Microseismic event reliability evaluation method and device, electronic equipment and medium

Abstract

The invention discloses a microseism event reliability evaluation method and device, electronic equipment and a medium. The method comprises the following steps: calculating a superposition channel ss (t), and carrying out microseism event identification by using a long and short time window energy ratio method; calculating the waveform similarity of the microseism event; and according to the waveform similarity, reliability evaluation and classification of the corresponding microseism events are carried out. According to the method, the waveform similarity coefficient is introduced as an evaluation basis, so that the false drop rate of automatic identification of the microseism event is reduced, the workload of effective microseism event screening in a subsequent artificial quality control link is also reduced, and the microseism processing interpretation efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of hydraulic fracturing microseismic monitoring data processing, and more specifically, to a method, device, electronic device and medium for evaluating the reliability of microseismic events. Background Art

[0002] The fracturing microseismic monitoring technology can monitor the fracturing process and evaluate the fracturing effect by monitoring the microseismic signals generated during the hydraulic fracturing process, and further guide the optimization of engineering parameters. Since the microseismic monitoring is a long-term continuous monitoring during the fracturing process, microseismic events may occur at any moment in the monitoring record. The long-short time window energy ratio method is the most commonly used microseismic event identification method at present. This method calculates the energy ratio of the records in the long time window and the short time window, and when the energy ratio exceeds a given threshold, it is considered that a microseismic event is detected. The defect of using signal energy as the identification criterion is that the identification result is greatly affected by the noise energy in the record, and it is easy to detect "false events" with a high false detection rate. For the microseismic events identified by the long-short time window energy ratio, at present, the effective events are mostly screened and classified manually, with low efficiency.

[0003] Therefore, it is necessary to develop a method, device, electronic device and medium for evaluating the reliability of microseismic events based on waveform similarity.

[0004] The information disclosed in the background art part of the present invention is only intended to deepen the understanding of the general background art of the present invention, and should not be regarded as an admission or any form of suggestion that this information constitutes the prior art known to those skilled in the art. Summary of the Invention

[0005] The present invention provides a method, device, electronic device and medium for evaluating the reliability of microseismic events, which can reduce the false detection rate of automatic identification of microseismic events by introducing the waveform similarity coefficient as the evaluation basis, and also reduce the workload of screening effective microseismic events in the subsequent manual quality control link, improving the efficiency of microseismic processing and interpretation.

[0006] In a first aspect, an embodiment of the present disclosure provides a method for evaluating the reliability of microseismic events, including:

[0007] Calculating the stacked trace ss(t), and identifying microseismic events by using the long-short time window energy ratio method;

[0008] Calculating the waveform similarity of the microseismic events;

[0009] According to the waveform similarity, performing reliability evaluation and classification of the corresponding microseismic events.

[0010] As a specific implementation manner of the embodiment of the present disclosure, the stacked trace is:

[0011]

[0012] Among them, ss(t) is the stacked trace, m is the number of ground microseismic monitoring records, and s i (t) is the signal recorded by the i-th geophone, and t = [t 1 , t 2 ,..., t n is the recording time, and n is the number of time sampling points.

[0013] As a specific implementation manner of the embodiment of the present disclosure, microseismic event recognition based on the stacked trace by using the short-term and long-term window energy ratio method includes:

[0014] Set a threshold threshold;

[0015] According to the short-term and long-term window energy ratio method, slide the short-term and long-term windows to complete the calculation of the short-term and long-term window energy ratio of the entire ss(t) trace. When STA(t 0 ) contains event information and LTA(t 0 ) contains noise, a local maximum value appears in the ratio;

[0016] If the value of R(t 0 ) is greater than the set threshold, a microseismic event is detected at the moment of t 0 .

[0017] As a specific implementation manner of the embodiment of the present disclosure, the short-term and long-term window energy ratio is:

[0018]

[0019] Among them, R(t 0 ) is the short-term and long-term window energy ratio at the moment of t 0 , STA(t 0 ) is the short-term window average energy, LTA(t 0 ) is the long-term window average energy, E(t) is the energy of the received signal of ss(t) at the moment of t, t 2 > t 0 > t 1 .

[0020] As a specific implementation manner of the embodiment of the present disclosure, calculating the waveform similarity of the microseismic event includes:

[0021] Open a time window at the corresponding moment of the microseismic event to intercept the waveform record;

[0022] Set a reference trace, and calculate the waveform similarity coefficient between the waveform record and the reference trace for each trace;

[0023] The waveform similarity coefficients corresponding to all channels in the waveform record constitute the waveform similarity.

[0024] As a specific implementation manner of an embodiment of the present disclosure, the waveform similarity coefficient is:

[0025]

[0026] As a specific implementation manner of an embodiment of the present disclosure, according to the waveform similarity, the reliability evaluation and classification of the corresponding microseismic events include:

[0027] Calculate the mean mean(R) and variance var(R) of the waveform similarity respectively;

[0028] The larger the absolute value of mean(R) and the smaller the value of var(R), the higher the reliability of the microseismic event;

[0029] The value of mean(R) is greater than the first threshold and the value of var(R) is greater than the second threshold, indicating that the reliability of the microseismic event is high, but the continuity of the in-phase axis between channels is poor;

[0030] The value of mean(R) is less than the third threshold and the value of var(R) is less than the fourth threshold, indicating that the reliability of the microseismic event is low;

[0031] The difference between the value of mean(R) and 0 is greater than the fifth threshold and the value of var(R) is less than the fourth threshold, indicating that the microseismic event is a "false" microseismic event.

[0032] In a second aspect, an embodiment of the present disclosure further provides a microseismic event reliability evaluation device, including:

[0033] An event recognition module that calculates the stacked trace ss(t) and uses the long-short time window energy ratio method to identify microseismic events;

[0034] A calculation module that calculates the waveform similarity of the microseismic event;

[0035] A classification module that performs reliability evaluation and classification of the corresponding microseismic events according to the waveform similarity.

[0036] As a specific implementation manner of an embodiment of the present disclosure, the stacked trace is:

[0037]

[0038] where ss(t) is the stacked trace, m is the number of ground microseismic monitoring records, and s i (t) is the signal recorded by the i-th geophone, and t = [t 1 ,t 2 ,...,tn , where \(m\) is for recording time and \(n\) is the number of time sampling points.

[0039] As a specific implementation manner of the embodiment of the present disclosure, microseismic event recognition based on the stacked trace by using the short-term and long-term window energy ratio method includes:

[0040] Set a threshold value threshold;

[0041] According to the short-term and long-term window energy ratio method, slide the short-term and long-term windows to complete the calculation of the short-term and long-term window energy ratio of the entire ss(t) trace. When STA(t 0 ) contains event information and LTA(t 0 ) contains noise, a local maximum value appears in the ratio;

[0042] If the value of R(t 0 ) is greater than the set threshold, a microseismic event is detected at time t 0 .

[0043] As a specific implementation manner of the embodiment of the present disclosure, the short-term and long-term window energy ratio is:

[0044]

[0045] where \(R(t 0 ) is the short-term and long-term window energy ratio at time t 0 , STA(t 0 ) is the short-term window average energy, LTA(t 0 ) is the long-term window average energy, E(t) is the energy of the received signal of ss(t) at time t, \(t 2 >t 0 >t 1 .

[0046] As a specific implementation manner of the embodiment of the present disclosure, calculating the waveform similarity of the microseismic event includes:

[0047] Open a time window at the corresponding time of the microseismic event to intercept the waveform record;

[0048] Set a reference trace and calculate the waveform similarity coefficient between the waveform record and the reference trace channel by channel;

[0049] The waveform similarity coefficients corresponding to all channels in the waveform record form the waveform similarity.

[0050] As a specific implementation manner of the embodiment of the present disclosure, the waveform similarity coefficient is:

[0051]

[0052] As a specific implementation manner of the embodiments of the present disclosure, the reliability evaluation and classification of corresponding microseismic events according to the waveform similarity include:

[0053] Calculate the mean mean(R) and variance var(R) of the waveform similarity respectively;

[0054] The larger the absolute value of mean(R) and the smaller the value of var(R), the higher the reliability of the microseismic event;

[0055] When the value of mean(R) is greater than the first threshold and the value of var(R) is greater than the second threshold, it represents that the reliability of the microseismic event is high, but the continuity of the in-phase axis between channels is poor;

[0056] When the value of mean(R) is less than the third threshold and the value of var(R) is less than the fourth threshold, it represents that the reliability of the microseismic event is low;

[0057] When the difference between the value of mean(R) and 0 is greater than the fifth threshold and the value of var(R) is less than the fourth threshold, it represents that the microseismic event is a "false" microseismic event.

[0058] In a third aspect, the embodiments of the present disclosure further provide an electronic device, which includes:

[0059] A memory storing executable instructions;

[0060] A processor, the processor runs the executable instructions in the memory to implement the microseismic event reliability evaluation method.

[0061] In a fourth aspect, the embodiments of the present disclosure further provide a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, it implements the microseismic event reliability evaluation method.

[0062] The method and device of the present invention have other characteristics and advantages, which will be obvious from the accompanying drawings incorporated herein and the subsequent specific embodiments, or will be described in detail in the accompanying drawings incorporated herein and the subsequent specific embodiments. These drawings and specific embodiments are used together to explain the specific principles of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] By describing the exemplary embodiments of the present invention in more detail in conjunction with the accompanying drawings, the above and other objects, features, and advantages of the present invention will become more obvious. Among them, in the exemplary embodiments of the present invention, the same reference numerals generally represent the same components.

[0064] Figure 1The flowchart showing the steps of the method for evaluating the reliability of microseismic events according to an embodiment of the present invention is presented.

[0065] Figure 2 The schematic diagram showing the synthetic microseismic record according to an embodiment of the present invention is presented.

[0066] Figure 3a and Figure 3b The schematic diagrams showing the stacking result and the similarity coefficient according to an embodiment of the present invention are presented respectively.

[0067] Figure 4 The schematic diagram showing the evaluation of the waveform similarity of four microseismic events according to an embodiment of the present invention is presented.

[0068] Figure 5 The block diagram showing a device for evaluating the reliability of microseismic events according to an embodiment of the present invention is presented.

[0069] Description of reference numerals:

[0070] 201, event recognition module; 202, calculation module; 203, classification module. Detailed implementation manners

[0071] The preferred embodiments of the present invention will be described in more detail below. Although the preferred embodiments of the present invention are described below, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein.

[0072] To facilitate understanding of the solutions and effects of the embodiments of the present invention, six specific application examples are given below. Those skilled in the art should understand that this example is only for facilitating the understanding of the present invention, and any specific details are not intended to limit the present invention in any way.

[0073] Example 1

[0074] Figure 1 The flowchart showing the steps of the method for evaluating the reliability of microseismic events according to an embodiment of the present invention is presented.

[0075] As Figure 1 shown, the method for evaluating the reliability of microseismic events includes: Step 101, calculating the stacked trace ss(t) and using the long-short time window energy ratio method to identify microseismic events; Step 102, calculating the waveform similarity of microseismic events; Step 103, performing reliability evaluation and classification of corresponding microseismic events according to the waveform similarity.

[0076] In one example, the stacked trace is:

[0077]

[0078] Among them, ss(t) is the stacked trace, m is the number of ground microseismic monitoring records, and s i (t) is the signal recorded by the i-th geophone, and t = [t 1 , t 2 ,..., t n is the recording time, and n is the number of time sampling points.

[0079] In one example, microseismic event identification based on the stacked trace using the short-term and long-term window energy ratio method includes:

[0080] Set a threshold threshold;

[0081] According to the short-term and long-term window energy ratio method, slide the short-term and long-term windows to complete the calculation of the short-term and long-term window energy ratio of the entire ss(t) trace. When STA(t 0 ) contains event information and LTA(t 0 ) contains noise, a local maximum value appears in the ratio;

[0082] If the value of R(t 0 ) is greater than the set threshold, a microseismic event is detected at time t 0 .

[0083] In one example, the short-term and long-term window energy ratio is:

[0084]

[0085] Among them, R(t 0 ) is the short-term and long-term window energy ratio at time t 0 , STA(t 0 ) is the short-term window average energy, LTA(t 0 ) is the long-term window average energy, E(t) is the energy of the received signal of ss(t) at time t, and t 2 > t 0 > t 1 .

[0086] In one example, calculating the waveform similarity of microseismic events includes:

[0087] Open a time window at the corresponding time of the microseismic event to intercept the waveform record;

[0088] Set a reference trace and calculate the waveform similarity coefficient between the waveform record and the reference trace for each trace;

[0089] The waveform similarity coefficients corresponding to all traces in the waveform record form the waveform similarity.

[0090] In one example, the waveform similarity coefficient is:

[0091]

[0092] In one example, the reliability evaluation and classification of corresponding microseismic events according to waveform similarity include;

[0093] Calculate the mean mean(R) and variance var(R) of waveform similarity respectively;

[0094] The larger the absolute value of mean(R) and the smaller the value of var(R), the higher the reliability of the microseismic event;

[0095] If mean(R) is greater than the first threshold and var(R) is greater than the second threshold, it means that the reliability of the microseismic event is high, but the continuity of the in-phase axis between channels is poor;

[0096] If mean(R) is less than the third threshold and var(R) is less than the fourth threshold, it means that the reliability of the microseismic event is low;

[0097] If the difference between the value of mean(R) and 0 is greater than the fifth threshold and var(R) is less than the fourth threshold, it means that the microseismic event is a "false" microseismic event.

[0098] Specifically, the present invention first uses the long-short time window energy ratio to identify hydraulic fracturing microseismic events, then calculates the similarity coefficient between the waveforms of each channel of the identified microseismic events and the standard waveform, and finally realizes the quantitative evaluation and automatic classification of the reliability of microseismic events by establishing a similarity evaluation criterion.

[0099] Suppose there are m sets of surface microseismic monitoring records in total, and the signal recorded by the i-th geophone is denoted as s i (t), where t = [t 1 , t 2 ,..., t n , is the recording time, and n is the number of time sampling points. Calculate the stacked trace ss(t) of the m sets of records:

[0100]

[0101] Based on the stacked trace ss(t), use the long-short time window energy ratio method to identify microseismic events. The long-short time window energy ratio R is defined as:

[0102]

[0103]

[0104]

[0105] where, t 2 > t0 > t 1 , where E(t) is the energy of the received signal of ss(t) at time t, and STA(t 0 ) is the short-time window average energy, and LTA(t 0 ) is the long-time window average energy. R(t 0 ) is defined as the function of the ratio of the short-time and long-time window energies at time t 0 .

[0106] The sliding short-time and long-time windows are used to calculate the energy ratio of the entire trace of ss(t). When STA(t 0 ) contains event information and LTA(t 0 ) contains noise, a local maximum appears in the ratio, indicating that the energy change has reached a local maximum. Set a threshold value threshold. If the value of R(t 0 ) is greater than the set threshold, a microseismic event is detected at time t 0 .

[0107] At time t 0 , time windows are opened before and after to intercept the waveform record ss(t), where t = [t 1 , t 2 ,... t n . Set a Ricker wavelet or extract the standard waveform w(t) of the microseismic signal from the original record as the reference trace, and calculate the waveform similarity coefficient between ss(t) and w(t) for each trace. The calculation method of the similarity coefficient of the i-th trace in ss(t) is as follows:

[0108]

[0109] R i The closer the absolute value of is to 1, the greater the similarity between the two waveforms used for calculation. The similarity between all traces in ss(t) and the standard waveform w(t) is denoted as R.

[0110] Calculate the mean value mean(R) and variance var(R) of R respectively. The larger the absolute value of mean(R) and the smaller the value of var(R), the higher the reliability of the microseismic event; the larger the value of mean(R) and the larger the value of var(R), the higher the reliability of the microseismic event, but the continuity of the in-phase axis between traces is poor; the smaller the value of mean(R) and the smaller the value of var(R), the lower the reliability of the microseismic event; the very small value of mean(R) (close to 0) and the smaller value of var(R) represent that the microseismic event is a "false" microseismic event. The reliability evaluation and classification of microseismic events based on waveform similarity are realized.

[0111] Example 2

[0112] The present invention also provides a device for evaluating the reliability of microseismic events, including:

[0113] An event recognition module that calculates the stacked trace ss(t) and uses the short-term and long-term window energy ratio method to identify microseismic events;

[0114] A calculation module that calculates the waveform similarity of microseismic events;

[0115] A classification module that performs reliability evaluation and classification of corresponding microseismic events based on waveform similarity.

[0116] In one example, the stacked trace is:

[0117]

[0118] where ss(t) is the stacked trace, m is the number of ground microseismic monitoring records, s i (t) is the signal recorded by the i-th geophone, and t = [t 1 , t 2 ,..., t n is the recording time, and n is the number of time sampling points.

[0119] In one example, using the short-term and long-term window energy ratio method to identify microseismic events based on the stacked trace includes:

[0120] Setting a threshold threshold;

[0121] According to the short-term and long-term window energy ratio method, sliding the short-term and long-term windows to complete the calculation of the short-term and long-term window energy ratio of the entire ss(t) trace. When STA(t 0 ) contains event information and LTA(t 0 ) contains noise, a local maximum value appears in the ratio;

[0122] If the value of R(t 0 ) is greater than the set threshold, a microseismic event is detected at time t 0 .

[0123] In one example, the short-term and long-term window energy ratio is:

[0124]

[0125] where R(t 0 ) is the short-term and long-term window energy ratio at time t 0 , STA(t 0 ) is the short-term window average energy, LTA(t 0 ) is the long-term window average energy, E(t) is the energy of the received signal of ss(t) at time t2 > t 0 > t 1 。

[0126] In one example, calculating the waveform similarity of microseismic events includes:

[0127] Opening a time window at the corresponding moment of the microseismic event to intercept the waveform record;

[0128] Setting a reference trace and calculating the waveform similarity coefficient between the waveform record and the reference trace for each trace;

[0129] The waveform similarity coefficients corresponding to all traces in the waveform record constitute the waveform similarity.

[0130] In one example, the waveform similarity coefficient is:

[0131]

[0132] In one example, according to the waveform similarity, performing the reliability evaluation and classification of the corresponding microseismic events includes;

[0133] Calculating the mean mean(R) and variance var(R) of the waveform similarity respectively;

[0134] The larger the absolute value of mean(R) and the smaller the value of var(R), the higher the reliability of the microseismic event;

[0135] The value of mean(R) is greater than the first threshold and the value of var(R) is greater than the second threshold, indicating that the reliability of the microseismic event is high, but the continuity of the in-phase axis between traces is poor;

[0136] The value of mean(R) is less than the third threshold and the value of var(R) is less than the fourth threshold, indicating that the reliability of the microseismic event is low;

[0137] The difference between the value of mean(R) and 0 is greater than the fifth threshold and the value of var(R) is less than the fourth threshold, indicating that the microseismic event is a "false" microseismic event.

[0138] Specifically, the present invention first uses the long-short time window energy ratio to identify the hydraulic fracturing microseismic events, then calculates the similarity coefficient between the waveforms of each trace of the identified microseismic events and the standard waveform, and finally realizes the quantitative evaluation and automatic classification of the reliability of the microseismic events by establishing a similarity evaluation criterion.

[0139] Assume that there are m ground microseismic monitoring records in total, and the signal recorded by the i-th geophone is denoted as s i (t), where t = [t 1 , t 2 ,..., t n, to record time, n is the number of time sampling points. Calculate the stacked trace ss(t) of m traces:

[0140]

[0141] Based on the stacked trace ss(t), use the short-term and long-term window energy ratio method to identify microseismic events. The short-term and long-term window energy ratio R is defined as:

[0142]

[0143]

[0144]

[0145] where t 2 > t 0 > t 1 , E(t) is the energy of the received signal of ss(t) at time t, STA(t 0 ) is the short-term window average energy, LTA(t 0 ) is the long-term window average energy, R(t 0 ) is defined as the short-term and long-term window energy ratio function at time t 0 .

[0146] Slide the short-term and long-term windows to complete the calculation of the energy ratio of the entire trace of ss(t). When STA(t 0 ) contains event information and LTA(t 0 ) contains noise, the ratio shows a local maximum, indicating that the energy change has reached a local maximum. Set a threshold threshold. If the value of R(t 0 ) is greater than the set threshold, a microseismic event is detected at time t 0 .

[0147] Open time windows before and after time t 0 to intercept the waveform record ss(t), where t = [t 1 , t 2 ,... t n . Set the Ricker wavelet or extract the standard waveform w(t) of the microseismic signal from the original record as the reference trace, and calculate the waveform similarity coefficient between ss(t) and w(t) trace by trace. The calculation method of the similarity coefficient of the i-th trace in ss(t) is:

[0148]

[0149] R i The closer the absolute value of is to 1, the greater the similarity of the two waveforms used for calculation. The similarity of all traces in ss(t) to the standard waveform w(t) is denoted as R.

[0150] Calculate the mean (mean(R)) and variance (var(R)) of R respectively. The larger the absolute value of mean(R) and the smaller the value of var(R), the higher the reliability of the microseismic event; when mean(R) is relatively large and var(R) is also relatively large, it means the reliability of the microseismic event is high, but the continuity of the in-phase axis between channels is poor; when mean(R) is relatively small and var(R) is also relatively small, it means the reliability of the microseismic event is relatively low; when mean(R) is very small (close to 0) and var(R) is also relatively small, it means the microseismic event is a "false" microseismic event. The reliability evaluation and classification of microseismic events based on waveform similarity are realized.

[0151] Example 3

[0152] Figure 2 A schematic diagram of a synthetic microseismic record according to an embodiment of the present invention is shown.

[0153] As Figure 2 shown, the synthetic microseismic record contains three effective microseismic events and a noise signal.

[0154] Figure 3a And Figure 3b schematically show the stacking result and the similarity coefficient according to an embodiment of the present invention respectively.

[0155] Four "microseismic events" are identified near 250 ms, 500 ms, 750 ms, and 1000 ms by the long-short time window energy ratio method, as Figure 3a and Figure 3b shown.

[0156] Figure 4 A schematic diagram of the waveform similarity evaluation of four microseismic events according to an embodiment of the present invention is shown.

[0157] As Figure 4 shown, through this method, it can be determined that the first three are effective microseismic events and the fourth is a false microseismic event.

[0158] This embodiment shows that the present invention has good capabilities for screening and classifying effective microseismic events.

[0159] Example 4

[0160] Figure 5 A block diagram of a microseismic event reliability evaluation device according to an embodiment of the present invention is shown.

[0161] As Figure 5 shown, the microseismic event reliability evaluation device includes:

[0162] The event recognition module 201 calculates the stacked trace ss(t) and uses the short-term and long-term window energy ratio method to identify microseismic events;

[0163] The calculation module 202 calculates the waveform similarity of microseismic events;

[0164] The classification module 203 performs reliability evaluation and classification of corresponding microseismic events according to the waveform similarity.

[0165] As an alternative, the stacked trace is:

[0166]

[0167] where ss(t) is the stacked trace, m is the number of ground microseismic monitoring records, and s i (t) is the signal recorded by the i-th geophone, and t = [t 1 , t 2 ,..., t n is the recording time, and n is the number of time sampling points.

[0168] As an alternative, based on the stacked trace, using the short-term and long-term window energy ratio method to identify microseismic events includes:

[0169] Set a threshold threshold;

[0170] According to the short-term and long-term window energy ratio method, slide the short-term and long-term windows to complete the calculation of the short-term and long-term window energy ratio of the entire ss(t) trace. When STA(t 0 ) contains event information and LTA(t 0 ) contains noise, a local maximum value appears in the ratio;

[0171] If the value of R(t 0 ) is greater than the set threshold, a microseismic event is detected at the moment of t 0 .

[0172] As an alternative, the short-term and long-term window energy ratio is:

[0173]

[0174] where R(t 0 ) is the short-term and long-term window energy ratio at the moment of t 0 , STA(t 0 ) is the short-term window average energy, LTA(t 0 ) is the long-term window average energy, E(t) is the energy of the received signal of ss(t) at the moment of t, t 2 > t 0 > t 1 .

[0175] As an alternative, calculating the waveform similarity of microseismic events includes:

[0176] Opening a time window at the corresponding moment of the microseismic event to intercept the waveform record;

[0177] Setting a reference trace, and calculating the waveform similarity coefficient between the waveform record and the reference trace for each trace;

[0178] The waveform similarities corresponding to all traces in the waveform record form the waveform similarity.

[0179] As an alternative, the waveform similarity coefficient is:

[0180]

[0181] As an alternative, according to the waveform similarity, performing the reliability evaluation and classification of the corresponding microseismic events includes;

[0182] Calculating the mean value mean(R) and variance var(R) of the waveform similarity respectively;

[0183] The larger the absolute value of mean(R) and the smaller the value of var(R), the higher the reliability of the microseismic event;

[0184] When the value of mean(R) is greater than the first threshold and the value of var(R) is greater than the second threshold, it represents that the reliability of the microseismic event is high, but the continuity of the in-phase axis between traces is poor;

[0185] When the value of mean(R) is less than the third threshold and the value of var(R) is less than the fourth threshold, it represents that the reliability of the microseismic event is low;

[0186] When the difference between the value of mean(R) and 0 is greater than the fifth threshold and the value of var(R) is less than the fourth threshold, it represents that the microseismic event is a "false" microseismic event.

[0187] Example 5

[0188] The present disclosure provides an electronic device, which includes: a memory storing executable instructions; a processor that runs the executable instructions in the memory to implement the above-mentioned method for evaluating the reliability of microseismic events.

[0189] The electronic device according to the embodiment of the present disclosure includes a memory and a processor.

[0190] The memory is used to store non - transitory computer - readable instructions. Specifically, the memory may include one or more computer program products, and the computer program products may include various forms of computer - readable storage media, such as volatile memory and / or non - volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory, etc. The non - volatile memory may include, for example, read - only memory (ROM), hard disk, flash memory, etc.

[0191] The processor may be a central processing unit (CPU) or other forms of processing units with data - processing capabilities and / or instruction - execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment of the present disclosure, the processor is used to run the computer - readable instructions stored in the memory.

[0192] Those skilled in the art should understand that, in order to solve the technical problem of how to obtain good user - experience effects, this embodiment may also include well - known structures such as communication buses, interfaces, etc., and these well - known structures should also be included in the protection scope of the present disclosure.

[0193] For the detailed description of this embodiment, reference may be made to the corresponding descriptions in the foregoing embodiments, and details will not be repeated here.

[0194] Example 6

[0195] An embodiment of the present disclosure provides a computer - readable storage medium storing a computer program, and when the computer program is executed by a processor, the micro - seismic event reliability evaluation method described above is implemented.

[0196] According to the computer - readable storage medium of the embodiment of the present disclosure, non - transitory computer - readable instructions are stored thereon. When the non - transitory computer - readable instructions are run by a processor, all or part of the steps of the methods of the foregoing embodiments of the present disclosure are executed.

[0197] The above - mentioned computer - readable storage media include, but are not limited to: optical storage media (such as CD - ROM and DVD), magneto - optical storage media (such as MO), magnetic storage media (such as magnetic tape or removable hard disk), media with built - in rewritable non - volatile memory (such as memory card), and media with built - in ROM (such as ROM cartridge).

[0198] Those skilled in the art should understand that the purpose of the above description of the embodiments of the present invention is only to exemplarily illustrate the beneficial effects of the embodiments of the present invention, and is not intended to limit the embodiments of the present invention to any of the examples given.

[0199] The embodiments of the present invention have been described above. The above description is exemplary and not exhaustive, and is also not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A method for evaluating the reliability of microseismic events, characterized in that, it includes: Calculating the stacked trace ss(t), and using the long-short time window energy ratio method to identify microseismic events; Calculating the waveform similarity of the microseismic events; According to the waveform similarity, performing reliability evaluation and classification of the corresponding microseismic events.

2. The method for evaluating the reliability of microseismic events according to claim 1, wherein, the stacked trace is: Among them, ss(t) is the stacked trace, m is the number of ground microseismic monitoring records, and s i (t) is the signal recorded by the i-th geophone, and t = [t 1 , t 2 ,..., t n is the recording time, and n is the number of time sampling points.

3. The method for evaluating the reliability of microseismic events according to claim 1, wherein, Based on the stacked trace, using the long-short time window energy ratio method to identify microseismic events includes: Setting a threshold value threshold; According to the short-term and long-term window energy ratio method, the sliding short-term and long-term windows are used to complete the calculation of the short-term and long-term window energy ratio of the entire ss(t) trace. When STA(t 0 ) contains event information and LTA(t 0 ) contains noise, a local maximum value appears in the ratio; If the value of R(t 0 ) is greater than the set threshold, then a microseismic event is detected at time t 0 .

4. The method for evaluating the reliability of microseismic events according to claim 3, wherein, The long-short time window energy ratio is: Among them, R(t 0 ) is the short-term and long-term window energy ratio at time t, STA(t 0 ) is the short-term window average energy, 0 ) is the short-term window average energy, LTA(t 0 ) is the long-term window average energy, E(t) is the energy of the received signal of ss(t) at time t, t 2 > t 0 > t 1 .

5. The method for evaluating the reliability of microseismic events according to claim 1, wherein, Calculating the waveform similarity of the microseismic events includes: Opening a time window at the corresponding moment of the microseismic event to intercept the waveform record; Setting a reference trace, and calculating the waveform similarity coefficient between the waveform record and the reference trace for each trace; The waveform similarity coefficients corresponding to all traces in the waveform record form the waveform similarity.

6. The method for evaluating the reliability of microseismic events according to claim 5, wherein, The waveform similarity coefficient is:

7. The method for evaluating the reliability of microseismic events according to claim 1, wherein, According to the waveform similarity, performing reliability evaluation and classification of the corresponding microseismic events includes; Respectively calculating the mean value mean(R) and variance var(R) of the waveform similarity; The larger the absolute value of mean(R) and the smaller the value of var(R), the higher the reliability of the microseismic event; When the value of mean(R) is greater than the first threshold value and the value of var(R) is greater than the second threshold value, it represents that the reliability of the microseismic event is high, but the continuity of the in-phase axis between traces is poor; When the value of mean(R) is less than the third threshold value and the value of var(R) is less than the fourth threshold value, it represents that the reliability of the microseismic event is low; When the difference between the value of mean(R) and 0 is greater than the fifth threshold value and the value of var(R) is less than the fourth threshold value, it represents that the microseismic event is a "false" microseismic event.

8. A device for evaluating the reliability of microseismic events, characterized in that, it includes: An event identification module, which calculates the stacked trace ss(t) and uses the long-short time window energy ratio method to identify microseismic events; A calculation module, which calculates the waveform similarity of the microseismic events; A classification module, which performs reliability evaluation and classification of the corresponding microseismic events according to the waveform similarity.

9. An electronic device, characterized in that, the electronic device includes: A memory, which stores executable instructions; A processor, and the processor runs the executable instructions in the memory to implement the method for evaluating the reliability of microseismic events according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, This computer-readable storage medium stores a computer program, and when this computer program is executed by a processor, it implements the method for evaluating the reliability of microseismic events according to any one of claims 1-7.

Images