A rock slope microseismic signal classification and identification method based on microseismic positioning

Abstract

The application provides a rock slope microseismic signal classification and identification method based on microseismic positioning, and relates to the technical field of geological disaster prevention engineering. The microseismic signal data monitored on the rock slope is acquired, the characteristics of the signal time domain and the frequency domain are analyzed, and the microseismic signal is preliminarily classified; according to the set positioning method, the microseismic events related to the slope stability are positioned; and the positioned microseismic events are classified again by using the signal and position characteristics of the microseismic events. The microseismic events are positioned, the microseismic events related to the slope stability are classified again, and the rockfall events and the crack events can be further distinguished. The method overcomes the deficiency of the existing microseismic signal classification method in the classification accuracy, and can more effectively evaluate the stability of the rock slope by using the microseismic events classified again.

Description

Technical Field

[0001] This invention relates to the field of geological disaster prevention and control engineering technology, and in particular to a method for classifying and identifying microseismic signals of rock slopes based on microseismic location. Background Technology

[0002] Rock slope instability can lead to geological disasters such as landslides, threatening the lives of surrounding residents, infrastructure, and engineering activities. Landslides are typically characterized by wide spread, rapid movement, and unpredictability, making it difficult to identify precursory information. To study landslide mechanisms and establish disaster early warning systems, it is necessary to monitor unstable rock slopes at risk of landslides. Among various geophysical techniques for monitoring natural disasters, microseismic monitoring is a promising method for monitoring unstable rock slopes. Using microseismic monitoring, signals generated by internal stress within the rock mass can be recorded by sensors pre-installed on or inside the rock mass. These signals contain useful information describing microseismic events, which can be analyzed to extract the time, location, trajectory, and volume of these events. In microseismic monitoring of unstable rock slopes, a large number of microseismic events can be detected and recorded. These events typically originate from various sources, such as cracks, rockfalls, electromagnetic noise, and other noise sources. Therefore, a classification method is needed to distinguish and filter microseismic events that may be related to rock slope instability from the microseismic data, eliminating other interfering data.

[0003] Microseismic events caused by crack propagation are an important type of microseismic event. The increased rate and intensity of these events, along with changes in signal frequency components, reveal the development of rock fracturing, indicating a significantly increased probability of landslides. Therefore, these events play a crucial role in constructing landslide early warning systems. Furthermore, the location of crack origin can be used to pinpoint unstable areas of rock slopes. Besides microseismic events caused by crack propagation, microseismic records also include rockfall events, electromagnetic noise associated with thunderstorms, and other noise sources. Many researchers use time and frequency parameters to distinguish different types of microseismic events. Events caused by electromagnetic noise associated with thunderstorms and other noise sources are easily distinguishable because they have distinct characteristics in the time and frequency domains (e.g., pulse amplitude in the time domain and broadband spectral components in the frequency domain, or no obvious characteristics). However, events caused by crack propagation are difficult to distinguish from those caused by rockfalls, mainly because they have similar characteristics in the time and frequency domains, especially for short-duration events. Therefore, there is still no clear and effective method for classifying rockfall and crack events. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method for classifying and identifying microseismic signals of rock slopes based on microseismic location.

[0005] A method for classifying and identifying microseismic signals of rock slopes based on microseismic location includes the following steps:

[0006] Step 1: Use a microseismic monitoring system to acquire microseismic signal data monitored on the rock slope;

[0007] Step 2: Analyze the characteristics of microseismic signal data in the time and frequency domains, and perform preliminary classification of microseismic signals;

[0008] Step 2.1: For each monitored microseismic signal, draw a time series diagram and a spectrum diagram;

[0009] Step 2.2: Based on the time and frequency characteristics of the signals, perform preliminary classification of the microseismic signals;

[0010] The preliminary classification includes the following three categories of events: The first category is microseismic events related to slope stability. The signal duration of these microseismic events is 0-20s, and the frequency range is 10-150Hz. The signal source is the propagation of internal cracks in the rock mass or the impact of falling rocks on the rock slope. The second category is electromagnetic events related to thunderstorms. The signal duration of these microseismic events is less than 20ms, and the frequency band is 0-400Hz. The signal source is the transient signal of lightning caused by electromagnetic activity during thunderstorms. The third category is noise events. The signals of these microseismic events are all random amplitudes near zero in the time domain and random amplitudes close to zero in the frequency domain.

[0011] Step 2.3: Select and retain Category I microseismic events, and exclude Category II and III microseismic events;

[0012] Step 3: Select a positioning method and locate the first type of microseismic event according to the set positioning method;

[0013] Step 3.1: Pick the first arrival time of the waveform for the first type of microseismic event;

[0014] Step 3.2: At the top of the rock slope, a tomographic imaging test was conducted to obtain a three-dimensional velocity model map of the monitored rock mass area;

[0015] Step 3.3: Select a positioning algorithm and evaluate its positioning error, which is denoted as d;

[0016] Step 3.4: Use the localization algorithm in Step 3.3 to locate the first type of microseismic event.

[0017] Step 4: Set discrimination rules, reclassify the located microseismic events according to the discrimination rules, and use the crack events obtained after classification to perform slope stability analysis.

[0018] The discrimination rule utilizes the signal and location characteristics generated by two types of events, namely cracks and rockfalls, and at the same time considers the error of microseismic event location to analyze the location results and distinguish between crack events and rockfall events. The specific discrimination rules are as follows:

[0019] When a microseismic event contains multiple vibration signals, that is, the number of vibration signals n≥2, and the locations of each vibration signal are all near the surface of the rock slope. The distance between the location and the surface of the rock slope is represented by h, that is, h<d, the microseismic event is determined as a rockfall event;

[0020] When a microseismic event contains multiple vibration signals, that is, the number of vibration signals n≥2, and at least one of the locations of each vibration signal is inside the rock slope, that is, h≥d, the microseismic event is determined as a mixed event of rockfall and crack;

[0021] When a microseismic event contains only a single vibration signal, that is, the number of vibration signals n = 1, and the location of each vibration signal is near the surface of the rock slope, that is, h<d, the microseismic event is determined as a rockfall or crack event;

[0022] When a microseismic event contains only a single vibration signal, that is, the number of vibration signals n = 1, and the location of each vibration signal is inside the rock slope, that is, h≥d, the microseismic event is determined as a crack event.

[0023] The beneficial effects produced by adopting the above technical solutions are as follows:

[0024] The present invention provides a method for classifying and identifying microseismic signals of a rock slope based on microseismic location. Based on the signal and location characteristics generated by microseismic events, by locating microseismic events, the microseismic signals related to the slope stability are classified again, which can further distinguish between rockfall events and crack events. This method overcomes the deficiencies of the existing microseismic signal classification methods in classification accuracy, and at the same time can more effectively evaluate the stability of the rock slope by using the microseismic events after reclassification. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a flowchart of the method for classifying and identifying microseismic signals of a rock slope based on microseismic location according to the present invention;

[0026] Figure 2 It is a time series diagram and a frequency spectrum diagram of the signal recorded by one channel of the first type of microseismic event in the embodiment of the present invention;

[0027] Figure 3 It is a time series diagram and a frequency spectrum diagram of the signal recorded by one channel of the second type of electromagnetic event in the embodiment of the present invention;

[0028] Figure 4This is a timing diagram and a spectrum diagram of the signal recorded in one channel of the third type of noise event in this embodiment of the invention;

[0029] Figure 5 This is a three-dimensional velocity model diagram in an embodiment of the present invention;

[0030] Figure 6 This is a localization error diagram for microseismic event localization using data from eight seismic sources in a tomographic imaging experiment in an embodiment of the present invention.

[0031] Figure 7 This is a cross-sectional view of the location result of a typical rockfall event in an embodiment of the present invention;

[0032] Figure (a) is the front view, and Figures (b) and (c) are the side views on both sides, respectively.

[0033] Figure 8 This is a cross-sectional view of a typical crack event location result in an embodiment of the present invention;

[0034] Figure (a) is the front view, and Figure (b) is the side view;

[0035] Figure 9 This is a cross-sectional view of the location result of a typical rockfall or crack event in an embodiment of the present invention;

[0036] Figure (a) is the front view and Figure (b) is the side view. Detailed Implementation

[0037] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0038] A method for classifying and identifying microseismic signals of rock slopes based on microseismic location, such as... Figure 1 As shown, it includes the following steps:

[0039] Step 1: Use a microseismic monitoring system to acquire microseismic signal data monitored on the rock slope;

[0040] In this embodiment, a microseismic monitoring system is used to monitor a rock slope at risk of collapse. The microseismic monitoring system includes five three-channel geophones. Based on the background noise conditions of the monitoring area, the system acquires microseismic signals by setting a trigger threshold. When the signal value detected by any channel of any geophone exceeds the threshold, the monitoring system triggers all geophones to acquire the microseismic signal, with a 2-second pre-trigger window set.

[0041] Step 2: Analyze the characteristics of microseismic signal data in the time and frequency domains, and perform preliminary classification of microseismic signals;

[0042] Step 2.1: For each monitored microseismic signal, plot the time series diagram and spectrum diagram; a typical signal schematic diagram is shown below. Figure 2 , Figure 3 and Figure 4 As shown;

[0043] Step 2.2: Based on the time and frequency characteristics of the signals, perform preliminary classification of the microseismic signals;

[0044] The preliminary classification includes the following three categories of events: The first category is microseismic events related to slope stability. The signal duration of these microseismic events is 0-20s, and the frequency range is 10-150Hz. The signal source is the propagation of internal cracks in the rock mass or the impact of falling rocks on the rock slope, and they are closely related to slope stability. The second category is electromagnetic events related to thunderstorms. The signal duration of these microseismic events is less than 20ms, and the frequency band is 0-400Hz. The signal source is the transient signal of lightning caused by electromagnetic activity during thunderstorms. The third category is noise events. The signals of these microseismic events are all random amplitudes near zero in the time domain and random amplitudes close to zero in the frequency domain.

[0045] Step 2.3: Select and retain Category I microseismic events, and exclude Category II and III microseismic events;

[0046] Step 3: Select a positioning method and locate the first type of microseismic event according to the set positioning method;

[0047] Step 3.1: Pick the first arrival time of the waveform for the first type of microseismic event;

[0048] Step 3.2: Conduct a tomographic imaging test at the top of the rock slope;

[0049] The tomographic imaging experiment combined a 24-channel Geode recording system with the existing microseismic monitoring system to jointly record the vibration signals generated by the artificial seismic source. Twenty-four single-component geophones were connected to the Geode system and arranged at approximately 3-meter intervals on the top of the rock slope. An 8-kg hammer was used as the artificial seismic source to strike the rock mass at 12 different locations. The data obtained from the tomographic imaging experiment were manually processed to extract the first arrival times of the waveforms. The SIRT (Sequential Iterative Reconstruction Technique) was then used to perform seismic wave travel-time inversion, resulting in a three-dimensional velocity model of the monitored rock mass area, as shown below. Figure 5 As shown. Figure 5 The three-dimensional velocity model only shows the velocity distribution model of the rock mass area through which rays pass during the inversion process; the velocity of other rock mass areas is constant.

[0050] Step 3.3: Select a positioning algorithm and evaluate its positioning error, which is denoted as d;

[0051] The positioning algorithm is a direct global grid search algorithm. The equal time difference method (EDT) is used to define the objective function, and the objective function is of the L2 type, as shown in the following formula:

[0052]

[0053] where and are the initial arrival times of the waveforms picked up from the monitored signals of geophones a and b respectively, and are the calculated propagation times from any grid point x in the three-dimensional velocity model to geophones a and b respectively. The positioning location of the microseismic event is the grid point corresponding to the minimum value of the objective function.

[0054] Eight known source data in the tomography experiment are selected, and the above positioning algorithm is used for the positioning of known sources. After positioning, the distance between the positioning location of the source and the actual location of the source is calculated as the positioning error of the algorithm. After calculation, the average positioning error of the algorithm is about d = 15m, as Figure 6 shown, the average positioning error is about 15m;

[0055] Step 3.4: Use the above positioning algorithm to locate the first type of microseismic events.

[0056] Step 4: Set the discrimination rules, reclassify the located microseismic events according to the discrimination rules, and use the obtained fracture events after classification for the analysis of slope stability.

[0057] The discrimination rules utilize the signal and position characteristics of two types of events, namely fractures and rockfalls, and also consider the positioning error of microseismic events to analyze the positioning results and distinguish fracture events from rockfall events. The specific setting of the discrimination rules includes:

[0058] When there are multiple vibration signals in a microseismic event, that is, the number of vibration signals n ≥ 2, and the positioning positions of each vibration signal are all near the surface of the rock slope, and the distance between the positioning position and the surface of the rock slope is represented by h, that is, h < d, the microseismic event is judged as a rockfall event;

[0059] When there are multiple vibration signals in a microseismic event, that is, the number of vibration signals n ≥ 2, and at least one of the positioning positions of each vibration signal is inside the rock slope, that is, h ≥ d, the microseismic event is judged as a mixed event of rockfall and fracture;

[0060] When there is only a single vibration signal in a microseismic event, that is, the number of vibration signals n = 1, and the positioning position of each vibration signal is near the surface of the rock slope, that is, h < d, the microseismic event is judged as a rockfall or fracture event. By reducing the positioning error of microseisms, that is, reducing d, the occurrence of such uncertain events during classification discrimination is reduced;

[0061] When a microseismic event contains only a single vibration signal, i.e., the number of vibration signals n=1, and the location of each vibration signal is inside the rock slope, i.e., h≥d, the microseismic event is identified as a crack event.

[0062] In this embodiment, as shown... Figure 7 , Figure 8 , Figure 9 As shown, the white grid represents the rock slope, the black grid represents the air, the gray squares represent the projected positions of the five detectors on the cross-section, the gray pentagrams represent the projected positions of the location vibration signals on the cross-section, and the numbers represent the order in which the vibration signals were generated. Figure 7 Cross-sectional views of the location results of the rockfall event; Figures (b) and (c) pass through the location of each vibration signal and are used to determine the distance between the location and the rock slope surface. The length, width and height of each grid are: x = 2m, y = 2m, z = 4m. Figure 8 A cross-sectional view of the crack event localization results. Figure 9 Cross-sectional views of the location results for rockfall or crack events, where both cross-sectional views pass through the location of the vibration signal;

[0063] By using the crack events obtained after classification, which reflect the generation and development of cracks inside the rock mass, and by analyzing the time, spatial distribution and energy magnitude of crack events, it is possible to more effectively assess and warn of unstable areas of slopes, avoiding the drawback of the original assessment method which mixed the two types of events and had poor early warning effect.

[0064] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

Claims

1. A method for classifying and identifying microseismic signals of rock slopes based on microseismic location, characterized in that, It includes the following steps: Step 1: Obtain the microseismic signal data monitored on the rock slope by using a microseismic monitoring system; Step 2: Analyze the characteristics of the microseismic signal data in the time domain and frequency domain, and conduct a preliminary classification of the microseismic signals; Step 3: Select a positioning method, and according to the set positioning method, locate the first type of microseismic events; Step 4: Set discrimination rules, re-classify the located microseismic events according to the discrimination rules, and conduct slope stability analysis by using the crack events obtained after classification; The discrimination rules utilize the signal and position characteristics of two types of events, namely cracks and rockfalls, and at the same time consider the error of microseismic event positioning, analyze the positioning results, and distinguish crack events and rockfall events; Setting the discrimination rules specifically includes: When a microseismic event contains multiple vibration signals, that is, the number of vibration signals n≥2, and the positions located by each vibration signal are all near the surface of the rock slope, and the distance between the located position and the surface of the rock slope is represented by h, that is, h<d, the microseismic event is judged as a rockfall event; When a microseismic event contains multiple vibration signals, that is, the number of vibration signals n≥2, and at least one of the positions located by each vibration signal is inside the rock slope, that is, h≥d, the microseismic event is judged as a mixed event of rockfall and crack; When a microseismic event contains only a single vibration signal, that is, the number of vibration signals n=1, and the position located by each vibration signal is near the surface of the rock slope, that is, h<d, the microseismic event is judged as a rockfall or crack event; When a microseismic event contains only a single vibration signal, that is, the number of vibration signals n=1, and the position located by each vibration signal is inside the rock slope, that is, h≥d, the microseismic event is judged as a crack event.

2. The method for classifying and identifying microseismic signals of rock slopes based on microseismic location according to claim 1, characterized in that, Step 2 specifically includes the following steps: Step 2.1: For each monitored microseismic signal, draw a time series diagram and a frequency spectrum diagram; Step 2.2: According to the time and frequency characteristics of the signal, conduct a preliminary classification of the microseismic signals; The preliminary classification includes the following three types of events: The first type is microseismic events related to slope stability. The signal time duration of this type of microseismic event is between 0-20s, and the frequency range is between 10-150Hz. The signal source is the crack expansion inside the rock mass or the rockfall hitting the rock slope; The second type is electromagnetic events related to thunderstorms. The signal of this type of microseismic event has a duration less than 20ms, and the frequency band is 0-400Hz. The signal source is the lightning transient signal caused by the electromagnetic activity in thunderstorm weather; The third type is noise events. The signals of this type of microseismic event are random amplitudes near zero in the time domain signal and random amplitudes close to zero in the frequency domain; Step 2.3: Screen and retain the first type of microseismic events, and exclude the second and third types of microseismic events.

3. The method for classifying and identifying microseismic signals of rock slopes based on microseismic location according to claim 1, characterized in that, Step 3 specifically includes the following steps: Step 3.1: Pick up the first arrival time of the waveform of the first type of microseismic events; Step 3.2: At the top of the rock slope, conduct a tomography experiment to obtain a three-dimensional velocity model diagram of the monitored rock mass area; Step 3.3: Select a positioning algorithm and evaluate the positioning error of the algorithm, which is represented as d; Step 3.4: Use the positioning algorithm in Step 3.3 to locate the first type of microseismic events.

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