Deep hidden rock mass boundary detection method, system, equipment, medium and product

Abstract

The invention discloses a deep hidden rock mass boundary detection method, system and device, a medium and a product, and relates to the field of rock mass boundary detection, and the method comprises the steps: obtaining multi-source data; the multi-source data comprises a surface wave profile, an electrical method profile, a seismic profile, a gravity plane, a magnetic method plane, geological drilling and physical property data; carrying out data processing and inversion on the surface wave profile and the electrical method profile, and determining an inversion profile; carrying out data processing and technical attack on the seismic section, and determining a geological interpretation section by combining physical property data; carrying out data processing on a gravity plane and a magnetic method plane, and determining 2.5 D same-section modeling by combining a geological interpretation section, geological drilling and physical property data; constructing a three-dimensional geological model according to the inversion profile, the geological interpretation profile, 2.5 D same-profile modeling, geological drilling and physical property data; and detecting the deep hidden rock mass boundary according to the three-dimensional geologic model. According to the method, the contact part of the rock mass and the surrounding rock and the deep hidden rock mass boundary can be accurately depicted.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of rock mass boundary detection, in particular to a deep concealed rock mass boundary detection method, system, device, medium and product. BACKGROUND

[0002] Rock mass provides material source for ore deposit, and most large and medium-sized metal mines are closely related to deep rock mass. How to finely depict the spatial distribution form of deep concealed rock mass and determine the properties of rock mass is one of the key technologies for delineating deep prospecting prospective areas, finding favorable metallogenic positions and achieving major breakthroughs in prospecting. It is urgent and necessary to summarize the deep concealed rock mass detection technology and promote its application.

[0003] The previous method does not have significant effect on deep prospecting breakthrough, and there is a lack of effective and high-precision deep concealed rock mass detection technology as effective support.

[0004] Disadvantages of prior art:

[0005] ①The degree of fine depiction of deep rock mass is not enough.

[0006] The previous three-dimensional geological-geophysical model mainly uses gravity and magnetic or gravity-magnetic-electric inversion technology to finely depict deep rock mass. The main target of comprehensive interpretation is the deep geological structure of ore concentration area, mainly aiming at faults, strata spatial distribution, and the shape and distribution characteristics of rock mass. However, the deep rock mass is not finely depicted, and the geophysical data used does not directly reflect the deep rock mass.

[0007] ②The contact part between rock mass and surrounding rock is not accurately depicted.

[0008] When the direct density, magnetism and resistivity difference between different rock masses and surrounding rocks is small, a single method cannot complete the identification. The previous deep concealed rock mass detection mainly uses gravity-magnetic-electric joint three-dimensional forward and inverse work, which has limitations, multi-solution, low vertical resolution and other shortcomings, resulting in that the contact part between the concealed rock mass and the surrounding rock is not accurate. The gravity and magnetic method mainly aims at the density and magnetic difference, and more reflects the plane geophysical information, and has no better effect on the vertical rock mass spatial distribution. The electrical method mainly aims at the vertical direction and mainly collects the resistivity difference, but the rock mass and old strata (such as Triassic limestone) both show high resistivity, which cannot be well identified. SUMMARY

[0009] The purpose of the present application is to provide a deep concealed rock mass boundary detection method, system, device, medium and product to solve the problems of low degree of fine depiction of deep rock mass and low accuracy of depicting the contact part between rock mass and surrounding rock.

[0010] To achieve the above purpose, the present application provides the following solutions:

[0011] The first aspect is a deep concealed rock mass boundary detection method, comprising:

[0012] Obtaining multi-source data, wherein the multi-source data comprises surface wave profiles, electrical profiles, seismic profiles, gravity planes, magnetic planes, geological drillings, and physical property data;

[0013] Processing and inverting the surface wave profiles and the electrical profiles to determine inversion profiles;

[0014] Processing and researching the seismic profiles, and combining the physical property data to determine geological interpretation profiles;

[0015] Processing the gravity planes and the magnetic planes, and combining the geological interpretation profiles, the geological drillings, and the physical property data to determine 2.5-dimensional same-profile modeling;

[0016] Constructing a three-dimensional geological model according to the inversion profiles, the geological interpretation profiles, the 2.5-dimensional same-profile modeling, the geological drillings, and the physical property data;

[0017] Detecting the deep concealed rock mass boundary according to the three-dimensional geological model.

[0018] The second aspect is a deep concealed rock mass boundary detection system, comprising:

[0019] A multi-source data obtaining module is configured to obtain multi-source data, wherein the multi-source data comprises surface wave profiles, electrical profiles, seismic profiles, gravity planes, magnetic planes, geological drillings, and physical property data;

[0020] An inversion profile determining module is configured to process and invert the surface wave profiles and the electrical profiles to determine inversion profiles;

[0021] A geological interpretation profile determining module is configured to process and research the seismic profiles, and combine the physical property data to determine geological interpretation profiles;

[0022] A 2.5-dimensional same-profile modeling determining module is configured to process the gravity planes and the magnetic planes, and combine the geological interpretation profiles, the geological drillings, and the physical property data to determine 2.5-dimensional same-profile modeling;

[0023] A three-dimensional geological model constructing module is configured to construct a three-dimensional geological model according to the inversion profiles, the geological interpretation profiles, the 2.5-dimensional same-profile modeling, the geological drillings, and the physical property data;

[0024] A detection module is configured to detect the deep concealed rock mass boundary according to the three-dimensional geological model.

[0025] In a third aspect, a computer device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to implement the method for detecting a deep concealed rock mass boundary.

[0026] In a fourth aspect, a computer readable storage medium stores a computer program, and the computer program, when executed by a processor, implements the method for detecting a deep concealed rock mass boundary.

[0027] In a fifth aspect, a computer program product comprises a computer program, and the computer program, when executed by a processor, implements the method for detecting a deep concealed rock mass boundary.

[0028] According to the embodiments provided in the present application, the following technical effects are disclosed.

[0029] The present application is based on multi-source data, which includes surface wave profile, electrical profile, seismic profile, gravity plane, magnetic plane, geological drilling and physical property data. The geological drilling and physical property data are used to accurately locate the ore-forming target area and provide effective data and direction for the next drilling work. Based on the surface wave profile, electrical profile and seismic profile, the inversion profile and the geological interpretation profile are determined. The wave velocity difference between the rock mass and the surrounding rock is used to finely depict the deep rock mass boundary and delineate the longitudinal rock mass boundary range, so as to accurately depict the contact position of the rock mass and the surrounding rock. The gravity plane and the magnetic plane are processed, and combined with the geological interpretation profile, the geological drilling and the physical property data to determine the 2.5-dimensional same profile modeling. According to the inversion profile, the geological interpretation profile, the 2.5-dimensional same profile modeling, the geological drilling and the physical property data, a three-dimensional geological model is constructed. On the basis of the previous three-dimensional geological-geophysical model, the seismic profile is added for constraint, which can improve the accuracy of the three-dimensional geological model, so as to accurately detect the deep concealed rock mass boundary. BRIEF DESCRIPTION OF DRAWINGS

[0030] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0031] Figure 1 A deep concealed rock mass boundary detection method flowchart is provided in the present application.

[0032] Figure 2 A gravity plane data processing flowchart is provided in the present application.

[0033] Figure 3A flow chart of the magnetic plane data processing provided in the present application;

[0034] Figure 4 A flow chart of the CSAMT processing and interpretation provided in the present application;

[0035] Figure 5 A schematic diagram of a three-dimensional geological structure spatial model of a certain area provided in the present application;

[0036] Figure 6 A schematic diagram of the effect of a rock mass model of a certain area provided in the present application;

[0037] Figure 7 A flow chart of another deep concealed rock mass boundary detection method provided in the present application. DETAILED DESCRIPTION

[0038] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0039] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0040] As shown in Figure 1 The present application provides a deep concealed rock mass boundary detection method, which comprises the following steps:

[0041] S1: acquiring multi-source data; the multi-source data comprises surface wave profile, electrical method profile, seismic profile, gravity plane, magnetic plane, geological drilling and physical property data.

[0042] S2: performing data processing and inversion on the surface wave profile and the electrical method profile to determine an inversion profile.

[0043] S3: performing data processing and technical research on the seismic profile, and combining the physical property data to determine a geological interpretation profile.

[0044] S4: performing data processing on the gravity plane and the magnetic plane, and combining the geological interpretation profile, the geological drilling and the physical property data to determine 2.5-dimensional same profile modeling.

[0045] S5: constructing a three-dimensional geological model according to the inversion profile, the geological interpretation profile, the 2.5-dimensional same profile modeling, the geological drilling and the physical property data.

[0046] S6: detecting the boundary of the deep concealed rock mass according to the three-dimensional geological model.

[0047] In one exemplary embodiment, S2 specifically comprises:

[0048] S21: data processing is performed on the surface wave profile to extract deep wave velocity information; the data processing comprises data preprocessing, spatial autocorrelation coefficient calculation and residual calculation; the data preprocessing comprises detrending, mean removal, cutting and time domain normalization.

[0049] S22: data processing is performed on the electrical profile to determine deep apparent chargeability and phase data for inversion; the data processing comprises original data editing and smoothing, static correction and near-field correction.

[0050] S23: inversion is performed according to the deep wave velocity information, deep apparent chargeability and the phase data to determine an inversion profile.

[0051] In practical applications, the data processing and inversion of the electrical profile and the surface wave profile specifically comprise:

[0052] Effective information (deep apparent chargeability and phase data and wave velocity information) is extracted from electrical and surface wave data, and electrical and natural source surface wave joint processing is selected.

[0053] Electrical profile data processing and imaging: data processing comprises original data editing and smoothing filtering, static effect correction and near-field correction, and finally apparent chargeability and phase data for inversion are obtained.

[0054] Full-band measured apparent chargeability and phase are edited in a man-machine interactive manner, and the overall shape and trend of the curve are used as the standard to edit the data of the frequency points that are more affected by interference, to obtain appropriate apparent chargeability and phase data.

[0055] The curve translation method and the spatial filtering method are commonly used for static effect correction. Since it is a three-dimensional acquisition, the area filtering method is used to perform static correction on the apparent chargeability data of all measurement points. The high-frequency apparent chargeability values of all measurement points are filtered in the area to obtain the high-frequency apparent chargeability value of each measurement point, and then each measurement point is corrected to this high-frequency apparent chargeability value to complete the static correction.

[0056] Near-field correction: an iterative method and a numerical approximation method are used to establish a near-field correction method, and the correction effect is good. In the case of a uniform earth, the equivalent resistivity or full-frequency domain apparent resistivity calculated by correction is very close to the true resistivity of the earth.

[0057] The data processing of surface wave profile: the extraction method of natural source surface wave dispersion curve is extended spatial autocorrelation method (ESPAC) for processing the obtained deep wave velocity information.

[0058] The data processing includes data preprocessing, spatial autocorrelation coefficient calculation and residual calculation.

[0059] The data preprocessing includes detrending, mean removal, cutting and time domain normalization. The cutting is for window calculation to improve signal-to-noise ratio by stacking. The purpose of time domain normalization is to remove the influence of the earth.

[0060] The spatial autocorrelation coefficient calculation includes window autocorrelation coefficient calculation and stacking.

[0061] The residual calculation is to fit the spatial autocorrelation coefficient between all receivers with the theoretical Bessel function to calculate the residual, so as to obtain the continuous and small value trend on the residual map, i.e. the surface wave dispersion curve.

[0062] In practical application, the gravity and magnetic data processing specifically includes:

[0063] The gravity and magnetic data are processed by anomaly separation, derivation and potential field conversion to extract the gravity and magnetic field information and geological body boundary information of local geological body on the plane.

[0064] The gravity plane processing is shown in Figure 2 The basic flow of gravity plane (gravity data) processing is as follows:

[0065] The measured gravity data, gravity anomaly calculation, data preprocessing, data processing (qualitative) and data processing (quantitative) and forward and inverse process. From known to unknown, from qualitative to quantitative, for geological interpretation.

[0066] Among them, the data preprocessing is mainly aimed at the field collected gravity original data: including the absolute gravity value of the collection point, the coordinate, the calculation of the Bouguer gravity data of the gravity data; after the calculation is completed, the gridding processing is completed to form the Bouguer gravity anomaly data body; there are Bouguer gravity data with large differences in the surrounding in the Bouguer gravity anomaly data, which is commonly known as distortion point; in the distortion point processing, the point with sudden large change in the Bouguer gravity anomaly map is mainly aimed at, and the field is re-measured and processed. Then re-grid the Bouguer gravity anomaly.

[0067] Data processing (qualitative), mainly including local and regional field extraction, linear information extraction; local and regional field extraction includes: local and regional field extraction, high and low pass filtering, underbalanced filtering, comparison with geological features after extraction, and selection of better processing results. Linear information extraction mainly includes: horizontal derivative, vertical derivative, horizontal total gradient; so as to identify gravity high, low, gravity gradient zone, and determine whether it is rock mass, stratum, fracture, etc. for geological discrimination.

[0068] Data processing (quantitative), mainly after qualitative, after data processing, gravity forward and inversion are carried out, deep rock mass model (depth, size) is described, and parameters (density) are attached to the geological body model.

[0069] Magnetic plane processing is as shown in Figure 3 The basic flow of magnetic plane (i.e. magnetic data processing) is as follows:

[0070] The measured magnetic data preprocessing includes distortion point processing, gridding and magnetization, qualitative data processing is carried out according to the existing verified geological results; local and regional field extraction: iterative continuation, sliding window, high, low and band pass filtering, vertical derivative; linear information extraction: horizontal and vertical derivative, horizontal total gradient, horizontal arbitrary direction derivative;

[0071] Data processing (quantitative), mainly after qualitative, after data processing, gravity forward and inversion are carried out, deep rock mass model (depth, size) is described, and parameters (magnetic susceptibility) are attached to the geological body model.

[0072] Based on the results of gravity and magnetic data processing, three-dimensional physical property inversion is carried out to obtain the distribution trend of three-dimensional space of geological body.

[0073] In practical application, the data processing of seismic profile:

[0074] Seismic data processing and technical research make fine processing of seismic profile, use the wave velocity difference of rock mass and surrounding rock, finely describe the boundary of deep rock mass, and circle out the longitudinal rock mass boundary range, so as to identify the rock mass intrusion. The main processing technologies include: static correction technology, signal-to-noise ratio improvement technology, post-stack time migration technology, pre-stack depth migration technology.

[0075] Among them, static correction processing is to compensate the influence of surface high layer change, shot well depth, weathering layer thickness and velocity change on seismic data. The purpose is to obtain the reflection wave arrival time in a plane, which is collected and does not exist weathering layer and low velocity zone. This is the premise and initial processing technology of all methods. Then carry out signal-to-noise ratio improvement.

[0076] Improving signal-to-noise ratio technology: the signal-to-noise ratio and resolution of seismic data is the most basic problem in seismic data processing. The key technology for improving the signal-to-noise ratio of seismic data is: ① suppress noise and highlight effective signal; ② strive to achieve in-phase stacking; ③ handle the relationship between resolution and signal-to-noise ratio, and improve the signal-to-noise ratio after post-stack time migration.

[0077] Post-stack time migration technology: make the reflected wave correctly positioned, make the diffraction wave positioned, and change the energy distribution law of the transmitted wave. Then perform pre-stack depth migration.

[0078] Pre-stack depth migration technology: is a processing technology to realize the spatial positioning of geological structure. The current widely used pre-stack time migration can only solve the problem of common reflection point stacking, but cannot solve the problem of non-coincidence of imaging points and underground diffraction points. Therefore, pre-stack time migration is mainly applied to areas with less complex lateral velocity variation.

[0079] In practical application, the processing and inversion of controlled source audio frequency magnetotelluric sounding (CSAMT) data (i.e. electrical profile data) are as follows:

[0080] The CSAMT data processing technology and basic flow are as follows:

[0081] The main processing technologies of GeoDeep are: data loading, offset calculation, two-dimensional smoothing filtering, two-dimensional static correction and two-dimensional regularization inversion.

[0082] (1) Smoothing filtering: filtering can eliminate local interference data. After filtering, local anomalies are eliminated, and the resistivity profile has better continuity.

[0083] (2) Two-dimensional static correction: the two-dimensional static correction processing method of CSAMT data in GeoDeep software is similar to MT.

[0084] (3) The data processing flow is as shown in Figure 4 , including data import and preprocessing, qualitative data processing, quantitative calculation and other steps.

[0085] The present application constructs a three-dimensional geological model through multi-science joint interpretation to determine the distribution and properties of rock mass.

[0086] Among them, the multi-science joint interpretation includes multi-disciplinary, multi-method and multi-information geophysical joint interpretation technology of gravity, magnetic, electromagnetic, seismic, logging and geological data. The display and collaborative interpretation effect of gravity, magnetic, electromagnetic, seismic and logging multi-information is obvious. By extracting different properties of geological bodies, and using pattern recognition and neural network method to classify and comprehensively analyze the properties (bodies) that can reflect specific geological characteristics.

[0087] Multi-disciplinary geophysical joint interpretation method and basic flow: after the gravity and magnetic data processing, the surface data interpretation is basically completed, and then the vertical data such as electrical method (i.e. controlled source audio frequency magnetotelluric sounding), surface wave and seismic data processing are processed. For the identification of deep rock mass boundary by multiple methods in the vertical direction, fine description. When fine description, the rock mass properties are qualitatively and quantitatively determined. Finally, the three-dimensional model is displayed.

[0088] As shown in Figures 5-6 , rock mass distribution and property identification:

[0089] Mainly according to the characteristics of gravity and magnetic anomalies combined with the physical properties of rock and mineral, and combined with the known (exposed or revealed by drilling) rock mass anomaly characteristics for analogy (but it should be noted that in the Luzong basin, because of the multi-period activity of magmatic rocks, the judgment of its properties should be relative, and the mineralization is closely related to the properties of rock mass, so when considering the mineralization prospect, this point should be fully considered), for example:

[0090] Acid rock mass: local gravity low anomaly, local weak magnetic anomaly.

[0091] Intermediate rock mass: moderate gravity anomaly, often located at the position of high and low gravity anomaly, local strong magnetic anomaly.

[0092] Basic rock mass: local gravity high anomaly, local strong magnetic anomaly.

[0093] In an exemplary embodiment, as shown in Figure 7 , S3 specifically includes:

[0094] S31: data processing and technical research on the seismic profile are performed to determine the stacked profile.

[0095] S32: the boundary of the concealed rock mass is described according to the stacked profile.

[0096] S33: the geological interpretation profile is determined according to the boundary of the concealed rock mass and the physical property data.

[0097] In an exemplary embodiment, S4 specifically includes:

[0098] S41: data processing is performed on the gravity plane and the magnetic plane to determine the processed plane.

[0099] S42: three-dimensional physical property inversion is performed according to the processed plane to determine the inverted plane.

[0100] S43: the rock mass plane and properties are determined according to the inverted plane.

[0101] S44: in combination with the geological drilling and the physical property data, according to the rock mass plane and property and the geological interpretation profile, determine 2.5D isopachous profile modeling.

[0102] In an exemplary embodiment, S5 specifically comprises:

[0103] S51: according to the inversion profile, the geological interpretation profile, the 2.5D isopachous profile modeling and the geological drilling, determine a comprehensive interpretation profile.

[0104] S52: according to the geological drilling, the physical property data, the comprehensive interpretation profile and the 2.5D isopachous profile modeling, determine 2.5D gravity-magnetic profile modeling.

[0105] S53: according to the geological drilling, the physical property data, the inversion profile, the comprehensive interpretation profile, the 2.5D gravity-magnetic profile modeling, build a three-dimensional geological model.

[0106] In an exemplary embodiment, S6 specifically comprises:

[0107] S61: according to the three-dimensional geological model and the 2.5D gravity-magnetic profile modeling, determine the three-dimensional spatial distribution and property of the concealed rock mass.

[0108] S62: based on the three-dimensional spatial distribution and property of the concealed rock mass, detect the boundary of the deep concealed rock mass.

[0109] The present application provides a deep concealed rock mass boundary detection system, comprising:

[0110] A multi-source data acquisition module is configured to acquire multi-source data, wherein the multi-source data comprises surface wave profile, electrical method profile, seismic profile, gravity plane, magnetic method plane, geological drilling and physical property data.

[0111] An inversion profile determination module is configured to process and invert the surface wave profile and the electrical method profile to determine an inversion profile.

[0112] A geological interpretation profile determination module is configured to process and research the seismic profile, and in combination with the physical property data, determine a geological interpretation profile.

[0113] A 2.5D isopachous profile modeling determination module is configured to process the gravity plane and the magnetic method plane, and in combination with the geological interpretation profile, the geological drilling and the physical property data, determine 2.5D isopachous profile modeling.

[0114] A three-dimensional geological model construction module is configured to construct a three-dimensional geological model according to the inversion profile, the geological interpretation profile, the 2.5D isopachous profile modeling, the geological drilling and the physical property data.

[0115] The detection module is configured to detect a boundary of the deep concealed rock mass according to the three-dimensional geological model.

[0116] In an example embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, and the processor implementing the steps in the above method embodiments when executing the computer program. The computer device can be a server or a terminal. The computer device includes a processor, a memory, an input / output interface (I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The database of the computer device is configured to store to-be-processed data. The input / output interface of the computer device is configured to exchange information between the processor and external devices. The communication interface of the computer device is configured to communicate with external terminals through a network connection. The computer program is executed by the processor to implement the above method.

[0117] In an example embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, and the processor implementing the steps in the above method embodiments when executing the computer program.

[0118] In an example embodiment, a computer readable storage medium is provided, storing a computer program, and the computer program is executed by a processor to implement the steps in the above method embodiments.

[0119] In an example embodiment, a computer program product is provided, including a computer program, and the computer program is executed by a processor to implement the steps in the above method embodiments.

[0120] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant regulations.

[0121] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by computer program instructions related to hardware. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiments of the method. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (Read-Only Memory, ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric memory (Ferroelectric Random Access Memory, FRAM), phase change memory (Phase Change Memory, PCM), graphene memory, etc. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc.

[0122] The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a blockchain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general processor, a central processor, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0123] The embodiments in the specification are described in a progressive manner, and each embodiment focuses on the difference from other embodiments. The same or similar parts between the embodiments can be referred to each other.

[0124] The principles and implementation modes of the present application are described by applying specific examples in the present application. The above description of the embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those skilled in the art, according to the idea of the present application, the specific implementation mode and application range will be changed. In conclusion, the content of the specification should not be understood as a limitation of the present application.

Claims

1. A method for detecting the boundary of a deep, concealed rock mass, characterized in that, include: Acquire multi-source data; the multi-source data includes surface wave profiles, electrical resistivity profiles, seismic profiles, gravity plane data, magnetic plane data, geological drilling data, and physical property data; Data processing and inversion are performed on the surface wave profile and the electrical resistivity profile to determine the inverted profile; Data processing and technical breakthroughs were carried out on the seismic profile, and geological interpretation profiles were determined by combining the physical property data. Data processing is performed on the gravity plane and the magnetic plane, and a 2.5-dimensional co-profile model is determined by combining the geological interpretation profile, the geological drilling data, and the physical property data. A three-dimensional geological model is constructed based on the inversion profile, the geological interpretation profile, the 2.5-dimensional co-profile modeling, the geological drilling data, and the physical property data. The boundaries of deep, concealed rock masses are detected using the three-dimensional geological model.

2. The method for detecting the boundary of deep concealed rock masses according to claim 1, characterized in that, Data processing and technical challenges were addressed on the seismic profile, and geological interpretation profiles were determined based on the physical property data, specifically including: Data processing and technical challenges were conducted on the seismic profiles to determine the stacking profiles; the technical challenges included static correction techniques, signal-to-noise ratio improvement techniques, post-stack time migration techniques, and pre-stack depth migration techniques. The boundaries of the concealed rock mass are delineated based on the superimposed profiles; Geological interpretation profiles were determined based on the boundaries of the concealed rock mass and the physical property data.

3. The method for detecting the boundary of deep concealed rock masses according to claim 1, characterized in that, Data processing is performed on the gravity plane and the magnetic plane, and a 2.5-dimensional co-profile model is determined by combining the geological interpretation profile, the geological drilling data, and the physical property data. Specifically, this includes: Data processing is performed on the gravity plane and the magnetic plane to determine the processed plane; Based on the processed plane, perform three-dimensional physical property inversion to determine the inverted plane; The rock mass plane and properties are determined based on the inverted plane. Based on geological drilling and physical property data, and according to the rock mass plane and properties and the geological interpretation profile, a 2.5-dimensional same-profile model was determined.

4. The method for detecting the boundary of deep concealed rock masses according to claim 1, characterized in that, A three-dimensional geological model is constructed based on the inversion profile, the geological interpretation profile, the 2.5-dimensional co-profile modeling, the geological drilling data, and the physical property data, specifically including: Based on the inversion profile, the geological interpretation profile, the 2.5-dimensional co-profile modeling, and the geological drilling, a comprehensive interpretation profile is determined; Based on the geological drilling data, physical property data, comprehensive interpretation profile, and 2.5-dimensional co-profile modeling, the 2.5-dimensional gravity and magnetic profile modeling is determined. A three-dimensional geological model is constructed based on the geological drilling data, physical property data, inversion profiles, comprehensive interpretation profiles, and 2.5-dimensional gravity and magnetic profiles.

5. The method for detecting the boundary of deep concealed rock masses according to claim 4, characterized in that, Based on the aforementioned three-dimensional geological model, the boundaries of deep, concealed rock masses are detected, specifically including: The three-dimensional spatial distribution and properties of the concealed rock mass are determined based on the three-dimensional geological model and the 2.5-dimensional gravity and magnetic profile modeling. Based on the three-dimensional spatial distribution and properties of the concealed rock mass, the boundary of the deep concealed rock mass is detected.

6. The method for detecting the boundary of deep concealed rock masses according to claim 1, characterized in that, Data processing and inversion are performed on the surface wave profile and the electrical resistivity profile to determine the inverted profile, specifically including: The surface wave profile is processed to extract deep wave velocity information; the data processing includes data preprocessing, spatial autocorrelation coefficient calculation, and residual calculation; the data preprocessing includes detrending, mean removal, segmentation, and time-domain normalization. The electrical resistivity profile is processed to determine the deep Carnia apparent resistivity and phase data for inversion; the data processing includes raw data editing and smoothing, static correction, and near-field correction; Inversion is performed based on the deep wave velocity information, the deep Kania apparent resistivity, and the phase data to determine the inversion profile.

7. A deep, concealed rock mass boundary detection system, characterized in that, include: The multi-source data acquisition module is used to acquire multi-source data, including surface wave profiles, electrical resistivity profiles, seismic profiles, gravity plane data, magnetic plane data, geological drilling data, and physical property data. The inversion profile determination module is used to process and invert the surface wave profile and the electrical resistivity profile to determine the inversion profile. The geological interpretation profile determination module is used to process the data and tackle technical challenges of the seismic profile, and to determine the geological interpretation profile in conjunction with the physical property data. The 2.5-dimensional co-profile modeling determination module is used to process data from the gravity plane and the magnetic plane, and combine them with the geological interpretation profile, the geological drilling data, and the physical property data to determine the 2.5-dimensional co-profile modeling. The three-dimensional geological model construction module is used to construct a three-dimensional geological model based on the inversion profile, the geological interpretation profile, the 2.5-dimensional same profile modeling, the geological well, and the physical property data. The detection module is used to detect the boundaries of deep concealed rock masses based on the three-dimensional geological model.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the deep concealed rock mass boundary detection method according to any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the method for detecting the boundary of deep concealed rock masses as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the method for detecting the boundary of deep concealed rock masses as described in any one of claims 1-6.

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