Active layer depth identification method based on envelope fusion algorithm

Abstract

The invention discloses an active layer depth identification method based on an envelope fusion algorithm, and belongs to the technical field of seasonal frozen depth survey. The method comprises the following steps: optimizing survey line arrangement and radar frequency through field survey and ground temperature data, and collecting data by using a ground penetrating radar; performing background removal, gain compensation, band-pass filtering and time-depth conversion preprocessing on the data; then, a gravity center method and an edge detection method are adopted to extract a bottom interface track, and noise and abnormal values are effectively suppressed according to self-adaptive weighted fusion of signal definition and continuity; and finally, carrying out cross validation and error correction by taking the earth temperature 0 DEG C isotherm depth as a reference, outputting a continuous and smooth interface fusion curve with centimeter-level precision, and calculating the depth of an active layer. According to the method, the high-efficiency and lossless advantages of the GPR are kept, meanwhile, the limitation of traditional interpretation precision is broken through, and a practical technical scheme which can be directly used for designing parameter values is provided for engineering investigation of the permafrost region.

Description

Technical Field

[0001] The present invention relates to the technical field of seasonal frost depth survey, and in particular to an active layer depth identification method based on an envelope fusion algorithm. Background Art

[0002] The active layer is the surface rock and soil layer in seasonally frozen areas that freezes in cold seasons and thaws in warm seasons. Its thickness directly determines the scale and distribution of cold-region disasters such as frost heave, thaw settlement, and frost pull, and is a core parameter for the frost-resistant design of projects such as roadbeds, pipelines, and pile foundations in cold regions. With climate warming and intensified engineering activities, interannual fluctuations in the thickness of the active layer have intensified. Traditional drilling and point-based geothermal measurements struggle to provide continuous, large-scale, and real-time thickness data, resulting in blind spots in surveys, conservative engineering designs, and escalating operation and maintenance costs. Therefore, there is an urgent need to develop a rapid, non-destructive, wide-area, and accurate active layer thickness identification technology to support the refined design and risk prevention of cold-region infrastructure.

[0003] Ground-penetrating radar (GPR) technology has been widely used in permafrost exploration, enabling efficient and continuous kilometer-scale profile detection, significantly improving data acquisition efficiency and coverage density. However, current GPR data interpretation and inversion still rely primarily on commercial software bundled with the equipment and manual interpretation, which can be subjective, inconsistent, and inefficient. Automated interpretation methods for permafrost radar imagery remain significant research gaps and application shortcomings. The center of gravity (COG) method identifies interfaces by calculating the weighted average depth of reflected energy. It offers advantages such as strong anti-interference capabilities and stable extraction results, and is commonly used to locate strong reflective layers in engineering geophysical exploration. Edge detection captures interface abrupt changes by identifying the first-order difference of the signal or the gradient extreme points. It is sensitive to subtle interface changes and has achieved promising results in fields such as medical imaging and remote sensing. However, neither the COG nor the edge detection method has been effectively applied in the field of permafrost radar detection and interpretation.

[0004] To address this bottleneck, this paper integrates the centroid method with the edge detection method for the first time, proposing an active layer depth identification method based on envelope weighted fusion. By combining the advantages of the two algorithms, the centroid method's ability to smooth noise and stably extract interfaces is utilized, while the edge detection method's ability to keenly capture interface mutations is also leveraged. This method, supplemented by geothermal data and other data for cross-validation and adaptive weight adjustment, effectively overcomes the problems of interface disconnection, dislocation, and false reflection that can easily arise from a single algorithm in complex environments. This method achieves continuous, accurate, and automatic extraction of active layer interfaces, significantly improving the interpretation accuracy and reliability of GPR in cold-region engineering surveys. Summary of the Invention

[0005] The purpose of the present invention is to provide an active layer depth identification method based on envelope fusion algorithm to address the technical defects in the prior art.

[0006] The technical solution adopted to achieve the purpose of the present invention is: An active layer depth identification method based on envelope fusion algorithm includes the following steps: Step 1: Select a study area in a seasonal frozen soil region, survey the geological and geophysical information of the study area, obtain a favorable collection area for ground penetrating radar data, divide the collection area into survey lines, and deploy ground penetrating radar data; Step 2: Acquire ground temperature data in the collection area and determine the position of the active layer to confirm the operating frequency of the ground penetrating radar. The ground penetrating radar collects data in the collection area along the survey line at the operating frequency. The electromagnetic waves are emitted, and the electromagnetic waves are reflected at the interface between the active layer and the unfrozen soil. The reflected electromagnetic waves are received to obtain the raw data of the ground penetrating radar. The propagation speed of the electromagnetic waves is then corrected by drilling and measured data. The raw data of the ground penetrating radar is preprocessed to obtain a preprocessed radar data signal. Step 3: Select the pre-processed radar data signal on a survey line, use multiple interface extraction methods to extract features from the selected radar data signal, identify the interface between the active layer and the unfrozen soil, and extract the interface position; Step 4, setting weight parameters for the interface positions extracted by different interface extraction methods in step 3 and fusing them using a weighted fusion strategy to generate a fused interface position; Step 5: The depth of the 0°C isotherm obtained from the geothermal data or the active layer depth obtained from drilling is used as the actual depth. The actual depth is compared with the fusion interface position obtained in step 4. If the fusion interface position is within the preset error range, the fusion interface position is used as the calibrated interface position. If the fusion interface position is not within the preset error range, the weight parameter of step 4 is adjusted or the electromagnetic wave propagation speed of step 2 is adjusted until the fusion interface position is within the preset error range. Step 6: Obtain the fused interface trajectory based on the calibrated interface position obtained in step 5, and perform secondary processing on the fused interface trajectory to obtain the fused interface curve.

[0007] In the above technical solution, in step 1, the vegetation coverage and topography of the study area are investigated to avoid factors that interfere with the ground penetrating radar signal, so as to obtain a favorable collection area for ground penetrating radar data based on the surface flatness and traffic conditions of the study area.

[0008] In the above technical solution, in step 2, the host of the ground penetrating radar equipment is connected to the computer via Ethernet for equipment control and real-time data transmission.

[0009] In the above technical solution, in step 2, the distance measuring wheel is communicatively connected to the ground penetrating radar equipment, and the displacement signal generated by the rotation of the distance measuring wheel is used to trigger the ground penetrating radar data acquisition, so that the ground penetrating radar can collect data at equal intervals along the survey line; the ground penetrating radar equipment is advanced at a uniform speed along the survey line, and the radar raw data is recorded in real time by a computer.

[0010] In the above technical solution, in step 2, the raw data of the ground penetrating radar are preprocessed in sequence by background removal, gain compensation, bandpass filtering and time-depth conversion to enhance the recognition effect of the reflection interface in the study area.

[0011] In the above technical solution, in step 3, the interface extraction method includes a center of gravity method (COG) and an edge detection method (Edge Detection).

[0012] In the above technical solution, the center of gravity method interface recognition: by performing envelope analysis on the pre-processed radar data signal, the envelope amplitude is calculated , extract the reflected energy distribution of each A-scan signal of the vertical radar waveform, select the target reflection window in each signal, and calculate the weighted average depth of the energy in the target reflection window as the interface position of the signal. The expression is: ; in: For the The envelope amplitude of each sampling point; For the The depth of the sampling point converted from sampling time to wave velocity; are the start and end sampling points of the target reflection window; is the interface position extracted by the center of gravity method.

[0013] It is usually obtained from the original radar waveform through Hilbert Transform. The analytical signal is obtained by Hilbert transform: , calculate the envelope amplitude ,in, represents the Hilbert transform, Represents a 90° phase-shifted version of the original signal.

[0014] In the above technical solution, the edge detection method is used to extract the interface position: the characteristic of the sudden change in reflection intensity of the preprocessed radar data signal at the interface is used to calculate the first-order difference or gradient image of the A-scan envelope signal, and the maximum gradient point is extracted as the potential interface position. The edge detection method can sensitively capture the sudden change edge of the reflection position, which is conducive to the fine extraction of the interface. Its mathematical expression is: ; Or in discrete form: ; in: For the The envelope amplitude of each sampling point; For the The envelope amplitude of each sampling point; Indicates the rate of change of envelope amplitude with depth; The interface position extracted by edge detection method; is the starting sampling point, The sampling point ends.

[0015] In the above technical solution, in step 4, the mathematical formula of the weighted fusion strategy is: ; in: , is the weight parameter. If the reflected signal is clear and the interface is continuous, set ; If the interface is fuzzy and seriously disturbed by noise, set , is the interface position after fusion.

[0016] In the above technical solution, in step 6, the secondary processing is curve smoothing, missing value smoothing and interpolation.

[0017] Compared with the prior art, the present invention has the following beneficial effects: The recognition method of the present invention combines the advantages of the center of gravity method and the edge detection method. Through the joint extraction and weighted processing of multi-feature information, it realizes the automatic recognition of the depth of the seasonal frozen soil active layer. Compared with the traditional single algorithm, it is superior in accuracy, stability and adaptability. By introducing pre-processing technologies such as background removal, gain compensation, bandpass filtering and time depth, the noise interference and false reflection effects in the radar data are effectively reduced, and the continuity and credibility of the interpretation results are improved. At the same time, this method supports the processing of radar data of different frequencies and is suitable for the task of identifying frozen interfaces under various soil hydrothermal conditions. This method optimizes survey line layout and radar frequency through field surveys and ground temperature data, improving the quality of the collected data from the source. The raw ground-penetrating radar data undergoes background removal, gain compensation, bandpass filtering, and time-depth conversion preprocessing, significantly enhancing the freeze-thaw interface reflection signal. The center of gravity method and edge detection methods are then used to extract the bottom interface trajectory, and adaptive weighted fusion is performed based on signal clarity and continuity to effectively suppress noise and outliers. Finally, cross-validation and error correction are performed using the 0°C ground temperature isotherm depth as a benchmark, outputting a continuous, smooth, centimeter-level accurate interface fusion curve for calculating the active layer depth. While maintaining the advantages of GPR's high efficiency and non-destructiveness, this method overcomes the limitations of traditional interpretation accuracy, providing a practical technical solution for engineering surveys in seasonally frozen areas that can be directly used to determine design parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a schematic diagram of the ground penetrating radar detection principle of the present invention.

[0019] Figure 2 This is an image of the active layer detected by the ground penetrating radar of the present invention.

[0020] Figure 3 Schematic diagram of the identification method of the present invention.

[0021] Figure 4 It is the radar image cross-section diagram and the active layer bottom interface recognition result diagram of the present invention. DETAILED DESCRIPTION

[0022] The present invention will be further described in detail below with reference to specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0023] An active layer depth identification method based on envelope fusion algorithm includes the following steps: like Figure 1-Figure 3 As shown, step 1 is to select a study area in the seasonal frozen soil area, conduct on-site geological and geophysical surveys of the study area, obtain a favorable acquisition area for ground penetrating radar data, divide the acquisition area into survey lines and deploy ground penetrating radar, and optimize the survey line division using ground temperature data; Furthermore, the vegetation coverage and environmental conditions in the study area were analyzed to avoid terrain and landforms that may interfere with the ground penetrating radar signal, such as accumulated water, snow, dense vegetation areas, and electromagnetic interference sources, so as to reduce the conditions that affect the propagation of radar signals; priority was given to areas with relatively flat surfaces, moderate hardness, and areas that are conducive to the stable advancement of the ground penetrating radar system, such as alpine grasslands, areas with frequent freeze-thaw cycles, or areas with obvious freeze-thaw interface changes, as favorable collection areas for ground penetrating radar data. This collection area is conducive to obtaining high-quality ground penetrating radar data, ensuring clear identification of the interface between the active layer and the unfrozen soil, and small signal reflection interference.

[0024] Furthermore, appropriate survey line layout areas are delineated within areas favorable for GPR data collection to ensure that the survey lines cover key areas of the collection area. Within the survey line layout area, several parallel or intersecting survey lines are laid out based on the lateral variation characteristics of seasonal frozen soil distribution to ensure the coverage and resolution of subsequent data collection.

[0025] Optimally, survey lines should be laid out based on the principle of "covering representative areas while taking into account the characteristics of frost depth variations." Priority should be given to areas with diverse landforms and significant frost depth variations. Survey lines should be placed perpendicular to the direction of frost depth variations whenever possible to enhance the clarity of radar reflection interfaces.

[0026] Step 2: Obtain ground temperature data in the collection area and determine the location of the active layer. This confirms that ground-penetrating radar antennas with operating frequencies of 250 MHz and 500 MHz are used for measurement to balance detection depth and resolution. The ground-penetrating radar collects image data of the collection area at equal intervals along the survey line. The ground-penetrating radar transmits electromagnetic waves, which are reflected at the interface between the active layer and the unfrozen soil. The reflected electromagnetic waves are received to obtain the raw data of the ground-penetrating radar. The electromagnetic wave propagation velocity is then corrected through drilling and measured data, and the raw data of the ground-penetrating radar is preprocessed to obtain the preprocessed radar data signal.

[0027] Furthermore, the GPR includes a host computer and transmitting and receiving antennas. The host computer is connected to a computer via Ethernet, establishing a real-time data transmission channel. The host computer is also connected to a distance measuring wheel (via a cable). During the data acquisition phase, computer software controls the initialization, parameter setting, and antenna configuration of the GPR. The displacement signal generated by the distance measuring wheel triggers GPR data acquisition. Preferably, the distance measuring wheel is synchronized with the GPR host computer. As the GPR moves along the survey line, the antenna maintains close contact with the ground and good coupling with the surface. Data acquisition is triggered every time the distance measuring wheel advances a set distance, ensuring uniform data acquisition spacing and accurate spatial positioning. The GPR automatically records raw (radar profile) data, including radar waveforms, sampling time, and location coordinates. This raw data is transmitted and stored in real time on the computer. The real-time waveform display function on the computer software monitors signal quality and performs preliminary interpretation of the acquired signal. If severe signal interference or echo anomalies are detected, the GPR acquisition parameters are promptly adjusted or acquisition is repeated to ensure data integrity and validity.

[0028] Furthermore, ground-penetrating radar data processing software was used to preprocess the raw ground-penetrating radar data, performing background removal, gain compensation, bandpass filtering, and time-depth conversion. Specifically, this enhanced the recognition of reflective interfaces in the study area, reduced random noise in the data signal, and improved the signal-to-noise ratio. As a result, the preprocessed data signal image showed less noise than before preprocessing and was clearer than the original data, as the filtering process helped highlight structural features. Energy gain compensated for energy attenuation in the radar data signal, making the energy distribution of the preprocessed radar data signal more uniform and improving signal consistency.

[0029] The background removal is to remove background noise and highlight local abnormal reflections. The removal method is the horizontal averaging method. The formula is as follows: ; in, is the signal after background removal, For the The road in The raw data signal of the ground penetrating radar at the time sampling point, N is the total number of channels, The average background value for each time sampling point (averaged along the columns).

[0030] Gain compensation artificially amplifies signals that gradually weaken over time (corresponding to detection depth). Essentially, it reduces the gain of early (shallow) signals and increases the gain of later (deeper) signals, visually "balancing" the radar profile and facilitating the identification of deep targets. Bandpass filtering is a crucial step, removing unwanted frequency components while retaining the valid signal components.

[0031] like Figure 1 As shown, ground-penetrating radar transmits an incident wave into the ground, generating a reflected wave at the interface between the active layer and the unfrozen soil. t2 is the total time it takes for the electromagnetic wave to propagate through the interface between the active layer and the unfrozen soil, and t1 is the time it takes for the electromagnetic wave to be captured by the receiving antenna, known as the "two-way travel time." The active layer depth = (electromagnetic wave propagation speed × two-way travel time) / 2. Time-to-depth conversion converts the time axis (in nanoseconds, ns) displayed visually on the radar profile into a more practical and easily understood depth axis (in meters, m). The electromagnetic wave propagation velocity v (in meters / nanosecond, m / ns) in a medium is not constant. Corrections are made to the electromagnetic wave velocity using the concentricity method or based on the dielectric properties of the medium as determined by drilling.

[0032] like Figure 1 In the GPR profile, mV and -mV represent the amplitude and polarity of the received electromagnetic wave signal in the radar image. If the echo is positive (mV), it indicates that the electromagnetic wave has entered the unfrozen soil (high ε) from the active layer (low ε).

[0033] Step 3: Select a preprocessed radar data signal on a survey line and use multiple interface extraction methods to extract features from the selected radar data signal, identify the interface between the active layer and the unfrozen soil, and extract the interface position. The interface extraction methods include the Center of Gravity (COG) method and the Edge Detection method.

[0034] Furthermore, the center of gravity method is used to extract the interface position: the envelope analysis is performed on the pre-processed radar data signal to calculate the envelope amplitude. , extract the reflected energy distribution of each vertical A-scan signal. In each signal, select the target reflection window and calculate the weighted average depth of the energy in the target reflection window as the interface position of the signal. The centroid method has strong anti-interference ability and can smoothly extract the position of the high-energy reflection area. The mathematical expression of the centroid method is: ; in: For the The envelope amplitude of each sampling point; For the The depth of the sampling point converted from sampling time to wave velocity; are the start and end sampling points of the target reflection window; is the interface position extracted by the center of gravity method. It is usually obtained from the original radar waveform through Hilbert Transform. The analytical signal is obtained by Hilbert transform: , calculate the envelope amplitude ,in, represents the Hilbert transform, Represents a 90° phase-shifted version of the original signal.

[0035] The edge detection method extracts the location of the sudden interface: Utilizing the characteristic of sudden changes in reflection intensity at the interface of the preprocessed radar data signal, the first-order difference or gradient image of the A-scan envelope signal is calculated, and the maximum gradient point is found as the potential interface. The edge detection method can sensitively capture the sudden change edge of the reflection position, which helps to accurately locate and extract the interface. Its mathematical expression is: ; Or in discrete form: ; in: For the The envelope amplitude of each sampling point; For the The envelope amplitude of each sampling point; Indicates the rate of change of envelope amplitude with depth; The interface position extracted by edge detection method; is the starting sampling point, The sampling point ends.

[0036] Step 4: setting weight parameters for the interface positions extracted by different interface extraction methods in step 3, fusing them using a weighted fusion strategy to generate a fused interface position, wherein the weight parameters are adjusted according to the interface position measured during drilling; The mathematical formula of the weighted fusion strategy is: ; in: , is the weight parameter of the centroid method, is the weight parameter of the edge detection method. If the reflection signal is clear and the interface is continuous, then set ; If the interface is fuzzy and seriously disturbed by noise, set , is the fusion interface position.

[0037] Step 5: The depth of the 0°C isotherm obtained from the geothermal data or the active layer depth obtained from drilling is used as the actual depth. The actual depth is compared with the fusion interface position obtained in step 4. If the fusion interface position is within the preset error range (±0.10 m), the fusion interface position is used as the calibrated interface position. If the fusion interface position is not within the preset error range, the weight parameter of step 4 or the electromagnetic wave propagation speed of step 2 is adjusted until the fusion interface position is within the preset error range. Step 6: Obtain the fused interface trajectory based on the calibrated interface position obtained in step 5, perform curve smoothing on the fused interface trajectory, smooth and interpolate missing values, and obtain the fused interface curve.

[0038] Furthermore, during the interface identification process, the active layer interface is defined as a reflective anomaly with a distinct envelope amplitude response in the radar image. Envelope calculation and waveform feature tracking algorithms based on the Hilbert transform are combined with multi-channel smoothing to extract the continuous reflective interface. Simultaneously, a ground temperature variation curve with depth is plotted based on ground temperature data measured by field-deployed ground temperature sensors. The 0°C isotherm of this curve is used as the boundary between the active layer and unfrozen soil in permafrost. By comparing this boundary with the reflective anomaly in the radar image profile, comprehensive and automatic identification of the frozen depth interface is achieved. To ensure the accuracy of the interpretation, manual drilling or other geophysical methods can also be used for cross-validation.

[0039] like Figure 4As shown, in order to show that the method of the present invention has good recognition accuracy and engineering applicability, the interface positions extracted by the centroid method and the interface positions extracted by the edge detection method are respectively processed twice to obtain the centroid method interface curve and the edge method interface curve, and a straight line (fitting curve) is fitted to the fusion interface curve obtained in step 4 according to its trend. The trend of the interface between the active layer and the unfrozen soil is intuitively felt through the fitted straight line. Finally, the above lines are visualized and superimposed, and the image is output and compared with the original radar image profile.

[0040] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for identifying the depth of an active layer based on an envelope fusion algorithm, characterized in that: The following steps are involved: Step 1: Select a study area in a seasonal frozen soil region, survey the geological and geophysical information of the study area, obtain a favorable collection area for ground penetrating radar data, divide the collection area into survey lines, and deploy ground penetrating radar data; Step 2: Acquire ground temperature data in the collection area and estimate the position of the active layer to confirm the operating frequency of the ground penetrating radar. The ground penetrating radar collects data in the collection area along the survey line at the operating frequency. The ground penetrating radar transmits electromagnetic waves, which are reflected by the interface between the active layer and the unfrozen soil. The reflected electromagnetic waves are received to obtain raw data of the ground penetrating radar. The raw data of the ground penetrating radar is preprocessed to obtain a preprocessed radar data signal. Step 3: Select the pre-processed radar data signal on a survey line, use multiple interface extraction methods to extract features from the selected radar data signal, identify the interface between the active layer and the unfrozen soil, and extract the interface position; Step 4, setting weight parameters for the interface positions extracted by different interface extraction methods in step 3 and fusing them using a weighted fusion strategy to generate a fused interface position; Step 5: The depth of the 0°C isotherm obtained from the geothermal data or the active layer depth obtained by drilling is used as the actual depth. The actual depth is compared with the fusion interface position obtained in step 4. If the fusion interface position is within the preset error range, the fusion interface position is used as the calibrated interface position. If the fusion interface position is not within the preset error range, return to step 4 to adjust the weight parameter of step 4. Step 6: Obtain the fused interface trajectory based on the calibrated interface position obtained in step 5, and perform secondary processing on the fused interface trajectory to obtain the fused interface curve.

2. The method for identifying the depth of the active layer according to claim 1, wherein: In step 1, the vegetation coverage and topography of the study area are investigated to avoid factors that interfere with the ground penetrating radar signal, so as to obtain a favorable collection area for ground penetrating radar data based on the surface flatness and traffic conditions of the study area; at the same time, the survey line division is optimized through ground temperature data.

3. The method for identifying the depth of the active layer according to claim 1, wherein: In step 2, the host of the ground penetrating radar device is connected to the computer via Ethernet for device control and real-time data transmission.

4. The method for identifying the depth of the active layer according to claim 3, wherein: In step 2, the distance measuring wheel is communicatively connected to the ground penetrating radar device, and the displacement signal generated by the rotation of the distance measuring wheel is used to trigger the ground penetrating radar data acquisition, so that the ground penetrating radar can collect data at equal intervals along the survey line; the ground penetrating radar device is advanced at a uniform speed along the survey line, and the radar raw data is recorded in real time by a computer.

5. The method for identifying the depth of an active layer according to claim 1, wherein: In step 2, the ground penetrating radar raw data is preprocessed in sequence by background removal, gain compensation, bandpass filtering and time-depth conversion.

6. The method for identifying the depth of an active layer according to claim 1, wherein: In step 3, the interface extraction method includes a center of gravity method and an edge detection method.

7. The method for identifying the depth of the active layer according to claim 6, characterized in that: Center of gravity interface recognition: Calculate the envelope amplitude by performing envelope analysis on the pre-processed radar data signal , extract the reflected energy distribution of each A-scan signal of the vertical radar waveform, select the target reflection window in each signal, and calculate the weighted average depth of the energy in the target reflection window as the interface position of the signal. The expression is: ； in: For the i The envelope amplitude of each sampling point; For the The depth of the sampling point converted from sampling time to wave velocity; 、 are the start and end sampling points of the target reflection window; is the interface position extracted by the center of gravity method.

8. The method for identifying the depth of the active layer according to claim 7, wherein: The edge detection method extracts the interface position: using the characteristic of sudden change in reflection intensity of the preprocessed radar data signal at the interface, the first-order difference or gradient image of the A-scan envelope signal is calculated, and the maximum gradient point is extracted as the potential interface position. The mathematical expression of the edge detection method is: ； Or in discrete form: ； in: For the The envelope amplitude of each sampling point; For the The envelope amplitude of each sampling point; Indicates the rate of change of envelope amplitude with depth; The interface position extracted by edge detection method; is the starting sampling point, The sampling point ends.

9. The method for identifying the depth of the active layer according to claim 8, characterized in that: In step 4, the mathematical formula of the weighted fusion strategy is: ； in: , is the weight parameter, is the interface position after fusion.

10. The method for identifying the depth of an active layer according to claim 1, wherein: In step 6, the secondary processing includes curve smoothing, missing value smoothing and interpolation.

Images