Method, device, storage medium and electronic device for estimating thickness of active layer

Abstract

The present application provides a method, device, storage medium and electronic device for estimating the thickness of the active layer, which relates to the geological field. Among them, the electronic device acquires the sample environmental parameters and the sample radar wave velocity at the sample location; generates a wave velocity prediction model according to the sample environmental parameters and the sample radar wave velocity; acquires the target environmental parameters at the target location; processes the target environmental parameters through the wave velocity prediction model to obtain the target radar wave velocity; and obtains the target thickness of the active layer at the target location according to the target radar wave velocity. In this way, since the wave velocity prediction model is generated based on a large amount of data, more accurate radar wave velocity can be obtained, and then a more accurate thickness of the active layer can be obtained.

Description

Technical Field

[0001] The present application relates to the field of geology, and more particularly, to a method, apparatus, storage medium, and electronic device for estimating the thickness of an active layer. Background Art

[0002] Permafrost refers to the rock and soil layer that remains at a negative temperature for two or more consecutive years within a certain thickness below the ground surface. In the permafrost region, within a certain thickness below the ground surface, summer melting and winter freezing occur, and this layer of soil is called the active layer. The active layer is the area where the heat and moisture exchange is most intense in the land surface process of the permafrost region. The thickness of the active layer refers to the distance from the ground surface to the maximum seasonal melting thickness (the bottom plate of the active layer), which is a key parameter affecting the hydrology, ecology, and engineering safety in the permafrost region.

[0003] Therefore, in the related art, it is proposed to use ground penetrating radar to detect the thickness of the active layer, and the setting of the radar wave velocity is of great significance for the accuracy of the active layer thickness. Since the radar wave velocity is affected by various factors, at present, the radar wave velocity is mostly determined based on expert experience, which results in a certain empirical error in the active layer thickness. Summary of the Invention

[0004] In order to overcome at least one deficiency in the prior art, the present application provides a method, apparatus, storage medium, and electronic device for estimating the thickness of an active layer, which is used to improve the accuracy of the thickness when determining the thickness of the active layer. Specifically, it includes:

[0005] In a first aspect, the present application provides a method for estimating the thickness of an active layer, the method including:

[0006] Obtaining sample environmental parameters and sample radar wave velocity at a sample location;

[0007] Generating a wave velocity prediction model according to the sample environmental parameters and the sample radar wave velocity;

[0008] Obtaining target environmental parameters at a target location;

[0009] Processing the target environmental parameters through the wave velocity prediction model to obtain a target radar wave velocity;

[0010] Obtaining the target thickness of the active layer at the target location according to the target radar wave velocity.

[0011] In a second aspect, the present application provides an apparatus for estimating the thickness of an active layer, the apparatus including:

[0012] A model generation module, configured to obtain sample environmental parameters and sample radar wave velocity at a sample location; generate a wave velocity prediction model according to the sample environmental parameters and the sample radar wave velocity;

[0013] A wave velocity prediction module, configured to obtain target environmental parameters of a target location; process the target environmental parameters through the wave velocity prediction model to obtain a target radar wave velocity.

[0014] A thickness estimation module, configured to obtain a target thickness of an active layer at the target location according to the target radar wave velocity.

[0015] In a third aspect, the present application provides a storage medium storing a computer program, which when executed by a processor, implements the active layer thickness estimation method described above.

[0016] In a fourth aspect, the present application provides an electronic device, which includes a processor and a memory, the memory stores a computer program, and when the computer program is executed by the processor, the active layer thickness estimation method described above is implemented.

[0017] Compared with the prior art, the present application has the following beneficial effects:

[0018] In the active layer thickness estimation method, device, storage medium and electronic device provided by the present application, the electronic device obtains sample environmental parameters and a sample radar wave velocity of a sample location; generates a wave velocity prediction model according to the sample environmental parameters and the sample radar wave velocity; obtains target environmental parameters of a target location; processes the target environmental parameters through the wave velocity prediction model to obtain a target radar wave velocity; and obtains a target thickness of an active layer at the target location according to the target radar wave velocity. Thus, since the wave velocity prediction model is generated based on a large amount of data, a more accurate radar wave velocity can be obtained, and then a more accurate active layer thickness can be obtained. Description of the Drawings

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings required in the embodiments. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0020] Figure 1 It is a schematic diagram showing the relationship between the active layer thickness and altitude provided by the embodiment of the present application;

[0021] Figure 2 It is a flowchart of the active layer thickness estimation method provided by the embodiment of the present application;

[0022] Figure 3 It is a schematic diagram showing the comparison of each method provided by the embodiment of the present application;

[0023] Figure 4Schematic structural diagram of the active layer thickness estimation device provided by the embodiment of the present application;

[0024] Figure 5 Schematic structural diagram of the electronic device provided by the embodiment of the present application.

[0025] Icons: 101 - Model generation module; 102 - Wave velocity prediction module; 103 - Thickness estimation module; 201 - Memory; 202 - Processor; 203 - Communication unit; 204 - System bus. Specific embodiments

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. Components of the embodiments of the present application usually described and illustrated in the accompanying drawings here can be arranged and designed in various different configurations.

[0027] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application claimed, but merely represents selected embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts fall within the scope of protection of the present application.

[0028] It should be noted that like reference numerals and letters denote like items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0029] In the description of the present application, it should be noted that the terms "first", "second", "third", etc. are only used for descriptive distinction and cannot be understood as indicating or implying relative importance. In addition, the terms "include", "comprise", or any other variant thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or device including a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or device. Without further limitation, an element defined by the phrase "including a..." does not exclude the presence of additional identical elements in the process, method, article, or device including the said element.

[0030] As the buffer layer between permafrost and the atmosphere, the active layer is relatively sensitive to climate change. With the warming of the climate, the active layer in permafrost regions generally shows a thickening trend to varying degrees. From the perspective of cold region engineering, the thickness of the active layer plays an important role in the stability of the foundation. Therefore, the thickness of the active layer is one of the important geological parameters that need to be investigated and mastered before construction in cold regions.

[0031] The existing investigations of the active layer thickness can be classified into two categories: destructive detection methods and non-destructive detection methods according to the implementation methods and the degree of damage to the surface environment. These investigations are generally carried out in the season when the active layer thickness is the largest (usually from the end of August to the beginning of November). In addition, some soil physical property parameters can also be obtained in engineering geological investigations, and the thickness of the active layer can be estimated using model methods.

[0032] Among them, destructive investigations include pit exploration, drilling, and penetration testing, etc. The pit exploration and drilling methods can determine the thickness of the active layer by directly observing the stratigraphic profile or core, or by installing a probe to monitor the continuity of the internal thermal conditions of the active layer, and using the method of drawing geothermal isotherm maps to extract the maximum seasonal melting depth within a year. This is currently the most accurate way to obtain the active layer thickness. The physical penetration testing method uses a probe or steel bar to insert into the soil layer for detection, and is only applicable to the strata with extremely thin active layer, soft soil, and low gravel content.

[0033] Although the destructive method has the best accuracy, it usually requires site excavation and drilling construction, which causes great damage to the original strata and the surrounding environment, consumes high labor and material costs, and is only applicable to single-point detection. The geophysical exploration method belongs to an indirect way to obtain the formation characteristics. To detect the active layer thickness, it needs to be used in combination with pit exploration and drilling methods, and is carried out in a man-machine interaction way. The geophysical exploration data has multiple solutions, and the interpretation accuracy is restricted by the degree of understanding of the formation information, the knowledge level and experience of the interpreters, and the error is relatively large. The advantage is that the construction method is flexible and the detection efficiency is high. It can not only carry out point detection, but also carry out line and surface detection.

[0034] The non-destructive investigation mainly uses geophysical methods such as electrical method, electromagnetic method, and seismic wave method to detect and interpret the active layer to obtain the formation structure information of the active layer. Among them, the electromagnetic method includes using ground penetrating radar to detect the thickness of the permafrost layer by the profile method or wide-angle method.

[0035] The profile method is suitable for the identification of continuously distributed and horizontally layered targets, and is often used for the identification of the bottom plate of the active layer of long-distance survey lines (the survey line length is from hundreds of meters to several kilometers), and can also be used for the site scale (the survey line length is usually from several meters to dozens of meters). As Figure 1 shown, it shows the change of the active layer with altitude in a certain place. It can be seen that the higher the altitude, the greater the thickness of the active layer.

[0036] The wide - angle method is applicable to the recognition of the morphology of point targets, etc., and is often used to measure the propagation speed of ground - penetrating radar electromagnetic waves in the active layer. During the investigation of the thickness of the active layer in permafrost regions, the two methods are generally used in combination. For the detection of the thickness of the active layer at a larger scale (regional scale), the profile method is often more applicable because more data can be obtained within a limited fieldwork time.

[0037] However, it should be understood that after the ground - penetrating radar obtains data in the field, some processing and interpretation processes are required. Among them, the setting of the radar wave speed has an important impact on the target depth, affecting the accuracy of the interpreted depth of the active layer thickness, and thus making it difficult to accurately understand its distribution law. For regional - scale detection, the wave speed has significant spatial heterogeneity along a long survey line, that is, the radar wave speed is different at different positions of the survey line. A more reasonable approach should be to measure the speed at different positions of the survey line to ensure the accuracy of the depth of the bottom plate of the active layer along the entire survey line. However, in the survey work, this wave - speed measurement method is too inefficient to implement. Therefore, the common implementation methods are either to only conduct measurements at the site scale or to use a semi - empirical method for speed assignment, that is, to assign speeds to the radar profiles using limited regional speed measurements or referring to soil texture based on expert experience.

[0038] However, assigning speeds to radar profiles based on expert experience is easily restricted by expert experience and introduces empirical errors, especially when some experts lack experience.

[0039] The research further found that in related technologies, the thickness of the active layer can also be estimated through different types of models, such as empirical models, equilibrium models, numerical models, etc. Among them, empirical statistical models often require a large amount of field - measured data of the active layer thickness as a driving force. When using equilibrium models and numerical models to estimate the thickness of the active layer, it is difficult to obtain model physical property parameters, and the simulation effect depends on the accuracy of the driving data and parameter settings. Moreover, most models require on - site - measured active layer data for parameter calibration and verification. Therefore, obtaining accurate information on the thickness of the active layer on - site as reference data as much as possible is the key to improving the simulation level of the active layer thickness and understanding its laws.

[0040] Similarly, in the method for estimating the thickness of the active layer provided in this embodiment, a more accurate radar wave speed is estimated through a wave - speed prediction model, thereby improving the accuracy of the active layer thickness. Among them, the electronic device implementing this method can be, but is not limited to, a mobile terminal, a tablet computer, a laptop computer, a server, etc.

[0041] When it is a server, the server can be a single server or a server group. The server group can be centralized or distributed (for example, the server can be a distributed system). In some embodiments, the server can be local or remote relative to the user terminal. In some embodiments, the server can be implemented on a cloud platform; by way of example only, the cloud platform can include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an inter-cloud, a multi-cloud, etc., or any combination thereof. In some embodiments, the server can be implemented on an electronic device having one or more components.

[0042] Based on the above related introduction, the following will combine Figure 1 to introduce in detail the method for estimating the thickness of the active layer provided in this embodiment. However, it should be understood that the operations in the flowchart may not be implemented in sequence, and steps without a logical context relationship can be reversed or implemented simultaneously. In addition, those skilled in the art can add one or more other operations to the flowchart or remove one or more operations from the flowchart under the guidance of the content of this application. As Figure 2 shown, the method includes:

[0043] S101, obtain the sample environmental parameters and the sample radar wave velocity at the sample position.

[0044] S102, generate a wave velocity prediction model according to the sample environmental parameters and the sample radar wave velocity.

[0045] In this embodiment, the sample environmental parameters and the sample radar wave velocity are subjected to function fitting to obtain a wave velocity prediction model. Among them, the expression of the wave velocity prediction model is:

[0046] y = a1·x1 + a2·x2 + a3·x3 + … + a n ·x n + b

[0047] In the formula, x n represents the nth parameter in the target environmental parameters, a n represents the coefficient of x n , b represents the bias coefficient, and y represents the target radar wave velocity.

[0048] Since the wave velocity of ground penetrating radar is mainly related to factors such as soil texture and water content, data on the composition of powder, clay, and sand grains in the soil at the velocity measurement points, as well as data on the surface vegetation conditions and ground temperature conditions affecting the soil structure, etc., are mainly collected. The sample environmental parameters collected in this embodiment include indicators such as soil sand content, clay content, and maximum composite normalized vegetation index (NDVI, Normalized Difference Vegetation Index).

[0049] Exemplarily, a certain ground penetrating radar profile in the permafrost area within Xinghai County in the northeastern part of the Qinghai-Tibet Plateau is selected as the research object. The starting point of this radar profile has an altitude of 4227m, the ending point has an altitude of 4142m, the elevation difference is less than 100m, and the length is about 750. The vegetation near the starting point is alpine meadow with a high vegetation coverage. Near the ending point, it is near the foot of the slope, close to the riverbed, and there is a free face about 1m high, and the vegetation type is degraded meadow.

[0050] Environmental parameters such as the terrain humidity index, soil clay content, soil sand content, soil organic matter density, annual average surface temperature, annual average precipitation, annual median composite normalized humidity index (NDMI, Normalized Difference Moisture Index), and annual maximum composite normalized vegetation index are collected at each sample position of this profile. Then, using the multiple linear regression optimal subset method and through the method of five-fold cross-validation, feature screening is carried out to screen the feature combination with the smallest mean square error loss (MSE, Mean Square Error).

[0051] The screening results show that the combination of soil sand content, soil clay content, and normalized vegetation index can obtain the optimal solution; the corresponding wave velocity prediction model is:

[0052] y = 0.048499·Snd - 0.288445·Cly - 0.05458·NDVI + 0.12504

[0053] Among them, Snd represents the soil sand content, Cly represents the soil clay content, and NDVI represents the normalized vegetation index. The regression determination coefficient R2 of this model is 0.689, and the root mean square error RMSE is 0.012m / ns.

[0054] Based on the above introduction of the wave velocity prediction model, continue to participate in Figure 2 , this method further includes:

[0055] S103, obtaining the target environmental parameters of the target position.

[0056] Among them, in the optional implementation manner, environmental indicators such as the soil sand content, clay content, and normalized vegetation index corresponding to the target location can be extracted through a Geographic Information System (GIS) platform.

[0057] S104. Process the target environmental parameters through the wave velocity prediction model to obtain the target radar wave velocity.

[0058] In this way, based on the wave velocity prediction model fitted by the above large amount of sample environmental data, the target radar wave velocity of the target location is obtained.

[0059] S105. Obtain the target thickness of the active layer at the target location according to the target radar wave velocity.

[0060] Considering that when using a ground penetrating radar to detect the active layer, a supporting processing tool is often provided to analyze the reflected wave of the ground penetrating radar and calculate the empirical thickness of the active layer based on the empirical radar wave velocity input by the user. Among them, the empirical radar wave velocity can be 0.1 m / ns. Therefore, in this embodiment, the empirical thickness is further corrected by the target radar wave velocity. That is, the specific implementation manner of step S105 includes:

[0061] S105-1. Obtain the empirical radar wave velocity of the target location.

[0062] S105-2. Obtain the empirical thickness of the active layer at the target location according to the empirical radar wave velocity.

[0063] In the specific implementation manner, the electronic device can obtain the radar reflection signal of the target location; then, extract the target reflection signal of the active layer from the reflection signal.

[0064] Among them, when extracting the target reflection information signal, the electronic device can preprocess the reflection signal to obtain a preprocessing signal, where the clarity of the preprocessing signal is higher than that of the reflection signal.

[0065] The preprocessing methods include at least one of the following:

[0066] (1) Subtract the average value of the signal saturation correction;

[0067] (2) Static correction for adjusting the time zero position;

[0068] (3) Signal for deep signal enhancement;

[0069] (4) Background removal for horizontal signal filtering;

[0070] (5) Band-pass filtering for removing specific frequency bands;

[0071] (6) Sliding average for filtering high-frequency noise signals;

[0072] (7) Terrain correction.

[0073] After preprocessing the radar reflection signal in these ways, the clarity of the ground penetrating radar signal is greatly enhanced, which facilitates the extraction of the reflection signal from the active layer bottom plate.

[0074] Since the bottom plate signal of the horizontal active layer generally presents a continuous, high-amplitude, in-phase reflection axis and a signal characteristic that follows the changes in terrain, the electronic equipment extracts the target reflection signal from the pre-processed signal based on the signal characteristics generated by the active layer; and obtains the empirical thickness of the active layer at the target position based on the target reflection signal and the empirical radar wave velocity.

[0075] S105-3, correcting the empirical thickness by the target radar wave velocity to obtain the target thickness.

[0076] Among them, the relationship between the target radar wave velocity, empirical radar wave velocity, empirical thickness and target thickness satisfies:

[0077]

[0078] Among them, Ap represents the target thickness, Vp represents the target radar wave velocity, Vg represents the empirical radar wave velocity, and Ag represents the empirical thickness.

[0079] The actual field verification shows that the active layer thickness obtained by the active layer thickness estimation method provided in this embodiment reflects the thickening characteristics of the active layer thickness caused by the decrease in temperature after the altitude decreases, and also reflects the superposition effect of the thickening of the active layer due to lateral thermal erosion and vegetation degradation at the foot of the slope. It can reflect the high spatial heterogeneity of the active layer thickness in discontinuous permafrost areas and conforms to the objective law of the active layer thickness distribution. Therefore, if Figure 3 As shown, compared with the existing methods for determining the thickness of the active layer (empirical wave velocity, average wave velocity), the consistency with the dynamic wave velocity method is better when the active layer is thin, but there is a large error when the active layer at the tail of the profile is thick. Therefore, the thickness determined by the target wave velocity estimated in this embodiment is more accurate.

[0080] The above is an introduction to the method for estimating the thickness of an active layer. Under the same inventive concept, this embodiment also provides an active layer thickness estimation device. Among them, the active layer thickness estimation device includes at least one software function module that can be stored in a memory in the form of software or solidified in the operating system (Operating System, referred to as OS) of the electronic device. The processor in the electronic device is used to execute the executable module stored in the memory. For example, the software function modules and computer programs included in the active layer thickness estimation device. Please refer toFigure 4 , functionally divided, the active layer thickness estimation device may include:

[0081] A model generation module 101, configured to obtain sample environmental parameters and sample radar wave velocities at sample positions; and generate a wave velocity prediction model according to the sample environmental parameters and the sample radar wave velocities.

[0082] In this embodiment, the model generation module 101 is used to implement Figure 2 Steps S101 and S102 in. For a detailed description of the model generation module 101, reference can be made to the detailed introduction of steps S101 and S102.

[0083] A wave velocity prediction module 102, configured to obtain target environmental parameters at a target position; and process the target environmental parameters through the wave velocity prediction model to obtain a target radar wave velocity.

[0084] In this embodiment, the wave velocity prediction module 102 is used to implement Figure 2 Steps S103 and S104 in. For a detailed description of the wave velocity prediction module 102, reference can be made to the detailed introduction of steps S103 and S104.

[0085] A thickness estimation module 103, configured to obtain the target thickness of the active layer at the target position according to the target radar wave velocity.

[0086] In this embodiment, the thickness estimation module 103 is used to implement Figure 2 Step S105 in. For a detailed description of the thickness estimation module 103, reference can be made to the detailed introduction of step S105.

[0087] It should be noted that since the model generation module 101, the wave velocity prediction module 102, and the thickness estimation module 103 have the same inventive concept as the active layer thickness estimation method, they can also be used to implement other steps or sub-steps of the method. In this regard, this embodiment does not make specific limitations.

[0088] In addition, in each embodiment of the present application, the functional modules can be integrated together to form an independent part, or each module can exist alone, or two or more modules can be integrated to form an independent part.

[0089] It should also be understood that if the above embodiments are implemented in the form of software functional modules 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 application, in essence, or the part that contributes to the prior art, or a part of this 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 for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present application.

[0090] Therefore, this embodiment also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the method for estimating the thickness of the active layer provided in this embodiment is implemented. Among them, the computer-readable storage medium can be various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk, or an optical disc.

[0091] Please refer to Figure 5 , this embodiment also provides an electronic device. The electronic device includes a processor 202 and a memory 201. The processor 202 and the memory 201 can communicate via a system bus 204. And, the memory 201 stores a computer program. The processor realizes the method for estimating the thickness of the active layer provided in this embodiment by reading and executing the computer program corresponding to the above embodiments in the memory 201.

[0092] Continue to refer to Figure 5 , in some embodiments, the electronic device may further include a communication unit 203. Each element of the memory 201, the processor 202, and the communication unit 203 is directly or indirectly electrically connected to each other through the system bus 204 to achieve data transmission or interaction.

[0093] Among them, the memory 201 can be an information recording device based on any electronic, magnetic, optical, or other physical principles, and is used to record execution instructions, data, etc. In some embodiments, the memory 201 can be, but is not limited to, a volatile memory, a non-volatile memory, a storage drive, etc.

[0094] Among them, by way of example only, the volatile memory may be a Random Access Memory (RAM). The non-volatile memory may be a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), an Electric Erasable Programmable Read-Only Memory (EEPROM), a flash memory, etc.; the storage drive may be a disk drive, a solid state drive, any type of storage disk (such as an optical disk, a DVD, etc.), or a similar storage medium, or a combination thereof, etc.

[0095] The communication unit 203 is configured to send and receive data through a network. In some embodiments, the network may include a wired network, a wireless network, an optical fiber network, a telecommunication network, an intranet, the Internet, a Local Area Network (LAN), a Wide Area Network (WAN), a Wireless Local Area Networks (WLAN), a Metropolitan Area Network (MAN), a Wide Area Network (WAN), a Public Switched Telephone Network (PSTN), a Bluetooth network, a ZigBee network, or a Near Field Communication (NFC) network, etc., or any combination thereof. In some embodiments, the network may include one or more network access points. For example, the network may include a wired or wireless network access point, such as a base station and / or a network switching node, and one or more components of the service request processing system may be connected to the network through the access point to exchange data and / or information.

[0096] The processor 202 may be an integrated circuit chip with signal processing capabilities, and the processor may include one or more processing cores (e.g., a single-core processor or a multi-core processor). By way of example only, the above-mentioned processor may include a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), an Application Specific Instruction-set Processor (ASIP), a Graphics Processing Unit (GPU), a Physics Processing Unit (PPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), a controller, a microcontroller unit, a Reduced Instruction Set Computing (RISC), or a microprocessor, etc., or any combination thereof.

[0097] Based on the above introduction, it should be understood that the devices and methods disclosed in the above embodiments can also be implemented in other ways. The device embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the drawings show the possible architectures, functions, and operations of devices, methods, and computer program products according to multiple embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0098] As described above, these are only various embodiments of the present application. However, the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for estimating the thickness of an active layer, characterized in that The method includes: Obtaining sample environmental parameters and sample radar wave velocity at a sample location, where the sample environmental parameters include soil sand content, soil clay content, and normalized difference vegetation index. The sample environmental parameters are subjected to feature screening using the multiple linear regression optimal subset method and through five-fold cross-validation to screen the feature combination with the minimum average mean square error loss. Performing function fitting on the sample environmental parameters and the sample radar wave velocity to obtain a wave velocity prediction model, and the expression of the wave velocity prediction model is: Among them, represents the soil sand content, represents the soil clay content, represents the normalized difference vegetation index; Obtaining target environmental parameters at a target location. Processing the target environmental parameters through the wave velocity prediction model to obtain a target radar wave velocity. Obtaining the empirical radar wave velocity at the target location. Obtaining the empirical thickness of the active layer at the target location based on the empirical radar wave velocity. Correcting the empirical thickness through the target radar wave velocity to obtain a target thickness, where the relationship among the target radar wave velocity, the empirical radar wave velocity, the empirical thickness, and the target thickness satisfies: Wherein, represents the target thickness, represents the target radar wave velocity, represents the empirical radar wave velocity, represents the empirical thickness.

2. The method for estimating the thickness of the active layer according to claim 1, wherein The obtaining the empirical thickness of the active layer at the target location based on the empirical radar wave velocity includes: Obtaining the radar reflection signal at the target location. Extracting the target reflection signal of the active layer from the reflection signal. Obtaining the empirical thickness of the active layer at the target location based on the target reflection signal and the empirical radar wave velocity.

3. The method for estimating the thickness of the active layer according to claim 2, wherein The extracting the target reflection signal of the active layer from the reflection signal includes: Preprocessing the reflection signal to obtain a preprocessed signal, where the clarity of the preprocessed signal is higher than that of the reflection signal. Extracting the target reflection signal from the preprocessed signal according to the signal characteristics generated by the active layer.

4. An active layer thickness estimation device, characterized in that, The device includes: A model generation module for obtaining sample environmental parameters and sample radar wave velocity at a sample location, where the sample environmental parameters include soil sand content, soil clay content, and normalized difference vegetation index. The sample environmental parameters are subjected to feature screening using the multiple linear regression optimal subset method and through five-fold cross-validation to screen the feature combination with the minimum average mean square error loss. Performing function fitting on the sample environmental parameters and the sample radar wave velocity to obtain a wave velocity prediction model, and the expression of the wave velocity prediction model is: Among them, represents the soil sand content, represents the soil clay content, represents the normalized difference vegetation index; A wave velocity prediction module for obtaining target environmental parameters at a target location; processing the target environmental parameters through the wave velocity prediction model to obtain a target radar wave velocity. A thickness estimation module for obtaining the empirical radar wave velocity at the target location. Obtaining the empirical thickness of the active layer at the target location based on the empirical radar wave velocity. Correcting the empirical thickness through the target radar wave velocity to obtain a target thickness, where the relationship among the target radar wave velocity, the empirical radar wave velocity, the empirical thickness, and the target thickness satisfies: Wherein, represents the target thickness, represents the target radar wave velocity, represents the empirical radar wave velocity, represents the empirical thickness.

5. A storage medium, characterized in that, The storage medium stores a computer program, and when the computer program is executed by a processor, it implements the active layer thickness estimation method according to any one of claims 1-3.

6. An electronic device, characterized in that, The electronic device includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, the method for estimating the thickness of the active layer described in any one of claims 1-3 is implemented.

Images