Railway line time sequence InSAR (Interferometric Synthetic Aperture Radar) and semi-aviation electromagnetism combined investigation method and device

Abstract

The invention relates to the technical field of geological exploration and geophysical exploration, in particular to a time sequence InSAR (Interferometric Synthetic Aperture Radar) and semi-aviation electromagnetic combined exploration method and device along a railway. Comprising the following steps: S1, acquiring time sequence InSAR data and ground auxiliary data along a target railway; s2, interference analysis and deformation monitoring are carried out on the time sequence InSAR data, and deformation information of the earth surface along the line is extracted; s3, determining an abnormal area based on the deformation information, and carrying out semi-aviation transient electromagnetic observation to obtain an electromagnetic response signal reflecting underground medium characteristics; s4, performing electromagnetic inversion processing on the electromagnetic response signal to obtain a semi-aviation electromagnetic inversion result; and S5, carrying out data fusion and correlation analysis on the deformation information, the semi-aviation electromagnetic inversion result and the ground auxiliary data to generate an underground structure and geological anomaly distribution result along the railway. According to the method, collaborative acquisition and fusion analysis of earth surface and underground information along the railway are realized, and hidden geological anomalies can be identified and earth surface deformation causes can be found out under complex terrain conditions.

Description

Technical Field

[0001] This invention relates to the field of geological exploration and geophysical exploration technology, and in particular to a method and apparatus for combined time-series InSAR and semi-airborne electromagnetic survey along railway lines. Background Technology

[0002] Existing geological surveys and safety monitoring along railway lines largely rely on single remote sensing or ground-based detection methods. Previous deformation monitoring methods primarily utilized differential or permanent scatterer InSAR (Interferometric Synthetic Aperture Radar) technology to calculate surface deformation through multi-temporal radar imagery, aiming to identify surface subsidence, landslides, or deformation. However, these methods only reflect the deformation characteristics of the surface layer and have limited ability to detect underground geological structures, making it difficult to reveal the deep causes of surface deformation. On the other hand, while traditional ground-based electromagnetic or geomagnetic surveys can be used to analyze the properties of underground media, they are often limited by the layout of measuring points, construction environment, and terrain conditions, resulting in problems such as small survey areas, complex deployment, low data acquisition efficiency, and high on-site operational risks, especially in mountainous areas or high-fill sections.

[0003] As railway networks extend into areas with complex geological conditions, single monitoring methods are no longer sufficient to meet the needs for early identification of hidden geological hazards. Existing technologies are inadequate in comprehensively acquiring surface and subsurface information in complex terrain, leading to a disconnect between deformation monitoring results and geological causal analysis, thus failing to achieve accurate early warning and structural assessment of potential hazards. Summary of the Invention

[0004] This application aims to address at least the technical problems existing in the prior art by proposing a geological exploration method that combines temporal InSAR with semi-airborne electromagnetic technology. This method utilizes temporal InSAR to achieve continuous monitoring of millimeter-level surface deformation, while semi-airborne electromagnetic technology penetrates the surface to detect subsurface geological structures, forming an integrated dynamic exploration system encompassing both surface and subsurface layers. Compared with traditional methods, this technology offers advantages such as high efficiency, low risk, and wide applicability in complex terrain environments. It significantly enhances the ability to identify concealed deformations and deep geological anomalies, providing a new solution for the accurate monitoring and early warning of geological disasters along railway lines.

[0005] In a first aspect, embodiments of this application provide a method for joint time-series InSAR and semi-airborne electromagnetic surveying along railway lines, which may include: S1. Acquire time-series InSAR data and ground-aided data along the target railway line, wherein the time-series InSAR data includes SAR imagery, digital elevation model data, atmospheric correction auxiliary data, and track data; S2. Perform interferometric analysis and deformation monitoring on the time-series InSAR data to extract deformation information of the land surface along the route; S3. Based on the deformation information, determine the abnormal area, and conduct semi-airborne transient electromagnetic observation in the abnormal area to obtain electromagnetic response signals reflecting the characteristics of the underground medium; S4. Perform electromagnetic inversion processing on the electromagnetic response signal to obtain semi-airborne electromagnetic inversion results and obtain the electrical distribution of the underground medium; S5. The deformation information, the semi-airborne electromagnetic inversion results, and the ground auxiliary data are fused and correlated to generate the underground structure and geological anomaly distribution results along the railway line.

[0006] The railway line time-series InSAR and semi-airborne electromagnetic joint survey method according to the embodiments of this application has at least the following beneficial effects: This application first utilizes temporal InSAR technology to continuously monitor surface deformation along the railway line. This allows for the acquisition of millimeter-level precision deformation time series without the need for ground control points, comprehensively reflecting the early deformation characteristics of minor subsidence, uplift, and landslides along the railway line. Then, through analysis of the deformation time series, areas with abnormal deformation rates or accumulations are automatically identified, accurately determining key exploration areas and providing target guidance for subsequent underground exploration. Next, semi-airborne transient electromagnetic observations are conducted within these abnormal areas. Using an unmanned aerial vehicle (UAV) equipped with an electromagnetic receiving device, survey lines are flexibly deployed under complex terrain conditions to quickly acquire underground electromagnetic response signals, achieving efficient detection of the underground medium structure. Subsequently, through filtering, frequency domain modeling, time domain transformation, and forward and inverse calculations, the electrical distribution information of the underground medium is obtained. Finally, the deformation information, the semi-airborne electromagnetic inversion results, and the ground-based auxiliary data are fused and correlated to reveal the coupling relationship between surface deformation and underground structural changes in a unified spatial coordinate system, generating a map of the underground structure and geological anomalies along the railway line. By establishing a dynamic linkage between surface monitoring and underground exploration, this invention enables integrated surface-underground collaborative exploration, significantly improving the accuracy and efficiency of identifying hidden geological anomalies along railway lines. This method not only reduces the deployment difficulty and operational risks of traditional ground surveys but also allows for large-scale, high-resolution dynamic monitoring under complex terrain conditions, providing a reliable technical means for early warning and risk assessment of geological disasters along railway lines.

[0007] According to some embodiments of this application, the interferometric analysis and deformation monitoring includes: registering and differentially interferometric processing of multi-temporal radar images to obtain phase difference information, and calculating the ground displacement along the radar line of sight based on the phase difference to establish a continuous deformation time series.

[0008] According to some embodiments of this application, the determination of the abnormal area includes: performing rate and cumulative analysis on the deformation time series, selecting areas where the deformation or deformation rate exceeds a preset threshold as abnormal areas, and determining the key investigation scope in combination with the results of on-site surveys.

[0009] According to some embodiments of this application, the semi-airborne electromagnetic observation includes: the semi-airborne transient electromagnetic observation includes: using an unmanned aerial vehicle (UAV) equipped with an electromagnetic receiving device to conduct grid-like data collection along a planned survey line in a key survey area, acquiring induced electromagnetic signals and recording spatial positions.

[0010] According to some embodiments of this application, the electromagnetic inversion processing includes: filtering, frequency domain modeling and time domain conversion of the acquired semi-airborne transient electromagnetic signals, establishing a forward model and performing inversion calculations by minimizing the difference between the observed data and the theoretical response, and obtaining the electrical distribution of the underground medium.

[0011] According to some embodiments of this application, the data fusion and correlation analysis includes: spatially registering the deformation information and the semi-airborne electromagnetic inversion results in a unified geographic coordinate system, analyzing the correlation between the two changes through a multi-source information fusion algorithm, and outputting the results of underground structure and geological anomaly distribution.

[0012] Secondly, embodiments of this application provide a time-series InSAR and semi-airborne electromagnetic joint survey device along a railway line, the device including: The data acquisition module is used to acquire time-series InSAR data and ground auxiliary data along the target railway line. The time-series InSAR data includes SAR imagery, digital elevation model data, atmospheric correction auxiliary data, and track data. The first calculation module is used to perform interferometric analysis and deformation monitoring on the time-series InSAR data and extract deformation information of the land surface along the route; The second calculation module is used to determine the abnormal area based on the deformation information, and to carry out semi-airborne transient electromagnetic observation in the abnormal area to obtain electromagnetic response signals reflecting the characteristics of the underground medium. The electromagnetic response signals are then subjected to electromagnetic inversion processing to obtain the semi-airborne electromagnetic inversion results and to obtain the electrical distribution of the underground medium. The data fusion module is used to perform data fusion and correlation analysis on the deformation information, the semi-airborne electromagnetic inversion results and the ground auxiliary data to generate the underground structure and geological anomaly distribution results along the railway line.

[0013] According to the embodiment of this application, the railway-line time-series InSAR and semi-airborne electromagnetic joint survey device first automatically accesses the time-series InSAR data and ground auxiliary data along the railway line through a data acquisition module, realizing centralized management and unified access to SAR imagery, digital elevation model data, atmospheric correction data, and track data, providing a stable data source for deformation monitoring. Then, a first calculation module performs interferometric registration, phase unwrapping, and deformation conversion processing to form a surface deformation time series, achieving continuous surface deformation monitoring with millimeter-level accuracy. Next, a second calculation module determines anomaly areas based on the deformation information and conducts semi-airborne transient electromagnetic observations in the anomaly areas to acquire electromagnetic response signals reflecting the characteristics of the underground medium. Electromagnetic inversion processing is performed on the electromagnetic response signals to obtain semi-airborne electromagnetic inversion results, yielding the electrical distribution of the underground medium. Finally, a data fusion module performs spatial registration and feature fusion of the deformation information, the semi-airborne electromagnetic inversion results, and the ground auxiliary data to output an underground structure model and geological anomaly distribution information along the railway line. Through modular integrated design, this device achieves efficient collaborative operation of time-series InSAR and semi-airborne electromagnetic detection, supporting automated data processing and integrated analysis, significantly improving the intelligence level and operational efficiency of geological exploration along railway lines. The device can rapidly complete data acquisition, inversion calculation, and result output under complex terrain conditions, reducing the risks of manual operations, improving monitoring accuracy and timeliness, and providing reliable technical support for geological disaster monitoring, hazard identification, and risk early warning along railway lines.

[0014] According to some embodiments of this application, the first calculation module is configured to perform multi-temporal interferometric registration, phase difference extraction, phase unwrapping and deformation conversion to form a surface deformation time series and output deformation rate results.

[0015] According to some embodiments of this application, the second calculation module is configured to perform filtering, forward modeling and inversion calculations to obtain the electrical distribution and geological modeling results of the subsurface medium.

[0016] According to some embodiments of this application, the data fusion module is configured to spatially register time-series InSAR deformation data and semi-airborne electromagnetic inversion results under a unified coordinate system, and generate geological anomaly distribution results through feature matching and trend analysis.

[0017] Other features and advantages of this application will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing this application. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the steps of a combined temporal InSAR and semi-airborne electromagnetic survey method along a railway line, as described in Embodiment 1 of this application. Figure 2 This is a flowchart of a railway line time-series InSAR and semi-airborne electromagnetic joint survey method according to Embodiment 2 of this application; Figure 3 This is a schematic diagram of the time-series InSAR method processing flow in Embodiment 2 of this application; Figure 4 This is a schematic diagram of the semi-aerospace electromagnetic processing flow of Embodiment 2 of this application; Figure 5 This is a schematic diagram of the semi-aviation electromagnetic operation mode of Embodiment 2 of this application. Detailed Implementation

[0019] The present application will now be described in further detail with reference to experimental examples and specific embodiments. However, this should not be construed as limiting the scope of the subject matter of the present application to the following embodiments. All technologies implemented based on the content of the present application fall within the scope of protection of the present application.

[0020] Unless otherwise specified, the use of terms such as "upper," "lower," "left," "right," "center," "inner," "outer," and "side" to indicate orientation or positional relationships in the description of specific embodiments of this application is based on the orientation or positional relationships shown in the accompanying drawings, or the orientation or positional relationship in which the product / equipment / device is usually placed during use. These terms are merely for the purpose of facilitating the description of the solution in this application or simplifying the description in specific embodiments, and for enabling those skilled in the art to quickly understand the solution, and do not indicate or imply that a particular device / component / element must have a specific orientation, or be constructed and operated in a specific positional relationship, and therefore should not be construed as a limitation of this application.

[0021] In the description of the embodiments of this application, technical terms such as "first" and "second" only distinguish one entity or operation from another, and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary or secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0022] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0023] Example 1 During the research process, the applicant found that when using a single InSAR deformation monitoring or ground electromagnetic detection technology to conduct geological surveys along railway lines, if it is necessary to obtain information on surface deformation and underground structure at the same time to analyze the causes of geological anomalies, it is necessary to carry out cumbersome operations such as multi-source data acquisition, independent modeling and result comparison. The processing flow is complicated and the timeliness is poor. Moreover, in complex terrains such as mountainous areas and valleys, only local measurement points can be set up, and continuous and dynamic spatial monitoring cannot be achieved.

[0024] In solving practical engineering problems, in order to achieve the technical goal of high-precision, wide-range, and low-risk collaborative investigation and early warning of hidden geological anomalies along railway lines, existing technologies cannot simultaneously take into account the unified acquisition and linkage analysis of surface and underground information, nor can they quickly deploy and automate data processing under complex terrain conditions. Therefore, they cannot meet the requirements of railway safety monitoring for real-time performance and completeness.

[0025] Therefore, after studying this problem, the applicant proposed a joint survey method using time-series InSAR and semi-airborne electromagnetic methods along railway lines. Addressing the technical issue of the disconnect between surface deformation monitoring and underground structure detection in complex terrain along railway lines, this method combines the continuous deformation monitoring capability of time-series InSAR with the underground penetration detection capability of semi-airborne transient electromagnetic methods. It establishes an integrated surface-to-subsurface joint inversion and data fusion analysis process, achieving collaborative analysis and dynamic correlation between surface deformation and underground electrical structures. This results in the efficient identification of hidden geological anomalies and improved accuracy and early warning capabilities for geological disaster monitoring under complex terrain conditions.

[0026] Please refer to Figure 1 , Figure 1 A schematic diagram illustrating the steps of the railway line time-series InSAR and semi-airborne electromagnetic joint survey method provided in this application embodiment. This may include: S1. Acquire time-series InSAR data and ground-aided data along the target railway line, wherein the time-series InSAR data includes SAR imagery, digital elevation model data, atmospheric correction auxiliary data, and track data; S2. Perform interferometric analysis and deformation monitoring on the time-series InSAR data to extract deformation information of the land surface along the route; S3. Based on the deformation information, determine the abnormal area, and conduct semi-airborne transient electromagnetic observation in the abnormal area to obtain electromagnetic response signals reflecting the characteristics of the underground medium; S4. Perform electromagnetic inversion processing on the electromagnetic response signal to obtain semi-airborne electromagnetic inversion results and obtain the electrical distribution of the underground medium; S5. The deformation information, the semi-airborne electromagnetic inversion results, and the ground auxiliary data are fused and correlated to generate the underground structure and geological anomaly distribution results along the railway line.

[0027] The railway line time-series InSAR and semi-airborne electromagnetic joint survey method according to the embodiments of this application has at least the following beneficial effects: This application first utilizes temporal InSAR technology to continuously monitor surface deformation along the railway line. This allows for the acquisition of millimeter-level precision deformation time series without the need for ground control points, comprehensively reflecting the early deformation characteristics of minor subsidence, uplift, and landslides along the railway line. Then, through analysis of the deformation time series, areas with abnormal deformation rates or accumulations are automatically identified, accurately determining key exploration areas and providing target guidance for subsequent underground exploration. Next, semi-airborne transient electromagnetic observations are conducted within these abnormal areas. Using an unmanned aerial vehicle (UAV) equipped with an electromagnetic receiving device, survey lines are flexibly deployed under complex terrain conditions to quickly acquire underground electromagnetic response signals, achieving efficient detection of the underground medium structure. Subsequently, through filtering, frequency domain modeling, time domain transformation, and forward and inverse calculations, the electrical distribution information of the underground medium is obtained. Finally, the deformation information, the semi-airborne electromagnetic inversion results, and the ground-based auxiliary data are fused and correlated to reveal the coupling relationship between surface deformation and underground structural changes in a unified spatial coordinate system, generating a map of the underground structure and geological anomalies along the railway line. By establishing a dynamic linkage between surface monitoring and underground exploration, this invention enables integrated surface-underground collaborative exploration, significantly improving the accuracy and efficiency of identifying hidden geological anomalies along railway lines. This method not only reduces the deployment difficulty and operational risks of traditional ground surveys but also allows for large-scale, high-resolution dynamic monitoring under complex terrain conditions, providing a reliable technical means for early warning and risk assessment of geological disasters along railway lines.

[0028] In some specific implementation schemes, the following steps may be included: First, acquire time-series InSAR data and ground-aided data along the target railway line. The time-series InSAR data includes SAR imagery, digital elevation model data, atmospheric correction auxiliary data, and track data. This data is used to support subsequent interferometric analysis and precise registration.

[0029] Secondly, interferometric analysis and deformation monitoring are performed on the time-series InSAR data. By registering and differentially interferometrically processing multi-temporal radar images, phase difference information between images is extracted, and the phase difference is converted into surface displacement along the radar line of sight, establishing a surface deformation time series. In this step, operations such as phase unwrapping, track refinement, and residual atmospheric phase removal can be performed to improve the accuracy and stability of the deformation results, thereby obtaining a continuous deformation rate field of the surface along the railway line.

[0030] Then, the abnormal areas are determined based on the deformation time series analysis results. By using threshold determination or cluster analysis of surface deformation and deformation rate, areas with large deformation amplitude or abnormal change rate are identified as key exploration areas. The anomaly range is confirmed by combining on-site reconnaissance, providing precise targets for subsequent electromagnetic detection.

[0031] Next, semi-airborne transient electromagnetic observations were conducted in the key survey area. Using an unmanned aerial vehicle (UAV) equipped with an electromagnetic receiving device, electromagnetic data was collected from the target area according to the planned grid survey lines. The spatial location of the measuring points and the amplitude of the induced signal were recorded in real time, enabling rapid and non-contact detection of the subsurface medium under complex terrain.

[0032] Subsequently, the acquired semi-airborne transient electromagnetic signals were inverted. First, filtering, frequency domain modeling, and time domain transformation were performed on the original signals to establish a forward model of the underground electromagnetic field. Then, based on the criterion of minimizing the difference between the observed data and the theoretical response, iterative optimization was used to obtain the electrical distribution of the underground medium, forming a three-dimensional electrical structure model that reflects the underground conductivity characteristics.

[0033] Finally, the deformation information, the semi-airborne electromagnetic inversion results, and the ground-based auxiliary data are fused and correlated. Under a unified geographic coordinate system, spatial registration and interpolation are performed on the two types of data. A multi-source information fusion algorithm is used to establish the correspondence between surface deformation and underground electrical changes. The influence of factors such as geological tectonic activity, groundwater changes, or strata loosening on surface deformation is comprehensively assessed, generating a map of the underground structure distribution along the railway line and geological anomaly analysis results.

[0034] Through the above steps, the method of this application realizes the collaborative investigation of surface deformation and underground structure along railway lines. It can obtain continuous deformation information with millimeter-level accuracy from time-series InSAR data, and obtain the electrical characteristics of underground media through semi-airborne electromagnetic technology. The two technologies complement each other in the temporal and spatial dimensions, forming a joint analysis framework integrating surface and underground. It can efficiently identify hidden geological anomalies under complex terrain conditions, and significantly improve the accuracy, efficiency and safety of geological disaster monitoring and early warning along railway lines.

[0035] The railway-line time-series InSAR and semi-airborne electromagnetic joint survey method provided in this application can be applied to various geological exploration and safety monitoring technologies, such as geological risk assessment of transportation infrastructure, monitoring of mine subsidence, deformation monitoring of reservoir areas, investigation of slope and landslide hazards, and monitoring of urban ground subsidence. This method is not only suitable for geological disaster prevention and control along railway lines, but can also be extended to monitoring tasks in highways, utility tunnels, tunnels, water conservancy, and energy projects that require simultaneous acquisition of surface deformation and underground structure information.

[0036] In the above implementation method, when controlling the monitoring process, the InSAR image acquisition cycle, observation mode, and processing strategy can be flexibly selected according to different terrain and geological conditions; the layout of the semi-airborne electromagnetic survey lines, excitation current intensity, and sampling interval can be adjusted to optimize the spatial resolution of the underground electromagnetic signal. By establishing an automated data registration and joint inversion model in the data processing stage, the synchronous updating and dynamic fusion of InSAR deformation monitoring results and semi-airborne electromagnetic inversion results can be achieved, thereby achieving high-precision, low-risk, and wide-coverage integrated surface-subsurface geological exploration under complex terrain conditions.

[0037] Example 2 As a further optimization of the preceding embodiments, this application proposes a specific implementation method for a combined temporal InSAR and semi-airborne electromagnetic survey method along railway lines. This method can efficiently and safely ascertain the evolution of surface deformation in the survey area, including information such as deformation boundaries and deformation rates, without requiring personnel to enter the deformation area, and further analyze the causes of deformation and the distribution of underground geological bodies.

[0038] Temporal InSAR is a radar interferometry technique applied in surveying and remote sensing. This method uses differential synthetic aperture radar interferometry and incorporates topographic phase difference analysis using an external digital elevation model to achieve precise monitoring of surface deformation. Semi-airborne Electromagnetic Method (SAEM) is an electromagnetic exploration method that employs a ground-based transmission and airborne reception mode. By receiving induced electromagnetic signals generated by underground geological bodies, it enables wide-area detection and temporal movement analysis of areas difficult to access on the surface.

[0039] This method includes the following: S1. Select survey targets and determine the scope of the survey targets through geological surveys along the railway line, and obtain multi-view SAR (synthetic aperture radar) image data, high-precision DEM (digital elevation model) data, atmospheric correction auxiliary data, and precise track data of the corresponding areas to provide basic data for subsequent processing.

[0040] S2. Calculate the location and deformation rate of the deformed area using time-series InSAR technology to obtain high-precision terrain deformation information for the selected area. For example... Figure 3 As shown, Figure 3 This is a schematic diagram of the time-series InSAR processing flow. The flow includes: preprocessing multiple SAR image data sets within a specific time frame by cropping regions; and registering the multiple SAR image data sets after correction with precise orbital data to obtain the master image. The process involves importing digital elevation model (DEM) data and atmospheric correction auxiliary data, performing differential interferometry (DI) calculations on the SAR data within a set time threshold to generate interferograms. A coherence threshold is set during the DI, selecting pixels with high coherence. The interferograms are then unwrapped to obtain a continuous phase field. After unwrapping, the process continues with editing interferometric pairs, track refinement, and re-leveling to eliminate track errors and system biases. Deformation efficiency and elevation coefficients are then estimated, and atmospheric phase and residual phase effects are removed. Finally, the surface deformation rate is calculated, and high-precision deformation results for the study area are output via geocoding. These processing steps provide the spatiotemporal distribution characteristics of surface deformation along the railway line, offering precise spatial positioning data for subsequent anomaly identification and semi-airborne electromagnetic surveys.

[0041] Any SAR image within the time threshold With the main image The generated differential interference phase can be expressed as:

[0042] The above formula Represents differential interference phase, and The phases represented by pixels B and A are respectively. For wavelength, and These are the cumulative deformations in the radar line-of-sight (LOS) direction corresponding to times B and A, respectively.

[0043] After screening eligible interferometric pairs, further orbit refinement and re-leveling operations were performed to correct orbital errors and systematic deviations. Subsequently, deformation rates and elevation coefficients were estimated, and the influence of atmospheric phase and residual phase was removed to obtain atmospherically corrected deformation rate results. After the deformation rate calculation was completed, the obtained deformation rate values ​​were statistically analyzed to determine whether they conformed to a normal distribution centered at 0 within the study area, thus verifying the reliability of the calculation results. After verification, the deformation results were geocoded and projected onto a geographic coordinate system to obtain information such as the surface deformation process and development patterns of the processed area.

[0044] S3. Compare and analyze historical optical images with the calculated topographic deformation information to delineate deformation boundaries. First, areas with deformation rates >10 mm / a (millimeters per year) are marked to identify areas with significant surface deformation. Then, compare the changes in optical images within the same time frame to exclude surface deformation caused by human activities. Based on this, areas requiring key investigation are delineated to determine their specific geographic coordinates and deformation boundary ranges.

[0045] S4. Conduct a comprehensive analysis of the temporal InSAR deformation rate results and select key survey areas along the railway line. After determining the key areas, measure their length along the topographic contour lines, using the central area as the zero point coordinate, and defining the intersections with the topographic contour lines on both sides as -L and L coordinates, respectively. The included area is the key survey area.

[0046] S5. Conduct on-site reconnaissance of key survey areas, and lay grounding wire sources in areas accessible to personnel. The length of the grounding wire sources should be greater than 2L to ensure signal coverage. Simultaneously, plan semi-airborne electromagnetic survey lines according to the terrain undulations, and determine the flight altitude and survey line path for different lines. The working mode of semi-airborne electromagnetic survey is as follows: Figure 5 As shown, by transmitting current through a ground-based grounding wire, the electromagnetic receiving device on the UAV synchronously receives induced electromagnetic signals in the air, enabling space detection of electromagnetic responses below the Earth's surface.

[0047] S6. A network-based data collection system is used in key deformation areas via receiving equipment mounted on a drone to collect induced electromagnetic information generated by underground geological bodies. Assume the center of the grounding conductor source is the origin, and the conductor extends L on both sides along the X-axis. The coordinates of each measuring point are... Semi-airborne electromagnetic frequency domain forward response The expression is as follows:

[0048] In the formula, This represents the forward frequency domain response of the semi-aerospace electromagnetic system. For transmitting current; 2 L The length of the grounding conductor source; the formula for calculating R is... ; Let be the coordinates of any point on the long conductor. The reflection coefficient; For integration variables; It is a first-order Bessel function of the first kind; The magnetic permeability in vacuum (4π*10) -7 H / m), This represents the portion of the electromagnetic field that attenuates or increases during propagation in the vertical (z-direction). After a sinusoidal transformation, the time-domain expression for the derivative of the vertical magnetic field with respect to time is obtained as follows:

[0049] In the formula, t is time, n is the number of sine and cosine filter coefficients, and Δ is the sampling interval. These are the sine and cosine filter coefficients.

[0050] Ultimately, the induced electromagnetic information can be represented as: In the formula, V represents the induced electromotive force, N represents the number of turns of the receiving coil, and S is the effective area of ​​the coil (unit: square meters).

[0051] S7. The collected induced electromagnetic information is imported into the data processing system. Multiple signals are superimposed to improve the signal-to-noise ratio. Secondary field information is extracted, and the signals are filtered to remove high-frequency noise and background interference. Multi-channel electromagnetic profile data is then generated, and inversion calculations are performed based on this data to obtain the electrical distribution of the subsurface medium. The objective function for the inversion calculation is:

[0052] In the formula, Φ is the overall objective function; the observation data vector d consists of the electromagnetic response values ​​collected by the receiving system at each measuring point and at each time sampling channel, and the number of data points i depends on the number of measuring points and the number of sampling channels recorded at each measuring point; m is the model parameter vector, and if the model has n layers, the corresponding parameters generally include formation resistivity. and stratum thickness There are a total of 2n-1 parameters; Δm is the damping factor; Δm is the model correction value. The weighting matrix for the observation data is a diagonal matrix whose elements are the reciprocals of the noise in the observation data. The theoretical electromagnetic response calculated for model m. It is a weighted matrix of model correction data. Then, Taylor's formula is used to... Expand and take the first-order linear terms:

[0053] In the formula, It is a Jacobian matrix that is updated with the number of iterations.

[0054] After unfolding Substituting the total objective function Φ into the inversion objective function and minimizing the objective function, we obtain:

[0055] The model correction amount can be obtained from the above formula. Then the model parameter vector for the next iteration can be obtained. , The model parameter vector from the previous iteration is used as the basis for repeated iterations using this formula until the iteration convergence condition is met, yielding a satisfactory inversion result. The resistivity profile is then summarized using the inversion results, and combined with regional geological information to ultimately obtain the subsurface geological distribution of the key target area. After the inversion calculation is completed, the semi-airborne transient electromagnetic data is processed and interpreted; the processing flow is shown in Figure 4. Figure 4The illustrated process comprises three main stages: data import, forward modeling, and inversion calculation. In the data import stage, full-time induction electromagnetic data is first input, and the raw signals from all channels are superimposed to improve the signal-to-noise ratio. Subsequently, a suitable power-off time is selected to filter the intercepted induction electromagnetic signals, removing high-frequency noise and interference. The filtered data is then synthesized to form multi-channel profile information, providing high-quality input data for subsequent inversion calculations. In the modeling and inversion stage, a forward model is established based on geological conditions to simulate the propagation of electromagnetic fields in the subsurface medium. Inversion calculations are then performed by comparing the model with observational data to obtain the electrical distribution of the subsurface medium. After the inversion is completed, geological modeling is conducted to generate resistivity profiles reflecting changes in the subsurface conductive structure. Through comprehensive analysis of these profiles and geological information, characteristics such as changes in overburden thickness, the degree of development of subsurface fissures, and increased water content can be identified, thereby clarifying the specific causes of surface deformation.

[0056] S8. Finally, through joint exploration using temporal InSAR and semi-airborne electromagnetic technology, surface deformation information and underground geological body distribution information of the target area were obtained, and a high-precision comprehensive geological model extending from the surface to a certain depth underground was established. This model integrates the process and development law of surface deformation and the spatial distribution characteristics of underground geological bodies, realizing the fusion expression of surface and underground information, and providing a scientific basis for the identification of hidden geological anomalies and the analysis of the causes of geological disasters.

[0057] Example 3 As a further optimization of the preceding embodiments, this application provides a railway-line time-series InSAR and semi-airborne electromagnetic joint survey device, including: The data acquisition module is used to acquire time-series InSAR data and ground-aided data along the target railway line. The data includes SAR imagery, digital elevation model data, atmospheric correction auxiliary data, and track data.

[0058] The first calculation module is used to perform interferometric analysis on the time-series InSAR data and extract surface deformation information. The first calculation module is configured to perform multi-temporal interferometric registration, phase difference extraction, phase unwrapping and deformation conversion to form a surface deformation time series and output deformation rate results.

[0059] The second calculation module is used to collect semi-airborne transient electromagnetic observation signals in the deformation anomaly area and perform inversion processing to obtain the electrical distribution of the subsurface medium; the second calculation module is configured to perform filtering, forward modeling and inversion calculations to obtain the electrical distribution of the subsurface medium and geological modeling results.

[0060] The data fusion module is used to perform data fusion and correlation analysis between the deformation information and the semi-airborne electromagnetic inversion results, and output the results of the underground structure and geological anomaly distribution along the railway line. The data fusion module is configured to spatially register the time-series InSAR deformation data and the semi-airborne electromagnetic inversion results under a unified coordinate system, and generate geological anomaly distribution results through feature matching and trend analysis.

[0061] It should be understood that the various modules of the railway time-series InSAR and semi-airborne electromagnetic joint survey device provided in the above embodiments are only illustrated by the division of each functional module in the above description when conducting joint surveys. In practical applications, the above functions can be assigned to different functional modules as needed. That is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0062] The functional modules in the above embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of the embodiments of this application.

[0063] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects.

[0064] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0065] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for joint temporal InSAR and semi-airborne electromagnetic survey along railway lines, characterized in that, include: S1. Acquire time-series InSAR data and ground-aided data along the target railway line, wherein the time-series InSAR data includes SAR imagery, digital elevation model data, atmospheric correction auxiliary data, and track data; S2. Perform interferometric analysis and deformation monitoring on the time-series InSAR data to extract deformation information of the land surface along the route; S3. Based on the deformation information, determine the abnormal area, and conduct semi-airborne transient electromagnetic observation in the abnormal area to obtain electromagnetic response signals reflecting the characteristics of the underground medium; S4. Perform electromagnetic inversion processing on the electromagnetic response signal to obtain semi-airborne electromagnetic inversion results and obtain the electrical distribution of the underground medium; S5. The deformation information, the semi-airborne electromagnetic inversion results, and the ground auxiliary data are fused and correlated to generate the underground structure and geological anomaly distribution results along the railway line.

2. The method according to claim 1, characterized in that, The interferometric analysis and deformation monitoring include: registering and differentially interferometric processing of multi-temporal radar images to obtain phase difference information, and calculating the ground displacement along the radar line of sight based on the phase difference to establish a continuous deformation time series.

3. The method according to claim 1, characterized in that, The determination of the abnormal area includes: performing rate and cumulative analysis on the deformation time series, selecting areas where the deformation or deformation rate exceeds a preset threshold as abnormal areas, and determining the key investigation scope in combination with the results of on-site surveys.

4. The method according to claim 3, characterized in that, The semi-airborne transient electromagnetic observation includes: using an unmanned aerial vehicle (UAV) equipped with an electromagnetic receiving device to conduct grid-like data acquisition along the planned survey line in key survey areas, obtaining induced electromagnetic signals and recording spatial positions.

5. The method according to claim 1, characterized in that, The electromagnetic inversion process includes: filtering, frequency domain modeling and time domain conversion of the acquired semi-airborne transient electromagnetic signals, establishing a forward model and performing inversion calculations by minimizing the difference between the observed data and the theoretical response to obtain the electrical distribution of the underground medium.

6. The method according to claim 1, characterized in that, The data fusion and correlation analysis includes: spatially registering the deformation information and the semi-airborne electromagnetic inversion results in a unified geographic coordinate system, analyzing the correlation between the two changes through a multi-source information fusion algorithm, and outputting the results of underground structure and geological anomaly distribution.

7. A combined temporal InSAR and semi-airborne electromagnetic survey device for railway lines, characterized in that, include: The data acquisition module is used to acquire time-series InSAR data and ground auxiliary data along the target railway line. The time-series InSAR data includes SAR imagery, digital elevation model data, atmospheric correction auxiliary data, and track data. The first calculation module is used to perform interferometric analysis and deformation monitoring on the time-series InSAR data and extract deformation information of the land surface along the route; The second calculation module is used to determine the abnormal area based on the deformation information, and to carry out semi-airborne transient electromagnetic observation in the abnormal area to obtain electromagnetic response signals reflecting the characteristics of the underground medium. The electromagnetic response signals are then subjected to electromagnetic inversion processing to obtain the semi-airborne electromagnetic inversion results and to obtain the electrical distribution of the underground medium. The data fusion module is used to perform data fusion and correlation analysis on the deformation information, the semi-airborne electromagnetic inversion results and the ground auxiliary data to generate the underground structure and geological anomaly distribution results along the railway line.

8. The apparatus according to claim 7, characterized in that, The first calculation module is configured to perform multi-temporal interferometric registration, phase difference extraction, phase unwrapping and deformation conversion to form a time series of surface deformation and output deformation rate results.

9. The apparatus according to claim 7, characterized in that, The second calculation module is configured to perform filtering, forward modeling, and inverse calculations to obtain the electrical distribution and geological modeling results of the underground medium.

10. The apparatus according to claim 7, characterized in that, The data fusion module is configured to spatially register time-series InSAR deformation data and semi-airborne electromagnetic inversion results under a unified coordinate system, and generate geological anomaly distribution results through feature matching and trend analysis.

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