave becomes very small or is submerged in noise, so that the primary motion pick-up error of the P wave is large, and the positioning accuracy of the seismic source is influenced.
The method in the embodiment not only solves the problem of positioning accuracy of the seismic source positioning target function constructed by L1 and L2 norms, but also solves the two problems, and the method in the embodiment can reduce the influence of an initial iteration center and improve the stability of seismic source positioning; in addition, P-wave first arrival data of a sensor with a longer propagation distance are removed, P-wave first arrival pickup error influence caused by high and low speed areas of a mine and waveform attenuation can be reduced, and the denoised P-wave first arrival data set is used for carrying out positioning again, so that high-precision positioning of a mine seismic source can be realized.
As shown in fig. 1, the microseismic source positioning method based on Log-Cosh function provided in this embodiment includes the following steps:
step 1: and establishing a microseismic seismic source positioning target function based on the Log-Cosh function. The formula is as follows:
min f(x0,y0,z0,t0)
wherein li(i＝1,2,3…,n1) Is the linear distance between each station to the event and is determined by the source location (x)0,y0,z0) And ith sensor position (x)i,yi,zi) Determining that n is the number of sensors; t is tiP-wave first arrival time v picked up for microseismic signal of ith sensorpIs the P wave propagation velocity, t0K is an integral scaling coefficient for the occurrence time of the seismic source, Cosh () refers to a hyperbolic cosine function, and min () refers to f (x)0,y0,z0,t0) Corresponding to a minimum value of (x)0,y0,z0,t0)。
Step 2: randomly generating a plurality of sampling points in a core mining area of the mine, in the embodiment, generating N1And (3) 1500-2500 sampling points, wherein the sampling points are used as an initial seismic source position to respectively calculate the seismic source positioning points based on the Log-Cosh seismic source positioning target function. When the random points are preferably generated in the embodiment, 1500-2500 sampling points are randomly generated in the core mining space area of the mine by using the uniformly distributed function, so that the positioning stability of a data field can be ensured, and the positioning stability of the data field can be ensuredA smaller amount of calculation can be used.
It should be noted that, the sensors in the lake of the mine area are used for acquiring seismic source signals, the positions of the sensors and the P-wave first arrival time of the picked microseismic events are also recorded, and a P-wave first arrival data set is constructed based on the recorded P-wave first arrival time of the microseismic events picked by each sensor.
Step 3: obtaining a plurality of corresponding seismic source positioning points, namely N, based on the Log-Cosh seismic source positioning target function1Positioning points of the seismic sources;
step 4: the coordinate mean value of the N seismic source positioning points with the largest data field potential value in the plurality of seismic source positioning points is used as an initial positioning result, in this embodiment, the value range of N is as follows: [30, 80 ];
step 5: p-wave first-arrival data, i.e., corresponding sensor data, with a propagation time greater than a threshold value is removed based on the initial positioning result, where a value range of the threshold value is [0.15, 0.20] s in this embodiment.
Wherein the preliminary positioning result is the preliminarily determined coordinate position (x) of the microseismic seismic source0,y0,z0) Based on the coordinate position (x)0,y0,z0) And the coordinates of the sensor can calculate the corresponding P wave propagation time when the sensor receives the P wave, and then the P wave propagation time is compared with the threshold value, the P wave propagation time larger than the threshold value is identified, and then the P wave first arrival data of the corresponding sensor is deleted, namely the P wave first arrival data of the sensor with a longer propagation distance is eliminated, so that the P wave first arrival pickup error caused by the mine high-low speed area and the waveform attenuation can be reduced, and the high-precision positioning of the mine event is realized.
Step 6: and (4) realizing the relocation of the microseismic event by using the microseismic seismic source positioning objective function on the basis of the new P wave first arrival data set to obtain the seismic source position.
After P-wave first-arrival data of the sensor with the longer propagation distance are removed, the seismic source position can be obtained through recalculation and calculation based on the new P-wave first-arrival data set. In this embodiment, preferably, a plurality of new seismic source positioning points are obtained by recalculation based on a plurality of random sampling points and a new P-wave first-arrival dataset, and then the seismic source positioning point with the highest data field potential value is selected as the seismic source position.
Therefore, the method in the embodiment applies the Log-Cosh function with better convergence to the seismic source positioning, and reduces the influence of P wave first arrival picking errors; meanwhile, a plurality of initial points are randomly generated in a main mining area of the mine, and the mean value of the high-density positioning result is used as a seismic source positioning result, so that the influence of an initial iteration center can be reduced, and the stability of the positioning method is improved; and P wave first arrival data of the sensor with a longer propagation distance are rejected according to the distance between the initial positioning result and the sensor, so that P wave first arrival pickup errors caused by high and low speed areas of the mine and waveform attenuation can be reduced.
In some embodiments, the invention further provides a system based on the microseismic source positioning method, which includes a microseismic signal acquisition module and a positioning module connected with each other. The microseismic signal acquisition module is used for acquiring microseismic signals sent by a seismic source, and if the microseismic signal acquisition module is realized by hardware, the microseismic signal acquisition module is generally a sensor arranged in a monitoring area. The positioning module is used for positioning a micro-seismic source by utilizing the position of the sensor, the P wave first arrival time picked up by the micro-seismic signal received by the sensor and a plurality of random sampling points in the monitoring area and based on a micro-seismic source positioning target function to obtain the position of the seismic source.
Wherein, corresponding to the above embodiment 1, the positioning module may be further divided into: the device comprises a microseismic source positioning target function acquisition unit, a sampling point generation unit, a positioning unit and a denoising unit. The microseismic seismic source positioning target function acquisition unit is used for acquiring a microseismic seismic source positioning target function based on a Log-Cosh function; the sampling point generating unit is used for randomly generating a plurality of sampling points in a core mining area of the mine; the positioning unit is used for obtaining a plurality of corresponding seismic source positions based on the Log-Cosh seismic source positioning target function; the denoising unit is used for taking the average value of the N seismic source positioning coordinates with the maximum data field potential value in the multiple seismic source positioning as an initial positioning result and removing P wave initial arrival data with the propagation time larger than a threshold value based on the initial positioning result. The positioning unit is also used for realizing the relocation of the microseismic event by using the microseismic seismic source positioning objective function to obtain the seismic source position on the basis of the new P wave first arrival data set.
For the implementation process of each module, please refer to the content of the above method, which is not described herein again. It should be understood that the above described division of functional blocks is merely a division of logical functions and that in actual implementation there may be additional divisions, for example, where multiple elements or components may be combined or integrated into another system or where some features may be omitted, or not implemented. Meanwhile, the integrated unit can be realized in a hardware form, and can also be realized in a software functional unit form.
In some specific examples, the invention provides an apparatus comprising a processor and a memory, the processor storing a computer program, the processor invoking the computer program to perform: and acquiring data, and positioning a micro seismic source by using the position of a sensor in the data, the P wave first arrival time picked up by a micro seismic signal received by the sensor and a plurality of random sampling points in a monitoring area and based on a micro seismic source positioning objective function to obtain the position of the seismic source.
The specific implementation process may also refer to the above method content.
In some specific examples, the invention provides a readable storage medium storing a computer program for execution by a processor to: and acquiring data, and positioning a micro seismic source by using the position of a sensor in the data, the P wave first arrival time picked up by a micro seismic signal received by the sensor and a plurality of random sampling points in a monitoring area and based on a micro seismic source positioning objective function to obtain the position of the seismic source.
The specific implementation process may also refer to the above method content.
It should be understood that in the embodiments of the present invention, the Processor may be a Central Processing Unit (CPU), and the Processor may also be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The memory may include both read-only memory and random access memory, and provides instructions and data to the processor. The portion of memory may also include non-volatile random access memory. For example, the memory may also store device type information.
The readable storage medium is a computer readable storage medium, which may be an internal storage unit of the controller according to any of the foregoing embodiments, for example, a hard disk or a memory of the controller. The readable storage medium may also be an external storage device of the controller, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the controller. Further, the readable storage medium may also include both an internal storage unit of the controller and an external storage device. The readable storage medium is used for storing the computer program and other programs and data required by the controller. The readable storage medium may also be used to temporarily store data that has been output or is to be output.
Data comparison and effect verification:
FIG. 2 is a diagram of sensors generating theoretical data and the location of an event to be tested. The distribution of the microseismic sensors is wide, wherein 12 sensors are respectively arranged near the height of 930m and 1080m, 4 sensors are arranged near the height of 1120m, and the sampling frequency of the sensors is 6000 Hz. In order to verify the effectiveness of the positioning method, a typical event testing positioning method is selected, and the event coordinates are as follows: (381400,2997000,1000). Due to the complex mine environment, the sensors cannot be completely triggered in real-world monitoring, and 15 sensors are randomly extracted from the 28 sensors for research. Theoretical propagation time is generated by P-wave velocity, seismic source position and sensor position, 5% Gaussian noise is added to the P-wave velocity, and 2ms Gaussian noise is added to the theoretical propagation time received by each sensor. In addition, on the basis of the above-mentioned noise, a large pickup error is added to a sensor that travels a long distance.
FIG. 3 is a plot of distance traveled versus time fitness for noisy microseismic events. Firstly, the distance between the event position and the trigger sensor is calculated, and the P wave propagation time is calculated by combining the wave velocity to obtain the time of the horizontal axis. The vertical axis is the distance between the event position and the sensor, and the P wave velocity is the slope to obtain an oblique straight line in the graph. The closer the circle is to the straight line in the figure, the smaller the P-wave first arrival pickup error. As can be seen from the figure, the last three circles deviate from the straight line to a greater extent, so that P-wave first-arrival data with propagation time greater than 0.2s should be removed during seismic source positioning.
Figure 4 is a graph of the error of the three functions versus the value of the objective function. The L1 function is the sum of the absolute values of the differences between the target value and the predicted value, and represents the average error amplitude of the predicted value, the L1 function is not conducive, and has larger jump at the extreme point, and smaller loss also causes larger error. The L2 function is the sum of the squares of the differences between the predicted values and the target values, and since the L2 function loses more weight at points where the error is larger than the L1 function, it gives more weight to outliers, resulting in a larger overall error. The Log-Cosh function is similar to the L2 function when the error is small, and is similar to the L1 function when the error is large, and the Log-Cosh function has the advantages of the L1 and L2 loss functions and cannot be influenced too much by abnormal values.
Fig. 5 is a diagram of randomly generated initial point positions. At this time, the main mining area of the mine is taken as a range, namely x belongs to (380900,381800), y belongs to (2995500,2998500), and z belongs to (900,1150), 2000 initial points are randomly generated in a uniform distribution mode, and the mean value of the coordinates of the high-density positioning points is taken as the positioning result of the seismic source, so that the influence of the initial iteration center is reduced.
FIG. 6 is a block diagram of positioning error for three different methods. In each method, the left side of the diagram is the seismic source positioning error containing long-distance P-wave first arrival data, and the right side is the seismic source positioning error containing no long-distance P-wave first arrival data. As can be seen from the graph, the seismic source positioning error based on the L2 norm and the Log-Cosh function is smaller than the seismic source positioning error based on the L1 norm as a whole, and the minimum seismic source positioning error based on the Log-Cosh function is smaller than the minimum seismic source positioning error based on the L2 norm. When P-wave first arrival data of the sensors with longer propagation distances are removed, the positioning errors of the three positioning methods are reduced to some extent, and the seismic source positioning errors based on the L2 norm and the Log-Cosh function are obviously reduced, so that the necessity of removing the P-wave first arrival data of the sensors with longer propagation distances and the positioning effectiveness of the patent are shown.
FIG. 7 is a diagram showing the positioning results of 2000 sampling points for determining whether to eliminate P-wave first arrival data of a sensor with a longer propagation distance. When P-wave first-arrival data of a sensor with a longer propagation distance is contained, the positioning results of 2000 initial points of the Log-Cosh function are shown as circles in fig. 7(a), and the deeper the color is, the larger the potential value of the data field is; when P-wave first-arrival data of a sensor with a longer propagation distance is rejected, the positioning results of 2000 initial points of the Log-Cosh function are shown in fig. 7 (b). As can be seen from the graph, when P-wave first-arrival data of the sensors with longer propagation distances are contained, the positioning result is more dispersed, the maximum potential value of the data field is smaller, and the positioning result of the P-wave first-arrival data of the sensors with longer propagation distances is rejected is more concentrated, which indicates that the positioning stability of the method is higher. The errors of the two positioning methods are respectively 9.94m and 8.74m, the stability of the positioning results of a plurality of sampling points of the potential values of the data field is shown, and the superiority of the P wave first arrival data positioning method for eliminating the sensor with a longer propagation distance is also proved.
It should be emphasized that the examples described herein are illustrative and not restrictive, and thus the invention is not to be limited to the examples described herein, but rather to other embodiments that may be devised by those skilled in the art based on the teachings herein, and that various modifications, alterations, and substitutions are possible without departing from the spirit and scope of the present invention.
Claims (10)
1. A microseismic seismic source positioning method based on a Log-Cosh function is characterized by comprising the following steps: the method comprises the following steps:
s1: acquiring a microseismic signal sent by a seismic source based on a sensor in a monitoring area;
s2: the method comprises the steps of utilizing the position of a sensor, the P wave first arrival time picked up by a microseismic signal received by the sensor and a plurality of random sampling points in a monitoring area, and carrying out microseismic source positioning based on a microseismic source positioning target function to obtain a seismic source position, wherein the microseismic source positioning target function is constructed based on a Log-Cosh function.
2. The method of claim 1, wherein: the microseismic seismic source positioning target function constructed based on the Log-Cosh function is as follows:
min f(x0,y0,z0,t0)
in the formula Ii(i ═ 1,2,3 …, n) is the source location (x)0,y0,z0) And ith sensor position (x)i,yi,zi) N is the number of sensors participating in the positioning calculation, tiP-wave first arrival time v picked up for microseismic signal of ith sensorpIs the P wave propagation velocity, t0K is an overall scaling factor for the time of occurrence of the seismic source, and Cosh () refers to a hyperbolic cosine function.
3. The method of claim 1, wherein: the acquisition process of the seismic source position is as follows:
obtaining an initial positioning result based on the plurality of random sampling points and the microseismic seismic source positioning objective function;
then, removing the sensor data corresponding to the propagation time greater than the threshold value by using the initial positioning result;
and finally, carrying out microseismic source positioning by using the removed data and the microseismic source positioning objective function to obtain the seismic source position.
4. The method of claim 3, wherein: and obtaining a seismic source positioning point corresponding to each random sampling point, and determining an initial positioning result according to the data field potential value of each seismic source positioning point.
5. The method of claim 4, wherein: and the initial positioning result is the coordinate mean value of the N seismic source positioning points with the maximum data field potential value in all the seismic source positioning points.
6. The method of claim 5, wherein: randomly setting N in the monitoring area1A sampling point, N1Has a value range of [1500,2500 ]](ii) a The value range of N is [30, 80]]The value range of the threshold is [0.15, 0.20]]s。
7. The method of claim 3, wherein: the process of using the removed data and the micro-seismic source positioning objective function to perform micro-seismic source positioning to obtain the seismic source position comprises the following steps:
based on the sampling point, the position of the residual sensor after data removal and the P wave first arrival time picked up by the microseismic signal received by the sensor, and based on the microseismic source positioning target function, the microseismic source positioning is carried out again to obtain a plurality of seismic source positioning points;
and finally, determining the position of the seismic source based on the data field potential value and the plurality of seismic source positioning points.
8. A system based on the method of any one of claims 1-7, characterized by: the method comprises the following steps:
microseismic signal acquisition module: the system is used for acquiring microseismic signals sent by a seismic source;
a positioning module: and the method is used for positioning the micro-seismic source based on the micro-seismic source positioning target function by utilizing the position of the sensor, the P wave first arrival time picked up by the micro-seismic signal received by the sensor and the sampling points in the monitoring area to obtain the position of the seismic source.
9. An apparatus, characterized by: comprising a processor and a memory, the processor storing a computer program that the processor calls to perform: and acquiring data, and positioning a micro seismic source by using the position of a sensor in the data, the P wave first arrival time picked up by a micro seismic signal received by the sensor and a plurality of random sampling points in a monitoring area and based on a micro seismic source positioning objective function to obtain the position of the seismic source.
10. A readable storage medium, characterized by: a computer program is stored, which is invoked by a processor to perform:
and acquiring data, and positioning a micro seismic source by using the position of a sensor in the data, the P wave first arrival time picked up by a micro seismic signal received by the sensor and a plurality of random sampling points in a monitoring area and based on a micro seismic source positioning objective function to obtain the position of the seismic source.
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