Geological forecasting method and device and detection equipment

Abstract

The invention discloses a geological forecasting method, a geological forecasting device and detection equipment. The method comprises the following steps: acquiring first three-dimensional information of a geological target body based on a geological radar, wherein the first three-dimensional information comprises spatial position, structure, electrical property or geometric morphology; acquiring second three-dimensional information of the geological target body based on transient electromagnetism; acquiring third three-dimensional information of the geological target body based on a seismic wave method; establishing an inversion algorithm model based on the first three-dimensional information, the second three-dimensional information and the third three-dimensional information; based on the inversion algorithm model, physical property parameters and spatial distribution of the geological target body are analyzed, and a three-dimensional geological model is determined; based on the three-dimensional geologic model and a pre-trained geologic anomalous body identification model, determining an unfavorable geologic body of the geologic target body; and determining the risk level of the geological target body based on the unfavorable geological body and performing early warning. The method has the technical effects that multi-source data are fused to jointly establish the geological model, and the accuracy and comprehensiveness of geological forecast are improved.

Description

Technical Field

[0001] The present application relates to the technical field of geological prediction, and in particular to a geological prediction method, device, and detection equipment. Background Art

[0002] With the continuous advancement of transportation infrastructure construction, tunnel projects are increasing in number and facing increasingly complex geological conditions. Accurately understanding the geological conditions ahead is crucial to ensuring construction safety and improving efficiency. Before and during tunnel construction, detailed geological surveys are required to formulate appropriate construction plans. Therefore, the accuracy and comprehensiveness of geological forecasts warrant attention. Summary of the Invention

[0003] In view of this, the embodiments of the present application provide a geological prediction method, device, and detection equipment to improve the accuracy of geological prediction. In the first aspect, a geological prediction method is provided, including: obtaining first three-dimensional information of a geological target body based on geological radar, the first three-dimensional information including: spatial position, structure, electrical properties, or geometric shape; obtaining second three-dimensional information of the geological target body based on transient electromagnetic, the second three-dimensional information including: electrical conductivity, electrical structure, or electrical interface position; obtaining third three-dimensional information of the geological target body based on seismic wave method, the third three-dimensional information including: longitudinal wave velocity, shear wave velocity, or wave impedance; establishing an inversion algorithm model based on the first three-dimensional information, the second three-dimensional information, and the third three-dimensional information; analyzing the physical parameters and spatial distribution of the geological target body based on the inversion algorithm model to determine a three-dimensional geological model; determining an unfavorable geological body of the geological target body based on the three-dimensional geological model and a pre-trained geological anomaly recognition model; determining the risk level of the geological target body based on the unfavorable geological body and issuing an early warning.

[0004] The above geological prediction methods, through three geological information acquisition methods, realize geological target detection tasks from multiple angles, integrate multi-source data to jointly establish geological models, and conduct geological analysis through inversion algorithm models to identify geological anomalies, determine unfavorable geological bodies, and improve the accuracy and comprehensiveness of geological predictions.

[0005] Optionally, obtaining the first three-dimensional information of the geological target body based on the geological radar includes: obtaining the first reflected signal of the geological radar electromagnetic wave; eliminating the DC component in the first reflected signal to determine the second reflected signal; smoothing the curve of the second reflected signal to eliminate sharp noise peaks to determine the third reflected signal; decomposing the third reflected signal by wavelet to remove high-frequency noise to determine the fourth reflected signal; performing electromagnetic wave analysis on the fourth reflected signal to determine the first three-dimensional information.

[0006] Optionally, obtaining second three-dimensional information of the geological target body based on transient electromagnetic includes: obtaining a first pulse signal of transient electromagnetic; performing background field correction on the first pulse signal to determine a second pulse signal; and performing resistivity inversion based on the second pulse signal to determine the second three-dimensional information.

[0007] Optionally, obtaining the third three-dimensional information of the geological target body based on the seismic wave method includes: obtaining the first seismic wave signal of the seismic wave; performing wavelength separation on the first seismic wave signal to determine multiple waveform signals, the multiple waveform signals including: first arrival wave signal, secondary wave signal, surface wave signal, or interference wave signal; performing wave velocity analysis on the multiple waveform signals to determine the third three-dimensional information.

[0008] Optionally, an inversion algorithm model is established based on the first three-dimensional information, the second three-dimensional information, and the third three-dimensional information, including: establishing a geological coordinate system; aligning the spatial dimensions and time dimensions of the first three-dimensional information, the second three-dimensional information, and the third three-dimensional information in the geological coordinate system; constructing a joint inversion objective function based on the aligned first three-dimensional information, the second three-dimensional information, and the third three-dimensional information; optimizing the joint inversion objective function and determining the inversion algorithm model.

[0009] Optionally, based on the three-dimensional geological model and a pre-trained geological anomaly recognition model, the unfavorable geological body of the geological target body is determined, including: training the geological anomaly recognition model based on a deep learning algorithm to determine the classification characteristics and edge characteristics of the unfavorable geological body; and determining the category of the unfavorable geological body of the geological target body by comparing the three-dimensional geological model and the edge characteristics.

[0010] In the second aspect, a geological prediction detection device is provided, which is used for the geological prediction method provided in the first aspect, including: a bracket, a telescopic rod, a detection body holder, an adjustment device, and a controller; the first part of the telescopic rod is connected to the bracket through the adjustment device, and the second part is connected to the detection body holder; the adjustment device is configured to adjust the angle, length and height of the telescopic rod, and is configured to adjust the height of the bracket; the detection body holder, on which a geological radar coil, a transient electromagnetic coil and a seismic wave generator are arranged, is configured to detect first three-dimensional information, second three-dimensional information and third three-dimensional information based on the movement of the telescopic rod; the controller is arranged on the third part of the telescopic rod, and is configured to control the length, angle and scanning movement of the telescopic rod.

[0011] The above geological prediction and detection equipment can replace the manual lifting of radar antennas and detection coils through structural design, avoiding human operation hazards and effectively improving the speed and accuracy of detection.

[0012] Optionally, the bracket includes multiple support rods, the first ends of the support rods are connected to the adjustment device, and a footrest fixing platform is provided away from the first ends of the support rods; the support rods include: a telescopic tensioner, a first rod body, a second rod body and multiple hinges; the telescopic tensioner is provided between the first rod body and the second rod body, and is configured to adjust the length of the support rod; the hinge connects the first end of the support rod and the adjustment device, and is configured to adjust the angle of the bracket.

[0013] Optionally, the adjusting device includes: a fixed platform, a lifting device, a rotating bearing, and a telescopic rod connector; the lower part of the fixed platform is connected to the first ends of the multiple support rods, and the upper part is connected to the lifting device; the rotating bearing is connected to the lifting device at the lower part and the telescopic rod connector at the upper part, and is configured to drive the telescopic rod to move horizontally; the telescopic rod connector is connected to the first part of the telescopic rod and is configured to drive the telescopic rod to move vertically; the telescopic rod connector includes: a rotating structure, a locking structure, and a fixed structure; the rotating structure is connected to the first part of the telescopic rod through the locking structure, and is configured to drive the telescopic rod to move vertically; the upper part of the fixed structure is connected to the rotating structure, and the lower part is connected to the lifting device.

[0014] According to a third aspect, a geological prediction device is provided, comprising: an acquisition unit configured to acquire first three-dimensional information of a geological target body based on geological radar, the first three-dimensional information including spatial position, structure, electrical properties, or geometric shape; acquire second three-dimensional information of the geological target body based on transient electromagnetic, the second three-dimensional information including electrical conductivity, electrical structure, or electrical interface position; and acquire third three-dimensional information of the geological target body based on a seismic wave method, the third three-dimensional information including longitudinal wave velocity, transverse wave velocity, or wave impedance; a modeling unit configured to establish an inversion algorithm model based on the first three-dimensional information, the second three-dimensional information, and the third three-dimensional information; a determination unit configured to analyze the physical parameters and spatial distribution of the geological target body based on the inversion algorithm model and determine a three-dimensional geological model; an identification unit configured to determine an unfavorable geological body of the geological target body based on the three-dimensional geological model and a pre-trained geological anomaly identification model; and an early warning unit configured to determine the risk level of the geological target body based on the unfavorable geological body and issue an early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The following is a brief introduction to the drawings used in describing the embodiments of this application:

[0016] Figure 1 A schematic flow chart of a geological prediction method provided in some embodiments of the present application is shown;

[0017] Figure 2 A schematic flow chart of a method for obtaining first three-dimensional information of a geological target body based on geological radar provided in some embodiments of the present application is shown;

[0018] Figure 3A schematic diagram of a process for obtaining second three-dimensional information of a geological target body based on transient electromagnetics provided in some embodiments of the present application is shown;

[0019] Figure 4 A schematic flow chart of a method for obtaining third-dimensional information of a geological target body based on a seismic wave method provided in some embodiments of the present application is shown;

[0020] Figure 5 A schematic flow chart of a method for establishing an inversion algorithm model provided in some embodiments of the present application is shown;

[0021] Figure 6 A schematic structural diagram of a geological prediction and detection device provided in some embodiments of the present application is shown;

[0022] Figure 7 A schematic diagram of a three-dimensional grid survey line layout of a geological radar provided in some embodiments of the present application is shown;

[0023] Figure 8 A schematic diagram of a matrix measurement point layout for transient electromagnetic detection of a tunnel face provided in some embodiments of the present application is shown;

[0024] Figure 9 A schematic structural diagram of a geological prediction device provided in some embodiments of the present application is shown. DETAILED DESCRIPTION

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the specific implementation methods of the present application will be described below with reference to the accompanying drawings. The drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings or embodiments can be obtained based on these drawings or embodiments without inventive work. Adjustments and improvements made without departing from the concept of the present application are all within the scope of protection of the present application.

[0026] To simplify the drawings, the figures schematically illustrate only the portions relevant to the embodiments and do not represent the actual structure of the products. Furthermore, to simplify the drawings and facilitate understanding, in some figures, only a portion of components with the same structure or function are schematically depicted; in practice, more or fewer components with the same structure or function may exist.

[0027] With the continuous advancement of transportation infrastructure construction, tunnel projects are increasing in number and facing increasingly complex geological conditions. Accurately understanding the geological conditions ahead is crucial to ensuring construction safety and improving efficiency. Detailed geological surveys are necessary to develop a reasonable construction plan in advance. In geological forecasting, geological radar can use the propagation characteristics of high-frequency electromagnetic waves in media to image shallow geological structures. Transient electromagnetic methods can effectively detect underground low-resistance bodies and identify water-bearing areas by observing the transient electromagnetic response of the subsurface medium. Seismic wave methods can analyze geological structures and rock mass integrity based on the differences in seismic wave propagation in different media. However, in practical applications, each method has limitations. For example, geological radar has a limited detection depth. It primarily uses high-frequency electromagnetic waves for detection, which attenuate rapidly when propagating in the subsurface medium. This results in a generally shallow effective detection depth of less than tens of meters. This makes it difficult to accurately detect deeply buried geological structures or deep, unfavorable geological bodies. Transient electromagnetic detection also has a low lateral resolution, focusing on the longitudinal electrical distribution of subsurface geological bodies. When detecting small, laterally complex geological anomalies, it's difficult to accurately determine their boundaries and morphology. Transient electromagnetic (TEM) signals are susceptible to instrument noise and surface interference in their early stages, resulting in low accuracy for shallow geological information and an inability to clearly reflect the detailed structure of shallow strata. Seismic wave detection and geological interpretation are subject to ambiguity. During seismic wave propagation, the reflected and refracted signals are affected by various factors, such as the shape, physical properties, and propagation path of the geological body. Consequently, the same set of elastic wave signals can yield multiple different geological interpretations, compromising the accuracy of geological inferences. Furthermore, the ability to identify some unique geological bodies, such as caves and cavities, is relatively weak, especially when these bodies are filled or have minimal physical property differences from the surrounding medium, making their presence and characteristics difficult to accurately determine. In traditional geological forecasting, data acquired by different detection technologies are often analyzed and used independently, without sufficient consideration of their complementary capabilities. This results in inadequate utilization of geological information and a failure to fully and accurately reflect the subsurface geology. Traditional, single-source geological exploration methods struggle to comprehensively and accurately acquire geological information ahead of tunnels under complex geological conditions. Furthermore, due to a lack of multi-source data integration and joint inversion, geological models constructed using traditional methods often only reflect partial geological features and fail to accurately reproduce the three-dimensional morphology and physical property distribution of underground geological structures, resulting in low accuracy and reliability in geological forecasts. Traditional geological forecast risk assessments rely primarily on human experience and subjective judgment, lacking scientific and objective evaluation standards and methods. This leads to significant subjectivity and uncertainty in risk assessment results, making them prone to omissions and misjudgments.This application aims to provide a geological prediction method, device, and detection equipment. By integrating geological radar three-dimensional detection, transient electromagnetic detection, and seismic wave comprehensive detection technology, it can achieve all-round and high-precision detection of the geological conditions ahead of the tunnel, provide a reliable geological basis for tunnel construction, effectively reduce construction risks, and reduce project delays and safety accidents caused by geological problems.

[0028] The following is a description with reference to the accompanying drawings:

[0029] Figure 1 The following is a flow chart of a geological prediction method provided in some embodiments of the present application. The geological prediction method includes at least the following steps:

[0030] S110: Acquire first three-dimensional information of the geological target body based on the geological radar, where the first three-dimensional information includes: spatial position, structure, electrical properties, or geometric shape;

[0031] S120: Acquire second three-dimensional information of the geological target body based on transient electromagnetics, where the second three-dimensional information includes: electrical conductivity, electrical structure, or electrical interface position;

[0032] S130: Acquire third and third-dimensional information of the geological target body based on a seismic wave method, where the third and third-dimensional information includes: longitudinal wave velocity, shear wave velocity, or wave impedance;

[0033] S140: establishing an inversion algorithm model based on the first three-dimensional information, the second three-dimensional information, and the third three-dimensional information;

[0034] S150: Based on the inversion algorithm model, analyze the physical parameters and spatial distribution of the geological target body and determine the three-dimensional geological model;

[0035] S160: Determine the unfavorable geological body of the geological target body based on the three-dimensional geological model and the pre-trained geological anomaly recognition model; determine the risk level of the geological target body based on the unfavorable geological body and issue an early warning.

[0036] In the above embodiments, when acquiring the first three-dimensional information of a geological target based on geological radar, high-frequency electromagnetic waves can be used in the form of broadband short pulses, transmitted from the ground through a transmitting antenna into the ground. When encountering targets such as stratum interfaces and geological bodies, part of the electromagnetic wave signal will be reflected and received by a receiving antenna. Based on information such as the travel time, amplitude, and waveform of the received reflected waves, the spatial position, structure, electrical properties, or geometric features of the geological target can be inferred, thereby obtaining basic three-dimensional information of the geological target and improving the accuracy and comprehensiveness of the detection. When acquiring the second three-dimensional information of a geological target based on transient electromagnetics, a pulsed magnetic field can be emitted into the ground using an ungrounded return line or a grounded line source. During the intervals of the pulsed magnetic field, a coil or grounded electrode is used to observe the secondary induced eddy current field in the underground medium, thereby detecting the electrical distribution of the underground geological body, determining the electrical conductivity, electrical structure, or electrical interface position of the geological target, and utilizing the sensitive response to low-resistance bodies to effectively detect underground water-bearing structures, faults, and other geological anomalies. When acquiring third-dimensional information about a geological target using the seismic wave method, parameters such as the propagation velocity, amplitude, and frequency of the seismic waves change. When seismic waves encounter geological interfaces or anomalies, they produce phenomena such as reflection, refraction, and transmission. Therefore, these seismic wave signals can be received by sensors placed at the tunnel face or wall, and analyzed and processed to determine the longitudinal wave velocity, shear wave velocity, or wave impedance, thereby inferring the structure and properties of the geological body ahead of the tunnel. Using the first, second, and third dimensional information determined by detection, an inversion algorithm model can be established for geological exploration. For example, this can be achieved through two stages: forward modeling and inversion optimization. During forward modeling, a hypothetical geological target model (initial model) can be constructed and assigned reasonable physical parameters (such as resistivity, dielectric constant, wave velocity, etc.). Numerical simulation methods (such as finite element method (FEM) and finite difference method (FDM)) are then used to calculate the theoretical response of the model under geological radar, transient electromagnetic, and seismic wave detection. Inversion optimization is then performed. For example, the error between the theoretical response and the actual measured data can be calculated. The geological model is then continuously adjusted using optimization algorithms such as least squares, gradient descent, Bayesian inversion, and Markov Chain Monte Carlo (MCMC). By establishing a geological model and inversion algorithm, the physical properties and spatial distribution of the subsurface geological bodies can be determined, and a three-dimensional geological model can be determined. A pre-trained geological anomaly recognition model can include a variety of complex and recognizable geological anomalies. By comparing the 3D geological model with this model, unfavorable geological bodies within the target geological body can be identified, and the risk level of the target geological body can be determined and an early warning can be issued.For example, unfavorable geological bodies can include faults, densely jointed zones, weak interlayers, isolated boulders, water-rich fault fracture zones, karst caves, or mined-out areas. For example, a fault structure is a fractured structure in which crustal rocks fracture under stress, with significant relative displacement along the fracture surface. In this structure, rock fragmentation in the fault zone and frequent groundwater activity can lead to disasters such as collapse, water and mud inrush during tunnel construction. By accurately identifying unfavorable geological bodies and conducting geological forecasts before construction begins, geological hazards can be effectively prevented during construction, minimizing production safety risks and economic losses.

[0037] In some embodiments, Figure 2 A schematic flow chart of a method for obtaining first three-dimensional information of a geological target body based on geological radar provided in some embodiments of the present application is shown, specifically including:

[0038] S210: Acquire a first reflected signal of the geological radar electromagnetic wave;

[0039] S220: Eliminate the DC component in the first reflected signal to determine a second reflected signal;

[0040] S230: Smoothing the curve of the second reflected signal to eliminate sharp noise peaks and determine a third reflected signal;

[0041] S240: Decompose the third reflected signal with wavelet to remove high-frequency noise and determine a fourth reflected signal;

[0042] S250: Perform electromagnetic wave analysis on the fourth reflected signal to determine first three-dimensional information.

[0043] In the above embodiments, for the structural identification of geological targets, the corresponding basic three-dimensional detection data can first be collected based on geological radar. First, the geological radar equipment is used to transmit electromagnetic wave signals to the underground target area, and the radar echo signals reflected by the interfaces of different geological layers are received to obtain the original first reflection signal. This signal contains both effective geological reflection information and a certain degree of noise and DC offset components. The first reflection signal is then preprocessed to remove its DC component, thereby eliminating the interference caused by the signal baseline drift and obtaining an improved second reflection signal. Furthermore, the waveform curve of the second reflection signal is smoothed to suppress the sharp noise peaks therein to remove abnormal points that may be caused by transient interference or equipment noise, thereby obtaining a more continuous and stable third reflection signal. The third reflection signal is subjected to wavelet decomposition processing, which can effectively separate the main echo characteristics of the geological target by retaining the main low-frequency components and filtering out the high-frequency noise, thereby obtaining a fourth reflection signal after noise reduction optimization. Finally, based on the fourth reflected signal, the electromagnetic wave propagation characteristics are analyzed. Combined with parameters such as the signal propagation time, amplitude change, and waveform characteristics, the electromagnetic property distribution of the underground medium is calculated to determine the first three-dimensional information of the geological target body, that is, the three-dimensional structural characteristics from the perspective of the geological radar.

[0044] In some embodiments, Figure 3 A schematic flow chart of a method for acquiring second three-dimensional information of a geological target body based on transient electromagnetics, provided in some embodiments of the present application, is shown, specifically comprising:

[0045] S310: Acquire a first transient electromagnetic pulse signal;

[0046] S320: Perform background field correction on the first pulse signal to determine a second pulse signal;

[0047] S330: Perform resistivity inversion based on the second pulse signal to determine second three-dimensional information.

[0048] In the above embodiment, a transient electromagnetic detection device can be deployed in the geological target area, a short-time pulse current is applied to the underground medium, and the response signal of the induced electromagnetic field decaying over time after the current is cut off is recorded, thereby obtaining the original first pulse signal. This signal reflects the response characteristics of the underground medium to the induced electromagnetic field and contains rich resistivity change information. The first pulse signal is then subjected to background field correction processing to eliminate interference components from the natural background electromagnetic field or instrument system errors, correct the signal baseline, and obtain a relatively stable and reliable second pulse signal, providing accurate input data for subsequent inversion. Based on the second pulse signal, resistivity inversion calculation can be performed, and a forward model that conforms to the physical characteristics of the transient electromagnetic method can be constructed. The distribution of underground resistivity changes in space is solved by a numerical inversion algorithm (such as Occam inversion or least squares inversion), thereby obtaining second three-dimensional information reflecting the underground conductive structure. Through the above processing, the electrical characteristics within different depth ranges of the underground can be determined more accurately, which can be used to identify structural information such as underground aquifers, conductive anomalies or geological faults.

[0049] In some embodiments, Figure 4 A schematic flow chart of a method for obtaining third-dimensional information of a geological target body based on a seismic wave method provided in some embodiments of the present application is shown, specifically including:

[0050] S410: Acquire a first seismic wave signal of a seismic wave;

[0051] S420: performing wavelength separation on the first seismic wave signal to determine multiple waveform signals, the multiple waveform signals including: a first arrival wave signal, a secondary wave signal, a surface wave signal, or an interference wave signal;

[0052] S43O: Perform wave velocity analysis on multiple waveform signals to determine third and third dimensional information.

[0053] In the above embodiments, a seismic wave detection system is deployed in the area involved in the geological target body. For example, a source device (such as an excitation hammer, a blasting source, etc.) can be used to excite seismic wave energy to the underground medium, and the vibration signal is collected by the deployed seismic detector array to obtain a first seismic wave signal. This signal records the reflection, refraction and scattering information generated by the propagation of seismic waves in different underground media, and contains rich geological structure information. The first seismic wave signal can then be subjected to wavelength separation processing, for example, by means of spectrum analysis or wavelet decomposition, the seismic wave signals of different frequency components and propagation paths are separated and identified to obtain a variety of seismic waveform signals. The various waveform signals may include but are not limited to: first arrival wave signals (P waves), reflecting the propagation path of longitudinal waves; secondary wave signals (S waves), reflecting the response of shear waves; surface wave signals, reflecting the energy characteristics propagating along the surface; interference wave signals, including stray waves, noise waves, etc. Furthermore, velocity analysis is performed on these various waveform signals. This involves measuring the propagation time and paths of different types of seismic waves in the subsurface medium. This allows for subsequent inversion modeling based on the seismic waves to determine the seismic velocity distribution across various subsurface regions. Because seismic velocity is closely related to geological parameters such as lithology, density, and water content, it can be used to determine third and third-dimensional information reflecting the subsurface structure and physical properties of the medium.

[0054] In some embodiments, Figure 5 A schematic flow chart of a method for establishing an inversion algorithm model provided in some embodiments of the present application is shown, including:

[0055] S510: Establishing geological coordinate system;

[0056] S520: aligning the spatial dimensions and the temporal dimensions of the first three-dimensional information, the second three-dimensional information, and the third three-dimensional information in the geological coordinate system;

[0057] S530: constructing a joint inversion objective function based on the aligned first three-dimensional information, the second three-dimensional information, and the third three-dimensional information; optimizing the joint inversion objective function, and determining an inversion algorithm model.

[0058] The above embodiments can fuse three-dimensional information obtained from multi-source geological exploration methods (including geological radar, transient electromagnetic, and seismic wave detection) to achieve more accurate geological structure identification and parameter estimation. When establishing a geological coordinate system, a unified spatial coordinate system can be established based on the geographical location of the target area, the measurement range, and the layout information of the multi-source sensing equipment. For example, the coordinate system can be a three-dimensional Cartesian coordinate system, or a local engineering coordinate system established according to the actual geological engineering situation, which is used as a unified reference space for data alignment and model inversion. Then, the first three-dimensional information, the second three-dimensional information, and the third three-dimensional information obtained by the geological radar, transient electromagnetic, and seismic wave methods are uniformly mapped to complete their spatial position alignment. If the data has different time acquisition bases or sampling frequencies, its time dimension can also be uniformly processed (such as time interpolation, resampling, etc.) to achieve consistent fusion of cross-modal data. On the basis of completing the multi-source data alignment, a joint inversion objective function can be constructed. This objective function is used to measure the error between the geological model prediction result and the actual multi-source observation data, and can comprehensively consider the physical constraints, noise model, and weight coefficients corresponding to each data source. For example, the objective function can be iteratively solved using an optimization algorithm (such as gradient descent, Newton's method, genetic algorithm, or Bayesian inference), ultimately obtaining the optimal model parameters that converge and stabilize, thereby constructing a complete inversion algorithm model. This model can output the spatial distribution and physical properties of the geological body within the target area, such as resistivity, wave velocity, and dielectric constant. Through the above method, it is possible to achieve deep fusion and inversion of multi-source geological data, effectively improving the accuracy and reliability of geological structure identification, and is particularly suitable for geological forecasting applications in complex engineering scenarios such as tunnels, mines, and hydrogeology.

[0059] In some embodiments, based on a three-dimensional geological model and a pre-trained geological anomaly recognition model, determining the poor geological body of the geological target body includes: training the geological anomaly recognition model based on a deep learning algorithm to determine the classification characteristics and edge characteristics of the poor geological body; and determining the category of the poor geological body of the geological target body by comparing the three-dimensional geological model and the edge characteristics.

[0060] The above embodiment uses a deep learning algorithm to train a geological anomaly recognition model. A training sample set can be constructed by collecting a large amount of 3D geological model data and its corresponding bad geological body label data from existing geological engineering projects, and using deep learning structures such as convolutional neural networks (CNNs), 3D U-Nets, and Transformers for modeling. During the training process, key features related to bad geological bodies are extracted, including but not limited to: classification features and edge features. Classification features may include, for example, lithology categories, wave velocity anomalies, resistivity mutations, dielectric parameter anomalies, etc.; edge features may include, for example, fracture interfaces, void boundaries, weak interlayer edges, etc. The trained recognition model is then applied to the 3D geological model obtained by joint inversion modeling. Combining various physical parameters and structural features in the model, potential bad geological body areas are identified and marked. Furthermore, by comparing the identified edge features with the structural change trend in the 3D geological model, the bad geological body category of the identified area can be determined, such as whether it is a fault fracture zone, weak interlayer, dissolution cavity, water-rich zone, or other engineering sensitive geological body, and classified, labeled, and output. Through the above method, the automatic identification and classification of adverse geological bodies in geological target bodies can be realized based on the results of joint inversion modeling, thereby improving the intelligence level and accuracy of geological forecasting. It is especially suitable for scenarios that are highly sensitive to geological risks, such as tunnel excavation, mining, and water conservancy projects.

[0061] Figure 6 A structural schematic diagram of a geological prediction detection device provided in some embodiments of the present application is shown, which is used for the geological prediction method provided in the above embodiments, including: a bracket 610, a telescopic rod 620, a detection body holder 630, an adjustment device 640, and a controller 650; the first part of the telescopic rod 620 is connected to the bracket 610 through the adjustment device 640, and the second part is connected to the detection body holder 630; the adjustment device 640 is configured to adjust the angle, length and height of the telescopic rod 620, and is configured to adjust the height of the bracket 610; the detection body holder 630 is provided with a geological radar coil, a transient electromagnetic coil and a seismic wave generator, and is configured to detect first three-dimensional information, second three-dimensional information and third three-dimensional information based on the movement of the telescopic rod 620; the controller 650 is provided on the third part of the telescopic rod 620, and is configured to control the length, angle and scanning movement of the telescopic rod 620.

[0062] The geological prediction detection device 600 can be used for geological prediction. For example, when using geological radar detection, the geological radar antenna is configured on the detection body fixture 630 to complete the data collection work of the grid survey line. The scanning survey line can adopt a three-dimensional grid survey line layout. Figure 7 The diagram shows a schematic diagram of a three-dimensional grid survey line layout of a geological radar provided in some embodiments of the present application. Figure 7In the figure, the tunnel profile 71 is the space for geological prediction detection equipment 600 to perform geological detection. The detection body holder 630 can be used to scan along the survey line 72. For example, when performing vertical detection, the controller 650 can control the telescopic rod 620 to move vertically under the drive of the adjustment device 640 for detection. When performing horizontal detection, the controller 650 can control the telescopic rod 620 to move horizontally under the drive of the adjustment device 650 for horizontal detection. Figure 8 A schematic diagram of a matrix measurement point layout of a transient electromagnetic detection tunnel face provided in some embodiments of the present application is shown. Figure 8 In the example, with tunnel outline 81 as the boundary, probe holder 630 can scan along survey line 82 and complete data collection of the tunnel face matrix measurement points at measurement point 83 using transient electromagnetic transmitting and receiving coils. This embodiment of the present application can replace the manual lifting of radar antennas and detection coils, eliminating operator hazards and effectively improving detection speed and accuracy.

[0063] In some embodiments, the bracket 610 includes multiple support rods 611, the first ends of the support rods 611 are connected to the adjustment device 640, and a footrest fixing platform 612 is set away from the first ends of the support rods 611; the support rods 611 include: a telescopic tensioner 613, a first rod body, a second rod body and multiple hinges 614; the telescopic tensioner 613 is set between the first rod body and the second rod body, and is configured to adjust the length of the support rod 611; the hinge 614 connects the first end of the support rod 611 with the adjustment device 640, and is configured to adjust the angle of the bracket 610.

[0064] In some embodiments, the adjustment device 640 includes: a fixed platform 641, a lifting device 642, a rotating bearing 643, and a telescopic rod connector 644; the lower part of the fixed platform 641 is connected to the first ends of the multiple support rods 611, and the upper part is connected to the lifting device 642; the rotating bearing 643 is connected to the lifting device 642 at the lower part and to the telescopic rod 620 connector at the upper part, and is configured to drive the telescopic rod 620 to move horizontally; the telescopic rod 620 connector is connected to the first part of the telescopic rod 620 and is configured to drive the telescopic rod 620 to move vertically; the telescopic rod 620 connector includes: a rotating structure 6441, a locking structure 6442, and a fixed structure 6443; the rotating structure 6441 is connected to the first part of the telescopic rod 620 through the locking structure 6442, and is configured to drive the telescopic rod 620 to move vertically; the upper part of the fixed structure 6443 is connected to the rotating structure 6441, and the lower part is connected to the lifting device 642.

[0065] Based on the same technical concept, Figure 9The schematic diagram of a geological prediction device provided in some embodiments of the present application is shown. The geological prediction device 900 includes: an acquisition unit 910 configured to acquire first three-dimensional information of a geological target body based on geological radar, the first three-dimensional information including spatial position, structure, electrical properties, or geometric form; acquire second three-dimensional information of the geological target body based on transient electromagnetic field, the second three-dimensional information including electrical conductivity, electrical structure, or electrical interface position; and acquire third three-dimensional information of the geological target body based on seismic wave method, the third three-dimensional information including longitudinal wave velocity, shear wave velocity, or wave impedance; a modeling unit 920 configured to establish an inversion algorithm model based on the first three-dimensional information, the second three-dimensional information, and the third three-dimensional information; a determination unit 930 configured to analyze the physical parameters and spatial distribution of the geological target body based on the inversion algorithm model to determine a three-dimensional geological model; an identification unit 940 configured to identify unfavorable geological bodies of the geological target body based on the three-dimensional geological model and a pre-trained geological anomaly identification model; and an early warning unit 950 configured to determine the risk level of the geological target body based on the unfavorable geological body and issue an early warning.

[0066] The specific implementation and beneficial effects of the above geological prediction device can be referred to the specific description of the embodiment of the geological prediction method above, and will not be repeated here. The division of the above units is merely a division of logical functions. In actual implementation, they can be fully or partially integrated into a physical entity, or physically separated. In addition, the above units can be implemented in the form of a processor calling software. Alternatively, the above units can be implemented in the form of hardware circuits, and the functions of some or all of the units can be realized by designing the hardware circuits. The hardware circuits can be understood as one or more processors. For example, in some embodiments, the hardware circuit is an application-specific integrated circuit (ASIC), and the functions of some or all of the above units are realized by designing the logical relationships between the components within the circuit. For another example, in another implementation, the hardware circuit can be implemented by a programmable logic device (PLD), which can include a large number of logic gate circuits. The logical relationships between the logic gate circuits are configured through a configuration file to realize the functions of some or all of the above units. The units of the above device can be implemented entirely by the processor calling a program, or entirely by the hardware circuit, or partially by the processor calling a program, with the remaining parts implemented in the form of hardware circuits.

[0067] In this application, unless otherwise expressly specified and limited, ordinal numbers such as "first" and "second" are only used to distinguish and describe related objects, and cannot be understood as indicating or implying the relative importance or order between related objects; in addition, they do not represent the number of related objects. "Multiple" includes two or more, and other quantifiers are similar. " / " is used to describe the relationship between related objects, which indicates an "or" relationship between related objects. "And / or" is used to describe the relationship between related objects, which includes any combination relationship between related objects, for example, "a and / or b" includes: "alone a", "alone b", or "a and b". "One or more" or "at least one" in multiple objects refers to any object or any combination of multiple objects, for example, "one or more of a1, a2, a3" or "at least one of a1, a2, a3" includes: "alone a1", "alone a2", "alone a3", "a1 and a2", "a1 and a3", "a2 and a3" or "a1, a2 and a3".

Claims

1. A geological prediction method, characterized in that: include: Acquiring first three-dimensional information of a geological target body based on a geological radar, wherein the first three-dimensional information includes: spatial position, structure, electrical properties, or geometric shape; Acquiring second three-dimensional information of the geological target body based on transient electromagnetics, wherein the second three-dimensional information includes: electrical conductivity, electrical structure, or electrical interface position; Acquiring third three-dimensional information of the geological target body based on a seismic wave method, wherein the third three-dimensional information includes: longitudinal wave velocity, shear wave velocity, or wave impedance; establishing an inversion algorithm model based on the first three-dimensional information, the second three-dimensional information, and the third three-dimensional information; Analyzing the physical parameters and spatial distribution of the geological target body based on the inversion algorithm model to determine a three-dimensional geological model; Determining the unfavorable geological body of the geological target body based on the three-dimensional geological model and a pre-trained geological anomaly recognition model; Based on the adverse geological body, the risk level of the geological target body is determined and an early warning is issued.

2. The geological prediction method according to claim 1, characterized in that: The method of obtaining the first three-dimensional information of the geological target body based on the geological radar includes: Obtaining the first reflected signal of the geological radar electromagnetic wave; Eliminating a DC component in the first reflected signal to determine a second reflected signal; smoothing a curve of the second reflected signal to eliminate sharp noise peaks and determine a third reflected signal; Decomposing the third reflected signal with wavelet to remove high frequency noise and determine a fourth reflected signal; Perform electromagnetic wave analysis on the fourth reflected signal to determine the first three-dimensional information.

3. The geological prediction method according to claim 2, characterized in that: The step of acquiring the second three-dimensional information of the geological target body based on transient electromagnetics includes: Acquiring a first transient electromagnetic pulse signal; performing background field correction on the first pulse signal to determine a second pulse signal; Resistivity inversion is performed based on the second pulse signal to determine the second three-dimensional information.

4. The geological prediction method according to claim 3, characterized in that: The obtaining of the third three-dimensional information of the geological target body based on the seismic wave method includes: Acquiring a first seismic wave signal of a seismic wave; performing wavelength separation on the first seismic wave signal to determine a plurality of waveform signals, wherein the plurality of waveform signals include: a first arrival wave signal, a secondary wave signal, a surface wave signal, or an interference wave signal; Perform wave velocity analysis on the multiple waveform signals to determine the third three-dimensional information.

5. The geological prediction method according to claim 4, characterized in that: The establishing of an inversion algorithm model based on the first three-dimensional information, the second three-dimensional information, and the third three-dimensional information includes: Establish a geological coordinate system; aligning the spatial dimensions and the temporal dimensions of the first three-dimensional information, the second three-dimensional information, and the third three-dimensional information in the geological coordinate system; constructing a joint inversion objective function based on the aligned first three-dimensional information, the second three-dimensional information, and the third three-dimensional information; The joint inversion objective function is optimized and the inversion algorithm model is determined.

6. The geological prediction method according to claim 5, characterized in that: The step of determining the unfavorable geological body of the geological target body based on the three-dimensional geological model and the pre-trained geological anomaly recognition model includes: Based on the deep learning algorithm, a geological anomaly recognition model is trained to determine the classification characteristics and edge characteristics of the adverse geological body; The category of the bad geological body of the geological target body is determined by comparing the three-dimensional geological model and the edge features.

7. A geological prediction detection device, characterized in that: The geological prediction method according to any one of claims 1 to 6 comprises: a bracket, a telescopic rod, a detector holder, an adjustment device, and a controller; The first portion of the telescopic rod is connected to the bracket via the adjusting device, and the second portion is connected to the probe holder; The adjustment device is configured to adjust the angle, length and height of the telescopic rod, and is configured to adjust the height of the bracket; The detection body holder is provided with a geological radar coil, a transient electromagnetic coil and a seismic wave generator, and is configured to detect the first three-dimensional information, the second three-dimensional information and the third three-dimensional information based on the movement of the telescopic rod; A controller is provided on the third portion of the telescopic rod and is configured to control the length, angle and scanning motion of the telescopic rod.

8. The geological prediction detection equipment according to claim 7, characterized in that: described The bracket includes a plurality of support rods, the first ends of the support rods are connected to the adjustment device, and a footrest fixing platform is provided away from the first ends of the support rods; The support rod comprises: a telescopic tensioner, a first rod body, a second rod body and a plurality of hinges; The telescopic tensioner is provided between the first rod body and the second rod body and is configured to adjust the length of the support rod; The hinge connects the first end of the support rod and the adjustment device and is configured to adjust the angle of the bracket.

9. The geological prediction detection equipment according to claim 8, characterized in that: The adjusting device includes: a fixed platform, a lifting device, a rotating bearing, and a telescopic rod connector; The lower portion of the fixed platform is connected to the first ends of the plurality of support rods, and the upper portion is connected to the lifting device; The rotating bearing is connected to the lifting device at the bottom and the telescopic rod connector at the top, and is configured to drive the telescopic rod to move horizontally; The telescopic rod connector is connected to the first portion of the telescopic rod and is configured to drive the telescopic rod to move vertically; The telescopic rod connector includes: a rotating structure, a locking structure, and a fixing structure; The rotating structure is connected to the first portion of the telescopic rod via the locking structure and is configured to drive the telescopic rod to move vertically; The upper portion of the fixed structure is connected to the rotating structure, and the lower portion is connected to the lifting device.

10. A geological prediction device, characterized in that: include: An acquisition unit is configured to acquire first three-dimensional information of a geological target body based on a geological radar, wherein the first three-dimensional information includes spatial position, structure, electrical property, or geometric form; acquire second three-dimensional information of the geological target body based on transient electromagnetics, wherein the second three-dimensional information includes electrical conductivity, electrical structure, or electrical interface position; and acquire third three-dimensional information of the geological target body based on a seismic wave method, wherein the third three-dimensional information includes longitudinal wave velocity, shear wave velocity, or wave impedance; a modeling unit configured to establish an inversion algorithm model based on the first three-dimensional information, the second three-dimensional information, and the third three-dimensional information; a determination unit configured to analyze the physical parameters and spatial distribution of the geological target body based on the inversion algorithm model to determine a three-dimensional geological model; an identification unit configured to determine an unfavorable geological body of the geological target body based on the three-dimensional geological model and a pre-trained geological anomaly identification model; The early warning unit is configured to determine the risk level of the geological target body based on the unfavorable geological body and issue an early warning.

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