Debris flow identification method and device based on seismic array signal time-frequency characteristics

Abstract

The invention relates to the technical field of mountain disaster risk identification, in particular to a debris flow identification method and device based on seismic array signal time-frequency characteristics, and the method comprises the steps: arranging a field debris flow vibration monitoring array at a preset position of a target debris flow gully, so as to collect continuous seismic oscillation waveform signals; performing short-time Fourier transform on the continuous seismic oscillation waveform signal to obtain local information of a time-frequency domain; solving the time-varying power spectral density of the continuous seismic oscillation waveform signal according to the local information of the time-frequency domain; and performing energy weighted average on the time-varying power spectral density to obtain a time-varying centroid frequency, and identifying whether a debris flow disaster event occurs according to the time-varying centroid frequency. Therefore, the problems that an existing classical long and short time window ratio identification method depends on amplitude characteristics of a high signal-to-noise ratio, the diagnosis distance is only within a range of tens of meters, and the detection effect fails under the condition that the observation distance is increased to hundreds of meters are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of mountain disaster risk identification, and particularly relates to a debris flow identification method based on the time-frequency characteristics of seismic array signals. Background Art

[0002] Conventional monitoring and early warning work for mountain flood and debris flow disasters mainly includes medium- and long-term risk early warning of disasters mainly based on the monitoring of meteorological and hydrological triggering factors and short-term and imminent alarms of disasters based on the monitoring of the dynamic processes of mountain disasters. Among them, the medium- and long-term risk early warning is based on an empirical model, and when the regional rainfall reaches the threshold for disaster initiation, disaster risk early warning is carried out. However, the time and location information provided by this method is relatively macroscopic and fuzzy; the existing short-term and imminent alarm systems are aimed at specific disaster points, and contact sensing devices such as GNSS displacement monitors, moisture content meters, debris flow disconnection alarm devices, ultrasonic water level meters, impact detectors, and mud level meters are installed nearby to monitor the dynamic processes of disasters within the basin and provide accurate disaster movement information. However, these observation devices have technical bottlenecks such as high costs and small coverage areas, and are often damaged by disasters, making it difficult to meet the requirements of accurate disaster early warning in complex environments.

[0003] In recent years, the research on analyzing geological disaster processes using seismic record signals has developed rapidly. Its non-contact sensing, high spatio-temporal resolution, and wide coverage characteristics have provided new ideas for geological disaster monitoring and early warning technologies. Identifying mountain disaster process events based on seismic record signals is a key issue in monitoring and early warning. Currently, the research mainly focuses on signal amplitude feature detection algorithms. For example, the classic short-term average / long-term average STA / LTA algorithm. This method avoids the dependence of a single threshold on different regions and different events and the influence of signal glitches, and is applicable to events with obvious first arrival phases, high signal-to-noise ratios, and short durations (second to minute scale); the average ratio of short-term and long-term window amplitudes was used to detect the Gadria debris flow event in the Eastern Alps of Italy. The waveform event lasted for 2-3 minutes, and the station layout was about 10 m away from the gully. Based on the high signal-to-noise ratio vibration signal, this method achieved good detection results. However, as the observation distance increases, the waveform signal ratio decreases rapidly, and this type of method cannot detect mountain flood and debris flow events more than hundreds of meters away from the gully.

[0004] Therefore, there is an urgent need to develop new methods for detecting mountain disaster events and tracking the movement process applicable to low signal-to-noise ratio seismic waveform records to meet the requirements of low cost, wide coverage, and accurate disaster early warning. Summary of the Invention

[0005] The present invention provides a debris flow identification method and device based on the time-frequency characteristics of seismic array signals, aiming to solve the problems that the existing classical long-short time window ratio identification method relies on the amplitude characteristics of high signal-to-noise ratio, the detection distance is within dozens of meters that can be detected, and debris flow events in mountain torrents can be detected within dozens of meters, but when the observation distance increases to hundreds of meters, the detection effect fails, etc.

[0006] In a first aspect embodiment of the present invention, a debris flow identification method based on the time-frequency characteristics of seismic array signals is provided, including the following steps: arranging a field debris flow vibration monitoring array at a preset position of a target debris flow gully to collect continuous ground motion waveform signals; performing a short-time Fourier transform on the continuous ground motion waveform signals to obtain local information in the time-frequency domain; solving the time-varying power spectral density of the continuous ground motion waveform signals according to the local information in the time-frequency domain; performing energy weighted averaging on the time-varying power spectral density to obtain a time-varying centroid frequency, and identifying whether a mountain torrent debris flow disaster event occurs according to the time-varying centroid frequency.

[0007] Optionally, the number of the field debris flow vibration monitoring arrays is greater than or equal to three, and the preset safe position is perpendicular to the target debris flow gully and non-contact with the target debris flow gully.

[0008] Optionally, the performing a short-time Fourier transform on the continuous ground motion waveform signals to obtain local information in the time-frequency domain includes:

[0009] using a Hamming window function to eliminate the sidelobes in the continuous ground motion waveform signals to obtain clean ground motion signals; dividing the clean ground motion signals into continuous ground motion waveform signals of multiple short time periods; performing a Fourier transform on the continuous ground motion waveform signals of each short time period to obtain the local information in the time-frequency domain.

[0010] Optionally, the performing energy weighted averaging on the time-varying power spectral density to obtain a time-varying centroid frequency, and identifying whether a mountain torrent debris flow disaster event occurs according to the time-varying centroid frequency includes: performing energy weighting on each time frame in the time-varying power spectral density, and averaging the power spectra of each frequency after weighting to obtain the time-varying centroid frequency; comparing the time-varying centroid frequency with a preset threshold, and determining that the mountain torrent debris flow disaster event occurs when the time-varying centroid frequency is higher than the preset threshold.

[0011] In the second aspect of the embodiments of the present invention, a debris flow identification device based on the time-frequency characteristics of seismic array signals is provided, including: a signal acquisition module, configured to deploy a field debris flow vibration monitoring array at a preset safe position of a target debris flow gully to acquire continuous seismic ground motion waveform signals; a short-time transformation module, configured to perform a short-time Fourier transform on the continuous seismic ground motion waveform signals to obtain local information in the time-frequency domain; a solution module, configured to solve the time-varying power spectral density of the continuous seismic ground motion waveform signals according to the local information in the time-frequency domain; an identification module, configured to perform energy weighted averaging on the time-varying power spectral density to obtain a time-varying centroid frequency, and identify whether a mountain flood debris flow disaster event has occurred according to the time-varying centroid frequency.

[0012] Optionally, the safe position is perpendicular to the target debris flow gully and non-contact with the target debris flow gully.

[0013] Optionally, the short-time transformation module includes:

[0014] An elimination unit, configured to eliminate sidelobes in the continuous seismic ground motion waveform signals by using a Hamming window function to obtain clean seismic ground motion signals;

[0015] A signal segmentation unit, configured to segment the clean seismic ground motion signals into continuous seismic ground motion waveform signals in multiple short time periods;

[0016] A signal transformation unit, configured to perform a Fourier transform on the continuous seismic ground motion waveform signals in each short time period to obtain the local information in the time-frequency domain.

[0017] Optionally, the identification module includes: an energy weighting unit, configured to perform energy weighting on each time frame in the time-varying power spectral density and average the power spectra of each frequency after weighting to obtain the time-varying centroid frequency; a comparison and identification unit, configured to compare the time-varying centroid frequency with a preset threshold, and determine that the mountain flood debris flow disaster event has occurred when the time-varying centroid frequency is higher than the preset threshold.

[0018] In the third aspect of the embodiments of the present invention, an electronic device is provided, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the program to implement the debris flow identification method based on the time-frequency characteristics of seismic array signals as described in the above embodiments.

[0019] In the fourth aspect of the embodiments of the present invention, a computer-readable storage medium is provided, where the computer-readable storage medium stores a computer program, and when the program is executed by a processor, it implements the debris flow identification method based on the time-frequency characteristics of seismic array signals as described above.

[0020] The debris flow identification method based on the time-frequency characteristics of seismic array signals proposed in the embodiments of the present invention comprehensively considers the waveform frequency and energy time-varying characteristics of different events. On the basis of statistically analyzing the peak frequency band of seismic ground motion signals, the energy level is added to obtain the centroid frequency that describes the main frequency component of the signal and the energy level changing with time. By using the centroid frequency, background noise, seismic events, and debris flow events of mountain floods more than hundreds of meters away from the gully can be effectively distinguished, and then it can be determined whether a mountain flood debris flow event has occurred. Compared with the existing classic long-short time window ratio identification method, this method not only improves the utilization rate of waveform information and the detection efficiency of debris flow disasters, but also can increase the detection range of mountain flood debris flows to 1.5 km, improve the safety distance of field observation equipment, and thus enhance the safety, reliability, and practicability of field observations.

[0021] Additional aspects and advantages of the present invention will be given in part in the following description, become apparent in part from the following description, or be understood through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The above and / or additional aspects and advantages of the present invention will become apparent and be readily understood from the following description of the embodiments in conjunction with the drawings, wherein:

[0023] Figure 1 is a flowchart of a debris flow identification method based on the time-frequency characteristics of seismic array signals provided by an embodiment of the present invention;

[0024] Figure 2 is a schematic layout diagram of a field debris flow vibration monitoring array provided by an embodiment of the present invention;

[0025] Figure 3 is a specific execution schematic diagram of a debris flow identification method based on the time-frequency characteristics of seismic array signals provided by an embodiment of the present invention;

[0026] Figure 4 is a simulation experiment comparison diagram of a debris flow identification method based on the time-frequency characteristics of seismic array signals provided by an embodiment of the present invention and the existing long-short time window method;

[0027] Figure 5 is a block schematic diagram of a debris flow identification device based on the time-frequency characteristics of seismic array signals provided by an embodiment of the present invention;

[0028] Figure 6 is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0029] Embodiments of the present invention will be described in detail below. Examples of the embodiments are shown in the accompanying drawings, where like or similar reference numerals denote like or similar elements or elements having like or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present invention, and should not be construed as limiting the present invention.

[0030] The method and device for debris flow identification based on the time-frequency characteristics of seismic array signals in the embodiments of the present invention will be described below with reference to the accompanying drawings. Aiming at the problem that the long-short time window ratio identification method mentioned in the above background technology mainly relies on the amplitude characteristics of high signal-to-noise ratio and can detect debris flow events within dozens of meters, but the detection effect fails when the observation distance increases to hundreds of meters, the present invention provides a method for debris flow identification based on the time-frequency characteristics of seismic array signals. In this method, the amplitude characteristics and frequency characteristics of different events are comprehensively considered. Since background noise belongs to the high-frequency band and low energy, earthquakes belong to the low-frequency band and high energy, and debris flow belongs to the high-frequency band and high energy, the present invention effectively distinguishes background noise, earthquake events, and debris flow events by statistically analyzing the time-varying information of high-energy frequency characteristics, so that the observation distance of debris flow can be increased to 1.5 km. Thus, the problem of effectively detecting mountain disasters such as debris flow based on low signal-to-noise ratio seismic waveform records under long-distance observation conditions is realized.

[0031] Specifically, Figure 1 FIG. is a schematic flowchart of a method for debris flow identification based on the time-frequency characteristics of seismic array signals provided by an embodiment of the present invention.

[0032] As Figure 1 shown, the method for debris flow identification based on the time-frequency characteristics of seismic array signals includes the following steps:

[0033] In step S101, a field debris flow vibration monitoring array is arranged at a preset safe position of the target debris flow gully to collect continuous ground motion waveform signals.

[0034] In some embodiments, the number of the field debris flow vibration monitoring arrays is greater than or equal to three, and the preset safe position is in the vertical direction where the target debris flow gully is located and is non-contact with the target debris flow gully.

[0035] In the actual execution process, as Figure 2As shown in the figure, at least three field debris flow vibration monitoring arrays are arranged in the vertical direction of the target debris flow gully, and are non-contact with the target debris flow gully. The spacing can be more than ten meters or even hundreds of meters, and continuous ground motion waveform signals of the target debris flow gully are collected. Specifically, one monitoring site is set every kilometer along the target debris flow gully, and there are at least three monitoring positions in the whole process. For example, one is set in the source area, the flowing area, and the accumulation area respectively. Instrument equipment such as field debris flow vibration monitoring arrays is installed within the vertical safety distance of the target debris flow gully to collect continuous ground motion waveform signals of the target debris flow gully, which can be applicable to common debris flow channels with a length of 1 - 10 kilometers in mountainous areas, or super-large debris flows with a length of dozens of kilometers.

[0036] In step S102, short-time Fourier transform is performed on the continuous ground motion waveform signal to obtain local information in the time-frequency domain.

[0037] In some embodiments, short-time Fourier transform is performed on the continuous ground motion waveform signal to obtain local information in the time-frequency domain, including:

[0038] The Hamming window function is used to eliminate the sidelobes in the continuous ground motion waveform signal to obtain a clean ground motion signal;

[0039] The clean ground motion signal is segmented into continuous ground motion waveform signals in multiple short time periods;

[0040] Fourier transform is performed on the continuous ground motion waveform signal in each short time period to obtain local information in the time-frequency domain.

[0041] As Figure 3 As shown in the figure, in the actual execution process, by analyzing the time-frequency characteristics of different events, considering that background noise belongs to the high-frequency band and low energy, earthquakes are in the low-frequency band and high energy, and debris flow events are in the high-frequency band and high energy. Therefore, after the continuous ground motion waveform signal is collected in the embodiment of the present invention, the entire frequency band of the continuous ground motion waveform signal is statistically analyzed. Specifically, first, the continuous ground motion waveform signal is processed by using the Hamming window smoothing weighting function to suppress the sidelobes of the continuous ground motion waveform signal, and then the processed clean ground motion signal is segmented into multiple short time periods, and Fourier transform is performed within each time period, so as to obtain local information in the time-frequency domain.

[0042] For example, the duration of a general debris flow event is several to more than ten minutes. For debris flow events, the acquisition rate can be set to data of 100Hz, the window length can be set to 256, and the window repetition rate is 50%. Therefore, for the discrete continuous ground motion waveform signal x[n] (n = 0, 1,..., L - 1) collected, after short-time Fourier transform, the local information in the time-frequency domain obtained is:

[0043]

[0044] where \(m = 0, 1, \cdots, M - 1\) is the time - frame index, \(k = 0, 1, \cdots, K - 1\) is the frequency index, \(w[n]\) is the Hamming window function, \(L\) is the length, and \(e\) -j2πkn / L is a complex exponential function used to transform the time - domain signal to the frequency domain.

[0045] In step S103, the time - varying power spectral density of the continuous ground motion waveform signal is solved according to the local information in the time - frequency domain.

[0046] As Figure 3 shown, in the actual execution process, the square modulus of the frequency amplitude in each time window of the local information in the time - frequency domain is taken to obtain the power spectral density \(S[m,k]\) at the \(k\) - th frequency point in the \(m\) - th frame, and the time - varying power spectral density is converted to decibel units dB. The specific expression is as follows:

[0047] \(S[m,k]=|STFT[m,k]|\) 2 .

[0048] In step S104, the time - varying power spectral density is weighted - averaged in energy to obtain the time - varying centroid frequency, and whether a debris - flow disaster event occurs is identified according to the time - varying centroid frequency.

[0049] In some embodiments, weighting - averaging the time - varying power spectral density in energy to obtain the time - varying centroid frequency, and identifying whether a debris - flow disaster event occurs according to the time - varying centroid frequency includes:

[0050] Weighting each time frame in the time - varying power spectral density in energy and averaging the power spectra of each frequency after weighting to obtain the time - varying centroid frequency;

[0051] Comparing the time - varying centroid frequency with a preset threshold, and determining that a debris - flow disaster event occurs when the time - varying centroid frequency is higher than the preset threshold.

[0052] As Figure 3 shown, in the actual execution process, each time frame \(m\) in the time - varying power spectral density is weighted in energy, and the power spectra of each frequency after weighting are averaged to obtain the time - varying centroid frequency. The specific expression is as follows:

[0053]

[0054] In the formula, \(f\) centroid [m]\) is the time - varying centroid frequency, is the discrete frequency of the \(k\) - th frequency component, \(f\) s is the sampling rate, and \(f\) k \(\cdot S[m,k]\) is the contribution of each frequency point to the spectrum center. The numerator is obtained by integrating \(f\)k · S[m, k] obtains the weighted average frequency of the frequency. The denominator integrates the total power density of S[m, k], representing the total energy of the signal. The time-varying centroid frequency f is obtained through the ratio. centroid [m].

[0055] Further, the time-varying centroid frequency is compared with a preset threshold to identify flash flood and debris flow events. Specifically, when the time-varying centroid frequency f centroid [m] is higher than the preset threshold, it indicates that a flash flood and debris flow disaster event is determined to have occurred.

[0056] Further, after determining that a flash flood and debris flow disaster event has occurred, in order to avoid the situation of no signal or weak signal in the mountainous area in the embodiments of the present invention, a 5G transmission network and / or a Beidou short message transmission module will be used to timely transmit the time-varying centroid frequency in the first two minutes before determination to the waveform data up to now to the disaster emergency warning center. The disaster emergency warning center will issue a debris flow event alarm to the masses and relevant departments to evacuate the masses in time.

[0057] As Figure 4 shown, compared with the existing long and short time window ratio method, the embodiments of the present invention can effectively distinguish background noise from earthquake events, and then can clearly identify whether a flash flood and debris flow event has occurred.

[0058] In summary, according to the debris flow identification method based on the time-frequency characteristics of seismic array signals proposed in the embodiments of the present invention, comprehensively considering the waveform frequency and energy time-varying characteristics of different events, on the basis of statistically analyzing the peak frequency band of ground motion signals, the energy level is increased to obtain the centroid frequency describing the main frequency component of the signal and the energy level changing with time. Through the centroid frequency, background noise, earthquake events, and flash flood and debris flow events more than hundreds of meters away from the gully are effectively distinguished, and then it is determined whether a flash flood and debris flow event has occurred. Compared with the existing classic long and short time window ratio identification method, this method not only improves the utilization rate of waveform information and the detection efficiency of debris flow disasters, but also can increase the detection range of flash flood and debris flow to 1.5 km, improve the safety distance of field observation equipment, and thus enhance the safety, reliability and practicability of field observation.

[0059] Secondly, a debris flow identification device based on the time-frequency characteristics of seismic array signals proposed in the embodiments of the present invention is described with reference to the accompanying drawings.

[0060] Figure 5 is a block diagram of the debris flow identification device based on the time-frequency characteristics of seismic array signals in the embodiments of the present invention.

[0061] As Figure 5As shown in the figure, the debris flow identification device 50 based on the time-frequency characteristics of seismic array signals includes: a signal acquisition module 501, a short-time transformation module 502, a solution module 503, and an identification module 504.

[0062] Among them, the signal acquisition module 501 is used to deploy a field debris flow vibration monitoring array at a preset safe position of the target debris flow gully to acquire continuous seismic ground motion waveform signals. The short-time transformation module 502 is used to perform short-time Fourier transform on the continuous seismic ground motion waveform signals to obtain local information in the time-frequency domain. The solution module 503 is used to solve the time-varying power spectral density of the continuous seismic ground motion waveform signals according to the local information in the time-frequency domain. The identification module 504 is used to perform energy weighted averaging on the time-varying power spectral density to obtain the time-varying centroid frequency, and identify whether a debris flow disaster event occurs according to the time-varying centroid frequency.

[0063] In some embodiments, the number of the field debris flow vibration monitoring arrays is greater than or equal to three, and the preset safe position is perpendicular to the target debris flow gully and non-contact with the target debris flow gully.

[0064] In some embodiments, the short-time transformation module 501 includes:

[0065] An elimination unit, which is used to eliminate the sidelobes in the continuous seismic ground motion waveform signals by using a Hamming window function to obtain clean seismic ground motion signals;

[0066] A signal segmentation unit, which is used to segment the clean seismic ground motion signals into continuous seismic ground motion waveform signals in multiple short time periods;

[0067] A signal transformation unit, which is used to perform Fourier transform on the continuous seismic ground motion waveform signals in each short time period to obtain local information in the time-frequency domain.

[0068] In some embodiments, the identification module 504 includes:

[0069] An energy weighting unit, which is used to perform energy weighting on each time frame in the time-varying power spectral density and average the power spectra of each frequency after weighting to obtain the time-varying centroid frequency;

[0070] A comparison and identification unit, which is used to compare the time-varying centroid frequency with a preset threshold, and determine that a debris flow disaster event occurs when the time-varying centroid frequency is higher than the preset threshold.

[0071] It should be noted that the foregoing explanation of the embodiments of the debris flow identification method based on the time-frequency characteristics of seismic array signals also applies to the debris flow identification device based on the time-frequency characteristics of seismic array signals in this embodiment, and will not be elaborated here.

[0072] The debris flow identification device based on the time-frequency characteristics of seismic array signals proposed in the embodiments of the present invention comprehensively considers the waveform frequency and energy time-varying characteristics of different events. On the basis of statistically analyzing the peak frequency band of ground motion signals, the energy level is added to obtain the centroid frequency that describes the main frequency component of the signal and the energy level changing with time. By using the centroid frequency, background noise, seismic events, and debris flow events of mountain torrents more than hundreds of meters away from the gully can be effectively distinguished, and then it can be determined whether a debris flow event of mountain torrents has occurred. Compared with the existing classic long-short time window ratio identification method, this method not only improves the utilization rate of waveform information and the detection efficiency of debris flow disasters, but also can increase the detection range of debris flow events of mountain torrents to 1.5 km, improve the safety distance of field observation equipment, and thus enhance the safety, reliability, and practicability of field observation.

[0073] Figure 6 The following is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. The electronic device may include:

[0074] A memory 601, a processor 602, and a computer program stored on the memory 601 and executable on the processor 602.

[0075] When the processor 602 executes the program, it implements the debris flow identification method based on the time-frequency characteristics of seismic array signals provided in the above embodiments.

[0076] Further, the electronic device further includes:

[0077] A communication interface 603 for communication between the memory 601 and the processor 602.

[0078] The memory 601 is used to store a computer program executable on the processor 602.

[0079] The memory 601 may include a high-speed RAM memory, and may also include a non-volatile memory, such as at least one disk memory.

[0080] If the memory 601, the processor 602, and the communication interface 603 are implemented independently, the communication interface 603, the memory 601, and the processor 602 may be interconnected through a bus and complete communication with each other. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For the sake of convenience of representation,Figure 6 It is represented only by a thick line, but it does not mean that there is only one bus or one type of bus.

[0081] Optionally, in a specific implementation, if the memory 601, the processor 602, and the communication interface 603 are integrated on a single chip, the memory 601, the processor 602, and the communication interface 603 can communicate with each other through an internal interface.

[0082] The processor 602 may be a central processing unit (CPU for short), or an application specific integrated circuit (ASIC for short), or one or more integrated circuits configured to implement the embodiments of the present invention.

[0083] The embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the debris flow identification method based on the time-frequency characteristics of the seismic array signals as described above is implemented.

[0084] In the description of this specification, the descriptions with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0085] In addition, the terms "first" and "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "N" is at least two, such as two, three, etc., unless otherwise specifically defined.

[0086] Any process or method description represented in a flowchart or otherwise described herein can be understood to represent a module, segment, or portion of code including one or N executable instructions for implementing a customized logical function or process. The scope of the preferred embodiments of the present invention includes additional implementations in which functions may be executed not in the order shown or discussed, including in a substantially simultaneous manner according to the functions involved or in a reverse order, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0087] Logic and / or steps represented in a flowchart or otherwise described herein, for example, can be considered a sequenced list of executable instructions for implementing a logical function and can be embodied specifically in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device. As used in this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection having one or N wires (electronic device), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable medium on which the program can be printed, as the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpretation, or other appropriate processing as necessary and then storing it in a computer memory.

[0088] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the N steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one of the following techniques known in the art or a combination thereof can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), and the like.

[0089] Those of ordinary skill in the art can understand that all or part of the steps carried out in the methods of the above embodiments can be completed by instructing relevant hardware through a program. The program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0090] In addition, each functional unit in various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically alone, or two or more units can be integrated into one module. The above integrated module can be implemented in the form of hardware or in the form of a software functional module. When the above integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0091] The above-mentioned storage medium can be a read-only memory, a magnetic disk, an optical disc, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A debris flow identification method based on the time-frequency characteristics of seismic array signals, characterized in that, It includes the following steps: Deploy a field debris flow vibration monitoring array at a preset safe position of the target debris flow gully to collect continuous ground motion waveform signals; Perform short-time Fourier transform on the continuous ground motion waveform signals to obtain local information in the time-frequency domain; Solve the time-varying power spectral density of the continuous ground motion waveform signals according to the local information in the time-frequency domain; Perform energy weighted averaging on the time-varying power spectral density to obtain the time-varying centroid frequency, and identify whether a mountain flood debris flow disaster event occurs according to the time-varying centroid frequency.

2. The debris flow identification method based on the time-frequency characteristics of seismic array signals according to claim 1, wherein The number of the field debris flow vibration monitoring arrays is greater than or equal to three, and the preset safe position is perpendicular to the target debris flow gully and non-contact with the target debris flow gully.

3. The debris flow identification method based on the time-frequency characteristics of seismic array signals according to claim 1, characterized in that The performing short-time Fourier transform on the continuous ground motion waveform signals to obtain local information in the time-frequency domain includes: Use a Hamming window function to eliminate the sidelobes in the continuous ground motion waveform signals to obtain clean ground motion signals; Divide the clean ground motion signals into continuous ground motion waveform signals of multiple short time periods; Perform Fourier transform on the continuous ground motion waveform signals of each short time period to obtain the local information in the time-frequency domain.

4. The debris flow identification method based on the time-frequency characteristics of seismic array signals according to claim 1, wherein The performing energy weighted averaging on the time-varying power spectral density to obtain the time-varying centroid frequency, and identifying whether a mountain flood debris flow disaster event occurs according to the time-varying centroid frequency includes: Perform energy weighting on each time frame in the time-varying power spectral density, and average the power spectra of each frequency after weighting to obtain the time-varying centroid frequency; Compare the time-varying centroid frequency with a preset threshold, and when the time-varying centroid frequency is higher than the preset threshold, determine that the mountain flood debris flow disaster event occurs.

5. A debris flow identification device based on the time-frequency characteristics of seismic array signals, characterized in that, It includes: A signal acquisition module for deploying a field debris flow vibration monitoring array at a preset safe position of the target debris flow gully to collect continuous ground motion waveform signals; A short-time transformation module for performing short-time Fourier transform on the continuous ground motion waveform signals to obtain local information in the time-frequency domain; A solution module for solving the time-varying power spectral density of the continuous ground motion waveform signals according to the local information in the time-frequency domain; An identification module for performing energy weighted averaging on the time-varying power spectral density to obtain the time-varying centroid frequency, and identifying whether a mountain flood debris flow disaster event occurs according to the time-varying centroid frequency.

6. The debris flow identification device based on the time-frequency characteristics of seismic array signals according to claim 5, wherein The number of the field debris flow vibration monitoring arrays is greater than or equal to three, and the preset safe position is perpendicular to the target debris flow gully and non-contact with the target debris flow gully.

7. The debris flow identification device based on the time-frequency characteristics of seismic array signals according to claim 5, characterized in that The short-time transformation module includes: An elimination unit for using a Hamming window function to eliminate the sidelobes in the continuous ground motion waveform signals to obtain clean ground motion signals; A signal segmentation unit for dividing the clean ground motion signals into continuous ground motion waveform signals of multiple short time periods; A signal transformation unit for performing Fourier transform on the continuous ground motion waveform signals of each short time period to obtain the local information in the time-frequency domain.

8. The debris flow identification device based on the time-frequency characteristics of seismic array signals according to claim 5, wherein, The identification module includes: An energy weighting unit, configured to perform energy weighting on each time frame in the time-varying power spectral density, and average the power spectra of each frequency after weighting, so as to obtain the time-varying centroid frequency; A comparison and identification unit, configured to compare the time-varying centroid frequency with a preset threshold, and determine that the debris flow disaster event occurs when the time-varying centroid frequency is higher than the preset threshold.

9. An electronic device, characterized in that, Comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the program to implement the debris flow identification method based on the time-frequency characteristics of seismic array signals according to any one of claims 1-4.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to be used for implementing the debris flow identification method based on the time-frequency characteristics of seismic array signals according to any one of claims 1-4.

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