Coal fire detection method based on sound wave active and passive detection and radar remote sensing technology

Abstract

The invention discloses a coal fire detection method based on sound wave active and passive detection and a radar remote sensing technology, which combines the sound wave active and passive detection technology and the radar remote sensing technology and comprehensively analyzes sound wave propagation characteristics, earth surface deformation, temperature change, combustion gas concentration and electromagnetic wave characteristics of a coal fire area. Judging the distribution range and the risk change trend of the coal fire; according to the method, real-time and all-around coal fire monitoring can be realized, and the precision and reliability of coal fire detection are effectively improved.

Description

Technical Field

[0001] The invention relates to the field of exploration technology, and in particular to a coal fire detection method based on active and passive acoustic wave detection and radar remote sensing technology. Background Art

[0002] The problem of spontaneous combustion in coal mines is a serious safety hazard that is prevalent in coal mine production around the world. The occurrence of coal fires not only threatens the lives of coal mine workers, but may also cause serious environmental pollution and even lead to catastrophic accidents in mining areas. Traditional coal fire detection methods usually rely on manual monitoring and local fire detection equipment. These methods not only have slow response speeds, but also have limited monitoring coverage due to factors such as deep coal seams and complex geological conditions, making it difficult to fully and real-timely capture the dynamic characteristics of coal fires. Therefore, how to improve the accuracy and real-time performance of coal fire detection has always been a difficult problem that needs to be solved in the field of coal mine safety management.

[0003] At present, the monitoring of coal fire mainly relies on single detection equipment such as temperature sensors and gas detection instruments. These devices can usually only detect the temperature or gas changes in the coal fire area, and cannot fully reflect the multi-faceted dynamic characteristics of coal fire. With the advancement of science and technology, acoustic wave detection and radar remote sensing technology have gradually been applied in mine disaster monitoring. Acoustic wave active and passive detection technology can reflect the changes in coal seams through the propagation characteristics of acoustic waves, such as the abnormality of coal fire media, which is of great significance for early warning of coal fire. However, the application of traditional acoustic wave detection methods in coal seam spontaneous combustion detection still faces some limitations, such as insufficient detection depth and resolution, and cannot achieve real-time and all-round monitoring.

[0004] Radar remote sensing technology can analyze the impact range of coal fire activities by monitoring surface deformation. Radar remote sensing can accurately measure the surface deformation of the coal fire area, analyze physical changes such as coal seam settlement and crack expansion, and provide important data for coal fire assessment. However, radar remote sensing technology also has certain limitations, such as insufficient sensitivity for early detection of coal fire activities, and low accuracy and reliability of coal fire monitoring. Summary of the invention

[0005] The purpose of the present invention is to provide a coal fire detection method based on acoustic wave active and passive detection and radar remote sensing technology, which can achieve real-time and all-round monitoring and improve the accuracy and reliability of coal fire monitoring.

[0006] To achieve the above object, the present invention provides a coal fire detection method based on active and passive acoustic wave detection and radar remote sensing technology. The method combines active and passive acoustic wave detection technology and radar remote sensing technology to judge the distribution range of coal fire and its dangerous change trend through comprehensive analysis of acoustic wave propagation characteristics, surface deformation, temperature change, combustion gas concentration and electromagnetic wave characteristics of the coal fire area. Specifically, the method comprises the following steps:

[0007] S1: Active and passive detection of acoustic waves, using active and passive detection technology to extract the propagation velocity, attenuation coefficient and spectrum characteristics of acoustic waves, and analyze the abnormal area of ​​acoustic waves;

[0008] S2: Radar surface deformation analysis, using radar remote sensing technology to extract surface deformation information, combined with the deformation rate and acceleration of the coal fire area and the distribution law of cracks, dynamically evaluate the trend of coal fire hazard changes;

[0009] S3: Radar scattering feature extraction, extracting radar backscatter coefficient and change rate, and using the dynamic changes of radar backscatter coefficient to evaluate the surface thermal anomaly and humidity changes in the coal fire area;

[0010] S4: Radar coherence and crack extension assessment, analyze the radar coherence coefficient, and infer the spatial distribution and extension of surface cracks in the coal fire area;

[0011] S5: Comprehensive assessment and model building, integrating acoustic and radar data, establishing comprehensive evaluation indicators for coal fire hazards, and predicting coal fire distribution trends.

[0012] As a further solution of the present invention: in step S1, the sound wave propagation velocity is defined as v, the attenuation coefficient β, the spectrum energy P(f), and the main frequency f0, wherein the sound wave attenuation coefficient β is calculated by the following formula:

[0013]

[0014] Where P(f r ) and P(f0) are the received spectrum energy and the transmitted spectrum energy respectively, and r is the propagation distance; when the attenuation coefficient β and When it increases significantly, it indicates that the coal fire medium is abnormal, resulting in increased danger; among them: It is the partial derivative of the sound wave attenuation coefficient β with respect to time t, that is, the instantaneous rate of change of the sound wave attenuation coefficient.

[0015] As a further solution of the present invention: the combustion gas concentration includes carbon monoxide (CO) and methane (CH4) combustion gas concentrations, and the CO and CH4 combustion gas concentrations in the coal fire area are monitored by gas detection equipment. When the CO concentration C CO and CH4 concentration When it increases significantly, it indicates that coal fire activity is increasing.

[0016] As a further solution of the present invention: by considering the time variation of the sound wave propagation velocity v and the main frequency f0, the sound wave variation trend is dynamically evaluated, and the formula is as follows:

[0017] Δv=v(t+Δt)-v(t), Δf0=f0(t+Δt)-f0(t)

[0018] When Δv<0 and Δf0<0, it indicates that medium damage is aggravated and coal fire activity is enhanced;

[0019] When Δv>0 and Δf0>0, it indicates that the coal fire tends to be stable.

[0020] As a further solution of the present invention: the deformation rate is defined in step S2 and deformation acceleration in:

[0021]

[0022] when When , it indicates that the surface deformation intensifies and the scope of coal fire influence expands;

[0023] when It indicates that the surface deformation tends to slow down and the impact range of coal fire is reduced.

[0024] As a further solution of the present invention: in step S2, the crack distribution includes crack density and crack opening, wherein the crack density calculation formula is:

[0025]

[0026] Where: D is the crack density; A is the area of ​​the study area; N is the number of cracks per unit area A; the crack opening calculation formula is:

[0027]

[0028] Where: W is the crack opening; Δu is the displacement difference between two points; θ is the radar observation angle;

[0029] Crack distribution law:

[0030] f(x,y)=v(x,y)+a(x,y)·t

[0031] Where: f(x, y) is the cumulative deformation at different positions; v(x, y) is the deformation rate at different positions; a(x, y) is the deformation acceleration at different positions; t is time;

[0032] The variation of crack distribution in space, where:

[0033]

[0034] Where: Δφ is the change in crack distribution; is the displacement gradient; k is the coefficient calibrated according to field observations.

[0035] As a further solution of the present invention: in step S3, a radar backscatter coefficient σ0 is defined, where:

[0036]

[0037] Wherein: P r is the radar receiving power, and P t is the transmitting power;

[0038] When there is an active coal fire area;

[0039] When it indicates that the coal fire activity weakens; wherein: is the partial derivative of the radar backscattering coefficient σ0 with respect to time t, that is, the instantaneous change rate of the radar backscattering coefficient.

[0040] As a further solution of the present invention: define the coherence coefficient C in step S4:

[0041]

[0042] Wherein: S i and R i are the echo intensities of two radar observations respectively, and are their means;

[0043] When C < 0.5, it indicates that the crack expansion is significant and the coal fire risk increases.

[0044] As a further solution of the present invention: define the comprehensive evaluation index R(t) of coal fire risk in step S5:

[0045]

[0046] Wherein: w1, w2, w3, w4, w5 are weight coefficients determined by training with historical data;

[0047] When R(t + Δt) > R(t), it indicates that the risk increases;

[0048] When R(t + Δt) < R(t), it indicates that the risk decreases.

[0049] Compared with the prior art, the present invention combines the active and passive acoustic detection technology and radar remote sensing technology. Through the multi-source data fusion algorithm, the monitoring results of the two are compared and comprehensively analyzed. The acoustic detection data can provide the preliminary location and activity range of the coal fire, while the radar remote sensing data provides more accurate surface deformation information for the activity of the coal fire. The combination of the two can realize real-time and all-round monitoring of the coal fire, effectively improving the accuracy and reliability of coal fire detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1The present invention is a flow chart of a coal fire detection method based on active and passive acoustic wave detection and radar remote sensing technology.

[0051] Figure 2 It is a flow chart of obtaining the coal fire area positioning information of the acoustic wave detection in the predetermined area in the present invention.

[0052] Figure 3 It is a flow chart of obtaining SAR data fusion and comprehensive analysis information of a predetermined area according to the SAR image of the predetermined area in the present invention.

[0053] Figure 4 It is a schematic diagram of the attenuation process of the sound wave in the coal fire zone over time in the present invention.

[0054] Figure 5 It is a schematic diagram of the change process of the surface deformation and radar backscattering of the coal fire area over time in the present invention.

[0055] Figure 6 It is a flow chart of obtaining the marking of coal fire high-risk areas in a predetermined area by integrating active and passive acoustic wave detection with radar data analysis in the present invention.

[0056] Figure 7 It is a schematic diagram of the changing process of the comprehensive acoustic wave detection and radar detection method of the coal fire area in the present invention. DETAILED DESCRIPTION

[0057] The present invention will be further described below in conjunction with the accompanying drawings.

[0058] like Figure 1 As shown, a coal fire detection method based on active and passive acoustic detection and radar remote sensing technology is provided. The method combines active and passive acoustic detection technology and radar remote sensing technology to judge the distribution range of coal fire and its dangerous change trend through comprehensive analysis of acoustic wave propagation characteristics, surface deformation, temperature change, combustion gas concentration and electromagnetic wave characteristics of the coal fire area. Specifically, the method includes the following steps:

[0059] S1: Active and passive detection of acoustic waves, using active and passive detection technology to extract the propagation velocity, attenuation coefficient and spectrum characteristics of acoustic waves, and analyze the abnormal area of ​​acoustic waves;

[0060] S2: Radar surface deformation analysis, using radar remote sensing technology to extract surface deformation information, combined with the deformation rate and acceleration of the coal fire area and the distribution law of cracks, dynamically evaluate the trend of coal fire hazard changes;

[0061] S3: Radar scattering feature extraction, extracting radar backscatter coefficient and change rate, and using the dynamic changes of radar backscatter coefficient to evaluate the surface thermal anomaly and humidity changes in the coal fire area;

[0062] S4: Radar coherence and crack extension assessment, analyze radar coherence coefficients, and infer the spatial distribution and extension of surface cracks in coal fire areas;

[0063] S5: Comprehensive assessment and model building, integrating acoustic and radar data, establishing comprehensive evaluation indicators for coal fire hazards, and predicting coal fire distribution trends.

[0064] In step S1, the sound wave propagation velocity is v, the attenuation coefficient β, the spectrum energy P(f), and the main frequency f0 are defined, wherein the sound wave attenuation coefficient β is calculated by the following formula:

[0065]

[0066] Where P(f r ) and P(f0) are the received spectrum energy and the transmitted spectrum energy respectively, and r is the propagation distance. When it increases significantly, it indicates that the coal fire medium is abnormal, leading to increased danger.

[0067] in: It is the partial derivative of the sound wave attenuation coefficient β with respect to time t, that is, the instantaneous rate of change of the sound wave attenuation coefficient.

[0068] The concentration of carbon monoxide (CO) and methane (CH4) in the coal fire area is monitored by gas detection equipment. The change in gas concentration can directly reflect the intensity of coal fire combustion:

[0069] When the CO concentration C CO and CH4 concentration When it increases significantly, it indicates that coal fire activity has increased;

[0070] When its concentration changes tend to stabilize or decrease, it indicates that the combustion intensity is weakening.

[0071] Gas concentration data can be combined with sound wave and radar data to further verify the scope and danger of coal fire activities.

[0072] By considering the time variation of the sound wave propagation velocity v and the main frequency f0, the sound wave variation trend Δv is dynamically evaluated, and the formula is as follows:

[0073] Δv=v(t+Δt)-v(t), Δf0=f0(t+Δt)-f0(t)

[0074] When Δv<0 and Δf0<0, it indicates that medium damage is aggravated and coal fire activity is enhanced;

[0075] When Δv>0 and Δf0>0, it indicates that the coal fire tends to be stable.

[0076] In step S2, the deformation rate is defined and deformation acceleration in:

[0077]

[0078] when When , it indicates that the surface deformation intensifies and the scope of coal fire influence expands;

[0079] when It indicates that the surface deformation tends to slow down and the impact range of coal fire is reduced.

[0080] In step S2, the crack distribution includes crack density and crack opening, wherein the crack density calculation formula is defined as:

[0081]

[0082] Where: D is the crack density; A is the area of ​​the study area; N is the number of cracks per unit area A; the crack opening calculation formula is defined as:

[0083]

[0084] Where: W is the crack opening; Δu is the displacement difference between two points; θ is the radar observation angle;

[0085] Define the crack distribution law:

[0086] f(x,y)=v(x,y)+a(x,y)·t

[0087] Where: f(x, y) is the cumulative deformation at different positions; v(x, y) is the deformation rate at different positions; a(x, y) is the deformation acceleration at different positions; t is time;

[0088] The variation of crack distribution in space, where:

[0089]

[0090] Where: Δφ is the change in crack distribution; is the displacement gradient; k is the coefficient calibrated according to field observations.

[0091] In step S3, the radar backscatter coefficient σ0 is defined as:

[0092]

[0093] Where: P r is the radar receiving power, P t is the transmission power.

[0094] when When there is an active coal fire area;

[0095] when When it is, it indicates that the coal fire activity weakens;

[0096] Wherein: is the partial derivative of the radar backscattering coefficient σ0 with respect to time t, that is, the instantaneous change rate of the radar backscattering coefficient.

[0097] Define the coherence coefficient C in step S4:

[0098]

[0099] In the formula: S i and R i are the echo intensities of two radar observations respectively, and is their mean value;

[0100] When C < 0.5, it indicates that the crack expansion is significant and the coal fire risk increases.

[0101] Define the comprehensive evaluation index R(t) of coal fire risk in step S5:

[0102]

[0103] In the formula: w1, w2, w3, w4, w5 are weight coefficients, which are determined by training with historical data;

[0104] When R(t + Δt) > R(t), it indicates that the risk increases;

[0105] When R(t + Δt) < R(t), it indicates that the risk decreases.

[0106] Embodiment:

[0107] Real-time monitor the acoustic wave signals in a predetermined area through the active and passive acoustic wave detection system; by arranging multiple acoustic wave detection points, collect the acoustic wave propagation characteristics in the coal mining area, inside the coal seam and on the ground surface, including sound speed, frequency, attenuation coefficient parameters. The occurrence and spread of coal fires will affect the physical properties of the coal seam, and thus change the speed, frequency and attenuation characteristics of acoustic wave propagation. Therefore, the active and passive acoustic wave detection technology can be used to monitor the initial signals generated by coal fires.

[0108] Adopt radar remote sensing technology to obtain radar images of a predetermined area through synthetic aperture radar (SAR); analyze the image sequence through interferometric radar technology to obtain surface deformation information. When a coal fire occurs, the surface will undergo small deformations due to phenomena such as coal seam expansion, contraction or settlement caused by high temperature. These deformations can be monitored with high precision through radar remote sensing technology, providing data support for coal fire activities on the surface deformation.

[0109] After obtaining the acoustic wave active and passive detection data and radar remote sensing data, the monitoring results of the two are compared and comprehensively analyzed through a multi-source data fusion algorithm. The acoustic wave detection data can provide the preliminary location and activity range of the coal fire, while the radar remote sensing data can provide more accurate surface deformation information for the activity of the coal fire. The combination of the two can effectively improve the accuracy and reliability of coal fire detection.

[0110] Based on the fused data, a comprehensive evaluation method for coal fire hazard is used to quantitatively evaluate the coal fire in the predetermined area. The hazard level of the coal fire is evaluated by analyzing the changes in the acoustic wave signal and the surface deformation, combined with the temperature and gas concentration environmental factors. Through spatial filtering and regional analysis, the range of the coal fire is accurately located, and a spatial distribution map of suspected coal fires is generated.

[0111] The above analysis results are fed back in real time through the early warning system. When the coal fire activity reaches a certain level of danger, the system automatically generates an alarm and provides effective decision-making basis for coal mine managers. Through the intelligent coal fire early warning system, coal mine workers and managers are reminded to take emergency measures in time to avoid safety hazards caused by the spread of coal fire.

[0112] Acquiring sound wave propagation data within the coal fire area specifically includes: deploying sound wave sensors in the coal fire monitoring area, regularly emitting sound wave signals of known frequency, and recording the spectrum data of the return signal. The sensor measures the energy of the transmitted signal and the energy of the received return signal, and obtains the specific path length of the sound wave propagation through the distance between the sensors. Using these data, the degree of attenuation of the sound wave during the propagation process is calculated. The attenuation information obtained reflects the physical changes in the medium in the coal fire area, especially when the coal fire activity intensifies, the attenuation coefficient changes significantly. Changes in the attenuation coefficient indicate that the medium in the coal fire area is abnormal, usually due to increased temperature or other combustion factors. Such as Figure 2 As shown in the figure, this process can monitor the changes in coal fire activity in real time and provide necessary data support for coal mine safety warning. When the attenuation coefficient increases significantly, it indicates that the threat of coal fire is increasing, especially in the early stage of coal fire spread or intensification, and potential dangers can be discovered in time.

[0113] The acoustic wave attenuation coefficient β is calculated by the following formula:

[0114]

[0115] Where: P(f r ) and P(f0) are the received spectrum energy and the transmitted spectrum energy respectively, and r is the propagation distance. When it increases significantly, it indicates that the coal fire medium is abnormal, resulting in increased danger. The sound wave attenuation coefficient in this formula can reflect the physical changes in the medium of the coal fire area, and through the changes in sound wave propagation, it can warn of the potential threat of coal fire.

[0116] Acquiring the sound wave propagation velocity (hereinafter referred to as sound velocity) and main frequency data of the coal fire area, specifically including: collecting the sound velocity and main frequency information of the coal fire area at different time points through the sound wave sensor. These data reflect the changes in the medium in the coal fire area and the strength of the coal fire activity. By comparing the data at different time points, the changes in the sound velocity and main frequency are calculated to determine whether the coal fire activity is enhanced. When the changes in the sound velocity and main frequency are both negative values, it indicates that the medium damage in the coal fire area is aggravated, the coal fire activity is enhanced, and the danger is increased. When the changes in the sound velocity and main frequency are both positive values, it indicates that the coal fire tends to be stable, the activity is weakened, and the danger is reduced. This process monitors the dangerous change trend of the coal fire area in real time, and can identify in advance whether the coal fire has entered the active period, and then take corresponding safety protection measures in time.

[0117] The formula for dynamically evaluating the trend of risk change is:

[0118] Δv=v(t+Δt)-v(t),Δf0=f0(t+Δt)-f0(t)

[0119] When Δv<0 and Δf0<0, it indicates that medium damage is aggravated and coal fire activity is enhanced;

[0120] When Δv>0 and Δf0>0, it indicates that the coal fire tends to be stable.

[0121] Obtaining surface deformation data of the coal fire area, specifically including: using radar remote sensing technology to conduct regular dynamic monitoring of the coal fire area and obtaining surface displacement data of the coal fire area, such as Figure 3 As shown. These displacement data reflect the impact of coal fire activity on the ground surface, especially the soil expansion or settlement caused by coal fire. Through continuous monitoring, the deformation rate and acceleration of the coal fire area are obtained. These data are helpful to analyze whether the coal fire has a lasting impact on the ground surface. When the coal fire activity intensifies, the surface deformation rate and acceleration increase, and the impact range of the coal fire expands. When the coal fire activity weakens, the surface deformation tends to slow down and the impact range of the coal fire shrinks. By dynamically monitoring these data, the dangerous changes of coal fire can be evaluated in real time, the expansion range of coal fire can be discovered in time, and the safety of coal mines can be ensured.

[0122] Among them, the calculation formula for dynamically monitoring and evaluating the changing trend of coal fire hazard is:

[0123]

[0124] when When , it indicates that the surface deformation intensifies and the scope of coal fire influence expands;

[0125] when It indicates that the surface deformation tends to slow down and the impact range of coal fire is reduced.

[0126] Figure 4 The trend of the acoustic wave attenuation coefficient in the coal fire area over time is intuitively displayed. When the attenuation coefficient increases significantly, it indicates that the coal fire activity has intensified and the physical medium in the coal fire area has changed, providing a dynamic early warning signal for coal fire hazard assessment.

[0127] The receiving power data of the radar signal is obtained, specifically including: using the radar equipment to scan the coal fire area and collect the echo signal of the coal fire area. The radar equipment evaluates the surface thermal anomaly and humidity changes in the coal fire area by emitting electromagnetic waves and receiving the reflected signals. The temperature and humidity characteristics of the coal fire area are evaluated by the radar signal data obtained in real time. When the coal fire activity intensifies, the temperature and humidity characteristics of the coal fire area change significantly, and the reflection intensity of the radar signal changes accordingly. When the reflection intensity of the signal received by the radar increases, it indicates that the activity in the coal fire area intensifies and the danger of the coal fire increases; when the reflection intensity of the radar signal decreases, it indicates that the coal fire activity weakens and the threat of the coal fire decreases. In this way, the radar monitors the temperature and humidity changes in the coal fire area in real time, provides real-time feedback for coal mine safety management, and ensures effective response to changes in coal fires.

[0128] Among them, the formula for evaluating the surface thermal anomaly and humidity changes in the coal fire area is:

[0129]

[0130] Where: P r is the radar receiving power, P t is the transmission power.

[0131] when When there is an active coal fire area;

[0132] when It indicates that the coal fire activity has weakened.

[0133] In this way, the temperature and humidity characteristics of the coal fire area can be analyzed through dynamically changing radar signals, and the intensity of coal fire activity can be evaluated.

[0134] Obtaining radar echo intensity data specifically includes: conducting two independent observations of the coal fire area through radar equipment, and recording the echo signal intensity of the two observations. Then compare the two observation data and analyze the change in echo intensity. If the surface cracks in the coal fire area expand, the echo signal changes significantly. Radar coherence analysis reflects the impact of coal fire on the surface by calculating the degree of change in the echo signal. When the difference in the echo intensity of the two observations is large, it indicates that the coal fire activity has caused significant crack expansion on the surface, and the danger of coal fire has increased. If the coherence coefficient is low, it indicates that the impact of coal fire activity has intensified and the safety of the coal mine area has decreased. By analyzing the changes in radar coherence, the impact of coal fire on surface cracks can be accurately judged, providing a basis for judging the extension range of coal fire and guiding coal mine safety management.

[0135] Among them, the radar coherence change is used to analyze the expansion of surface cracks, and the coherence coefficient C formula is defined as:

[0136]

[0137] Where: S i and R i are the echo intensities of the two radar observations, and is its mean value;

[0138] When C<0.5, it indicates that the cracks have expanded significantly and the risk of coal fire has increased.

[0139] Through coherence analysis, the impact of coal fire on surface cracks can be accurately determined, providing a basis for determining the expansion range of coal fire.

[0140] Figure 5 The relationship between surface deformation and radar signal changes in the coal fire area is shown. When the surface deformation intensifies, the radar backscatter coefficient increases, indicating that the coal fire activity intensifies and may lead to the expansion of the coal fire; when the surface deformation slows down, the reflection intensity of the radar signal decreases, indicating that the coal fire activity tends to stabilize or weaken.

[0141] Obtain multiple monitoring data of the coal fire area, including: obtaining acoustic wave data, radar data and surface deformation data of the coal fire area from monitoring means. These data comprehensively reflect the strength and scope of coal fire activities. By analyzing the changing trends of various types of data and combining historical data, the weight coefficient of each data is determined, and then the comprehensive hazard evaluation index of the coal fire area is calculated. By weighted fusion of various monitoring data, a comprehensive evaluation index of coal fire hazard is formed, such as Figure 6 Specifically, when the temperature, humidity, and surface deformation of the coal fire area show an increase in danger, the value of the comprehensive evaluation index will also increase, indicating that the threat of coal fire is intensifying; when multiple indicators show a decrease in danger, the value of the comprehensive evaluation index will decrease, indicating that the threat of coal fire is weakening, such as Figure 7 This process provides accurate coal fire risk assessment for coal mine safety managers, helping them to take preventive measures in a timely manner to ensure safe and stable coal mine production.

[0142] Among them, the formula for the comprehensive evaluation index R(t) of coal fire hazard is:

[0143]

[0144] Where: w1, w2, w3, w4, w5 are weight coefficients, which are determined through historical data training;

[0145] The comprehensive evaluation index formula weights and integrates the changes in various physical parameters to provide a comprehensive evaluation result that fully reflects the changes in coal fire hazards.

[0146] When R(t+Δt)>R(t), it indicates that the danger increases;

[0147] When R(t+Δt)

Claims

1. A coal fire detection method based on active and passive acoustic detection and radar remote sensing technology, characterized in that: This method combines active and passive acoustic detection technology and radar remote sensing technology. By comprehensively analyzing the acoustic wave propagation characteristics, surface deformation, temperature change, concentration of combustion gases, and electromagnetic wave characteristics in the coal fire area, it determines the distribution range of coal fires and the changing trend of their danger. Specifically, it includes the following steps: S1: Active and passive acoustic detection. Using active and passive acoustic detection technology, extract the acoustic wave propagation speed, attenuation coefficient, and spectral characteristics, and analyze the acoustic wave anomaly area. S2: Radar surface deformation analysis. Using radar remote sensing technology to extract surface deformation information, and combining the deformation rate, deformation acceleration, and fracture distribution law in the coal fire area, dynamically evaluate the changing trend of coal fire danger. S3: Radar scattering characteristic extraction. Extract the radar backscattering coefficient and its change rate, and use the dynamic change of the radar backscattering coefficient to evaluate the surface thermal anomaly and humidity change in the coal fire area. S4: Radar coherence and fracture propagation evaluation. Analyze the radar coherence coefficient, and infer the spatial distribution and propagation of surface fractures in the coal fire area. S5: Comprehensive evaluation and model construction. Integrate acoustic wave and radar data, establish a comprehensive evaluation index for coal fire danger, and predict the distribution trend of coal fires.

2. A coal fire detection method based on acoustic wave active and passive detection and radar remote sensing technology according to claim 1, characterized in that: In step S1, define the acoustic wave propagation speed as v, the attenuation coefficient as β, the spectral energy as P(f), and the main frequency as f0. Among them, the acoustic wave attenuation coefficient β is calculated by the following formula: Where P(f r ) and P(f0) are the received spectrum energy and the transmitted spectrum energy respectively, and r is the propagation distance; when the attenuation coefficient β and When it increases significantly, it indicates that the coal fire medium is abnormal, resulting in increased danger; among them: It is the partial derivative of the sound wave attenuation coefficient β with respect to time t, that is, the instantaneous rate of change of the sound wave attenuation coefficient.

3. The coal fire detection method based on active and passive acoustic wave detection and radar remote sensing technology according to claim 1 is characterized in that: The combustion gas concentration includes carbon monoxide (CO) and methane (CH4) combustion gas concentration. The CO and CH4 combustion gas concentrations in the coal fire area are monitored by gas detection equipment. When the CO concentration C CO and CH4 concentration When it increases significantly, it indicates that coal fire activity is increasing.

4. The coal fire detection method based on acoustic wave active and passive detection and radar remote sensing technology according to claim 2 is characterized in that: By considering the time variation of the acoustic wave propagation speed v and the main frequency f0, dynamically evaluate the changing trend of the acoustic wave. The formula is as follows: Δv = v(t + Δt) - v(t), Δf0 = f0(t + Δt) - f0(t) When Δv < 0 and Δf0 < 0, it indicates that the medium damage intensifies and the coal fire activity increases. When Δv > 0 and Δf0 > 0, it indicates that the coal fire tends to be stable.

5. The coal fire detection method based on acoustic wave active and passive detection and radar remote sensing technology according to claim 1 is characterized in that: In step S2, the deformation rate is defined and deformation acceleration in: when When , it indicates that the surface deformation intensifies and the scope of coal fire influence expands; when It indicates that the surface deformation tends to slow down and the impact range of coal fire is reduced.

6. The coal fire detection method based on active and passive acoustic wave detection and radar remote sensing technology according to claim 1 is characterized in that: In step S2, the fracture distribution includes fracture density and fracture aperture. The formula for fracture density is: In the formula: D is the fracture density; A is the area of the research area; N is the number of fractures in the unit area A; The formula for fracture aperture is: In the formula: W is the fracture aperture; Δu is the displacement difference between two points; θ is the radar observation angle; Fracture distribution law: f(x, y) = v(x, y) + a(x, y)·t In the formula: f(x, y) is the cumulative deformation at different positions; v(x, y) is the deformation rate at different positions; a(x, y) is the deformation acceleration at different positions; t is the time; The change of fracture distribution in space, where: Where: Δφ is the change in crack distribution; is the displacement gradient; k is the coefficient calibrated according to field observations.

7. The coal fire detection method based on active and passive acoustic wave detection and radar remote sensing technology according to claim 1 is characterized in that: In step S3, define the radar backscattering coefficient σ0, where: Where: P r is the radar receiving power, P t is the transmitting power; when When there is an active coal fire area; when When , it indicates that the coal fire activity is weakening; in: It is the partial derivative of the radar backscatter coefficient σ0 with respect to time t, that is, the instantaneous rate of change of the radar backscatter coefficient.

8. The coal fire detection method based on active and passive acoustic wave detection and radar remote sensing technology according to claim 1 is characterized in that: In step S4, define the coherence coefficient C: Where: S i and R i are the echo intensities of the two radar observations, and is its mean; When C < 0.5, it indicates that the fracture propagation is significant and the coal fire danger increases.

9. The coal fire detection method based on active and passive acoustic wave detection and radar remote sensing technology according to claim 1, characterized in that: In step S5, define the comprehensive evaluation index R(t) for coal fire danger: In the formula: w1, w2, w3, w4, w5 are weight coefficients, which are determined by training with historical data; When R(t + Δt) > R(t), it indicates that the danger increases; When R(t + Δt) < R(t), it indicates that the danger decreases.

Images