Surface shallow creep detection method and system based on time reversal technology

Abstract

The invention discloses a surface shallow creep detection method and system based on a time reversal technology, relates to the technical field of geological engineering, and solves the problem that the surface shallow creep detection precision is relatively low. The method comprises the following steps: acquiring multi-source physical field data of an earth surface shallow layer of a target area; establishing a simulation model of the surface shallow layer based on the multi-source physical field data; sending a first signal to the surface shallow layer through an antenna, and receiving a returned second signal; performing time reversal on the second signal to obtain a third signal; based on the simulation model, simulating a signal propagation path of a third signal sent by the antenna to the surface shallow layer to obtain target time inversion data; and comparing the target time inversion data with the historical time inversion data to obtain a creep abnormal region. Creep detection is carried out by adopting the time reversal technology, the creep abnormal area can be accurately positioned by utilizing the time reversibility of the wave equation and combining the self-consistent focusing characteristic of the time reversal signal at the real source point, and the creep detection precision is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of geological engineering, and in particular to a surface shallow layer creep detection method and system based on time inversion technology. BACKGROUND

[0002] In the field of geological engineering, civil engineering and geological disaster monitoring, the creep detection of surface shallow layer fissures is of great significance for evaluating the stability of the foundation, the safety of underground pipelines and preventing shallow geological disasters. Surface shallow layer fissures are mostly distributed within 0-50 meters underground, and their creep is characterized by slow displacement accumulation, with a deformation rate usually in the order of millimeters or even micrometers, which may cause accidents such as ground subsidence, pipeline rupture and foundation instability of buildings.

[0003] Traditional methods for detecting the creep of surface shallow layer fissures include borehole inclinometer measurement, layered settlement instrument monitoring, geological radar detection and acoustic wave transmission method. The borehole inclinometer requires drilling and embedding near the fissure, which disturbs the underground medium and has a limited range of single-hole measurement. When two shallow fissures are close to each other, it is difficult to accurately correspond to a single fissure at the drilling location, which may lead to interference between the creep data of the two fissures. The layered settlement instrument monitors the displacement by embedding magnetic rings at different depths, but the measurement points are discrete. If two close fissures are located in adjacent measurement layers, the insufficient sampling density may not be able to distinguish the creep characteristics of each fissure. Although the geological radar can achieve non-contact detection, it is significantly affected by the underground medium (such as soil moisture content and lithological differences), and the signal resolution is usually greater than 0.5 meters within a range of 5 meters, which makes it difficult to identify the boundaries of close fissures, resulting in ambiguous division of the creep area. The acoustic wave transmission method relies on pre-buried acoustic pipes, and when the distance between two fissures is close, the acoustic signal will be reflected and superimposed between the two fissures, making it impossible to separate the creep information of a single fissure from the wave velocity curve.

[0004] The above problems may lead to misjudgment in engineering practice, such as misjudging the coupled creep of two close shallow fissures as the expansion of a single fissure, or underestimating the actual activity intensity of one of the fissures, which may pose a risk to the safety assessment of underground engineering.

[0005] Therefore, there is a need for a surface shallow layer creep detection method and system based on time inversion technology. SUMMARY

[0006] To solve the problem of low creep detection accuracy of the existing surface shallow layer, the present application provides a surface shallow layer creep detection method and system based on time inversion technology, which can improve the creep detection accuracy of the surface shallow layer. The specific technical solutions are as follows: In a first aspect, the present application provides a surface shallow layer creep detection method based on time inversion technology, comprising: Obtaining multi-source physical field data of a surface shallow layer of a target area, the multi-source physical field data including underground structure, underground water level and underground medium physical property parameter data within a preset depth range; establishing a simulation model of the surface shallow layer based on the multi-source physical field data; sending a first signal to the surface shallow layer through an antenna and receiving a second signal returned by the surface shallow layer based on the first signal; time-reversing the second signal to obtain a third signal; simulating a signal propagation path of the antenna sending the third signal to the surface shallow layer based on the simulation model to obtain target time-reversal data; and comparing the target time-reversal data with historical time-reversal data to obtain a creep abnormal area within the preset depth range.

[0007] Preferably, after the creep abnormal area within the preset depth range is obtained, the method further includes: calculating a creep parameter of the creep abnormal area based on the target time-reversal data and the historical time-reversal data, the creep parameter including one or more of a creep rate, a cumulative displacement amount, a strain direction angle and a creep activity index.

[0008] Preferably, after the creep parameter of the creep abnormal area is calculated, the method further includes: importing the creep area and the creep parameter into a three-dimensional visualization platform to generate a dynamic creep cloud chart; and the spatial resolution of the dynamic creep cloud chart is not less than 0.5 meters.

[0009] Preferably, before the first signal is sent to the surface shallow layer through the antenna and the second signal returned by the surface shallow layer based on the first signal is received, the method further includes: arranging a ground synthetic aperture radar and a sensor array on the ground in the target area; and the first signal is sent to the surface shallow layer through the antenna of the ground synthetic aperture radar, and the second signal is received through the sensor array.

[0010] Preferably, the sensor array includes a three-component seismic detector irregularly arranged on the ground, or a distributed optical fiber acoustic sensor in a well; and the aperture of the sensor array is greater than 1.5 times of the preset depth range to ensure the spatial resolution.

[0011] Preferably, before the second signal is time-reversed to obtain the third signal, the method further includes: pre-processing the second signal, the pre-processing including mean filtering and time alignment; and the second signal is time-reversed to obtain the third signal, including: time-reversing the pre-processed second signal to obtain the third signal.

[0012] In a second aspect, an embodiment of the present application provides a surface shallow layer creep detection system based on time-reversal technology, which is applied to the method of the first aspect, and the system includes: The acquisition module is configured to acquire multi-source physical field data of a surface shallow layer of a target area, the multi-source physical field data including underground structure data, underground water level data and underground medium physical property parameter data within a preset depth range; The modeling module is configured to establish a simulation model of the surface shallow layer based on the multi-source physical field data. The signal transmitting and receiving module is configured to transmit a first signal to the surface shallow layer through an antenna and receive a second signal returned by the surface shallow layer based on the first signal. The time reversal module is configured to perform time reversal on the second signal to obtain a third signal. The simulation module is configured to simulate a signal propagation path of the antenna transmitting the third signal to the surface shallow layer based on the simulation model to obtain target time reversal data. The comparison module is configured to compare the target time reversal data with historical time reversal data to obtain a creep abnormal area within the preset depth range.

[0013] Preferably, the comparison module is further configured to calculate a creep parameter of the creep abnormal area based on the target time reversal data and the historical time reversal data, the creep parameter including one or more of a creep rate, a cumulative displacement amount, a strain direction angle and a creep activity index.

[0014] In a third aspect, an embodiment of the present application provides a computing device, including a memory configured to store a program, and a processor configured to load the program to execute the method according to the first aspect.

[0015] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, including a stored program, wherein the program controls a device where the computer-readable storage medium is located to execute the method according to the first aspect when the program runs.

[0016] Compared with the prior art, the present application has the following beneficial effects: by adopting the time reversal technology, using the time reversibility of the wave equation, automatically compensating the complexity of the wave propagation path, and combining the self-consistent focusing characteristics of the time reversal signal at the real source point, the creep abnormal area can be accurately located, and the positioning accuracy of the surface shallow layer creep detection is improved. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the drawings needed in the specific embodiments or the prior art description will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual proportions.

[0018] Figure 1A flowchart of a shallow surface creep detection method based on time reversal technology is provided for the embodiment of the present application. Figure 2 A structural diagram of a shallow surface creep detection system based on time reversal technology is provided for the embodiment of the present application. Figure 3 A structural diagram of a computing device is provided for the embodiment of the present application. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present application.

[0020] It should be understood that, when used in the specification and the appended claims, the terms "comprise" and "include" indicate the presence of the described features, integers, steps, operations, elements, and / or components, but do not exclude one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0021] It should also be understood that the terms used in the present application specification are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the present application specification and the appended claims, the singular forms "a", "an" and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0022] It should be further understood that the term "and / or" used in the present application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations thereof, and includes these combinations.

[0023] To solve the problem of low creep detection precision of the shallow surface in the prior art, the present application provides a shallow surface creep detection method and system based on time reversal technology, which can improve the creep detection precision of the shallow surface.

[0024] Please refer to Figure 1 , Figure 1 A flowchart of a shallow surface creep detection method based on time reversal technology is provided for the embodiment of the present application, which is applied to a computing device. Figure 1 As shown in the figure, the method comprises the following steps. Step 101, the computing device acquires multi-source physical field data of the shallow surface of a target area.

[0025] The computing device can be a server or a terminal. The computing device can be arranged on site in the target area or in a remote machine room.

[0026] The target area refers to a geological or engineering area where the shallow surface layer may have a creep phenomenon. It is the object of the embodiments of the present application, i.e., a specific spatial range that needs to be monitored, analyzed, and determined whether there is a shallow surface layer creep phenomenon.

[0027] The shallow surface layer refers to a rock-soil body layer within a certain depth range below the earth's surface, which is closely related to the surface environment and significantly affected by surface dynamics (such as precipitation, temperature changes, human activities, etc.). The depth range usually varies depending on the specific geological environment, engineering requirements, and monitoring targets. Therefore, a depth range needs to be preset as the detection range based on the known geological information of the target area.

[0028] The multi-source physical field data includes underground structure, underground water level, and underground medium physical parameter data within the preset depth range.

[0029] Specifically, the spatial distribution pattern, layering characteristics, and structural information of the underground structure rock-soil body reflect the macroscopic spatial arrangement of underground matter, including surface layering data, geological structure data, and artificial underground structure data. The underground water level refers to the burial characteristics and dynamic change information of underground water, which directly affects the mechanical properties of rock-soil body, including water level depth, water level dynamic change, and aquifer distribution. The underground medium physical parameter data refers to the physical and mechanical properties of the rock-soil body that constitutes the shallow surface layer, which is the core input for simulating the shallow surface layer, including physical parameters such as density, water content, and porosity, mechanical parameters such as elastic parameters, rheological parameters, and strength parameters, and permeability parameters.

[0030] The preset depth range is 0 to 50 meters, for example.

[0031] The computing device can obtain the multi-source physical field data from the database of the relevant institution in the target area, or the multi-source physical field data can be collected and detected by the operator before detection, and the multi-source physical field data is preset in the computing device or the storage connected with the computing device.

[0032] After obtaining the multi-source physical field data, the operator can arrange a sensor array and an antenna on the ground according to the terrain and detection requirements, and ensure the effectiveness of the detection signal, the integrity of the data, and the reliability of the results through reasonable spatial arrangement. Then, ensure the accurate position of each sensor and record the sensor coordinates using positioning technology.

[0033] Step 102, the computing device establishes a simulation model of the shallow surface layer based on the multi-source physical field data.

[0034] The computing device can construct a digital simulation model reflecting the geological structure, medium characteristics and dynamic changes of the near-surface by integrating the observation data of different physical fields in the multi-source physical field data.

[0035] Specifically, the computing device builds the framework of the simulation model based on the geological prior information and the spatial distribution characteristics of the multi-physical field data after preprocessing the multi-source physical field data; then divides the near-surface area into discrete three-dimensional grid cells, and sets the grid size according to the detection accuracy requirement to ensure that the simulation model can reflect local details and control the amount of calculation; and then converts the preprocessed physical field data into physical parameters of the model grid cells to realize the mapping of data to model parameters.

[0036] Step 103, the computing device sends a first signal to the near-surface through an antenna and receives a second signal returned by the near-surface based on the first signal.

[0037] In the embodiment of the application, the time reversal (TR) technology is mainly used for detecting and positioning the creep area, so it is necessary to capture the electromagnetic wave emitted by the creep area as a wave source; therefore, the computing device can actively send a first signal to the near-surface to obtain a second signal reflected by the near-surface based on the first signal.

[0038] Preferably, the device arranged by the operator on the surface of the target area includes a ground synthetic aperture radar and a sensor array; the computing device can send a first signal to the near-surface through the antenna of the ground synthetic aperture radar; and receive the second signal through the sensor array.

[0039] Preferably, the sensor array includes a three-component seismic detector irregularly arranged on the surface, or a distributed optical fiber acoustic sensor in a well; and the aperture of the sensor array is greater than 1.5 times of the preset depth range to ensure the spatial resolution.

[0040] Step 104, the computing device time-reverses the second signal to obtain a third signal.

[0041] The time reversal technology has many significant advantages in the near-surface creep detection, which uses the time reversal invariance of the wave equation and the third signal obtained after time reversal to automatically focus on the original source of the second signal, that is, the secondary wave source relative to the first signal, to realize high-resolution imaging and automatic focusing of the underground structure in a complex geological environment, and accurately locate and identify small changes in the formation.

[0042] As a non-invasive detection method, time reversal technology can effectively detect without damaging the surface, suitable for monitoring tasks in environmentally sensitive areas or under urban infrastructure. In addition, this technology has strong anti-interference ability, can effectively filter noise and enhance useful signals, and adapts to complex geological conditions. Time reversal technology also supports the establishment of long-term monitoring systems, tracking the development trend of surface shallow creep through regular repeated testing, providing a scientific basis for geological disaster warning. Therefore, TR technology is a very potential technical means in the field of modern geophysical exploration.

[0043] Preferably, the computing device can preprocess the second signal before time reversing the second signal to obtain the third signal, the preprocessing including mean filtering and time alignment; and then time reversing the preprocessed second signal to obtain the third signal.

[0044] Among them, mean filtering is a commonly used linear smoothing filtering method, mainly used to eliminate noise in signals or images, and the computing device can replace the value of the center data point by calculating the average value of the data points in the local area, thereby achieving the effect of smoothing.

[0045] Among them, the computing device can obtain the time stamp corresponding to each sensor data, and then align the second signals collected by each sensor in the sensor array according to the time stamps.

[0046] Among them, the computing device can construct a time reversal operator for the preprocessed second signal, that is, define a mathematical rule of time reversal; and then process the second signal based on the time reversal operator, so that the time axis of the second signal is reversed to obtain the third signal of the forward time axis.

[0047] Step 105, the computing device simulates the signal propagation path of the antenna sending the third signal to the surface shallow layer based on the simulation model to obtain target time reversal data.

[0048] Among them, the computing device can simulate the signal propagation path of the third signal when the antenna sends the third signal to the surface shallow layer based on the simulation model of the surface shallow layer, combined with the position and orientation corresponding to the antenna arranged on site.

[0049] Due to the time reversal invariance of the wave equation, the third signal is automatically focused on the original source of the second signal. By simulating the propagation path of the third signal, the position and depth of each secondary wave source in the surface shallow layer can be obtained.

[0050] The process aims to reconstruct the original signal path and identify the changes in medium properties caused by stratum creep. Traditional signal processing methods cannot distinguish very close surface shallow creep, while time reversal technology can distinguish them by analyzing their electromagnetic wave responses.

[0051] The conventional method is sensitive to soil moisture changes. Since the water content in the soil affects the propagation speed of electromagnetic waves, the conventional signal processing method can be affected by the change of soil moisture, thereby affecting the identification of shallow surface creep. The time reversal technique is relatively not affected by the change of soil moisture, because the time reversal principle based on wave equation is not affected by the change of soil moisture.

[0052] Step 106, the computing device compares the target time reversal data and the historical time reversal data to obtain the creep abnormal area in the preset depth range.

[0053] Among them, the computing device can use signal processing software to compare and analyze the received target time reversal data and historical time reversal data to find out the creep abnormal area that has changed. Combined with the multi-physical field data obtained in step 101, a scientific judgment can be made on the possible creep phenomenon and its influence range.

[0054] The time reversal image generated by the target time reversal data can improve the image quality and make the shallow surface creep more clearly visible. Combined with the historical time reversal image display, it is beneficial to research and manage geological disasters.

[0055] Among them, the historical time reversal data is also obtained based on the same antenna sending the third signal.

[0056] Preferably, after obtaining the creep abnormal area in the preset depth range, the method further comprises: based on the target time reversal data and the historical time reversal data, calculating the creep parameters of the creep abnormal area, the creep parameters including one or more of creep rate, cumulative displacement amount, strain direction angle and creep activity index.

[0057] Among them, the accuracy of the creep rate is 0.01 mm / day.

[0058] Among them, the computing device can convert the linear strain to the deformation displacement by using Hooke's law: , where L is the effective length of the creep abnormal area in the strain direction (determined by the analysis result, such as the extension length of the creep direction); then the displacement sequence {uᵢ} is fitted by using the linear least squares method to obtain the displacement-time function u(t)=k×t+b, where the slope k is the instantaneous displacement rate (unit: mm / d); then the rate values of the continuous 3 detection periods are averaged to obtain the final creep rate: v= / 3. The computing device can control the sampling frequency (0.1-1 Hz) and the fitting window length (not less than 72 hours) of the sensor to ensure that the calculation accuracy reaches ±0.01 mm / d.

[0059] Wherein, for the cumulative displacement in three-dimensional space, the components of x, y, z directions are calculated respectively, and then the size and direction of the total cumulative displacement are obtained by vector composition.

[0060] Wherein, the computing device can calculate the creep strain field distribution of the creep abnormal area based on the simulation model; then solve the eigenvalues and eigenvectors of the strain tensor to obtain the principal strain direction; the strain direction angle is defined as the included angle between the maximum principal strain direction and the horizontal reference direction.

[0061] Wherein, the computing device can obtain the historical creep rate sequence of the creep abnormal area based on the historical inversion data, and calculate the mean value of the historical creep rate; then define that the greater the difference between the current creep rate and the mean value of the historical creep rate, the higher the creep activity index, and vice versa. Preferably, the computing device can also introduce a formation stability coefficient K to correct the creep activity index. For example, when the creep abnormal area is in a complete rock formation, K is 0.8, and when it is in a loose soil layer, K is 0.3.

[0062] Preferably, after calculating the creep parameters of the creep abnormal area, the method further comprises: importing the creep area and the creep parameters into a three-dimensional visualization platform to generate a dynamic creep cloud chart; wherein the spatial resolution of the dynamic creep cloud chart is not less than 0.5 meters.

[0063] In specific applications, the core of TR technology is to use a ground-based synthetic aperture radar (GB-InSAR) system to monitor the target area with high precision and continuity. The radar system is fixedly installed in a safe position close to the monitoring area, regularly transmits signals and receives reflected echoes from the ground. By comparing radar images obtained at different times and processing multi-temporal data, millimeter-level or even sub-millimeter-level displacement change information of the ground surface can be extracted.

[0064] This method is suitable for monitoring the slow and continuous ground surface creep process. Since ground surface creep often manifests as slow sliding of slopes or soil bodies along the direction of gravity, its deformation rate is usually very low, and traditional monitoring methods are difficult to capture in time. TR technology has high temporal and spatial resolution and wide coverage, which can effectively identify the displacement trend of each point on the ground surface, including horizontal and vertical changes, and draw detailed deformation maps accordingly.

[0065] Furthermore, in practical engineering and disaster prevention and control, the TR system can be used in conjunction with other monitoring methods (such as GNSS, inclinometers, crack gauges, etc.) to form a comprehensive monitoring network that integrates multi-source data, thereby improving the reliability and accuracy of monitoring results. By analyzing the extent, rate, and evolution trend of surface creep, technicians can assess the stability of potential risk areas, establish corresponding early warning models, and issue warnings in advance, providing scientific basis and technical support for relevant departments to formulate emergency response plans, implement personnel evacuation, or take protective measures.

[0066] Therefore, TR technology not only improves the efficiency and accuracy of geological disaster monitoring, but also plays a key role in the early warning and risk management of geological disasters such as landslides, ground subsidence, and slope instability.

[0067] In this embodiment, by employing time reversal technology and utilizing the time reversibility of the wave equation, the complexity of the wave propagation path is automatically compensated. Combined with the characteristic of the time-reversed signal self-consistently focusing at the real source point, the positioning accuracy of shallow surface creep detection is improved.

[0068] The method provided in the embodiments of this application has been described above. The system provided in the embodiments of this application will be described below.

[0069] Please see Figure 2 , Figure 2 A schematic diagram of a shallow surface creep detection system based on time-reversal technology provided in this application embodiment is shown below. Figure 2 As shown, the system 20 includes: The acquisition module 201 is used to acquire multi-source physical field data of the shallow surface of the target area. The multi-source physical field data includes data on underground structure, groundwater level and physical properties of underground medium within a preset depth range. Modeling module 202 is used to establish a simulation model of the shallow surface layer based on the multi-source physical field data; The signal receiving and transmitting module 203 is used to send a first signal to the shallow surface layer through an antenna and to receive a second signal returned by the shallow surface layer based on the first signal. The time reversal module 204 is used to reverse the time of the second signal to obtain the third signal; Simulation module 205 is used to simulate the signal propagation path of the antenna sending the third signal to the shallow surface layer based on the simulation model, and obtain target time inversion data; The comparison module 206 is used to compare the target time-inversion data with historical time-inversion data to obtain the creep anomaly area within the preset depth range.

[0070] Preferably, the comparison module 206 is further used to calculate the creep parameters of the creep anomaly region based on the target time inversion data and the historical time inversion data. The creep parameters include one or more of the following: creep rate, cumulative displacement, strain direction angle, and creep activity index.

[0071] Preferably, the comparison module 206 is also used to import the creep region and the creep parameter into a three-dimensional visualization platform to generate a dynamic creep cloud map; wherein the spatial resolution of the dynamic creep cloud map is not less than 0.5 meters.

[0072] Preferably, before transmitting a first signal to the shallow subsurface via an antenna and receiving a second signal returned by the shallow subsurface based on the first signal, a ground-based synthetic aperture radar and a sensor array are deployed on the surface of the target area; the acquisition module 201 is specifically used to transmit the first signal to the shallow subsurface via the antenna of the ground-based synthetic aperture radar and to receive the second signal via the sensor array.

[0073] Preferably, the sensor array includes irregularly distributed three-component seismic detectors on the ground surface, or distributed fiber optic acoustic sensors in the well; the aperture of the sensor array is greater than 1.5 times the preset depth range to ensure spatial resolution.

[0074] Preferably, the time reversal module 204 is further used to preprocess the second signal, the preprocessing including mean filtering and time alignment; and to reverse the time of the preprocessed second signal to obtain the third signal.

[0075] Preferably, the target time-inversion data includes the coordinates of the creep anomaly region; the simulation module 205 is also used to generate the theoretical wave field signal reflected by the creep anomaly region in the simulation model based on the coordinates of the creep anomaly region in the initial target time-inversion data; calculate the residual signal between the theoretical wave field signal and the actual acquired signal; and repeat the time-inversion process using the residual signal as the second signal until the coordinates of the creep anomaly region converge.

[0076] The surface shallow creep detection system based on time-reversal technology provided in this application can be understood by referring to the relevant content in the foregoing method embodiment section, and will not be repeated here.

[0077] like Figure 3 As shown, Figure 3 This is a schematic diagram of a possible logical structure of a computing device provided in an embodiment of this application. The computing device 30 includes a processor 301, a communication interface 302, a memory 303, and a bus 304. The processor 301, the communication interface 302, and the memory 303 are interconnected via the bus 304. In an embodiment of this application, the processor 301 is used to control and manage the operation of the computing device 30. For example, the processor 301 is used to execute...Figure 1 The steps in the embodiments and / or other processes used in the techniques described herein. Communication interface 302 is used to support communication by computing device 30. Memory 303 is used to store program code and data of computing device 30.

[0078] The processor 301 can be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a digital signal processor and a microprocessor, etc. The bus 304 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0079] In another embodiment of this application, a computer-readable storage medium is also provided, the computer-readable storage medium including instructions that, when executed on a computer, cause the computer to perform the above-described... Figure 1 The method described in the embodiments.

[0080] Those skilled in the art will recognize that the units of the various examples described in connection with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of the invention.

[0081] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0082] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0083] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0084] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0085] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0086] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for detecting shallow surface creep based on time-reversal technology, characterized in that, The method includes: Acquire multi-source physical field data of the shallow surface layer of the target area, including data on underground structure, groundwater level and physical properties of underground medium within a preset depth range; A simulation model of the shallow Earth surface layer is established based on the multi-source physical field data. A first signal is transmitted to the shallow surface layer via an antenna, and a second signal is received from the shallow surface layer based on the first signal. The second signal is inverted in time to obtain the third signal; Based on the simulation model, the signal propagation path of the antenna sending the third signal to the shallow surface is simulated to obtain target time inversion data. By comparing the target time-inversion data with the historical time-inversion data, the creep anomaly region within the preset depth range is obtained.

2. The method according to claim 1, characterized in that, After obtaining the creep anomaly region within the preset depth range, the method further includes: Based on the target time inversion data and the historical time inversion data, the creep parameters of the creep anomaly region are calculated. The creep parameters include one or more of the following: creep rate, cumulative displacement, strain direction angle, and creep activity index.

3. The method according to claim 2, characterized in that, After calculating the creep parameters of the creep anomaly region, the method further includes: The creep region and creep parameters are imported into a three-dimensional visualization platform to generate a dynamic creep cloud map; wherein the spatial resolution of the dynamic creep cloud map is not less than 0.5 meters.

4. The method according to claim 1, characterized in that, Before transmitting a first signal to the shallow subsurface via an antenna and receiving a second signal returned by the shallow subsurface based on the first signal, the method further includes: A ground-based synthetic aperture radar and sensor array are deployed on the surface of the target area. The step of transmitting a first signal to the shallow subsurface via an antenna and receiving a second signal returned by the shallow subsurface based on the first signal includes: The first signal is transmitted to the shallow surface layer through the antenna of the ground synthetic aperture radar; The second signal is received through the sensor array.

5. The method according to claim 4, characterized in that, The sensor array includes irregularly deployed three-component seismic detectors on the ground surface, or distributed fiber optic acoustic sensors in wells; the aperture of the sensor array is greater than 1.5 times the preset depth range to ensure spatial resolution.

6. The method according to claim 1, characterized in that, Before inverting the second signal to obtain the third signal, the method further includes: The second signal is preprocessed, including mean filtering and time alignment; The step of time-reversing the second signal to obtain the third signal includes: The preprocessed second signal is time-reversed to obtain the third signal.

7. A shallow surface creep detection system based on time-reversal technology, characterized in that, The system, applied to the method of any one of claims 1-6, comprises: The acquisition module is used to acquire multi-source physical field data of the shallow surface of the target area. The multi-source physical field data includes data on underground structure, groundwater level and physical properties of underground medium within a preset depth range. The modeling module is used to establish a simulation model of the shallow surface layer based on the multi-source physical field data; The signal receiving and transmitting module is used to send a first signal to the shallow surface layer via an antenna and to receive a second signal returned by the shallow surface layer based on the first signal. The time reversal module is used to reverse the time of the second signal to obtain the third signal; The simulation module is used to simulate the signal propagation path of the antenna transmitting the third signal to the shallow surface layer based on the simulation model, and obtain target time inversion data; The comparison module is used to compare the target time-inversion data with the historical time-inversion data to obtain the creep anomaly region within the preset depth range.

8. The system according to claim 7, characterized in that, The comparison module is also used to calculate the creep parameters of the creep anomaly region based on the target time inversion data and the historical time inversion data. The creep parameters include one or more of the following: creep rate, cumulative displacement, strain direction angle, and creep activity index.

9. A computing device, characterized in that, include: Memory, used to store programs; A processor for loading the program to perform the method as described in any one of claims 1-6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the method of any one of claims 1-6.

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