Underground disease detection method and system based on energy analysis

Abstract

The invention discloses an underground disease detection method and system based on energy analysis, and relates to geological disasters. The method comprises the steps that seismic signals of an underground structure are acquired; dividing the seismic signals into different time windows, wherein each time window corresponds to different underground depth ranges; calculating the energy spectrum of each signal in each time window; the energy change rate P of each channel of signal is calculated by comparing the energy spectrum of each channel of signal in each time window with the energy spectrum of each channel of signal of the known dense stratum; performing Hilbert-Huang transform on the acquired seismic signal to obtain an IMF component energy distribution structure chart and Hilbert marginal spectrum features; according to the energy change rate P, the IMF component energy distribution structure and the Hilbert marginal spectrum characteristics, the underground disease type and position are judged. Aiming at the problems that traditional frequency analysis is limited in deep structure recognition precision and different types of holes, loose bodies and the like are difficult to accurately distinguish, different types of underground diseases are accurately recognized, and the spatial positions and depth ranges of the underground diseases are positioned.

Description

Technical Field

[0001] This application relates to the field of geological disasters, and particularly to a method and system for detecting underground diseases based on energy analysis. Background Art

[0002] With the continuous development of urban construction and underground engineering, the technology for detecting underground diseases is of great significance for ensuring project safety. If underground diseases such as cavities and loose bodies cannot be detected and treated in time, they will pose a serious threat to ground buildings and transportation facilities. Therefore, accurately detecting the types and locations of underground diseases is a key link in engineering investigation and safety assessment.

[0003] The detection of underground diseases mainly relies on the acquisition and analysis of seismic signals. Extracting relevant information by transforming signals is an important method used in signal analysis. Currently, the technology for detecting underground diseases is mainly based on various signal processing methods, such as Fourier transform, short-time Fourier transform (STFT), wavelet transform, and seismic imaging method, etc.

[0004] The Fourier transform establishes the connection between the time domain and the frequency domain of a signal, constituting two ways of observing the signal. This method is convenient and easy to use, and has a clear physical meaning. For stationary signals, the Fourier transform can usually meet the analysis requirements. However, the Fourier transform has obvious defects: (1) Only signals that are absolutely integrable in the time domain may have a Fourier transform; (2) Due to the infinite time integration interval, the time-frequency resolution cannot be satisfied simultaneously; (3) When analyzing the seismic signals of underground diseases, in order to mathematically fit the non-stationary waveform of the original data, a large number of high-frequency pseudo-harmonic components will be introduced, resulting in an underestimation of the low-frequency energy in the Fourier spectrum and affecting the detection accuracy.

[0005] For the analysis of non-stationary signals, the short-time Fourier transform (STFT) is usually adopted. Its characteristic is that it can obtain the frequency composition of the overall signal in the local time domain through the plane composed of the two coordinates of the time axis and the frequency axis, and observe the distribution and arrangement of each frequency band of the entire signal in the local time. However, when using this method, it must first be assumed that the signal is piecewise stationary. Once the window function is determined, its time resolution and frequency resolution are constants in the entire time-frequency band of the signal, lacking flexibility and being difficult to adapt to the complex and changeable characteristics of underground media.

[0006] The wavelet transform segments and analyzes the signal by adding a variable-scale sliding window to the signal. The widths of the time window and the frequency window are variable, and it has good localization properties in both the time domain and the frequency domain, with different resolutions at different positions in the time-frequency plane, which better solves the contradiction between time resolution and frequency resolution. However, the wavelet transform is restricted by many conditions in practical applications, such as algorithm complexity and stationarity constraints, etc., and still has limitations when dealing with non-linear and non-stationary signals in underground disease detection.

[0007] The seismic reflection imaging method (also known as high-density seismic exploration and seismic multi-wave exploration) is developed based on the optimal offset technique in the reflection wave method. This method can use various seismic waves as effective waves for detection, or only use a specific seismic wave as the effective wave according to the requirements of the detection purpose. When the detection purpose is relatively single and only the lateral geological changes need to be studied, the seismic reflection imaging method has good effects; when there are more detection target layers, it is not easy to determine the optimal offset. Since the same offset is used for each recording channel, the time changes on the seismic record are mainly the reflection of underground geological bodies, which brings convenience to data interpretation. However, the traditional seismic reflection imaging analysis for judging the underground structure is usually manual analysis, which is not automated and intelligent enough, and often requires frequent changes in excitation conditions to obtain different responses during the detection process, with complex operations and poor consistency.

[0008] In the prior art, the underground disease detection method generally uses frequency analysis rather than energy analysis as the main judgment basis, and has limited ability to identify deep structure changes. At the same time, the existing methods have insufficient ability to process non-linear and non-stationary seismic signals, and it is difficult to accurately identify and locate various underground diseases, especially to distinguish different types of diseases such as cavities and loose bodies under complex geological conditions. Summary of the Invention

[0009] Aiming at the problems that the traditional frequency analysis has limited accuracy in identifying deep structures and is difficult to accurately distinguish different types of diseases such as cavities and loose bodies, the present application provides a method and system for detecting underground diseases based on energy analysis. The underground disease detection method based on the combination of energy analysis of each channel and time window depth mapping under the condition of constant excitation energy can effectively process non-linear and non-stationary seismic signals, accurately identify different types of underground diseases, and accurately locate their spatial positions and depth ranges while keeping the excitation conditions consistent.

[0010] One aspect of the present application provides a method for detecting underground diseases based on energy analysis, including: acquiring seismic signals of an underground structure; dividing the seismic signals into different time windows, and each time window corresponds to a different depth range underground; calculating the energy spectrum of each channel signal within each time window; calculating the energy change rate P of each channel signal by comparing the energy spectrum of each channel signal within each time window with the energy spectrum of each channel signal of a known solid stratum; performing Hilbert-Huang transform on the acquired seismic signals to obtain Hilbert marginal spectrum features; and judging the types and positions of underground diseases according to the energy change rate P and the Hilbert marginal spectrum features.

[0011] Further, obtaining the seismic signals of the underground structure includes: collecting seismic reflection wave signals through a geophone system as the seismic signals; wherein, the geophone system includes: arranging a plurality of geophones according to a preset equal spacing to form a geophone array, so that the geophone spacing is constant; controlling the height and weight of each hammer drop to be the same, so that the same energy is excited by each hammer drop; and making the distance between the excitation point and the first geophone constant in each measurement through a positioning mark, so that the small offset distance is the same.

[0012] Further, dividing the seismic signals into different time windows, each time window corresponding to a different depth range underground, includes: obtaining the time-depth conversion relationship according to the average propagation speed of the seismic signals in the underground medium; dividing the seismic signals into a plurality of time windows at a preset time interval; and establishing a mapping relationship between each time window and the corresponding depth range according to the time-depth conversion relationship, and the mapping relationship is used for hierarchical identification of the underground structure and disease types; wherein, layering refers to the underground structure layers of different depth ranges, and each depth layer contains multiple signals, and each signal corresponds to the data collected by geophones at different positions.

[0013] Further, calculating the energy spectrum of each signal in each time window includes: preprocessing the seismic signals in each time window; calculating the energy spectrum of each signal in each time window according to the preprocessed seismic signals; dividing the test section according to a preset interval, and calculating the average energy value of each divided section.

[0014] Further, calculating the energy change rate of each signal includes: selecting a known dense formation as a reference benchmark; calculating the energy change rate P of each signal relative to the reference benchmark according to the average energy value of each divided section; and dividing the dense state of the underground structure in each divided section according to the energy change rate.

[0015] In particular, first, from the perspective of wave theory, the wave propagation in a dense and homogeneous medium follows the classical wave equation, and the energy mainly decays geometrically. For waves of the same frequency, its decay rate is stable and predictable. This stability provides a theoretical basis for establishing a "standard reference system". Second, from the perspective of energy transfer, the energy-frequency distribution curve in a dense formation has a stable morphological characteristic, usually showing an energy peak in a specific frequency band and an expected energy decay slope. This characteristic makes the dense formation an ideal "baseline" for discriminating the states of other media. Third, from the perspective of statistical robustness, the energy characteristics of the dense formation are highly consistent and repeatable in space, reducing the influence of random errors on the determination. By using the dense formation as a reference, the system actually establishes a "relative energy measurement" mechanism, effectively eliminating the influence of environmental variables and equipment differences.

[0016] Furthermore, the energy change rate P of each signal relative to the reference standard is calculated using the following formula: P = (Em- Er) / Er, where P is the energy change rate, Em is the average energy value of the channel signal to be measured in the corresponding divided segment, and Er is the average energy value of the corresponding reference standard dense channel signal in the same divided segment.

[0017] In particular, the energy change rate P = (Em - Er) / Er. On the one hand, this formula essentially quantifies the degree of modulation of the wave energy by the medium to be tested. A positive change rate indicates that the medium to be tested has a smaller attenuation or stronger reflection of the wave energy than the reference medium; a negative change rate indicates that the medium to be tested absorbs or scatters more wave energy. On the other hand, the difference in the absolute value of the original energy is eliminated, making the measurement results under different geological backgrounds and different excitation conditions comparable. This processing method is similar to the idea of ​​adaptive filtering, but its goal is to extract energy modulation features rather than the signal itself.

[0018] Furthermore, the compaction state of the underground structure in each divided section is divided according to the energy change rate, including: when the energy change rate is less than the threshold value ε1, it is judged as moderate to severe loose disease, and the compaction state is low density; when the energy change rate is greater than or equal to the threshold value ε1 and less than the threshold value ε2, it is judged as having abnormal characteristics of loose disease, and the compaction state is medium density; when the energy change rate is greater than or equal to the threshold value ε2 and less than the threshold value ε3, it is judged as a normal section, and the compaction state is dense; when the energy change rate is greater than or equal to the threshold value ε3 and less than the threshold value ε4, it is judged as small voids or loose disease, and the compaction state is medium density; when the energy change rate is greater than the threshold value ε4, it is judged as large voids or severe loose disease, and the compaction state is low density.

[0019] In particular, firstly, from the perspective of wave propagation in multiphase media, this segmented judgment method accurately captures the bidirectional variation characteristics of "loose-dense-void". Traditional methods often use one-way threshold judgment (such as abnormality below a certain value), which cannot distinguish between loose media and voids, two abnormal bodies with completely different physical properties. Secondly, from the perspective of energy conversion mechanism, the division of thresholds accurately corresponds to different physical processes: a significant decrease in energy (P < ε1) corresponds to the absorption of wave energy by loose media; a slight decrease (ε1 ≤ P < ε2) corresponds to slight looseness; the normal range (ε2 ≤ P < ε3) corresponds to dense layers; a slight increase (ε3 ≤ P < ε4) corresponds to boundary reflection of small voids; a significant increase (P ≥ ε4) corresponds to strong reflection of large voids. Thirdly, from the perspective of the mapping relationship between signal characteristics and physical structures, this nonlinear segmented judgment method effectively solves the limitations of traditional linear mapping methods. The relationship between underground structure and signal energy is essentially nonlinear, and simple linear mapping cannot accurately reflect complex underground disease conditions.

[0020] ε1 (usually taken from -50% to -40%): represents the typical upper limit of wave energy absorption by loose media. Values less than this indicate that the medium has significant energy attenuation characteristics; ε2 (usually taken from -40% to -20%): represents the lower limit of energy fluctuations allowed for measurement errors and natural geological variations; ε3 (usually taken from 10% to +20%): represents the upper limit of energy fluctuations allowed for measurement errors and natural geological variations; ε4 (usually taken from 20% to +40%): represents the lower limit of the energy increase caused by reflections at the typical cavity boundary.

[0021] Furthermore, perform empirical mode decomposition and Hilbert-Huang transform on the acquired seismic signals to obtain the energy structure distribution characteristics and Hilbert marginal spectrum characteristics of the IMF components, including: perform EMD decomposition on the acquired seismic signals to obtain IMF components, and obtain the energy structure distribution diagram of the IMF components of each channel of data, which reflects the energy proportion of IMF components at different frequencies. When there are diseases underground, the energy structure distribution of the IMF components will change significantly.

[0022] Perform Hilbert-Huang transform on the signal to obtain Hilbert marginal spectrum characteristics. Extract the maximum energy, the instantaneous frequency corresponding to the maximum energy, and the attenuation rate after the energy reaches the maximum value from the Hilbert marginal spectrum as the Hilbert marginal spectrum characteristics:

[0023] (1) Maximum energy: In normal compact strata, the maximum value of the marginal spectrum energy is much larger than that of the strata containing loose bodies.

[0024] (2) Instantaneous frequency of maximum energy: In normal strata, the energy reaches the maximum value at low frequencies, while in some strata containing loose bodies, the instantaneous frequency of the maximum energy will increase significantly.

[0025] (3) Energy attenuation rate: In normal strata, as the instantaneous frequency increases, the energy reaches the maximum value at low frequencies and then decays rapidly; in strata containing loose bodies, after the energy reaches the maximum value, the attenuation rate is relatively slow.

[0026] Furthermore, based on the energy change rate P, the energy structure diagram of the IMF components, and the Hilbert marginal spectrum characteristics, judge the type and location of underground diseases, including: when the energy change rate P is greater than or equal to the threshold Δ1, judge that the corresponding disease type is a cavity; when the energy change rate P is less than the threshold Δ2, and the Hilbert marginal spectrum characteristics and the energy distribution characteristics of the IMF components meet any of the following conditions, judge that the disease type is a loose body: (1) the decrease amplitude of the maximum energy in the Hilbert marginal spectrum is greater than the threshold β1; (2) the increase amplitude of the instantaneous frequency corresponding to the maximum energy in the Hilbert marginal spectrum is greater than the threshold β2; (3) the decrease amplitude of the attenuation rate after the energy reaches the maximum value in the Hilbert marginal spectrum is greater than the threshold β3; (4) the energy structure similarity of the IMF components is less than the threshold β4;

[0027] Determine the location and depth range of the underground diseases based on the mapping relationship between each time window and the corresponding depth range in step S2, and the division results of the compactness state of the underground structure in step S4.

[0028] Specifically, the energy change rate P essentially reflects the modulation effect of different media on seismic waves from the perspective of wave propagation: In the case of a cavity: When a seismic wave encounters a cavity (solid-gas interface), due to the huge difference in acoustic impedance (the acoustic impedance of the solid is about 10 4 times that of air), the reflection coefficient R≈-1, meaning that almost all of the wave energy is reflected. This strong reflection on the multi-channel record will form superposition enhancement between adjacent channels, resulting in a significantly positive energy change rate P. In the case of a loose body: When a wave propagates in a loose medium, the energy loss comes from three physical mechanisms: ① Thermal loss due to friction at the contact surface between particles (internal friction attenuation); ② Viscous damping effect of pore fluid (viscous attenuation); ③ Scattering effect caused by the irregular distribution of particles and pores (scattering attenuation). The combined action of these three mechanisms makes the energy change rate P significantly negative.

[0029] In addition, the Hilbert marginal spectrum analysis is based on the Hilbert-Huang transform (HHT). By extracting the intrinsic oscillation modes of the signal through empirical mode decomposition (EMD), it avoids the limitations of the pre-set basis functions of the Fourier transform and is more suitable for non-linear and non-stationary signals such as earthquakes; it can accurately characterize the changes in instantaneous frequency and instantaneous amplitude, and capture the subtle modulation characteristics of the medium on the signal; it can effectively characterize the non-linear modulation characteristics of different media on waves, which is difficult to achieve by traditional linear analysis methods.

[0030] Finally, the accurate identification of a cavity is achieved in principle by the energy change rate P≥Δ1: The Δ1 threshold (usually set to 150% - 200%) corresponds to the energy ratio of the reflected wave to the incident wave caused by the acoustic impedance difference at the cavity boundary. The cavity reflection shows three typical characteristics on the Hilbert marginal spectrum: Wideband energy enhancement: reflecting the high reflectivity of the cavity boundary to each frequency band; Phase jump: reflecting the phase characteristics of the reflected wave relative to the direct wave; Time-frequency aggregation: reflecting the locality and suddenness of the reflection event; This dual-condition discrimination mechanism effectively avoids misjudgment that may be caused by relying solely on energy changes (for example, a hard interlayer with increased density may also cause energy enhancement).

[0031] The combined discrimination strategy of the energy change rate P<Δ2 and any one of the three characteristics of the Hilbert marginal spectrum realizes the highly adaptable identification of loose bodies:

[0032] The Δ2 threshold (usually set to -30% to -50%) corresponds to the comprehensive effect threshold of the three main attenuation mechanisms (scattering, internal friction, and viscous damping) in the loose medium. According to the wave attenuation theory, the attenuation coefficient α is proportional to the nth power of the frequency, where n depends on the dominant attenuation mechanism: for scattering attenuation, n≈4 (Rayleigh scattering region) or n≈1 (geometric scattering region); for internal friction attenuation, n≈1; for viscous attenuation, n≈2.

[0033] The three characteristics of the Hilbert marginal spectrum correspond to different physical property manifestations of the loose body:

[0034] Decrease in the maximum energy (threshold β1): It reflects the overall attenuation effect of the loose body on the wave energy. β1 (usually 20% - 40%) is set based on the typical attenuation amplitude of the loose medium on the total energy of the signal.

[0035] Increase in the maximum energy frequency (threshold β2): It reflects the dispersion effect and selective attenuation characteristics of the loose body. The loose medium attenuates the high-frequency components more strongly, resulting in the spectral centroid shifting towards the low frequency, and the frequency corresponding to the maximum energy point relatively increases. β2 (usually 15% - 30%) is set based on the change in the shape of the dispersion curve.

[0036] Decrease in the energy attenuation rate (threshold β3): It reflects the modulation effect of the loose body on the energy spectrum shape. Multiple scattering in the loose medium will cause the redistribution of energy in the frequency domain, manifested as a slower attenuation of energy in the high-frequency band. β3 (usually 25% - 45%) is set based on the typical intensity of the spectral tailing effect.

[0037] The characteristics of the IMF component energy structure diagram correspond to the physical property manifestations of the loose body:

[0038] Decrease in the similarity of the IMF component energy structure (threshold β4): It reflects a significant change in the proportion of components in different frequency bands in the loose body accounting for the total energy of the signal. (β4 is usually 35% - 45%) is set based on the change in the proportion of the energy of each IMF component in each channel accounting for the total energy of the signal in that channel.

[0039] Adopting the decision-making strategy of "meeting any one condition" is the key innovation of this solution, which reflects the adaptation to the diversity of loose bodies: Loose bodies with different origins and structures may show different performances in these three characteristics, and the flexible decision-making logic greatly improves the adaptability of recognition.

[0040] Another aspect of the present application further provides an underground disease detection system based on energy analysis, including: a signal acquisition module that collects seismic reflection wave signals through a geophone system, where the geophone system includes a geophone array formed by a plurality of geophones arranged at preset equal intervals; a time window module that divides the seismic signals into different time windows and establishes a mapping relationship between each time window and the corresponding depth range according to the time-depth conversion relationship; an energy spectrum module that preprocesses the seismic signals within each time window, calculates the energy spectrum of each signal within each time window, and calculates the average energy value of each divided section; an energy change rate module that calculates the energy change rate P of each signal by comparing the energy spectra of each signal within each time window with the energy spectra of each signal in the known dense strata; a Hilbert-Huang module that performs empirical mode decomposition and Hilbert-Huang transform on the acquired seismic signals to obtain Hilbert marginal spectrum features; and a disease judgment module that determines the type and location of underground diseases based on the energy change rate P and the Hilbert marginal spectrum features, and determines the depth range of the underground diseases according to the mapping relationship between the time window and the depth range.

[0041] Compared with the prior art, the advantages of the present application are as follows:

[0042] When detecting different types and locations of underground diseases, the prior art generally adopts a frequency analysis method based on Fourier transform or a seismic imaging method that requires frequent changes in excitation conditions. However, there are defects such as limited processing ability for non-linear and non-stationary seismic signals, low recognition accuracy of deep structures, and difficulty in accurately distinguishing different types of diseases. The present application combines energy spectrum analysis under constant excitation energy conditions with time window-depth mapping, and applies Hilbert-Huang transform processing technology to achieve effective processing of non-linear and non-stationary seismic signals, significantly improving the recognition accuracy of underground disease types and the accuracy of depth positioning, especially enhancing the ability to distinguish cavities and loose bodies. Brief Description of the Drawings

[0043] The present application will be further described in the form of exemplary embodiments, and these exemplary embodiments will be described in detail through the drawings. These embodiments are not restrictive. In these embodiments, the same numbers represent the same structures, where:

[0044] Figure 1 is an exemplary flowchart of an underground disease detection method based on energy analysis shown in some embodiments of the present application;

[0045] Figure 2 is a map of the experimental site shown in some embodiments of the present application;

[0046] Figure 3 is a schematic diagram of the experimental survey line layout shown in some embodiments of the present application;

[0047] Figure 4 is a schematic diagram of different formation energy curves shown in some embodiments of the present application;

[0048] Figure 5 is a structural diagram of waveforms and IMF energy distributions of loose formations and compact formations shown in some embodiments of the present application;

[0049] Figure 6 is a schematic diagram of the marginal spectrum of a compact formation shown in some embodiments of the present application;

[0050] Figure 7 is a schematic diagram of the marginal spectrum of a loose formation shown in some embodiments of the present application;

[0051] Figure 8 is a schematic diagram of the terrain structure of a test section shown in some embodiments of the present application;

[0052] Figure 9 is a comparison chart of the energies of two tests on a test section shown in some embodiments of the present application. Detailed implementation manners

[0053] The methods and systems provided in the embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0054] The present application is mainly applied to fields such as urban engineering exploration and detection of underground diseases of roads. On the premise of ensuring the same small offset distance and the same hammer excitation energy for each channel, by analyzing the energy spectrum change rate within different time windows of the signal, and at the same time performing Hilbert-Huang transform analysis on the energy characteristics of the IMF components and the marginal spectrum characteristics of the signal, the compactness state of the soil within different depth ranges underground can be determined.

[0055] Through experimental verification, frequency does not have much significance for distinguishing between compact formations and cavities. It has a good effect to observe the changes in signal energy and in-phase axis morphology using seismic imaging. For the detection of loose bodies, the characteristics of obvious reduction in signal amplitude (energy) and in-phase axis disconnection are presented in seismic imaging. Therefore, signal energy can be mainly used for analysis.

[0056] The IMF energy structure distribution diagram and Hilbert marginal spectrum obtained by HHT can reflect the distribution of energies at different frequencies. When seismic waves propagate underground, if there is a disease damage at a certain location, then the energy transmission will be affected, and the energy values corresponding to each instantaneous frequency will inevitably show a certain change law, causing fluctuations and mutations in the IMF energy distribution structure and marginal spectrum. We can compare the marginal spectra of each channel to find the location of the suspected disease. This method is relatively effective for the determination of loose bodies.

[0057] Finally, it is determined that the main application is to analyze the underground structure using signal energy, and the HHT transform is used to assist in the analysis and determination of loose bodies.

[0058] As Figure 1 shown, obtain the seismic signal of the underground structure; divide the seismic signal into different time windows, and each time window corresponds to a different depth range underground; calculate the energy spectrum of each trace signal within each time window; by comparing the energy spectrum of each trace signal within each time window with the energy spectrum of each trace signal of the known dense strata, calculate the energy change rate P of each trace signal; perform Hilbert-Huang transform on the obtained seismic signal to obtain the Hilbert marginal spectrum characteristics; based on the energy change rate P and the Hilbert marginal spectrum characteristics, judge the type and location of underground diseases.

[0059] Specifically, obtaining the seismic signal of the underground structure includes: collecting the seismic reflection wave signal through a geophone system as the seismic signal; wherein, the geophone system includes: setting a plurality of geophones according to a preset equal spacing to form a geophone array so that the geophone spacing is constant; this uniform sampling enables the accurate calculation of the transfer and attenuation laws of energy in space during subsequent signal processing.

[0060] Control the height and weight of each hammer drop to be the same, so that each hammer drop excites the same energy; this is in sharp contrast to the method of frequently changing the excitation conditions in traditional technologies. The constant excitation energy ensures that the normalization benchmark of all measurement data is consistent, enabling the accurate reflection of the energy changes solely caused by the differences in underground media, thereby avoiding multi-variable interference and improving the processing ability for non-linear and non-stationary signals.

[0061] Through positioning marks, make the distance between the excitation point and the first geophone constant in each measurement, so that the small offset distance is the same. It provides a stable reference baseline for establishing an accurate time-depth correspondence relationship and effectively solves the accuracy problem in the identification of deep structures by traditional methods.

[0062] This application uses the method of controlling variables to take the characteristics of the underground medium as the only variable, so that the response of the system to underground diseases only depends on the underground structure itself, thereby overcoming the limitations of traditional frequency analysis methods in dealing with non-linear and non-stationary signals.

[0063] Divide the seismic signal into different time windows, and each time window corresponds to a different depth range underground. According to the average propagation speed of the seismic signal in the underground medium, obtain the time-depth conversion relationship; establish an accurate time and space mapping through the average propagation speed of the seismic signal in the underground medium. This conversion relationship is based on the physical laws of wave propagation, but different from traditional methods, this solution not only considers a single average speed but also implicitly has the ability to adapt to the layered characteristics of the underground medium, laying a foundation for precise positioning under complex geological conditions.

[0064] The seismic signals are divided into multiple time windows at a preset time interval; the traditional Fourier transform method will lose local features when processing the overall signal, while this solution realizes the segmented quasi-stationary processing of non-stationary signals through time window division. The signal characteristics within each window are more stable, facilitating subsequent detailed analysis. This processing method is essentially a time-frequency localization processing of the signal, but it avoids the limitation of the fixed window in the short-time Fourier transform.

[0065] According to the time-depth conversion relationship, a mapping relationship between each time window and the corresponding depth range is established. This mapping relationship is used for the layered identification of underground structures and disease types; among them, layering refers to the underground structure layers in different depth ranges. Each depth layer contains multiple signals, and each signal corresponds to the data collected by geophones at different positions. Through this mapping, the system can directly locate abnormal bodies in the actual geological space, rather than only identifying features in the abstract signal space. In particular, this mapping mechanism introduces the concept of "layered identification", decomposing the three-dimensional space problem into a combination of two-dimensional problems that can be processed, that is, analyzing the lateral changes of multiple signals in each depth layer, which greatly reduces the computational complexity under complex geological conditions.

[0066] Through this design, this application can focus on analyzing the energy changes purely caused by the differences in underground media, providing a theoretical basis for accurately distinguishing different types of diseases such as cavities and loose bodies. Traditional methods often have difficulty distinguishing different disease types under signal superposition interference, while this solution effectively isolates the mutual interference of signals at different depths through depth layering and lateral comparison, significantly improving the recognition accuracy.

[0067] Calculate the energy spectrum of each signal within each time window, including: preprocessing the seismic signals within each time window; calculating the energy spectrum of each signal within each time window according to the preprocessed seismic signals; dividing the test section according to a preset interval and calculating the average energy value of each divided section.

[0068] In particular, this solution uses energy spectrum analysis rather than traditional spectrum analysis as the core discrimination means. The energy spectrum directly reflects the energy transfer and absorption characteristics of seismic waves in different media and is more sensitive to changes in the physical properties of underground media. Suspected disease points are identified by judging the change rate of the energy of each signal within the same time window. This discrimination method based on relative spatial changes is more adaptable than the traditional absolute threshold discrimination method and can effectively meet the detection requirements under different geological backgrounds.

[0069] More specifically, on the one hand, although the cavity is not obvious in terms of frequency characteristics, this solution finds that the cavity appears as a bright spot reflection feature with strong amplitude on the seismic image, and the energy increases significantly. This energy enhancement phenomenon is due to the strong reflection of seismic waves at the air-rock interface, and it is difficult for traditional frequency analysis methods to effectively capture this feature.

[0070] On the other hand, the main frequency of the loose body is lower than that of the hard stratum. Through HHT decomposition, the energy structure of the intrinsic mode function (IMF) components of the loose body is significantly different from that of the dense stratum. In the HHT marginal spectrum, the loose body presents a unique "three decreases and one slow" feature - the maximum energy value decreases significantly, the maximum energy frequency is postponed, and the energy attenuation rate decreases. In addition, this solution also identifies the phenomenon of "in-phase axis pulling down and energy reduction" generated by the loose body on the seismic section, and this discovery provides an additional discriminant basis for the identification of the loose body.

[0071] In summary, this solution not only realizes the identification and positioning of underground diseases, but more importantly, it can distinguish different types of underground diseases, especially distinguish the two most common and easily confused disease types of cavity (energy increase) and loose body (energy attenuation). This distinguishing ability comes from the profound understanding of the physical mechanism of seismic wave propagation in different media and the application of innovative signal processing methods.

[0072] In this embodiment, for the amplitude energy of the seismic image, in the same time window, by comparing the energy of each trace signal and relative to the energy of the known dense stratum trace, through multiple experiments, we set the energy change rate and the corresponding underground density and hidden danger development characteristics as shown in Table 1.

[0073] Table 1 Classification criteria for compactness status

[0074] Rate of energy change (P) Development characteristics of underground hidden dangers Dense state P<-60% Medium to severe loosening diseases Low density -60≤P<-30% With abnormal characteristics of loosening diseases Medium density -30≤P<20% Normal section Dense 20≤P<40% Small cavities, loosening diseases Medium density P≥40% Larger cavities, severe loosening diseases Low density

[0075] The location of this experiment is the loose backfill part of the fish pond in the yard of a certain construction engineering company. The backfill area is about 10 meters long, 2 meters wide, and 1.5 meters deep. The survey line goes from south to north and extends to the cement hard road surface (dense stratum). The experimental location is as Figure 2 shown, and the schematic diagram of the experimental survey line layout is as Figure 3 shown, and the obtained energy curves of different strata are as Figure 4 shown. The energy comparison of the dense hard road surface, the soil layer outside the backfill area, and the soil layer inside the backfill area is shown in Table 2:

[0076] Table 2 Energies and energy change rates of different strata

[0077] Location Dense road surface Land outside the fish pond Land inside the fish pond Average energy 51.08155152 35.11079002 27.39678127 Rate of energy change / -31.27% -46.37%

[0078] Set the standard according to the energy change rate corresponding to different degrees of diseases. The backfill part is determined to have the abnormal characteristics of loose diseases, and it is less than the medium and severe loose degrees, which basically conforms to the on-site soil layer state.

[0079] We analyze the percentage of the energy of each IMF component in the total energy, observe the distribution of the energy of the IMF components, and express the percentages of each component in different colors. The more the percentage, the longer the corresponding color bar.

[0080] The waveforms and IMF energy distribution structures of loose strata and dense strata are as Figure 5 shown. It can be observed that in the IMF energy spectrum, there are also obvious characteristic differences in the energy ratio structures of the IMF components in the loose part (left side) and the dense strata (right side).

[0081] In this application, the marginal spectra of the loose channel and the dense channel are compared to obtain the marginal spectrum of the dense strata, as Figure 6 shown, and the marginal spectrum of the loose strata, as Figure 7 shown. By comparing the normal dense strata with the strata containing loose bodies, we can notice four obvious parameter changes:

[0082] (1) Maximum energy: In the normal dense strata, the maximum energy is much larger than the maximum energy of the strata containing loose bodies. The reduction amplitude of the maximum energy in the loose strata is greater than the threshold β1;

[0083] (2) Instantaneous frequency of the maximum energy: In the normal strata, the energy reaches the maximum value at low frequencies, while in some strata containing loose bodies, the instantaneous frequency of the maximum energy will increase significantly, and the increase amplitude is greater than the threshold β2;

[0084] (3) Energy decay rate: In the normal strata, as the instantaneous frequency increases, the energy reaches the maximum value at low frequencies and then decays rapidly; in the strata containing loose bodies, after the energy reaches the maximum value, the decay rate is relatively slow. The reduction amplitude of the decay rate is greater than the threshold β3;

[0085] (4) The similarity of the IMF component energy structure is less than the threshold β4;

[0086] In this application, a certain section of the road to be detected in a certain area is monitored. The monitored section is the bicycle lane road from north to south of the section to be detected. From the red iron cage to about 20 meters southward, there is a newly excavated subway new station under the road surface, with a top depth of 13 meters. The underground structure schematic diagram is as Figure 8 shown. Data is collected for the section to be detected on August 16, 2024 and November 11, 2024 respectively. Data is collected for the same section according to the coordinates, and the instrument moves forward at a constant speed during the collection process.

[0087] Within a depth of 3 meters underground, the collected data was subjected to bad track removal and band-pass filtering. The energy spectra of the two sets of data were analyzed, and the changes in their energy spectra were compared and analyzed. The energy diagrams of the two tests are as follows Figure 9 As shown, the overall quality of the data in the first phase is good. The overall formation of the monitored section is relatively dense. The formation density starting from the north side (left) is slightly lower than that on the south (right) side. The energy of the data on the south side is slightly higher than that on the north side. When the equipment passes over the manhole cover (void), there will be a discontinuity in the event axis and a sudden increase in amplitude.

[0088] The overall quality of the data in the second phase is good. The overall formation of the monitored section is relatively dense. The formation density starting from the north side (left) is slightly lower than that on the south (right) side. The energy of the data on the south side is slightly higher than that on the north side. When the equipment passes over the manhole cover (void), there will be an obvious discontinuity in the event axis and a sudden increase in amplitude, which is relatively consistent with the data in the first phase.

[0089] When the structure state of the underground soil mass changes, especially when it is disturbed and loosened, energy dissipation will occur during the propagation of seismic waves, the energy spectrum will decrease, and as the loosening state intensifies, the reduction rate will increase; on the other hand, when the soil mass is severely loosened or even forms a void, the wave group frame during the propagation of seismic waves is significantly enhanced, strong reflection occurs, and the energy spectrum will increase again. Therefore, it is proposed to use the change rate of the energy spectrum as the classification of the soil mass compactness state to reflect the influence of the disturbance of the soil mass structure on the formation and development of underground disease bodies, so as to facilitate the timely discovery of potential underground disease hazards that may cause collapse.

[0090] At the same time, the waveform data collected by the broadband vibration monitoring system studied in this project and the processing and analysis can also identify the development of potential underground disease hazards. Through the collection and analysis of the data, if the development characteristics of the underground disease body are identified, it reflects that potential underground disease hazards have been formed, and it is necessary to carry out planned exploration. Therefore, identifying the development characteristics of the underground disease body is used as a standard for monitoring the compactness state.

[0091] Therefore, a method was established to identify the change in the soil mass compactness state based on the development degree of underground diseases and the change rate of the energy spectrum, and to classify it.

[0092] Taking every 2 - 4 meters as a window section, the average energy analysis was carried out on a 20-meter section of the road, and the energy changes in each small window section were analyzed to determine the change in the compactness state. The changes in the energy spectra of the two tests are shown in Table 3.

[0093] Table 3 Changes in the energy spectra of the two tests

[0094] Section number Starting coordinates End coordinates First energy Second energy Rate of energy change Soil density 1 X = 4418302.9 Y = 454130.75 X = 4418300.2 Y = 454130.76 22.02 22.23 0.95% Dense 2 X = 4418300.2 Y = 454130.76 X = 4418296.33 Y = 454130.76 35.98 34.07 -5.31% Dense 3 X = 4418296.33 Y = 454130.75 X = 4418292.00 Y = 454130.75 26.00 25.38 -2.38% Dense 4 X = 4418292.00 Y = 454130.78 X = 4418288.1 Y = 454130.78 17.84 14.56 -18.39 Dense 5 X = 4418288.1 Y = 454130.79 X = 4418285.32 Y = 454130.79 40.45 40.05 -0.99% Dense 6 X = 4418285.32 Y = 454130.80 X = 4418283.33 Y = 454130.80 26.74 27.93 4.45% Dense

[0095] The overall condition of the monitored section of the road to be measured is good. The road surface stratum above the north of the underground subway station is slightly looser than that of the south. The overall stratum is relatively dense, and the difference between the two monitoring data is small. It can be seen that there is no obvious change in the density of the stratum under the second lane from north to south of the road section to be measured.

[0096] The present application and its implementation manners have been schematically described above. The description is not restrictive. Without departing from the spirit or basic characteristics of the present application, the present application can be implemented in other specific forms. What is shown in the drawings is only one of the implementation manners of the present application, and the actual structure is not limited thereto. Any reference signs in the claims should not limit the claims involved. Therefore, if those of ordinary skill in the art are inspired by it and design similar structural manners and embodiments to the technical solution without creative efforts without departing from the purpose of the present invention, they should all fall within the protection scope of the present application. In addition, the term "comprising" does not exclude other elements or steps, and the term "a" before an element does not exclude including "a plurality of" such elements. The plurality of elements stated in the product claims can also be implemented by one element through software or hardware. The terms such as first and second are used to indicate names and do not indicate any specific order.

Claims

1. An underground disease detection method based on energy analysis, characterized in that, Including: Obtaining seismic signals of the underground structure; Dividing the seismic signals into different time windows, with each time window corresponding to a different depth range underground; Calculating the energy spectrum of each signal trace within each time window; By comparing the energy spectra of each signal trace within each time window with the energy spectra of each signal trace of the known compact strata, calculating the energy change rate P of each signal trace; Performing Hilbert-Huang transform on the obtained seismic signals to obtain Hilbert marginal spectrum features; Based on the energy change rate P and the Hilbert marginal spectrum features, determining the types and locations of underground diseases; Obtaining seismic signals of the underground structure, including: Collecting seismic reflection wave signals through a geophone system as the seismic signals; Wherein, the geophone system includes: arranging a plurality of geophones according to a preset equal spacing to form a geophone array to keep the geophone spacing constant; controlling the height and weight of each hammer drop to be the same to make each hammer drop excite the same energy; through positioning marks, making the distance between the excitation point and the first geophone constant in each measurement to keep the small offset distance the same; Dividing the seismic signals into different time windows, with each time window corresponding to a different depth range underground, including: obtaining the time-depth conversion relationship according to the average propagation speed of the seismic signals in the underground medium; dividing the seismic signals into multiple time windows at a preset time interval; According to the time-depth conversion relationship, establishing a mapping relationship between each time window and the corresponding depth range, and the mapping relationship is used for hierarchical identification of the underground structure and disease types; wherein, layering refers to underground structure layers of different depth ranges, and each depth layer contains multiple signal traces, and each signal trace corresponds to data collected by geophones at different positions.

2. The method for detecting underground diseases based on energy analysis according to claim 1, characterized in that: Calculating the energy spectrum of each signal trace within each time window, including: Preprocessing the seismic signals within each time window; According to the preprocessed seismic signals, calculating the energy spectrum of each signal trace within each time window; Dividing the test section according to a preset interval and calculating the average energy value of each divided section.

3. The method for detecting underground diseases based on energy analysis according to claim 2, characterized in that: Calculating the energy change rate of each signal trace, including: Selecting the known compact strata as the reference benchmark; According to the average energy values of each divided section, calculating the energy change rate P of each signal trace relative to the reference benchmark; Dividing the compactness state of the underground structure of each divided section according to the energy change rate.

4. The method for detecting underground diseases based on energy analysis according to claim 3, characterized in that: Calculating the energy change rate P of each signal trace relative to the reference benchmark, using the following formula: P =(Em - Er) / Er Wherein, P is the energy change rate, Em is the average energy value of the signal trace to be measured within the corresponding divided section, and Er is the average energy value of the corresponding reference benchmark compact signal trace within the same divided section.

5. The method for detecting underground diseases based on energy analysis according to claim 3, characterized in that: Dividing the compactness state of the underground structure of each divided section according to the energy change rate, including: When the energy change rate is less than the threshold ε1, it is determined as medium to severe loose disease, and the dense state is low density; When the energy change rate is greater than or equal to the threshold ε1 and less than the threshold ε2, it is determined as having abnormal characteristics of loose disease, and the dense state is medium density; When the energy change rate is greater than or equal to the threshold ε2 and less than the threshold ε3, it is determined as a normal section, and the dense state is dense; When the energy change rate is greater than or equal to the threshold ε3 and less than the threshold ε4, it is determined as small cavities or loose disease, and the dense state is medium density; When the energy change rate is greater than the threshold ε4, it is determined as large cavities or severe loose disease, and the dense state is low density.

6. The underground disease detection method based on energy analysis according to claim 5, characterized in that: The value range of ε1 is -50% to -40%; The value range of ε2 is -40% to -20%; The value range of ε3 is +10% to +20%; The value range of ε4 is +20% to +40%.

7. The underground disease detection method based on energy analysis according to claim 3, characterized in that: Performing Hilbert-Huang transform on the acquired seismic signals, including: Performing EMD decomposition on the acquired seismic signals to obtain IMF components; Calculating the energy structure distribution map of each channel's IMF components, and the energy structure distribution map reflects the energy proportion of IMF components with different frequencies; Performing Hilbert-Huang transform on the seismic signals to obtain the Hilbert marginal spectrum; Extracting the following characteristic parameters from the Hilbert marginal spectrum: (1) The maximum energy; (2) The instantaneous frequency corresponding to the maximum energy; (3) The attenuation rate after the energy reaches the maximum value; Taking the extracted characteristic parameters as the Hilbert marginal spectrum characteristics for judging the disease type and location.

8. The underground disease detection method based on energy analysis according to claim 7, characterized in that: Judging the underground disease type and location according to the energy change rate P and the Hilbert marginal spectrum characteristics, including: When the energy change rate P is greater than or equal to the threshold Δ1, it is judged that the corresponding disease type is a cavity; When the energy change rate P is less than the threshold Δ2, and when the Hilbert marginal spectrum characteristics and the IMF component energy structure distribution characteristics meet any of the following conditions, it is determined that the disease type is a loose body: (1) The decrease amplitude of the maximum energy in the Hilbert marginal spectrum is greater than the threshold β1; (2) The increase amplitude of the instantaneous frequency corresponding to the maximum energy in the Hilbert marginal spectrum is greater than the threshold β2; (3) The decrease amplitude of the attenuation rate after the energy reaches the maximum value in the Hilbert marginal spectrum is greater than the threshold β3; (4) The similarity of the IMF component energy structure is less than the threshold β4; Determining the location and depth range of the underground disease according to the mapping relationship between each time window and the corresponding depth range in step S2, and the division result of the underground structure dense state in step S4.

9. The underground disease detection method based on energy analysis according to claim 8, characterized in that: The value range of β1 is: 20% - 40%; The value range of β2 is: 15% - 30%; The value range of β3 is: 25% - 45%; The value range of β4 is: 35% - 45%.

10. An underground disease detection system based on energy analysis, characterized in that, Including: A signal acquisition module that collects seismic reflection wave signals through a geophone system, where the geophone system includes a geophone array formed by multiple geophones arranged at preset equal intervals; A time window module that divides seismic signals into different time windows and establishes a mapping relationship between each time window and the corresponding depth range according to the time-depth conversion relationship; An energy spectrum module that preprocesses the seismic signals within each time window, calculates the energy spectrum of each signal trace within each time window, and calculates the average energy value of each divided section; An energy change rate module that calculates the energy change rate P of each signal trace by comparing the energy spectrum of each signal trace within each time window with the energy spectrum of each signal trace of the known compact strata; A Hilbert-Huang module that performs empirical mode decomposition and Hilbert-Huang transform on the acquired seismic signals to obtain the energy structure distribution of IMF components and the characteristics of the Hilbert marginal spectrum; A disease judgment module that determines the type and location of underground diseases based on the energy change rate P, the energy structure distribution of IMF components, and the characteristics of the Hilbert marginal spectrum, and determines the depth range of underground diseases according to the mapping relationship between the time window and the depth range.

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