CN115930852A - Method and device for estimating thickness of active layer, storage medium and electronic device - Google Patents
Method and device for estimating thickness of active layer, storage medium and electronic device Download PDFInfo
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Abstract
The application provides an active layer thickness estimation method, an active layer thickness estimation device, a storage medium and electronic equipment, and relates to the field of geology. The electronic equipment acquires sample environment parameters of a sample position and a sample radar wave speed; generating a wave velocity prediction model according to the sample environment parameters and the sample radar wave velocity; acquiring target environment parameters of a target position; processing the target environment parameters through a wave velocity prediction model to obtain the wave velocity of a target radar; and obtaining the target thickness of the active layer at the target position according to the wave velocity of the target radar. In this way, 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 thus a more accurate thickness of the active layer can be obtained.
Description
Technical Field
The application relates to the field of geology, in particular to an active layer thickness estimation method and device, a storage medium and electronic equipment.
Background
The permafrost refers to a rock-soil layer which is buried in a certain thickness below the ground surface and is kept in a negative temperature state for two or more years. The phenomenon that the soil melts in summer and freezes in winter can occur in a certain thickness below the earth surface of the permafrost region, and the soil layer is called as an active layer. The active layer is the region with the most intense water heat exchange in the process of land surface of the permafrost region, the thickness of the active layer refers to the distance from the ground surface to the maximum season melting thickness (active layer bottom plate), and is a key parameter influencing the hydrology, ecology and engineering safety of the permafrost region.
Therefore, in the related art, it is proposed to use a ground penetrating radar to detect the thickness of the active layer, and the setting of the radar wave speed has great significance to the accuracy of the thickness of the active layer. Because the radar wave speed is influenced by various factors, the radar wave speed is determined according to expert experience at present, and certain empirical errors exist in the thickness of the active layer.
Disclosure of Invention
In order to overcome at least one of the deficiencies in the prior art, the present application provides an active layer thickness estimation method, apparatus, storage medium, and electronic device for improving the accuracy of the thickness when determining the thickness of the active layer. The method specifically comprises the following steps:
in a first aspect, the present application provides a method for estimating a thickness of an active layer, the method comprising:
acquiring sample environment parameters of a sample position and a sample radar wave speed;
generating a wave velocity prediction model according to the sample environment parameters and the sample radar wave velocity;
acquiring target environment parameters of a target position;
processing the target environment parameters through the wave velocity prediction model to obtain the wave velocity of a target radar;
and obtaining the target thickness of the active layer at the target position according to the target radar wave velocity.
In a second aspect, the present application provides an active layer thickness estimation apparatus, the apparatus comprising:
the model generation module is used for acquiring sample environment parameters of a sample position and the sample radar wave velocity; generating a wave velocity prediction model according to the sample environment parameters and the sample radar wave velocity;
the wave velocity prediction module is used for acquiring target environment parameters of a target position; processing the target environment parameters by the wave velocity prediction model to obtain the wave velocity of the target radar;
and the thickness estimation module is used for obtaining the target thickness of the active layer at the target position according to the target radar wave speed.
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.
In a fourth aspect, the present application provides an electronic device, which includes a processor and a memory, where the memory stores a computer program, and the computer program, when executed by the processor, implements the active layer thickness estimation method.
Compared with the prior art, the method has the following beneficial effects:
in the method and the device for estimating the thickness of the active layer, the storage medium and the electronic equipment, the electronic equipment acquires the sample environment parameters of the sample position and the sample radar wave velocity; generating a wave velocity prediction model according to the sample environment parameters and the sample radar wave velocity; acquiring target environment parameters of a target position; processing the target environment parameters by a wave velocity prediction model to obtain the wave velocity of a target radar; and obtaining the target thickness of the active layer at the target position according to the wave velocity of the target radar. In this way, 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 thus a more accurate thickness of the active layer can be obtained.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 is a schematic diagram illustrating a relationship between a thickness of an active layer and an altitude according to an embodiment of the present disclosure;
FIG. 2 is a schematic flowchart of a method for estimating a thickness of an active layer according to an embodiment of the present disclosure;
FIG. 3 is a schematic diagram comparing methods provided in embodiments of the present application;
FIG. 4 is a schematic structural diagram of an apparatus for estimating a thickness of an active layer according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Icon: 101-a model generation module; 102-wave velocity prediction module; 103-a thickness estimation module; 201-a memory; 202-a processor; 203-a communication unit; 204-system bus.
Detailed Description
In order to make the objects, 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 drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
In the description of the present application, it should be noted that the terms "first", "second", "third", etc. are used only for distinguishing the description, and are not intended to indicate or imply relative importance. Furthermore, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.
The active layer is used as a buffer layer between the permafrost and the atmosphere, is sensitive to climate change, and generally shows thickening trends of different degrees along with warming of climate. From the perspective of cold region engineering, the thickness of the active layer plays an important role in the stability of the foundation, and therefore the thickness of the active layer is one of important geological parameters which need to be investigated and mastered before the construction of the cold region is carried out.
The existing survey of the thickness of the active layer can be divided into two types, namely a destructive detection mode and a nondestructive detection mode according to the implementation mode and the damage degree to the earth surface environment. These surveys are typically conducted during the season when the thickness of the active layer is the greatest (typically, from 8 months to 11 months). In addition, some soil physical property parameters can be obtained in engineering geological exploration, and the thickness of the movable layer can be estimated by using a model method.
The method comprises the steps of carrying out destructive investigation on the movable layer, wherein the destructive investigation comprises a spot exploration mode, a drilling mode, a contact exploration mode and the like, wherein the thickness of the movable layer can be determined by directly observing a stratum section or a rock core, or the thermal condition continuity monitoring inside the movable layer is carried out by installing a probe, and the maximum seasonal melting depth in the year is extracted by utilizing a mode of drawing a ground temperature contour map, which is the most accurate mode for obtaining the thickness of the movable layer at present. The physical penetration mode utilizes a probe or a steel chisel and the like to be inserted into a soil layer for detection, and is only suitable for strata with extremely thin active layers, soft soil and low broken stone content.
Although the destructive mode has the best accuracy, excavation and drilling construction of a field are generally required, original strata and surrounding environments are damaged greatly, high manpower and material resource costs are consumed, and the method is only suitable for single-point detection. The geophysical detection method belongs to a mode of indirectly acquiring stratum characteristics, the detection of the thickness of a movable layer needs to be matched with a pit detection method and a drilling method for use, a man-machine interaction mode is adopted for detection, geophysical data have multiple resolvability, interpretation precision is limited by the mastering degree of stratum information and the knowledge level and experience of interpretation personnel, errors are relatively large, and the geophysical detection method has the advantages of flexible construction mode and high detection efficiency, can be used for point detection and can also be used for on-line and on-plane detection.
The nondestructive survey mainly adopts geophysical methods such as an electrical method, an electromagnetic method, a seismic wave method and the like to detect and interpret the active layer to obtain the stratum structure information of the active layer. The electromagnetic law comprises the step of detecting the thickness of the permafrost layer by using a ground penetrating radar through a profile method or a wide angle method.
The section method is suitable for the identification of continuously distributed and horizontal layered targets, is commonly used for the identification of the movable floor of a long-distance measuring line (the length of the measuring line is hundreds of meters to several kilometers), and can also be used for a field scale (the length of the measuring line is usually several meters to dozens of meters). As shown in fig. 1, the variation of the active layer of a certain place with the altitude is shown, and it can be seen that the higher the sea wave is, the larger the thickness of the active layer is.
The wide angle method is suitable for recognition of a point-like target pattern, and is often used for the propagation speed of the ground penetrating radar electromagnetic wave in the active layer. In the process of investigating the thickness of the permafrost active layer, the two methods are generally combined. For active layer thickness detection on a larger scale (area scale), profiling is often more suitable because more data can be acquired within a limited field working time.
It should be understood that after the ground penetrating radar acquires data in the field, some processing and interpretation process is required. The setting of the radar wave velocity has an important influence on the target depth, and affects the accuracy of the interpretation depth of the thickness of the active layer, so that the distribution rule of the active layer is difficult to obtain accurate knowledge. For the detection of the regional scale, the wave velocity on a long survey line has larger spatial heterogeneity, that is to say, the radar wave velocities at different positions of the survey line are different. In a reasonable mode, speed measurement should be carried out at different positions of the measuring line so as to ensure the accuracy of the depth of the bottom plate of the movable layer on the whole measuring line. However, in the investigation work, the wave velocity measurement mode is too inefficient to be implemented. Therefore, in the conventional implementation, only field scale measurement is performed, or speed assignment is performed in a semi-empirical mode, that is, speed assignment is performed on a radar profile according to expert experience by using regional limited speed measurement or by referring to soil texture.
However, the assignment of the velocity to the radar profile based on expert experience is easily limited by the expert experience, and an empirical error is introduced, especially when part of the expert experience is insufficient.
It has further been found in the related art that the thickness of the active layer can also be estimated by different types of models, such as empirical models, equilibrium models, numerical models, etc. The empirical statistical model usually needs a large amount of data of the thickness of the movable layer measured in the field as a drive, when the thickness of the movable layer is estimated by using a balanced model and a numerical model, the difficulty in obtaining physical parameters of the model is high, the simulation effect depends on the drive data and the accuracy of parameter setting, and most models need the data of the movable layer measured in the field to carry out parameter calibration and verification. Therefore, accurate active layer thickness information needs to be acquired as reference data on site as much as possible in a model estimation mode, and the method is the key for improving the active layer thickness simulation and the rule understanding level.
Similarly, in the method for estimating the thickness of the active layer provided by the embodiment, the radar wave velocity is estimated more accurately through the wave velocity prediction model, so that the precision of the thickness of the active layer is improved. The electronic device implementing the method may be, but is not limited to, a mobile terminal, a tablet computer, a laptop computer, a server, etc.
When a server, the server may be a single server or a group of servers. The set of servers can be centralized or distributed (e.g., the servers can be a distributed system). In some embodiments, the server may be local or remote to the user terminal. In some embodiments, the server may be implemented on a cloud platform; by way of example only, the Cloud platform may include a private Cloud, a public Cloud, a hybrid Cloud, a Community Cloud, a distributed Cloud, a cross-Cloud (Inter-Cloud), a Multi-Cloud (Multi-Cloud), and the like, or any combination thereof. In some embodiments, the server may be implemented on an electronic device having one or more components.
Based on the above description, the method for estimating the thickness of the active layer provided in this embodiment is described in detail below with reference to fig. 1. It should be understood that the operations of the flow diagrams may be performed out of order, and steps without logical context may be performed in reverse order or concurrently. One skilled in the art, under the guidance of this application, may add one or more other operations to, or remove one or more operations from, the flowchart. As shown in fig. 2, the method includes:
s101, obtaining sample environment parameters of a sample position and a sample radar wave speed.
And S102, generating a wave velocity prediction model according to the sample environment parameters and the sample radar wave velocity.
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. The wave velocity prediction model has the expression as follows:
y＝a 1 ·x 1 +a 2 ·x 2 +a 3 ·x 3 +…+a n ·x n +b
in the formula, x n Representing the nth parameter, a, of the parameters of the target environment n Denotes x n B represents a bias coefficient, and y represents a target radar wave velocity.
Because the wave speed of the ground penetrating radar is mainly related to factors such as soil texture, water content and the like, the data of the composition of powder, viscosity and sand grains of soil at a speed measuring point, the condition of surface vegetation, the condition of ground temperature influencing the soil structure and the like are mainly collected. The environmental parameters of the samples collected in this embodiment include the soil sand content, the aggregate content, and the maximum value, such as the Normalized Difference Vegetation Index (NDVI).
Illustratively, a certain ground penetrating radar section of a permafrost region in Xinghai county in northeast of Qinghai-Tibet plateau is selected as a research object. The elevation of the radar cross section is 4227m at the starting point, 4142m at the ending point, less than 100m in elevation difference and about 750 in length. The vegetation close to the starting point is a alpine meadow vegetation with high coverage, the vegetation close to the end point is close to the toe and the riverbed, the height of the vegetation is about 1m, the vegetation is a degraded meadow.
And collecting environmental parameters such as a terrain humidity Index, soil aggregate content, soil sand content, soil organic matter density, annual average surface temperature, annual average precipitation, annual median synthetic Normalized humidity Index (NDMI) and annual maximum synthetic Normalized vegetation Index of each sample position of the section. Then, feature screening is carried out by utilizing a multivariate linear regression optimal subset method and a five-fold cross validation mode, and feature combinations with minimum Mean Square Error loss (MSE) are screened.
The screening result shows that the combination of the soil sand content, the soil clay content and the normalized vegetation index can obtain the optimal solution; the corresponding wave velocity prediction model is as follows:
y＝0.048499·Snd-0.288445·Cly-0.05458·NDVI+0.12504
wherein Snd represents the soil sand content, cly represents the soil clay content, and NDVI represents the normalized vegetation index. The regression coefficient R2 of the model was 0.689, and the root mean square error RMSE was 0.012m/ns.
Continuing with fig. 2 based on the above description of the wave velocity prediction model, the method further includes:
s103, acquiring target environment parameters of the target position.
In an optional embodiment, environmental indexes such as soil sand content, clay content and normalized vegetation index corresponding to the target position can be extracted through a Geographic Information System (GIS) platform.
And S104, processing the target environment parameters through a wave velocity prediction model to obtain the wave velocity of the target radar.
In this way, the target radar wave velocity at the target position is obtained based on the wave velocity prediction model fitted by the large amount of sample environment data.
And S105, obtaining the target thickness of the active layer at the target position according to the wave velocity of the target radar.
In consideration of the fact that a ground penetrating radar is used for detecting the active layer, a matched processing tool is usually provided for analyzing reflected waves of the ground penetrating radar and calculating the empirical thickness of the active layer based on the wave speed of the empirical radar input by a user. Wherein the empirical radar wave velocity may be 0.1m/ns. Therefore, the present embodiment further corrects the empirical thickness by the target radar wave velocity. Namely, the specific implementation of step S105 includes:
s105-1, acquiring the empirical radar wave speed of the target position.
And S105-2, obtaining the empirical thickness of the active layer at the target position according to the empirical radar wave speed.
In a specific embodiment, the electronic device may obtain a radar reflection signal of the target location; then, a target reflection signal of the active layer is extracted from the reflection signal.
When the target reflection information number is extracted, the electronic equipment can preprocess the reflection signal to obtain a preprocessed signal, wherein the definition of the preprocessed signal is higher than that of the reflection signal.
The pretreatment mode comprises at least one of the following modes:
(1) Subtracting the average of the signal saturation correction;
(2) A static correction for adjusting the time zero position;
(3) A signal for deep signal enhancement;
(4) Background removal for horizontal signal filtering;
(5) Band-pass filtering for removing a specific frequency band;
(6) A sliding average for filtering the high frequency noise signal;
(7) And (7) terrain correction.
After radar reflection signals are preprocessed in the modes, the definition of ground penetrating radar signals is greatly enhanced, and the extraction of the reflection signals of the bottom plate of the movable layer is facilitated.
Because the horizontal active layer baseboard signal generally presents continuous, high-amplitude, same-phase reflection axis and signal characteristics changing along with terrain, the electronic equipment extracts a target reflection signal from the preprocessed signal according to the signal characteristics generated by the active layer; and obtaining the empirical thickness of the active layer at the target position according to the target reflection signal and the empirical radar wave velocity.
And S105-3, correcting the empirical thickness through the wave speed of the target radar to obtain the target thickness.
Wherein, the relation between the target radar wave velocity, the empirical thickness and the target thickness satisfies:
where 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.
Through actual field verification, the thickness of the movable layer obtained by the movable layer thickness estimation method provided by the embodiment reflects the thickness thickening characteristic of the movable layer caused by temperature drop after the altitude is reduced, and simultaneously reflects the superposition effect of the slope toe part on the movable layer thickening due to lateral thermal erosion, vegetation degradation and the like, can reflect the height spatial heterogeneity of the thickness of the movable layer in the discontinuous permafrost region, and accords with the objective rule of the thickness distribution of the movable layer. Therefore, as shown in fig. 3, the method is more consistent with the dynamic wave velocity method when the active layer is thin than the conventional method (empirical wave velocity, average wave velocity) for determining the thickness of the active layer, but has a large error when the active layer is thick at the tail of the cross section. Therefore, the thickness determined by the estimated target wave velocity is more accurate.
The above is about the method for estimating the thickness of the active layer, and the embodiment further provides an apparatus for estimating the thickness of the active layer under the same inventive concept. The active layer thickness estimation device includes at least one software functional module that can be stored in a memory in a software form or solidified in an Operating System (OS) of the electronic device. A processor in the electronic device is used to execute the executable modules stored in the memory. For example, the active layer thickness estimation apparatus includes software functional modules, computer programs, and the like. Referring to fig. 4, functionally, the active layer thickness estimation apparatus may include:
the model generation module 101 is used for acquiring sample environment parameters of a sample position and a sample radar wave velocity; and generating a wave velocity prediction model according to the sample environment parameters and the sample radar wave velocity.
In the present embodiment, the model generating module 101 is used to implement steps S101 and S102 in fig. 2, and for a detailed description of the model generating module 101, reference may be made to detailed descriptions of steps S101 and S102.
The wave velocity prediction module 102 is configured to obtain a target environment parameter of a target location; and processing the target environment parameters by a wave velocity prediction model to obtain the wave velocity of the target radar.
In the present embodiment, the wave velocity prediction module 102 is configured to implement steps S103 and S104 in fig. 2, and for a detailed description of the wave velocity prediction module 102, reference may be made to detailed descriptions of steps S103 and S104.
And the thickness estimation module 103 is used for obtaining the target thickness of the active layer at the target position according to the wave speed of the target radar.
In this embodiment, the thickness estimation module 103 is used to implement step S105 in fig. 2, and for a detailed description of the thickness estimation module 103, refer to a detailed description of step S105.
It should be noted that, since the same inventive concept is provided as the active layer thickness estimation method, the above model generation module 101, the wave velocity prediction module 102, and the thickness prediction module 103 may also be used to implement other steps or sub-steps of the method, and the embodiment is not limited in particular.
In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
It should also be understood that the above embodiments, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application.
Therefore, the present embodiment also provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program implements the active layer thickness estimation method provided by the present embodiment. The computer-readable storage medium may be various media that can store program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
Referring to fig. 5, the present embodiment further provides an electronic device, which includes a processor 202 and a memory 201. The processor 202 and memory 201 may communicate via a system bus 204. Moreover, the memory 201 stores a computer program, and the processor reads and executes the computer program corresponding to the above embodiment in the memory 201 to realize the active layer thickness estimation method provided in the present embodiment.
With continued reference to fig. 5, in some embodiments, the electronic device may further include a communication unit 203. The memory 201, processor 202 and communication unit 203 are electrically connected to each other directly or indirectly through a system bus 204 to enable data transmission or interaction.
The memory 201 may be an information recording device based on any electronic, magnetic, optical or other physical principle for recording execution instructions, data, etc. In some embodiments, the memory 201 may be, but is not limited to, volatile memory, non-volatile memory, a storage drive, and the like.
Wherein the volatile Memory may be, for example only, a Random Access Memory (RAM). The nonvolatile Memory may be a Read Only Memory (ROM), a Programmable Read Only Memory (PROM), an Erasable Programmable Read Only Memory (EPROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a flash Memory, or the like; the storage drive may be a magnetic disk drive, a solid state drive, any type of storage disk (e.g., optical disk, DVD, etc.), or similar storage medium, or combinations thereof, etc.
The communication unit 203 is used for transmitting and receiving data via a network. In some embodiments, the Network may include a wired Network, a Wireless Network, a fiber optic Network, a telecommunications Network, an intranet, the internet, a Local Area Network (LAN), a Wide Area Network (WAN), a Wireless Local Area Network (WLAN), a 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, or the like, or any combination thereof. In some embodiments, the network may include one or more network access points. For example, the network may include wired or wireless network access points, such as base stations and/or network switching nodes, through which one or more components of the service request processing system may connect to the network to exchange data and/or information.
The processor 202 may be an integrated circuit chip having signal processing capabilities, and may include one or more processing cores (e.g., a single-core processor or a multi-core processor). Merely by way of example, the 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 Physical 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 computer (Reduced Instruction Set computer), a microprocessor, or the like, or any combination thereof.
Based on the above description, it should be understood that the apparatuses and methods disclosed in the above embodiments may be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The above description is only for various embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of changes or substitutions within the technical scope of the present application, and all such changes or substitutions are included in the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method for estimating a thickness of an active layer, the method comprising:
acquiring sample environment parameters of a sample position and a sample radar wave speed;
generating a wave velocity prediction model according to the sample environment parameters and the sample radar wave velocity;
acquiring target environment parameters of a target position;
processing the target environment parameters through the wave velocity prediction model to obtain the wave velocity of a target radar;
and obtaining the target thickness of the active layer at the target position according to the target radar wave velocity.
2. The method for estimating thickness of an active layer according to claim 1, wherein the generating a wave velocity prediction model describing a relationship between an environmental parameter and a radar wave velocity based on the environmental parameter and the radar wave velocity of the sample comprises:
and performing function fitting on the sample environment parameters and the sample radar wave velocity to obtain the wave velocity prediction model.
3. The method for estimating thickness of active layer according to claim 1, wherein the expression of the wave velocity prediction model is:
y＝a 1 ·x 1 +a 2 ·x 2 +a 3 ·x 3 +…+a n ·x n +b
in the formula, x n Representing the nth parameter, a, of the parameters of the target environment n Denotes x n B represents a bias coefficient, and y represents the target radar wave velocity.
4. The method for estimating thickness of active layer according to claim 1, wherein said obtaining thickness of active layer at the target position based on the target radar wave velocity comprises:
acquiring an empirical radar wave speed of the target position;
obtaining the empirical thickness of the active layer at the target position according to the empirical radar wave speed;
and correcting the empirical thickness through the wave speed of the target radar to obtain the target thickness.
5. The active layer thickness estimation method according to claim 4, wherein a relationship among the target radar wave velocity, the empirical thickness, and the target thickness satisfies:
wherein 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.
6. The method according to claim 4, wherein said obtaining an empirical thickness of the active layer at the target position based on the empirical radar wave velocity comprises:
acquiring a radar reflection signal of the target position;
extracting a target reflection signal of an active layer from the reflection signal;
and obtaining the empirical thickness of the active layer at the target position according to the target reflection signal and the empirical radar wave speed.
7. The method according to claim 6, wherein the extracting the target reflection signal of the active layer from the reflection signal comprises:
preprocessing the reflection signal to obtain a preprocessed signal, wherein the definition of the preprocessed signal is higher than that of the reflection signal;
and extracting a target reflection signal from the preprocessed signal according to the signal characteristics generated by the active layer.
8. An active layer thickness estimation device, comprising:
the model generation module is used for acquiring sample environment parameters of a sample position and the sample radar wave velocity; generating a wave speed prediction model according to the sample environment parameters and the sample radar wave speed;
the wave speed prediction module is used for acquiring target environment parameters of a target position; processing the target environment parameters by the wave velocity prediction model to obtain the wave velocity of the target radar;
and the thickness estimation module is used for obtaining the target thickness of the active layer at the target position according to the target radar wave speed.
9. A storage medium, characterized in that the storage medium stores a computer program which, when executed by a processor, implements the active layer thickness estimation method according to any one of claims 1 to 7.
10. An electronic device, comprising a processor and a memory, wherein the memory stores a computer program which, when executed by the processor, implements the active layer thickness estimation method of any of claims 1-7.
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| CN116592774A (en) * | 2023-07-18 | 2023-08-15 | 成都洋湃科技有限公司 | Pipe wall dirt detection method and device, storage medium and electronic equipment |
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2023
- 2023-01-03 CN CN202310004656.XA patent/CN115930852A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116592774A (en) * | 2023-07-18 | 2023-08-15 | 成都洋湃科技有限公司 | Pipe wall dirt detection method and device, storage medium and electronic equipment |
| CN116592774B (en) * | 2023-07-18 | 2023-09-19 | 成都洋湃科技有限公司 | Pipe wall dirt detection method and device, storage medium and electronic equipment |
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