Microseismic source location method without accurate pick-up

Abstract

The present application belongs to the technical field of microseismic source positioning, and particularly relates to a microseismic source positioning method without accurate time picking, which comprises the following steps: obtaining microseismic monitoring data of a plurality of sensors arranged in a monitoring area, extracting a microseismic wave arrival time segment of each sensor, and calculating an observed time difference interval of a sensor pair. Then, a theoretical time difference calculation model of the sensor pair is constructed based on a source position coordinate and a wave velocity. A time uncertainty parameter is introduced to establish a probability matching relationship between the theoretical time difference and the observed time difference interval, and a likelihood function is constructed. Finally, the source position and the wave velocity that maximize the likelihood function in a search space are searched as the final positioning result. The present application replaces the traditional accurate time point, relaxes the requirement for microseismic data picking accuracy, and effectively reduces the picking error caused by wave field complexity, equipment error and artificial factors.

Description

Technical Field

[0001] This invention belongs to the field of microseismic source location technology, and in particular relates to a method for locating microseismic sources without requiring precise time-based picking. Background Technology

[0002] Human survival and social development are inseparable from mineral resources. With the gradual depletion of shallow resources, venturing into the deeper parts of the earth has become an inevitable trend in mining development and a strategic technological challenge that must be faced. However, deep resource extraction faces a complex geological environment characterized by "three highs and one disturbance": high ground stress, high ground temperature, high osmotic pressure, and intense engineering disturbances. This significantly increases the risk of dynamic disasters such as rock bursts, coal and gas outbursts, and rock bursts. To achieve safe and efficient extraction of deep mineral resources, effective monitoring and early warning systems for dynamic disasters are urgently needed.

[0003] Acoustic emission / microseismic monitoring technology, by collecting and analyzing micro-vibration signals generated during the occurrence and propagation of fractures in underground rock masses, can reveal information about the damage evolution of the internal structure of the rock mass. It is one of the most effective technologies for monitoring and early warning of dynamic disasters. Among these technologies, microseismic source localization is the core component and the foundation for subsequent research on disaster mechanisms and the development of prevention and control measures.

[0004] Existing microseismic source location methods primarily utilize the arrival time or time difference of seismic waves at different monitoring stations. Through mathematical calculations and iterative optimization, the error between the theoretical travel time and the actual arrival time is minimized, thereby determining the spatial location of the seismic source. This approach can be traced back to the Geiger iterative algorithm of 1912. With the advancement of computer technology, since the 1960s, this method has gradually become the mainstream algorithm for seismic source location in engineering. Subsequently, researchers have proposed a series of improvements based on this idea, such as the HYPOCENTER algorithm improved by BR Lienert et al. based on the Geiger algorithm, and the QUAKE3D method based on a three-dimensional velocity model proposed by GD Nelson and JE Vidale. In China, Dong Longjun et al. proposed a seismic source location method that does not require prior velocity measurement, and Zhang Yanbo et al. proposed a location algorithm based on atomic orbital search. These studies have, to some extent, promoted the development of seismic source location technology.

[0005] However, a review of widely used microseismic location methods both domestically and internationally reveals that their core logic largely relies on accurate arrival picking. In real-world engineering environments, however, the complex wavefield and noise interference mean that even the most accurate first-arrival picking methods currently available suffer from unavoidable systematic errors. The lack of effective means to measure and control arrival picking errors, coupled with the fact that most existing location algorithms lack robustness to input errors, means that algorithms that perform well in ideal simulations often exhibit source location errors far exceeding expectations in practical engineering applications. Furthermore, the current lack of accurate a posteriori methods for arrival picking accuracy, with research often relying on manual picking results as a standard, further amplifies the adverse impact of arrival picking errors on the final location accuracy.

[0006] Therefore, it is of great significance to improve the reliability of dynamic disaster analysis in deep mining by getting rid of the over-reliance on time-accuracy, measuring and controlling the time-accuracy picking error, and exploring a high-precision seismic source location method under controllable error. Summary of the Invention

[0007] To address the problem that positioning errors are amplified due to inaccurate timing pickup in existing positioning methods, this invention proposes a microseismic source positioning method that does not require precise timing pickup.

[0008] This invention proposes a microseismic source location method that does not require precise arrival time picking. By picking an arrival time segment containing the actual arrival time, the observation is transformed from a point into an interval, and theoretical values ​​are matched with the observation interval. Finally, the most reliable source location and wave velocity are found in the search space through a particle swarm optimization search algorithm.

[0009] This invention is achieved through the following technical solution:

[0010] A method for locating microseismic sources without requiring precise time-based picking includes the following steps:

[0011] S1. Acquire microseismic monitoring data from N sensors deployed in the monitoring area, and pick up the arrival time segment of the microseismic wave corresponding to each sensor;

[0012] S2. Based on the arrival time segment, calculate the observation time difference interval of any two sensors forming a sensor pair;

[0013] S3. Based on the source location coordinates and wave velocity to be solved, construct a theoretical time difference calculation model for the sensor pair;

[0014] S4. Introduce the arrival time uncertainty parameter, establish the probability matching relationship between the theoretical arrival time difference and the observed time difference interval, and construct a likelihood function that reflects the possibility that the theoretical arrival time difference falls into the observed time difference interval;

[0015] S5. Find the source location coordinates and wave velocity in the search space that maximize the likelihood function, and use them as the final microseismic source location result.

[0016] Furthermore, in S1, the arrival segment of the i-th station is represented as [ ],in To reach the lower realm, For the time to reach the upper realm, the actual time to reach. The following relationship must be satisfied:

[0017] .

[0018] Furthermore, in S2, for a station pair consisting of the i-th station and the j-th station, the observed time difference interval... It is expressed as follows:

[0019] This formula represents the range of time difference between stations i and j.

[0020] Furthermore, in S3, let the source parameters be denoted as... The coordinates of the i-th station are The coordinates of the j-th station are Then the geometric distance from the epicenter to station i is... Represented as:

[0021] ,in Indicates the coordinates of the earthquake source. , This indicates the Euclidean distance between the epicenter and the station;

[0022] The theoretical arrival time difference between station i and station j is expressed as:

[0023] .

[0024] Furthermore, the method for calculating the uncertainty parameter at the time of arrival is as follows:

[0025] Let the length of the arrival segment of the i-th station be . Assuming the arrival time points are uniformly distributed within the interval, the uncertainty at arrival time is... Represented as:

[0026] .

[0027] Furthermore, in S4, for any two stations i and j, assuming their arrival errors are relatively independent, the uncertainty of their arrival time difference is expressed as:

[0028] .

[0029] Furthermore, in S4, assuming that the theoretical time difference and the observed time difference segments conform to a Gaussian distribution, then for any candidate seismic source point in space... The probability of it matching with the i-th and j-th stations. for:

[0030] ;in This is the cumulative distribution function of the standard normal distribution.

[0031] Furthermore, the likelihood function is obtained by summing the logarithms of the matching probabilities of all station pairs, and its total probability is expressed logarithmically as follows:

[0032] .

[0033] Furthermore, in S5, the method for determining the optimal seismic source point is as follows:

[0034] The most reliable source point is found in the search space using the particle swarm optimization algorithm. With wave speed ,but:

[0035] Among them, wave speed It is a constant.

[0036] The beneficial effects of this invention are:

[0037] (1) This invention replaces a single time point by acquiring time segments containing the actual arrival time, which greatly relaxes the stringent requirements for the accuracy of picking up the original microseismic data, effectively eliminates picking errors caused by wave field complexity, equipment system errors and human subjective factors, and significantly reduces the engineering difficulty of data processing.

[0038] (2) This invention scientifically quantifies the discreteness of arrival time picking by calculating the observed time difference interval and introducing the arrival time uncertainty parameter, combined with the interval length and probability distribution assumptions. On this basis, the deterministic problem of minimizing the absolute residual between theoretical travel time and actual arrival time in the traditional positioning method is transformed into a statistical problem of maximizing the probability that the theoretical arrival time difference falls into the observation interval, so that the algorithm has a strong tolerance and anti-interference ability for small fluctuations and noise in the original input data;

[0039] (3) This invention constructs a likelihood function that reflects the matching probability and uses an optimization algorithm to find the global optimization. Even in harsh engineering environments with limited on-site picking accuracy and uncontrollable systematic errors, this method can still converge to the most reliable true source location, providing high-precision and high-reliability basic data support for the mechanism research and prevention of dynamic disasters such as rock bursts, coal and gas outbursts and rock bursts in deep resource mining. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is a flowchart illustrating a microseismic source location method proposed in this invention that does not require precise time-based picking.

[0042] Figure 2 This is a schematic diagram of a terminal device for a microseismic source location method that does not require precise time-based picking, as proposed in this invention.

[0043] Figure 3 This is a schematic diagram of a readable storage medium for a microseismic source localization method that does not require precise time-based picking, as proposed in this invention.

[0044] In the diagram, 200 is the terminal device, 210 is the memory, 211 is the RAM, 212 is the cache, 213 is the ROM, 214 is the program / utility, 215 is the program module, 220 is the processor, 230 is the bus, 240 is the external device, 250 is the I / O interface, 260 is the network adapter, and 300 is the program product. Detailed Implementation

[0045] 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 embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0046] Example 1

[0047] refer to Figure 1 This implementation relies on a high-precision microseismic monitoring system built on geotechnical engineering sites such as deep metal mines, coal mining faces, underground powerhouses for water conservancy and hydropower projects, or tunnel excavation projects. At the physical implementation level, to achieve three-dimensional spatial coverage of potential microseismic sources, several microseismic sensors need to be deployed at key locations along the boundaries and within the monitoring area. The number of sensors, N, is typically determined based on the volume of the monitoring area and the complexity of the geological structure, generally ranging from 6 to 30. This embodiment uses N=16 as an example for illustration.

[0048] The sensor should be a high-sensitivity piezoelectric accelerometer or velocity sensor with a frequency response range that covers the main frequency band of the micro-vibration signal, typically from 10Hz to 2000Hz. The sensitivity should be no less than 10V / g to ensure that the weak rupture signal after long-distance propagation can be captured.

[0049] To ensure efficient acoustic coupling between the sensor and the rock mass, installation holes at least 2 meters deep are drilled in the tunnel wall. Rigid anchor bolts and cement grouting are used to couple the sensor deep into the original rock, thus avoiding the attenuation and scattering effects of loose zones and fractures on the tunnel surface on high-frequency waves. Each sensor is connected to the nearest data acquisition substation via shielded twisted-pair cable or fiber optic grating. The acquisition substation has a built-in high-precision analog-to-digital converter (A / D) with a sampling frequency set to 20kHz and a quantization bit depth of 24 bits to ensure wide dynamic range signal recording. All acquisition substations are connected to the ground data processing server via an industrial ring network, employing the IEEE 1588 PTP precision clock synchronization protocol or GPS timing system to ensure that the time synchronization error of all channels across the network is controlled within 1 microsecond, thereby eliminating time base errors introduced by the hardware system and providing a clean time reference for subsequent algorithm processing.

[0050] When a fracturing event occurs within the rock mass, elastic waves propagate outwards in the form of spherical waves. Due to the inhomogeneity and anisotropy of the geological medium, as well as the presence of voids caused by excavation, the waveform undergoes reflection, refraction, scattering, and attenuation during propagation. By the time the waveform reaches the sensor, it has often already undergone significant distortion, and the background is frequently accompanied by continuous industrial noise such as drilling operations and ventilation equipment operation.

[0051] In traditional positioning technology systems, positioning algorithms such as the Geiger method or the simplex method require the precise first arrival time point to be extracted from this complex waveform, that is, the instant the P wave arrives. However, in actual engineering data, the signal-to-noise ratio is often low. The initial arrival signal may be submerged in noise or exhibit a slow upward trend, resulting in the initial arrival point not being a steep inflection point on the waveform, but rather an ambiguous range. In this case, the picking results given by different human processors or different automatic picking algorithms may differ by several milliseconds or even tens of milliseconds. In hard rock with a wave velocity of 5000 m / s, a 1 ms time error corresponds to a 5-meter distance error. This error will be further amplified in the subsequent solution of the nonlinear equations.

[0052] To address the aforementioned technical challenges, this embodiment no longer focuses on finding a precise point that may be physically obscured by noise. Instead, it employs an interval picking strategy. The specific operation process is as follows:

[0053] The data processing server receives waveform data uploaded from each channel in real time. First, it performs bandpass filtering to remove power frequency interference and extremely low-frequency ground pulsations. Then, it calculates the characteristic envelope of the signal using the long short time window energy ratio (STA / LTA) algorithm. When the characteristic envelope value exceeds a preset trigger threshold, the system determines that a micro-vibration event has been detected in that channel.

[0054] At this point, the system does not directly define the trigger time as the arrival time, but instead defines an arrival time segment based on the trigger time and the signal-to-noise ratio characteristics of the waveform. ]. Among them, the lower bound The determination of the upper bound is usually made by looking back a certain amount of time from the trigger moment. This is to accommodate the error caused by the energy accumulation delay due to the weak initial signal, which leads to delayed triggering; The determination is usually made by extending the trigger time by a certain amount of time or by taking the moment of the first peak of the waveform. This interval [ The physical meaning of this is that although it is impossible to determine exactly at which microsecond the P-wave arrives, there is a very high degree of confidence that the actual arrival time is... It must fall within this time window, meaning the actual arrival time and the pickup time satisfy the following relationship:

[0055] This approach elevates the observation of microseismic signals from point observation to interval observation. While it may seem to reduce precision, it actually preserves the true physical information by introducing an uncertainty range, avoiding model bias caused by forcibly specifying an incorrect point. The interval length... The size can be dynamically adjusted according to engineering needs: for waveforms with extremely high signal-to-noise ratio and clear initial and final values, the interval can be set very narrow; for waveforms with low signal-to-noise ratio and large background interference, the interval will be automatically widened. This adaptability allows this method to utilize both high-quality and low-quality data simultaneously without being misled by the mispicking of low-quality data.

[0056] After completing the interval picking of all triggered sensors, the algorithm enters the second stage to calculate the observed time difference segments. The time difference principle is usually used for microseismic source localization to eliminate the time of seismic occurrence. Unknown influences, assuming there is a seismic source in space to be solved, whose source parameters are denoted as... ,in The coordinates of the earthquake source are... For the wave speed, for any two stations i and j (i≠j), if the precise arrival time can be obtained... and The actual time difference is expressed as follows:

[0057] .

[0058] However, in this embodiment, there are two intervals. and According to the rules of interval arithmetic, the difference between two intervals is still an interval, and the minimum value of this difference interval occurs when the minuend is at its minimum and the subtrahend is at its maximum. The maximum value of this difference interval occurs when the minuend is at its maximum and the subtrahend is at its minimum, i.e. .

[0059] Therefore, since only the time fragment is obtained, the above formula can be written as follows:

[0060] This formula represents the observation interval range of the time difference between stations i and j. This step constructs the target domain for subsequent probability matching. If N stations participate in the localization, theoretically, it is possible to construct... Such interval combinations.

[0061] Next, the algorithm needs to establish a theoretical physical model from the epicenter location to the time difference, that is, to calculate the theoretical time difference. Assume there exists a candidate epicenter point in the search space. The geometric distance from the epicenter to station i can be expressed as follows:

[0062] In the formula Indicates the coordinates of the earthquake source. , The coordinates of station i are denoted as . , The Euclidean distance between the epicenter and the station is expressed as follows:

[0063] Assume the wave speed is constant. The theoretical arrival time difference between station i and station j can be expressed as follows:

[0064] ,this It is a definite scalar value, representing the hypothetical location of the earthquake source. The time difference that should theoretically be observable at that time.

[0065] The core issue at this point is how to evaluate this definite theoretical value. Compared with the aforementioned broad observation range The traditional approach to determining the degree of matching between the theoretical values ​​is to check whether the theoretical values ​​fall within the range. If they do, the error is 0; otherwise, the error is infinite. This is a hard constraint and is not conducive to optimization.

[0066] This invention employs a soft-constraint probabilistic statistical perspective, establishing the relationship between observed time difference segments and theoretical time differences. Since the observed value is an interval segment and the theoretical value is a specific point, to quantify the probability that the theoretical value falls within the observed interval, this embodiment introduces a new variable, denoted as the time uncertainty. Uncertainty at that time It is used to characterize the dispersion of arrival time error, based on which the probability of the theoretical arrival time difference falling within the observation interval can be calculated.

[0067] about The value of is set in this embodiment based on the principle of maximum entropy, within the interval Internally, due to a lack of further information, this embodiment assumes that the probability of the actual arrival time occurring within this interval is equal, i.e., it follows a uniform distribution. For a length of... The uniform distribution of , according to statistical principles, has a standard deviation that is 1 / 3 of the interval length. Therefore, the interval length of station i is Assume that the arrival times are uniformly distributed within the interval, and their It can be expressed as follows:

[0068] This formula transforms the length of the geometric attribute of an interval into the standard deviation of the statistical attribute, making it possible to use statistical tools to process interval data.

[0069] Furthermore, this embodiment needs to consider the uncertainty after differential measurement of the two stations. For any two stations i and j, their monitoring processes are independent, and their background noise is also uncorrelated. Therefore, their arrival errors are relatively independent. According to the error propagation law, the variance of the difference between two independent random variables is equal to the sum of the variances of each variable. Therefore, the uncertainty of the arrival time difference is expressed as follows: This parameter reflects the pickup quality of the two stations involved in the calculation. If one station has a wide pickup range (poor quality), then... The weight of that station pair will be very large, and in subsequent probability calculations, its weight will automatically decrease.

[0070] Based on this, this embodiment needs to construct a likelihood function. Although the error of a single station is assumed to be uniformly distributed, when two random variables are subtracted, especially when multiple random factors are superimposed, according to the Central Limit Theorem, the distribution of the difference will gradually approach a Gaussian distribution. Therefore, this embodiment reasonably assumes that the observed time difference segment and the theoretical arrival time difference may not perfectly match. Assuming that both conform to a Gaussian distribution, they can be expressed as:

[0071] .

[0072] The above formula indicates that the theoretical arrival time difference and the observed time difference segments conform to a Gaussian distribution, with the mean being the theoretical arrival time difference. The standard deviation is .

[0073] Based on the Gaussian distribution assumption, a candidate earthquake source can be calculated. The probability of it being the actual epicenter is equivalent to calculating the probability of it being at the mean of the epicenter. Standard deviation is Under a normal distribution, the random variable falls within the observed difference interval. The probability area within the space. For any candidate source point in space. The probability of it matching with the i-th and j-th stations is:

[0074] Using the cumulative distribution function (CDF) of the standard normal distribution, denoted as... The integration process can be simplified to the difference between two CDF values:

[0075]

[0076] The physical meaning of the above formula is that if the theoretical time difference... It is exactly within the observation interval The center, then the two When the absolute values ​​of the independent variables of a function are equal but opposite in sign, the largest difference indicates the highest probability of a match.

[0077] If the picking interval is very narrow (data quality is high), that is Very small, then The independent variable of the function will be very large, and the function curve will become very steep, which means that the theoretical value... As long as it deviates slightly from the range, the probability The sharp drop demonstrates the algorithm's ability to fully consider and effectively utilize high-quality data.

[0078] If the picking range is very wide (data quality is low), that is... It's very big, so The curve of the function will become very flat, meaning that even the theoretical value... Moving within the interval, probability The changes are not significant, which reflects the algorithm's insensitivity to low-quality data and its fault tolerance. It will not drastically change the final positioning result due to fluctuations in low-quality data.

[0079] In order to make comprehensive use of the information of all stations, this embodiment also needs to construct a global objective function. Assuming that the observations of each station pair are statistically independent, the joint probability of all station pairs simultaneously satisfying the observation data is equal to the product of the probabilities of each station pair. In order to facilitate numerical calculation and avoid numerical underflow caused by the product of probability values, the log-likelihood function is usually used.

[0080] For all station pairs, the total probability expressed in logarithmic form is:

[0081] ,this The functions constitute a solution space in four dimensions. The search topographic map shows that the coordinates of the highest peak on the map are the most likely location of the earthquake source and the wave velocity.

[0082] Finally, we proceed to find the optimal source point, based on the log-likelihood function constructed above. It is a highly nonlinear, multimodal function. Especially when the sensor array is unevenly distributed or there are abnormal observations, the objective function may have multiple local extrema. Traditional gradient descent-based methods or linearized iterative methods are very prone to getting trapped in local optima, leading to localization failure. Therefore, this embodiment uses a heuristic global optimization algorithm, particle swarm optimization, to solve the problem.

[0083] During the solution process, the system first operates within a preset three-dimensional monitoring area and a reasonable wave velocity range, for example... Within the coordinate range of the mining area, wave velocity A swarm of particles is randomly initialized between 3000 and 6500 m / s, with each particle representing a potential source solution vector. Let the particle swarm size be M, for example, M=50. In each iteration, the algorithm calculates the position corresponding to each particle. The value serves as fitness; each particle updates itself by tracking two extreme values.

[0084] One is the historical best position experienced by the particle itself, i.e., the individual extreme value pBest; the other is the best position found by the entire population so far, i.e., the global extreme value gBest.

[0085] The formulas for updating the particle's velocity and position are as follows:

[0086] Speed ​​updates: ;

[0087] Location update: ;

[0088] in, Inertial weights are used to balance global search and local exploit capabilities; , The learning factor is used to adjust the step size of the particle's flight towards the individual extremum and the global extremum, respectively. , The random number between [0, 1] is used to increase the randomness of the search and escape local optima.

[0089] The algorithm stops after several iterations, such as 100-200 iterations, or when the change in the global optimal fitness value is less than the set convergence threshold. At this point, the coordinates corresponding to the global optimum gBest are... and wave speed This is output as the final source location result:

[0090] .

[0091] Through the complete implementation process described above, this invention successfully transforms the deterministic localization problem, which originally relied on precise single-point pickup, into a statistical inference problem based on interval probability matching. This method is mathematically closed-loop and physically clear, capable of adaptively handling microseismic data of varying qualities. In practical deep mining applications, this method demonstrates extremely strong robustness. Even when some sensors experience pickup intervals as long as 20ms due to noise interference, as long as a certain number of effective constraints exist in the array, the algorithm can still utilize the characteristics of the probability density function to automatically reduce the weight of ambiguous data and focus on high-probability areas, thereby obtaining localization results close to the true location. Simultaneously, because the algorithm synchronously inverts wave velocity... This also eliminates, to some extent, the systematic errors caused by inaccurate preset wave velocity models, making it very suitable for deep mining environments with complex and varied lithology. The entire processing flow runs automatically in computer software, eliminating the need for repeated manual corrections, which greatly improves the efficiency and intelligence level of microseismic monitoring.

[0092] Example 2

[0093] refer to Figure 2 Based on Example 1, this example proposes a terminal device for a microseismic source location method that does not require precise time picking. The terminal device 200 includes at least one memory 210, at least one processor 220, and a bus 230 connecting different platform systems.

[0094] The memory 210 may include a readable medium in the form of volatile memory, such as RAM 211 and / or cache memory 212, and may further include ROM 213.

[0095] The memory 210 also stores a computer program that can be executed by the processor 220, causing the processor 220 to perform any of the above-described microseismic source location methods in this application embodiment that do not require precise time-based picking. The specific implementation method and the achieved technical effects are consistent with those described in the above-described application embodiments, and some details will not be repeated here. The memory 210 may also include a program / utility 214 having a set (at least one) of program modules 215. Such program modules include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment.

[0096] Accordingly, processor 220 can execute the aforementioned computer program, as well as executable program / utility 214.

[0097] Bus 230 can represent one or more of several types of bus structures, including a memory bus or memory controller, peripheral bus, graphics acceleration port, processor, or a local bus using any of the various bus structures.

[0098] Terminal device 200 can also communicate with one or more external devices 240, such as keyboards, pointing devices, Bluetooth devices, etc., and with one or more devices capable of interacting with it, and / or with any device that enables it to communicate with one or more other computing devices (e.g., routers, modems, etc.). This communication can be performed via I / O interface 250. Furthermore, terminal device 200 can communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via network adapter 260. Network adapter 260 can communicate with other modules of terminal device 200 via bus 230. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with terminal device 200, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

[0099] Example 3

[0100] This embodiment proposes a readable storage medium for a microseismic source location method that does not require precise time-based picking. The computer-readable storage medium stores instructions that, when executed by a processor, implement any of the aforementioned microseismic source location methods that do not require precise time-based picking. The specific implementation method and the technical effects achieved are consistent with those described in the above application embodiments, and some details will not be repeated.

[0101] Figure 3The present embodiment illustrates a program product 300 for implementing the above-described applications. This product may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may run on a terminal device, such as a personal computer. However, the program product 300 of the present invention is not limited thereto. In this embodiment, the readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device. The program product 300 may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0102] Computer-readable storage media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof. Program code for performing operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on a user computing device, partially on a user device, as a standalone software package, partially on a user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing devices can be connected to user computing devices via any type of network, including local area networks (LANs) or wide area networks (WANs), or they can be connected to external computing devices (e.g., via the Internet using an Internet service provider).

[0103] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for locating microseismic sources without requiring precise time-based picking, characterized in that, Includes the following steps: S1. Acquire microseismic monitoring data from N sensors deployed in the monitoring area, and pick up the arrival time segment of the microseismic wave corresponding to each sensor; S2. Based on the arrival time segment, calculate the observation time difference interval of any two sensors forming a sensor pair; S3. Based on the source location coordinates and wave velocity to be solved, construct a theoretical time difference calculation model for the sensor pair; S4. Introduce the arrival time uncertainty parameter, establish the probability matching relationship between the theoretical arrival time difference and the observed time difference interval, and construct a likelihood function that reflects the possibility that the theoretical arrival time difference falls into the observed time difference interval; S5. Find the source location coordinates and wave velocity in the search space that maximize the likelihood function, and use them as the final microseismic source location result.

2. The method for locating microseismic sources without requiring precise time-based picking, as described in claim 1, is characterized in that... In S1, the arrival segment of the i-th station is represented as [ ],in To reach the lower realm, For the time to reach the upper realm, the actual time to reach. The following relationship must be satisfied: 。 3. The method for locating microseismic sources without requiring precise time-based picking, as described in claim 1, is characterized in that... In S2, for a station pair consisting of the i-th station and the j-th station, the observed time difference interval is... It is expressed as follows: This formula represents the range of time difference between stations i and j.

4. The method for locating microseismic sources without requiring precise time-based picking, as described in claim 1, is characterized in that... In S3, let the source parameters be denoted as... The coordinates of the i-th station are The coordinates of the j-th station are Then the geometric distance from the epicenter to station i is... Represented as: ,in Indicates the coordinates of the earthquake source. , This indicates the Euclidean distance between the epicenter and the station; The theoretical arrival time difference between station i and station j is expressed as: 。 5. A method for locating microseismic sources without requiring precise time-based picking, as described in claim 2, characterized in that... The method for calculating the uncertainty parameter at the time of arrival is as follows: Let the length of the arrival segment of the i-th station be . Assuming the arrival time points are uniformly distributed within the interval, the uncertainty at arrival time is... Represented as: 。 6. A method for locating microseismic sources without requiring precise time-based picking, as described in claim 5, characterized in that, In S4, for any two stations i and j, assuming their arrival errors are relatively independent, the uncertainty of their arrival time difference is expressed as: 。 7. A method for locating microseismic sources without requiring precise time-based picking, as described in claim 6, characterized in that... In S4, assuming that the theoretical time difference and the observed time difference segments conform to a Gaussian distribution, then for any candidate seismic source point in space... The probability of it matching with the i-th and j-th stations. for: ;in This is the cumulative distribution function of the standard normal distribution.

8. A method for locating microseismic sources without requiring precise time-based picking, as described in claim 7, characterized in that, The likelihood function is obtained by summing the logarithmic probabilities of all station pairs, and its total probability is expressed logarithmically as follows: 。 9. A method for locating microseismic sources without requiring precise time-based picking, as described in claim 1, characterized in that, In S5, the method for finding the optimal seismic source point is as follows: The most reliable source point is found in the search space using the particle swarm optimization algorithm. With wave speed ,but: Among them, wave speed It is a constant.

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