Geological exploration method and device based on muon detection, detector and equipment

Abstract

The present disclosure relates to a geological exploration method and device based on muon detection, a detector and equipment. The method comprises: obtaining muon flux and angle parameters of muons at each depth in each target borehole in a plurality of target boreholes; determining a density sequence and a density distribution according to the muon flux, the angle parameters and a preset density determination model; if a density gradient corresponding to a target depth is greater than a preset gradient threshold, it is determined that a stratigraphic boundary exists at the target depth; determining a closed contour of the stratigraphic boundary in the density distribution based on an edge extraction algorithm; determining a layered geological model according to the position information of the closed contour and the density sequence corresponding to each target borehole, and completing geological exploration. The method combines the density sequence, the density distribution and the closed contour of the stratigraphic boundary to determine the layered geological model and complete the geological exploration, realizes accurate identification of the stratigraphic boundary, accurately restores the structure of the stratigraphic boundary, and improves the efficiency of geological exploration.

Description

Technical Field

[0001] This disclosure relates to the field of muon detection and geological exploration technology, specifically to a geological exploration method, apparatus, detector and equipment based on muon detection. Background Technology

[0002] While muon-based deep geological stratification exploration technology can locate density anomalies in ore bodies and underground cavities, it struggles to accurately identify the boundaries between soft and hard strata, cannot generate layered 3D geological models, and cannot efficiently and accurately depict the spatial morphology of these boundaries. Due to these limitations, muon-based exploration technology is mostly confined to the investigation of small-scale, key anomalies, making it difficult to support large-scale deep geological exploration and resulting in low exploration efficiency. Summary of the Invention

[0003] To overcome the problems existing in related technologies, this disclosure provides a geological exploration method, apparatus, detector and equipment based on muon detection.

[0004] According to a first aspect of the present disclosure, a geological exploration method based on muon detection is provided, the method comprising: The muon flux and angle parameters of muons at various depths are obtained for each of the multiple target boreholes, wherein the number of muons is at least one, the muon flux is determined by the outer fiber layer of the muon detector, and the angle parameters are determined by the inner fiber layer of the muon detector. Based on the muon flux, angle parameters, and a preset density determination model for each target borehole at each depth, a density sequence along the axial direction and a density distribution along the radial direction of the target borehole are determined. The density sequence includes a first density value corresponding to each depth along the axial direction of the target borehole. The density distribution is used to characterize a second density value for the target position in the plane containing the radial direction of the target borehole at different depths, which has a different radial distance from the geometric center of the plane. A density gradient is determined based on multiple first density values ​​in the density sequence. If the density gradient corresponding to the target depth is greater than a preset gradient threshold, it is determined that a stratigraphic interface exists at the target depth. The closed contour of the formation interface is determined in the density distribution based on the edge extraction algorithm; Based on the density sequence and the location information of the corresponding closed contour for each of the multiple target boreholes, a layered geological model of the strata is determined, and the geological exploration of the strata is completed.

[0005] In some embodiments, the outer fiber layer includes a first detection module, wherein acquiring the muon flux and angle parameters of the muons at various depths for each of the plurality of target boreholes includes: The parameters of the first detection module are corrected to determine the first effective signal corresponding to the first signal output by the first detection module and the first correction gain of the first detection module. The muon flux of the muon is determined based on the first correction gain; The angle parameters of the muon in the geodetic coordinate system are determined based on the initial angle parameters of the muon in the coordinate system of the muon detector and the attitude angle parameters of the muon detector in the geodetic coordinate system.

[0006] In some embodiments, the inner fiber layer includes a second detection module, and the determination of the initial angle parameter includes: The parameters of the second detection module are corrected to determine the second valid signal corresponding to the second signal output by the second detection module and the second correction gain of the second detection module. The incident position of the muon at each depth is determined based on the second correction gain; By fitting the incident positions, the trajectory of the muon inside the muon detector is obtained. In the coordinate system of the muon detector, the initial angle parameter is determined based on the angle between the trajectory and the central axis of the muon detector.

[0007] In some embodiments, the number of muons is at least two, and determining the density sequence of the target borehole along the axial direction and the density distribution of the target borehole along the radial direction based on the muon flux at each depth, the angle parameter, and a preset density determination model for each target borehole includes: The angle parameters include a first angle parameter and a second angle parameter. The first angle parameter indicates that the muon is incident on the muon detector in a vertical direction, and the second angle parameter indicates that the muon is incident on the muon detector in an oblique direction. The density sequence is determined based on the first flux of the first muon at each of the aforementioned depths and the preset density determination model, wherein the first muon is the muon incident with the first angle parameter; The density distribution is determined based on the second flux of the second muon at each of the stated depths, the trajectory of the second muon, and the preset density determination model, wherein the second muon is the muon incident with the second angle parameter.

[0008] In some embodiments, the method for determining the closed contour of the formation interface based on the edge extraction algorithm in the density distribution includes: The density distribution is subjected to gradient enhancement processing to obtain the processed target density distribution; The closed contour of the formation interface is determined based on the edge extraction algorithm in the target density distribution.

[0009] In some embodiments, determining a layered geological model of the strata based on the density sequence and the location information of the corresponding closed contour of each of the plurality of target boreholes, and completing the geological exploration of the strata, includes: Based on the density sequence and the position information of the closed contour corresponding to each of the multiple target boreholes, an objective function is constructed, which includes a data fitting term, a total variation regularization term, and a boundary constraint term. Determine the three-dimensional density field based on the objective function; Based on the three-dimensional density field, multiple voxels belonging to the same stratigraphic unit are determined. The multiple voxels belonging to the same stratigraphic unit are spatially connected and the difference in the third density value between two adjacent voxels is less than a preset difference. The three-dimensional density field includes the third density value corresponding to each of the multiple voxels. The stratigraphic units are calibrated according to a preset density range to determine the attribute information of the stratigraphic units; Based on the three-dimensional density field and the attribute information of the stratigraphic unit, a layered geological model characterizing the stratigraphic interface is obtained, and the geological exploration of the stratigraphy is completed.

[0010] According to a second aspect of the present disclosure, a geological exploration apparatus based on muon detection is provided, the apparatus comprising: A muon detection unit is configured to acquire the muon flux and angle parameters of muons at various depths for each of a plurality of target boreholes, wherein the number of muons is at least one, the muon flux is determined by the outer fiber layer of the muon detector, and the angle parameters are determined by the inner fiber layer of the muon detector. The first determining unit is configured to determine, based on the muon flux, the angle parameter, and a preset density determining model, a density sequence along the axial direction and a density distribution along the radial direction of the target borehole; the density sequence includes a first density value corresponding to each depth along the axial direction of the target borehole; the density distribution is used to characterize a second density value of the target position in the plane containing the radial direction of the target borehole at different depths, which has a different radial distance from the geometric center of the plane. The second determining unit is configured to determine a density gradient based on a plurality of first density values ​​in the density sequence, and if the density gradient corresponding to the target depth is greater than a preset gradient threshold, determine that a stratigraphic interface exists at the target depth. The third determining unit is configured to determine the closed contour of the formation interface in the density distribution based on an edge extraction algorithm; The fourth determining unit is configured to determine the layered geological model of the strata based on the density sequence and the position information of the corresponding closed contour of each of the plurality of target boreholes, and to complete the geological exploration of the strata.

[0011] According to a third aspect of the present disclosure, a muon detector is provided, the muon detector comprising: Shell, outer fiber layer, isolation layer, inner fiber layer; In this structure, the first optical fiber in the outer optical fiber layer is wound with a first pitch, and the second optical fiber in the inner optical fiber layer is wound with a second pitch. The winding direction of the optical fiber in the outer optical fiber layer is opposite to that of the optical fiber in the inner optical fiber layer. A first detection module is configured at both ends of each first optical fiber in the outer optical fiber layer, and a second detection module is configured at both ends of each second optical fiber in the inner optical fiber layer. The muon flux is determined by the signal output by the first detection module, and the angle parameter is determined by the signal output by the second detection module.

[0012] In some embodiments, the internal cavity of the inner fiber layer further includes: The sensing unit is configured to detect parameters inside the formation and output sensing signals, which are used to correct the parameters of the first detection module and the second detection module inside the muon detector. The preprocessing unit is configured to perform parameter correction on the first detection module to determine a first valid signal corresponding to the first signal output by the first detection module and a first correction gain of the first detection module; and to perform parameter correction on the second detection module to determine a second valid signal corresponding to the second signal output by the second detection module and a second correction gain of the second detection module. The storage unit is configured to store the muon flux and the angle parameters.

[0013] According to a fourth aspect of the present disclosure, a computer device is provided, including a processor; a memory for storing processor-executable instructions; wherein the processor is configured to perform the steps of the muon-based geological exploration method as described in the first aspect.

[0014] The technical solutions provided by the embodiments of this disclosure can include the following beneficial effects: The method provided by the embodiments of this disclosure acquires the muon flux and angle parameters of each target borehole at various depths based on the outer and inner fiber layers of the muon detector. Through the functional division of the inner and outer fiber layers, the acquisition of the two types of parameters is independent, realizing signal decoupling of muon flux and angle parameters. Furthermore, it combines a preset density determination model to determine the density sequence in the axial direction and the density distribution in the radial direction, overcoming the limitation of only acquiring the density sequence and providing data basis for determining the layered geological model. Then, based on the density gradient and a preset gradient threshold, it determines the existence of stratigraphic interfaces at the target depth, and obtains the closed contour of the stratigraphic interface through an edge extraction algorithm, achieving accurate identification of the stratigraphic interface. Finally, by combining the density sequence and closed contour location information corresponding to multiple target boreholes, a layered geological model is determined, completing the geological exploration of the strata. This reduces the number of traditional target boreholes, accurately reconstructs the structure of stratigraphic interfaces, thereby supporting large-area deep geological exploration work and improving the efficiency of geological exploration.

[0015] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0017] Figure 1 This is a flowchart illustrating a geological exploration method based on muon detection according to an exemplary embodiment; Figure 2 This is a flowchart illustrating a method for determining the closed profile of a formation interface according to an exemplary embodiment; Figure 3 This is a flowchart illustrating an exemplary embodiment for obtaining muon flux and angle parameters; Figure 4 This is a flowchart illustrating the determination of initial angle parameters according to an exemplary embodiment; Figure 5 This is a flowchart illustrating a method for determining a layered geological model according to an exemplary embodiment; Figure 6 This is a block diagram illustrating a muon-based geological exploration apparatus according to an exemplary embodiment; Figure 7 This is a schematic diagram illustrating the structure of a muon detector according to an exemplary embodiment; Figure 8 This is a block diagram of a computer device according to an exemplary embodiment. Detailed Implementation

[0018] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the invention as detailed in the appended claims.

[0019] Currently, commonly used techniques for deep geological stratification exploration include core drilling, surface geophysical exploration, and muon detection. These techniques mainly suffer from the following technical limitations: 1. Core drilling exploration methods can only obtain single-point columnar geological data at the borehole location. The stratigraphic boundary information between borehole locations relies on linear interpolation for estimation, making it prone to missing complex geological structures such as underground faults, pinch-outs, and lenticular weak interlayers. Furthermore, traditional methods of constructing layered geological models have low modeling accuracy, and large-scale borehole deployment leads to extremely high drilling costs.

[0020] 2. Surface geophysical exploration methods, such as seismic exploration, gravity exploration, and electrical exploration, although they have a wide lateral detection coverage, lack vertical stratification resolution, making it difficult to identify thin interlayers and small-scale lithological interfaces, and unable to output the spatial location of stratigraphic interfaces.

[0021] To address the aforementioned technical deficiencies, this disclosure provides a geological exploration method based on muon detection. It employs a double-layered spiral compact muon detector adapted to the target borehole, and performs parameter calibration on the first and second detection modules within the muon detector. Based on density gradient enhancement algorithms, edge extraction algorithms, and stratigraphic model reconstruction algorithms, it achieves automatic extraction of the interface between soft and hard lithological strata, as well as automatic division of independent stratigraphic units. Ultimately, it outputs a continuous three-dimensional layered geological model covering the entire area, significantly reducing the number of exploration boreholes and improving the accuracy of depicting deep stratigraphic structures.

[0022] This disclosure provides an exemplary embodiment of a geological exploration method based on muon detection, applied to computer equipment. Figure 1 This is a flowchart illustrating a geological exploration method based on muon detection according to an exemplary embodiment, such as... Figure 1 As shown, the geological exploration method based on muon detection includes the following steps: Step S101: Obtain the muon flux and angle parameters of the muons at various depths for each of the multiple target boreholes.

[0023] The number of muons is at least one, the muon flux is determined by the outer fiber layer of the muon detector, and the angle parameter is determined by the inner fiber layer of the muon detector.

[0024] For example, muon flux is the amount of energy deposited after a muon penetrates the strata, and the angle parameter is the zenith angle of the muon in the geodetic coordinate system, which is the angle between the muon's trajectory and the vertical line of the earth.

[0025] Muons are secondary charged elementary particles generated by nuclear reactions of cosmic rays in the upper atmosphere. They have extremely strong penetrating power, and the density of underground rock masses can be inverted by the muon flux after passing through the medium.

[0026] It should be noted that in muon-based detection technology, no external power supply or active emission of detection particles is required. After the muon detector is lowered to the designated depth of the target borehole and powered on, naturally occurring muons from the universe penetrate the rock strata and enter the muon detector. The muon detector captures the muon's signal (e.g., light signal) and converts it into a corresponding electrical signal, thereby completing the data acquisition of muon events.

[0027] In one example, the muon detector is a coaxial double-fiber layer structure, including an outer fiber layer and an inner fiber layer. When muons are incident inside the double-fiber layer of the muon detector, they interact with the optical fibers and generate optical signals; that is, the interaction signal of the muons is an optical signal. The outer fiber layer of the muon detector includes a first detection module, and the inner fiber layer includes a second detection module. The muon flux is measured by the first detection module, and the angular parameters are measured by the second detection module.

[0028] For example, both the first detection module and the second detection module are silicon photomultiplier tubes (SiPMs). The silicon photomultiplier tubes have a built-in miniature preamplifier circuit for amplifying electrical signals.

[0029] In one example, the first fiber in the outer fiber layer is wound left-hand with a first pitch. The first fiber is a high-efficiency plastic scintillation fiber with a diameter of 1.0 mm, a single first fiber is 18 m long, and has a first pitch of 12 mm. SIPMs are coupled to both ends of the first fiber. The outer fiber layer is primarily used to obtain the energy deposition of muons after they pass through the formation, thereby obtaining the muon flux. The obtained muon flux is then used for density inversion calculations of the first density value.

[0030] In another example, the second fiber in the inner fiber layer is wound right-handed with a second pitch. The second fiber is a 0.8mm diameter plastic scintillation fiber, with a single fiber length of 12m and a second pitch of 10mm. SIPMs are coupled to both ends of the second fiber. The inner fiber layer is used to acquire the muon's trajectory along the target borehole depth and the muon's incident position, thereby obtaining the muon's angular parameters.

[0031] Understandably, during muon detection, a muon detector is lowered to various depths within the target borehole. At each depth, multiple natural muons from space penetrate the strata and enter the muon detector. Based on the muon detector, the muon flux and angle parameters of the muons at each depth within the target borehole are obtained.

[0032] Step S102: Based on the muon flux, angle parameters, and preset density determination model of each target borehole at various depths, determine the density sequence of the target borehole along the axial direction and the density distribution of the target borehole along the radial direction.

[0033] The density sequence includes the first density value corresponding to each depth along the axial direction of the target borehole; the density distribution is used to characterize the second density value of the target position in the plane containing the radial direction of the target borehole at different depths, which has a different radial distance from the geometric center of the plane.

[0034] For example, in a one-dimensional density sequence, the first density value corresponding to each depth is represented by ρ(z), where z is the depth along the axial direction. In a two-dimensional density distribution, the second density value corresponding to each depth is represented by ρ(z, r), where z is the depth along the axial direction, and r is the radial distance in the plane containing that depth relative to the geometric center of that plane (or the geometric center of the target borehole). For example, the second density value ρ(20, 1.5) = 2.45 g / cm³ corresponds to a depth of 20 m and a radial distance of 1.5 m from the geometric center of the target borehole. 3 .

[0035] In one example, a density determination model for a single depth is established based on Moliere's multiple Coulomb scattering and muon energy decay theory. The predetermined density model is expressed by the following formula:

[0036] in, The muon flux measured by the muon detector at depth z and zenith angle θ; is the initial baseline flux at the Earth's surface, and this parameter is pre-calibrated; exp is the natural exponential function. Given a zenith angle θ, the trajectory of a muon incident from the Earth's surface to a depth z. The muon attenuation coefficient, Let Z be the first density value at depth z on the trajectory of the muon.

[0037] Understandably, the initial baseline flux at the Earth's surface is known. By inputting the muon flux and angle parameters of the target borehole at various depths into the preset density determination model, the density sequence of the target borehole along the axial direction and the density distribution of the target borehole along the radial direction can be obtained by inversion.

[0038] In some embodiments, the number of muons is at least two. Based on the muon flux, angle parameters, and a preset density determination model at each depth for each target borehole, the density sequence along the axial direction and the density distribution along the radial direction of the target borehole are determined, including: The angle parameters include a first angle parameter and a second angle parameter. The first angle parameter represents the muon incident on the muon detector in a vertical direction, and the second angle parameter represents the muon incident on the muon detector in an oblique direction. Based on the first flux of the first muon at each depth and the preset density, a density sequence is determined, wherein the first muon is a muon incident with a first angular parameter. The density distribution is determined based on the second flux of the second muon at various depths, the trajectory of the second muon, and a preset density determination model, wherein the second muon is a muon incident with a second angle parameter.

[0039] In one example, the first muon is incident vertically onto the muon detector, with a zenith angle of 0°. Referring to the implementation method in step S101, the first flux corresponding to the first muon at each depth is collected, and the first flux at each depth is input into a preset density determination model. The propagation path of the first muon coincides with the axis of the target borehole. By solving the preset density determination model, the first density value corresponding to each depth along the axis of the target borehole is obtained through inversion. The first density values ​​at all depths are combined to form a density sequence along the axis of the target borehole.

[0040] In another example, the second muon is incident on the muon detector at an oblique angle, meaning the trajectory of the second muon is an inclined line, and the zenith angle of the corresponding muon is not 0. The detection of the second muon includes parameters in two dimensions: zenith angle and depth. Referring to the implementation method in step S101, the second flux of the second muon at various depths is obtained, and the trajectory of the second muon is obtained based on the inner fiber layer. The relevant parameters of the second flux and the trajectory are input into a preset density determination model. By solving the preset density determination model, the second density values ​​corresponding to different depths and different radial distances are obtained through inversion, thereby constructing a two-dimensional density distribution with depth and radial distance as variables.

[0041] It should be noted that the trajectory of the second muon differs depending on the zenith angle. The larger the zenith angle, the greater the radial distance of the second muon from the geometric center of the target borehole in that depth plane. Therefore, the density distribution of the second muon with respect to depth and radial distance can be determined based on the second muon at different zenith angles.

[0042] Step S103: Determine the density gradient based on multiple first density values ​​in the density sequence. If the density gradient corresponding to the target depth is greater than the preset gradient threshold, it is determined that there is a stratigraphic interface at the target depth.

[0043] The density gradient is the first partial derivative of the first density value with respect to depth, which characterizes the degree of significance of the change in rock mass density with depth. When soft and hard strata come into contact, the density changes significantly, resulting in a large density gradient.

[0044] For example, the preset gradient threshold can be pre-set, such as 0.25 g / cm³ or 0.5 g / cm³, and can be flexibly adjusted according to actual working conditions. Furthermore, the preset gradient thresholds corresponding to different types of stratigraphic interfaces can be configured differently; for example, the preset gradient threshold for the interface between soft soil and mudstone is 0.38 g / cm³, and the preset gradient threshold for the interface between mudstone and granite is 0.52 g / cm³.

[0045] In one example, the density gradient corresponding to the first density value at each depth is determined by the following gradient calculation formula:

[0046] in, This represents the first density value corresponding to the depth z position. This represents the density gradient at depth z.

[0047] In one example, taking a preset gradient threshold of 0.25 g / cm³ as an example, based on the density sequence determined in step S102, each depth point is traversed, and the gradient of the first density value at each depth in the density sequence is calculated according to the gradient calculation formula to obtain the density gradient corresponding to the first density value at each depth. If the density gradient at the target depth of 220m is greater than or equal to 0.25 g / cm³, it indicates that a stratigraphic interface exists at that target depth.

[0048] Step S104: Determine the closed contour of the formation interface in the density distribution based on the edge extraction algorithm.

[0049] Understandably, this step takes the density distribution determined in step S102 as input, and combines it with the depth of the formation interface initially obtained in the axial direction of the target borehole in step S103, and performs edge extraction processing of the formation interface to obtain the closed contour of the formation interface within the density distribution in the radial direction.

[0050] In some embodiments, see Figure 2 The closed contour of the formation interface is determined from the density distribution based on the edge extraction algorithm, including: Step S1041: Perform gradient enhancement processing on the density distribution to obtain the processed target density distribution.

[0051] In one example, an anisotropic diffusion gradient enhancement process is applied to the density distribution of the target borehole along the radial direction to obtain the gradient-enhanced target density distribution. The anisotropic diffusion gradient enhancement process is performed according to the following formula:

[0052] in, This is the second density value in the density distribution. This is the second gradient value of the second density value. The gradient of the second gradient value. is the second gradient value of the second density value, and c(·) is the gradient diffusion constraint function.

[0053] The gradient boosting algorithm in this example uses the gradient of the second gradient value as the diffusion constraint. When the value is large (corresponding to the stratigraphic interface edge), the diffusion constraint function suppresses diffusion and preserves the corresponding stratigraphic interface edge, making the stratigraphic interface edge response sharper; when... When the density is relatively small (corresponding to a homogeneous stratum), the diffusion constraint function acts as a diffusion smoothing function, achieving noise suppression in the homogeneous stratum. By enhancing the density distribution gradient, the edges of the formation interface are strengthened while the noise is smoothed, thus obtaining the target density distribution after gradient enhancement.

[0054] Step S1042: Determine the closed contour of the formation interface in the target density distribution based on the edge extraction algorithm.

[0055] Understandably, after completing the gradient enhancement processing of the density distribution, an edge extraction operation is performed to determine the closed contour of the formation interface in the target density distribution.

[0056] In one example, the edge extraction algorithm employs an adaptive threshold edge extraction algorithm, using a sliding window traversal to extract the closed contours of the formation interfaces. The window size and sliding step size of the sliding window can be flexibly adjusted according to the actual formation conditions. During the traversal process, all pixels of the formation interfaces are extracted using the following formula:

[0057] in, The mean gradient within the sliding window. Let be the standard deviation of the gradient within the sliding window, and k be the adaptive adjustment coefficient. This is the adaptive edge threshold corresponding to this sliding window.

[0058] The gradient value of each pixel within the sliding window is compared with the corresponding adaptive edge threshold. If the gradient value of a pixel is greater than the adaptive edge threshold corresponding to the sliding window, the pixel is determined to belong to the stratigraphic interface. The identified discrete pixels are then processed to form a closed contour of a complete and continuous stratigraphic interface.

[0059] It should be noted that this edge extraction algorithm uses the mean and standard deviation of the gradient within the local sliding window as adjustment criteria to generate a dynamic adaptive edge threshold, without the need for manually setting a single threshold.

[0060] Step S105: Based on the density sequence and location information of the corresponding closed contour of each target borehole in the multiple target boreholes, determine the layered geological model of the strata and complete the geological exploration of the strata.

[0061] The location information is represented in the form of coordinates.

[0062] In one example, an objective function is constructed based on the density sequence and the location information of the corresponding closed contour of each target borehole in multiple target boreholes. The three-dimensional density field of the layered geology is determined by solving the objective function, thereby determining the layered geological model of the strata. Based on the layered geological model, the geological exploration of the strata is completed, which can efficiently and accurately depict the morphological space of the interface between soft and hard strata.

[0063] The method provided in this disclosure acquires the muon flux and angle parameters of each target borehole at various depths using the outer and inner fiber layers of a muon detector. Through the functional division of the inner and outer fiber layers, the acquisition of these two types of parameters is independent, achieving signal decoupling between muon flux and angle parameters. Furthermore, it combines a preset density determination model to determine the density sequence in the axial direction and the density distribution in the radial direction, overcoming the limitation of only acquiring the density sequence and providing data for determining the layered geological model. Then, based on the density gradient and a preset gradient threshold, it determines the existence of stratigraphic interfaces at the target depth and obtains the closed contours of these interfaces through an edge extraction algorithm, achieving accurate identification of the stratigraphic interfaces. Finally, by combining the density sequences and the location information of the closed contours corresponding to multiple target boreholes, a layered geological model is determined, completing the geological exploration of the strata. This reduces the number of traditional target boreholes and can accurately reconstruct the structure of stratigraphic interfaces, thereby supporting large-area deep geological exploration and improving the efficiency of geological exploration.

[0064] In some embodiments, see Figure 3 , Figure 3 This is a flowchart illustrating the acquisition of muon flux and angle parameters according to an exemplary embodiment, including the following steps: Step S301: Perform parameter calibration on the first detection module to determine the first effective signal corresponding to the first signal output by the first detection module and the first calibration gain of the first detection module.

[0065] For example, the parameters to be corrected include vibration, temperature, and pressure. Because the performance of the first detection module (e.g., photoelectric gain) is easily affected by interference factors such as drilling rig vibration, temperature, and pressure within the target borehole, errors may occur in the muon flux determined by the first detection module. Therefore, before obtaining the muon flux and angle parameters, the parameters of the first detection module must first be corrected to ensure the accuracy of the muon flux determined by the first detection module.

[0066] In one example, variational mode decomposition (VMD) algorithm is used for vibration noise decomposition and correction. The corresponding objective function is expressed as follows:

[0067]

[0068] in, K represents the original timing signal (i.e., the first signal) output by the first detection module; K is the total number of mode decompositions. This is the k-th modal component; The center frequency corresponding to the k-th modal component; Let t be the Dirac function, and t be time. For modal components The Hilbert transform.

[0069] The constraints of the objective function are: That is, the superposition of all modal components after variational mode decomposition equals the first signal. Under constraints, a set of modal components corresponding to the minimum objective function is obtained. and center frequency This minimizes the bandwidth of each modal component.

[0070] Furthermore, if the center frequency is greater than a preset frequency threshold, the modal component corresponding to that center frequency is determined to be a high-frequency noise component and is discarded; if the center frequency is less than or equal to the preset frequency threshold, the modal component corresponding to that center frequency is determined to be a valid signal component and is retained. All valid signal components are superimposed and reconstructed to obtain the first valid signal after removing vibration noise.

[0071] In one example, the first detection module is further corrected for temperature and pressure using the following formula:

[0072] in, The first correction gain of the first detection module after temperature and pressure correction; The original gain of the first detection module is calculated from the first effective signal; T is the temperature collected by the first detection module at the depth position, and P is the pressure collected by the first detection module at the depth position. Reference temperature, As the reference pressure; This is the temperature gain correction factor; This is the pressure gain correction factor. and All of these are pre-calibrated constant parameters.

[0073] In this example, the systematic drift of the first detection module caused by changes in the underground temperature and pressure environment is corrected by temperature and pressure correction, and the first correction gain after temperature and pressure correction is obtained.

[0074] Step S302: Determine the muon flux of the muon based on the first correction gain.

[0075] Understandably, the signal output by the first detection module, configured with a photoelectric gain of the first correction gain in the outer fiber layer of the muon detector, is processed to invert and determine the muon flux.

[0076] Step S303: Determine the angle parameters of the muon in the geodetic coordinate system based on the initial angle parameters of the muon in the coordinate system of the muon detector and the attitude angle parameters of the muon detector in the geodetic coordinate system.

[0077] The initial angle parameter is the angle between the trajectory of the muon and the central axis of the muon detector in the coordinate system of the muon detector.

[0078] Among them, the attitude angle parameters are the tilt angle and rotation angle of the muon detector in the geodetic coordinate system, which are obtained by the gyroscope.

[0079] Understandably, the inner fiber layer of the muon detector can obtain the initial angle parameters of the muon in the muon detector's coordinate system. Since the muon detector may tilt or rotate during detection, there is an angular deviation between the muon detector's coordinate system and the Earth's coordinate system. Directly using the initial angle parameters as the angle parameters (i.e., the zenith angle) would lead to errors in both the first and second density values. Therefore, coordinate transformations are performed based on the initial angle parameters and the attitude angle parameters to determine the muon's angle parameters in the Earth's coordinate system.

[0080] In some embodiments, see Figure 4 , Figure 4 This is a flowchart illustrating the determination of initial angle parameters according to an exemplary embodiment, including the following steps: Step S401: Perform parameter calibration on the second detection module to determine the second effective signal corresponding to the second signal output by the second detection module and the second calibration gain of the second detection module.

[0081] Understandably, the second detection module is also susceptible to interference from vibration, temperature, and pressure, which can easily cause systematic drift in the second detection module and affect the accuracy of the calculated angle parameters of the inner fiber layer. This step refers to the embodiment in step S301 above to perform parameter correction on the second detection module, and will not be elaborated further here.

[0082] Step S402: Determine the incident position of the muon at each depth based on the second correction gain.

[0083] Understandably, the photoelectric gain of the second detection module after parameter correction is the second correction gain. All electrical signals in this step are output signals from the parameter-corrected second detection module.

[0084] In one example, each second fiber in the inner fiber layer is equipped with a second detection module at both ends. When a muon is incident on any second fiber in the inner fiber layer, it excites an optical signal, which propagates simultaneously to both ends of the second fiber. Since the propagation speed of the optical signal in the second fiber is fixed, the optical path distance between the incident position of the muon on the second fiber and the second detection modules at both ends is different, resulting in a propagation time difference between the optical signals collected by the second detection modules at both ends of the second fiber, and thus a propagation time difference between the electrical signals output by the second detection modules. Furthermore, the incident position of the muon can be determined based on the propagation time difference of the electrical signals output by the second detection modules at both ends of the same second fiber and the propagation speed in the second fiber.

[0085] Step S403: Fit the incident positions to obtain the trajectory of the muon inside the muon detector.

[0086] Understandably, during the process of a muon penetrating the inner fiber layer in a single pass, it will pass through multiple second fibers wound on the inner fiber layer. Each second fiber is equipped with a second detection module at both ends. Referring to the method for determining the incident position in step S402, a set of different incident positions can be obtained.

[0087] In one example, the least squares linear fitting algorithm is used to fit a straight line at each incident position to obtain the trajectory of the muon inside the muon detector.

[0088] Step S404: In the coordinate system of the muon detector, determine the initial angle parameters based on the angle between the trajectory and the central axis of the muon detector.

[0089] Understandably, the angle between the muon's trajectory obtained from step S403 and the vertical central axis of the muon detector is calculated to obtain the angle between the two spatial lines. This angle is the initial angle parameter in the coordinate system of the muon detector.

[0090] In some embodiments, see Figure 5 , Figure 5 This is a flowchart illustrating the determination of a layered geological model according to an exemplary embodiment, including the following steps: Step S501: Based on the density sequence and the location information of the closed contour corresponding to each target borehole in the multiple target boreholes, construct an objective function. The objective function includes a data fitting term, a total variation regularization term, and a boundary constraint term.

[0091] The position information of the closed contour can be the coordinate information of the closed contour.

[0092] Understandably, the location information of the closed contours determined in the density sequence and density distribution corresponding to all target boreholes is used as a priori constraints to construct an objective function, which is a compressed sensing three-dimensional density reconstruction objective function with stratigraphic interface regularization.

[0093] In one example, the objective function consists of three parts: a data fitting term, a total variation regularization term, and a boundary constraint term. The objective function is shown in the following equation:

[0094] in, For data fitting terms; For total variation regularization; , where A is the interface constraint term; A is the projection matrix, used to establish the mapping relationship between the three-dimensional density field and the one-dimensional density sequence; It is a density sequence; It is a three-dimensional density field; The gradient of the three-dimensional density field; These are the weighting coefficients for the total variation regularization term; These are the weight coefficients for the interface constraint terms; It is a total variational operator; This is a regular operator for interface constraints.

[0095] Step S502: Determine the three-dimensional density field based on the objective function.

[0096] In one example, the objective function is iteratively optimized to obtain the three-dimensional density field corresponding to the minimum objective function.

[0097] Step S503: Based on the three-dimensional density field, determine multiple voxels belonging to the same stratigraphic unit. The multiple voxels belonging to the same stratigraphic unit are spatially connected and the difference in the third density value between two adjacent voxels is less than a preset difference.

[0098] For example, the preset difference value is pre-set and can be flexibly adjusted according to the actual working conditions.

[0099] The three-dimensional density field includes the third density value corresponding to each of the multiple voxels.

[0100] It should be noted that, since the objective function in step S501 constrains the third density value of the three-dimensional density field, there will be a deviation between the third density value and the second density value.

[0101] Understandably, this step traverses all voxels in the three-dimensional density field and assigns multiple voxels that are spatially connected and whose third density value difference between two adjacent voxels is less than a preset difference to the same stratigraphic unit.

[0102] In one example, the extracted closed contour of the stratigraphic interface is used as a constraint, and a 3D region growing algorithm is employed to divide the stratigraphic units into independent units. The third density value of all voxels in the 3D density field within the closed contour is traversed, and adjacent and spatially connected voxels are selected. When the density difference between any two adjacent voxels is less than a preset difference, the adjacent voxels are determined to belong to the same stratigraphic unit. Through voxel-by-voxel growing clustering, the precise division of all stratigraphic units is finally completed.

[0103] Step S504: Calibrate the stratigraphic units according to the preset density range and determine the attribute information of the stratigraphic units.

[0104] For example, the preset density range is pre-defined, and the attribute information of the stratigraphic unit uniquely corresponds to the preset density range. The attribute information of the stratigraphic unit is the stratigraphic property, including properties such as hard rock, soft soil, and weak interlayered strata.

[0105] In one example, the average density of a stratigraphic unit is calculated. If the average density of a stratigraphic unit is within a preset density range, the attribute information of the stratigraphic unit is labeled according to the correspondence between the attribute information of the stratigraphic unit and the preset density range.

[0106] Step S505: Based on the three-dimensional density field and the attribute information of the stratigraphic units, a layered geological model representing the stratigraphic interface is obtained, and the geological exploration of the stratigraphy is completed.

[0107] Understandably, a three-dimensional density field is a set of discrete data. Based on this three-dimensional density field and combined with the attribute information of each stratigraphic unit, a complete layered geological model is ultimately constructed, and geological exploration of the strata is completed based on this model. This layered geological model can intuitively display the three-dimensional spatial distribution of all soft and hard strata interfaces, interlayer pinch-outs, and fault contact relationships.

[0108] As an application example, a double-helix muon detector is placed into the target borehole. The winch controls the measuring cable to lower the muon detector at a constant speed. The depth encoder records the depth of the muon detector in real time. Downhole sensors collect real-time time-series data such as raw events, vibration, temperature, and pressure of the muon and cache them in the local storage unit. The ground control unit retrieves time-series data acquired downhole, performs parameter corrections such as vibration decomposition, temperature and pressure compensation, and angle normalization, and outputs muon flux and angle parameters. Based on muon energy and a preset density determination model, it inverts the density sequence of the target borehole along the axial direction, calculates the density gradient at each depth, and marks the depth points where the density gradient exceeds the gradient threshold. It constructs a two-dimensional density profile (i.e., density distribution) of the target borehole along the radial direction, uses anisotropic diffusion gradient enhancement filtering to strengthen the formation boundary, and an adaptive threshold algorithm to extract the complete soft and hard strata interface edge. The three-dimensional region growth clustering algorithm divides the formation into independent stratigraphic units based on the closed contour of the formation interface and automatically distinguishes between three types of formation attributes: hard rock, soft soil, and weak interlayers. Based on the one-dimensional density sequence of all target boreholes and the coordinate information of the corresponding closed contour of the formation interface, it performs a three-dimensional reconstruction of multiple boreholes with interface constraints to generate a continuous layered geological model.

[0109] In one example, the geological exploration area consists of the following strata from top to bottom: backfilled silty soft soil (ρ=1.8g / cm³), weathered mudstone weak interlayer (ρ=2.1g / cm³), and intact granite hard rock (ρ=2.7g / cm³). There are two obvious soft-hard interfaces (i.e., stratigraphic interfaces) among the three strata. Three 110mm exploratory boreholes (i.e., target boreholes) were drilled to a depth of 220m. The borehole-type muon detector disclosed in this paper was used for detection, and the stratification accuracy was verified by comparing the results with those obtained from traditional core sampling.

[0110] Based on the preset density, the model inverts the one-dimensional density sequence and two-dimensional density distribution, and calculates the density gradient of the first density value in the density sequence. The density gradients of the two soft-hard strata interfaces, soft soil-mudstone and mudstone-granite, are 0.38 g / cm³ / m and 0.52 g / cm³ / m, respectively, both greater than their corresponding adaptive gradient thresholds. The strata interfaces at these two depths are automatically marked, with a deviation of less than 1.1 m from the depth detected by core stratification. Furthermore, the traditional fixed threshold method would lose the boundary of thin weathered mudstone interlayers.

[0111] After gradient enhancement of density distribution, the stratigraphic boundaries are clear and continuous, with no pseudo-edges of homogeneous rock masses. The adaptive edge extraction algorithm completely extracts the closed contour of the stratigraphic interface and accurately identifies the weathered weak interlayer with a thickness of only 4.2m. Conventional interlayer without gradient enhancement algorithm has blurred interlayer boundaries and cannot distinguish thin interlayers.

[0112] Finally, the density sequence and density distribution of the three target boreholes were integrated in the cloud, and the three-dimensional interface regular constraints were introduced to reconstruct the layered geological three-dimensional model, which fully restored the spatial undulation of the two sets of soft and hard strata interfaces and the lenticular pinch-out morphology of mudstone interlayers; traditional single borehole independent imaging can only obtain local profiles and cannot determine the lateral extension range of the interlayers.

[0113] Figure 6 This is a block diagram of a muon-based geological exploration apparatus, configured on a computer device, according to an exemplary embodiment. See also... Figure 6 The device includes: Muon detection unit 601 is configured to acquire the muon flux and angle parameters of muons at various depths for each of multiple target boreholes, wherein the number of muons is at least one, the muon flux is determined by the outer fiber layer of the muon detector, and the angle parameters are determined by the inner fiber layer of the muon detector. The first determining unit 602 is configured to determine the density sequence of the target borehole along the axial direction and the density distribution of the target borehole along the radial direction based on the muon flux, angle parameters and a preset density determining model of each target borehole at each depth; the density sequence includes the first density value corresponding to each depth of the target borehole along the axial direction; the density distribution is used to characterize the second density value of the target position in the plane containing the radial direction of the target borehole at different depths, which has a different radial distance from the geometric center of the plane. The second determining unit 603 is configured to determine the density gradient based on multiple first density values ​​in the density sequence, and if the density gradient corresponding to the target depth is greater than a preset gradient threshold, determine that there is a formation interface at the target depth. The third determining unit 604 is configured to determine the closed contour of the formation interface in the density distribution based on the edge extraction algorithm; The fourth determining unit 605 is configured to determine the layered geological model of the strata based on the density sequence and the location information of the corresponding closed contour of each of the multiple target boreholes, and to complete the geological exploration of the strata.

[0114] In some embodiments, the outer fiber layer includes a first detection module, and a muon detection unit 501 is configured to: The parameters of the first detection module are corrected to determine the first effective signal corresponding to the first signal output by the first detection module and the first correction gain of the first detection module. The muon flux of the muon is determined based on the first correction gain. Based on the initial angle parameters of the muon in the coordinate system of the muon detector and the attitude angle parameters of the muon detector in the geodetic coordinate system, the angle parameters of the muon in the geodetic coordinate system are determined.

[0115] In some embodiments, the inner fiber layer includes a second detection module, and the muon detection unit 501 is further configured to: The parameters of the second detection module are corrected to determine the second effective signal corresponding to the second signal output by the second detection module and the second correction gain of the second detection module. The incident position of the muon at each depth is determined based on the second correction gain; By fitting the data at each incident position, the trajectory of the muon inside the muon detector is obtained. In the coordinate system of the muon detector, the initial angle parameters are determined based on the angle between the trajectory and the central axis of the muon detector.

[0116] In some embodiments, the number of muons is at least two, and the first determining unit 602 is configured to: The angle parameters include a first angle parameter and a second angle parameter. The first angle parameter represents the muon incident on the muon detector in a vertical direction, and the second angle parameter represents the muon incident on the muon detector in an oblique direction. Based on the first flux of the first muon at each depth and the preset density, a density sequence is determined, wherein the first muon is a muon incident with a first angular parameter. The density distribution is determined based on the second flux of the second muon at various depths, the trajectory of the second muon, and a preset density determination model, wherein the second muon is a muon incident with a second angle parameter.

[0117] In some embodiments, the third determining unit 604 is configured to: Gradient enhancement processing is applied to the density distribution to obtain the processed target density distribution; The closed contour of the formation interface is determined in the target density distribution based on the edge extraction algorithm.

[0118] In some embodiments, the fourth determining unit 606 is configured to: Based on the density sequence and the location information of the closed contour corresponding to each target borehole in multiple target boreholes, an objective function is constructed. The objective function includes a data fitting term, a total variation regularization term, and a boundary constraint term. Determine the three-dimensional density field based on the objective function; Based on the three-dimensional density field, multiple voxels belonging to the same stratigraphic unit are determined. The multiple voxels belonging to the same stratigraphic unit are spatially connected and the difference in the third density value between two adjacent voxels is less than a preset difference. The three-dimensional density field includes the third density value corresponding to each voxel among the multiple voxels. The stratigraphic units are calibrated according to the preset density range to determine the attribute information of the stratigraphic units; Based on the three-dimensional density field and the attribute information of stratigraphic units, a layered geological model characterizing stratigraphic interfaces is obtained, and the geological exploration of strata is completed.

[0119] In some embodiments, the geological exploration device based on muon detection further includes a winch, an armored logging cable, and a depth encoder supporting the exploration drill rig. The determining device is further configured to record the depth coordinates of the lowering of the muon detector, make the muon events in the deep stratum correspond to the depths one by one, and establish a depth-muon event mapping relationship. The muon events include muon flux, angle parameters, and the like.

[0120] In an example, the muon detection unit 601, the first determination unit 602, and the second determination unit 603 in the geological exploration device based on muon detection are all implemented by a ground edge computing industrial control unit. The ground edge computing industrial control unit is equipped with a Graphics Processing Unit (GPU) parallel computing module, and executes the respective corresponding operations of the muon detection unit 601, the first determination unit 602, and the second determination unit 603 in real time. The third determination unit 604 and the fourth determination unit 605 are implemented by a cloud server, and the cloud server executes the respective corresponding operations of the third determination unit 604 and the fourth determination unit 605 in real time.

[0121] Each module in the above geological exploration device based on muon detection may be implemented entirely or partially by software, hardware, or a combination thereof. Each of the above modules may be embedded in or independent of the processor in the computer device in the form of hardware, or may be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0122] Figure 7 is a schematic structural diagram of a muon detector according to an exemplary embodiment, and the muon detector 700 includes: a housing 701, an outer optical fiber layer 702, an isolation layer 703, and an inner optical fiber layer 704; wherein the first optical fibers in the outer optical fiber layer 702 are wound at a first pitch, the second optical fibers in the inner optical fiber layer 704 are wound at a second pitch, the winding direction of the optical fibers in the outer optical fiber layer 702 is opposite to the winding direction of the optical fibers in the inner optical fiber layer 704, both ends of each first optical fiber in the outer optical fiber layer 702 are configured with a first detection module, both ends of each second optical fiber in the inner optical fiber layer 704 are configured with a second detection module, the muon flux is determined by the signal output by the first detection module, and the angle parameter is determined by the signal output by the second detection module.

[0123] In one example, the muon travels along its trajectory through the housing 701, the outer fiber layer 702, the isolation layer 703, and the inner fiber layer 704 in sequence. Neither the housing 701 nor the isolation layer 703 affects the muon flux, angular parameters, or trajectory of the muon.

[0124] In one example, the isolation layer 703 is located between the outer fiber layer 702 and the inner fiber layer 704. The isolation layer 703 is filled with insulating, elastic, and thermally insulating support rings with a ring spacing of 20 cm. This isolates the crosstalk between the outer fiber layer 702 and the inner fiber layer 704, while also buffering the vibration during drilling and lowering to prevent the fiber from bending or breaking. Furthermore, the isolation layer 703 has reserved micro-fluid channels to balance the hydrostatic pressure inside and outside the muon detector 700.

[0125] In one example, the muon detector 700 is a sealed, pressure-resistant cylindrical structure with an overall outer diameter of 88 mm, suitable for drilling standard target holes of Φ90 mm or Φ110 mm. The housing 701 is made of a high-strength titanium alloy pressure-resistant sealing cylinder, capable of withstanding pressures of 15 MPa and temperatures from -20°C to 85°C. Waterproof sealed cable connectors are provided at both ends of the housing 701. Inside the muon detector 700, the first optical fiber of the outer fiber layer 702 is wound in a left-hand spiral, while the second optical fiber of the inner fiber layer 704 is wound in a right-hand spiral, forming a cross-grid three-dimensional sensing network to achieve 360-degree three-dimensional grid sensing.

[0126] In one example, the top of the muon detector 700 is connected to an armored measuring cable, which integrates an optical fiber readout signal line, a sensor unit power supply line, and a depth encoding communication line. These are used to transmit the signals output by the first and second detection modules, power the sensor unit, and enable the interaction of ground and deep-seated data.

[0127] In some embodiments, the internal cavity of the inner fiber layer 704 further includes: The sensing unit is configured to detect parameters inside the formation and output sensing signals, which are used to correct the parameters of the first and second detection modules inside the muon detector. The preprocessing unit is configured to perform parameter correction on the first detection module to determine the first valid signal corresponding to the first signal output by the first detection module and the first correction gain of the first detection module; and to perform parameter correction on the second detection module to determine the second valid signal corresponding to the second signal output by the second detection module and the second correction gain of the second detection module. The storage unit is configured to store muon flux and angle parameters.

[0128] In one example, the sensing unit includes a triaxial miniature vibration sensor, a high-temperature temperature sensor, a high-precision hydrostatic pressure sensor, and an attitude gyroscope. The sensing signal lines connected to the sensing unit are neatly routed along the fiber gap formed by the outer fiber layer 702 and the inner fiber layer 704, without obstructing the light-sensitive area of ​​the fiber.

[0129] In one example, the storage unit is a high-capacity solid-state storage device that can continuously store raw muon events (e.g., muon flux, angle parameters) for 6 months and transmit them to the ground-based industrial control host via armored cables.

[0130] In some embodiments, the internal cavity of the inner fiber layer 704 further includes a field-programmable gate array (FPGA). The Programmable Gate Array (FPGA) signal processing chip amplifies, converts analog to digital, and buffers the output signals of the first and second detection modules in real time.

[0131] The muon detector disclosed herein uses an internally and externally reversed double-helix scintillation fiber to replace the traditional flat or block scintillator, achieving 360-degree three-dimensional grid sensing within the narrow cylindrical space of the target borehole. The coverage of muon angle parameters is increased by 75%, and the volume and weight are reduced by 42%, making it more suitable for deployment in small-diameter boreholes. At the same time, it separates the two types of physical quantities, angle parameters (inner fiber layer) and muon flux (outer fiber layer), resulting in higher signal decoupling accuracy. The lightweight fiber structure reduces the weight of the muon detector, making it less prone to jamming when deployed to the target borehole and reducing drilling construction risks.

[0132] In one exemplary embodiment, a computer device is provided, including a processor and a memory, the memory storing a computer program, the processor executing the computer program to implement the steps of any of the above-described muon-based geological exploration methods.

[0133] In one exemplary embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the above-described muon-based geological exploration methods. The computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, or optical data storage device, etc.

[0134] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of any of the above-described muon-based geological exploration methods.

[0135] refer to Figure 8The following description serves as a structural block diagram of the computer device disclosed herein. The computer device includes a computing unit 801, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. The RAM 803 may also store various programs and data required for the operation of the computer device 800. The computing unit 801, ROM 802, and RAM 803 are interconnected via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0136] Multiple components in computer device 800 are connected to I / O interface 805, including: input unit 806, output unit 807, storage unit 808, and communication unit 809. Input unit 806 can be any type of device capable of inputting information into computer device 800. Input unit 806 can receive input numerical or character information and generate key signal inputs related to user settings and / or function control of computer device 800, and may include, but is not limited to, a mouse, keyboard, touchscreen, trackpad, trackball, joystick, microphone, and / or remote control. Output unit 807 can be any type of device capable of presenting information, and may include, but is not limited to, a monitor, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 808 may include, but is not limited to, a hard disk and an optical disk. Communication unit 809 allows computer device 800 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers, and / or chipsets, such as Bluetooth™ devices, WiFi devices, WiMax devices, cellular communication devices, and / or the like.

[0137] The computing unit 801 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 801 performs the various methods and processes described above, such as a muon-based geological exploration method. For example, in some embodiments, the muon-based geological exploration method can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 808. In some embodiments, part or all of the computer program can be loaded and / or installed on the computer device 800 via ROM 802 and / or communication unit 809. When the computer program is loaded into RAM 803 and executed by the computing unit 801, one or more steps of the muon-based geological exploration method described above can be performed. Alternatively, in other embodiments, computing unit 801 may be configured by any other suitable means (e.g., by means of firmware) to perform a muon-based geological exploration method.

[0138] The computer device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described muon-based geological exploration method.

[0139] Those skilled in the art will understand that all or part of the steps in the above methods can be implemented by a program instructing related hardware, and the program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk. Optionally, all or part of the steps in the above embodiments can also be implemented using one or more integrated circuits. Accordingly, each module / unit in the above embodiments can be implemented in hardware or as a software functional module. This invention is not limited to any particular combination of hardware and software.

[0140] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the following claims.

[0141] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A geological exploration method based on muon detection, characterized in that, The method includes: The muon flux and angle parameters of muons at various depths are obtained for each of the multiple target boreholes, wherein the number of muons is at least one, the muon flux is determined by the outer fiber layer of the muon detector, and the angle parameters are determined by the inner fiber layer of the muon detector. Based on the muon flux, angle parameters, and a preset density determination model for each target borehole at each depth, a density sequence along the axial direction and a density distribution along the radial direction of the target borehole are determined. The density sequence includes a first density value corresponding to each depth along the axial direction of the target borehole. The density distribution is used to characterize a second density value for the target position in the plane containing the radial direction of the target borehole at different depths, which has a different radial distance from the geometric center of the plane. A density gradient is determined based on multiple first density values ​​in the density sequence. If the density gradient corresponding to the target depth is greater than a preset gradient threshold, it is determined that a stratigraphic interface exists at the target depth. The closed contour of the formation interface is determined in the density distribution based on the edge extraction algorithm; Based on the density sequence and the location information of the corresponding closed contour for each of the multiple target boreholes, a layered geological model of the strata is determined, and the geological exploration of the strata is completed.

2. The geological exploration method based on muon detection according to claim 1, characterized in that, The outer fiber layer includes a first detection module, which acquires the muon flux and angle parameters of muons at various depths for each of the multiple target boreholes, including: The parameters of the first detection module are corrected to determine the first effective signal corresponding to the first signal output by the first detection module and the first correction gain of the first detection module. The muon flux of the muon is determined based on the first correction gain; The angle parameters of the muon in the geodetic coordinate system are determined based on the initial angle parameters of the muon in the coordinate system of the muon detector and the attitude angle parameters of the muon detector in the geodetic coordinate system.

3. The geological exploration method based on muon detection according to claim 2, characterized in that, The inner fiber layer includes a second detection module, and the determination of the initial angle parameters includes: The parameters of the second detection module are corrected to determine the second valid signal corresponding to the second signal output by the second detection module and the second correction gain of the second detection module. The incident position of the muon at each depth is determined based on the second correction gain; By fitting the incident positions, the trajectory of the muon inside the muon detector is obtained. In the coordinate system of the muon detector, the initial angle parameter is determined based on the angle between the trajectory and the central axis of the muon detector.

4. The geological exploration method based on muon detection according to claim 2, characterized in that, The number of muons is at least two. The step of determining the density sequence along the axial direction and the density distribution along the radial direction of the target borehole based on the muon flux at each depth, the angle parameter, and a preset density determination model for each target borehole includes: The angle parameters include a first angle parameter and a second angle parameter. The first angle parameter indicates that the muon is incident on the muon detector in a vertical direction, and the second angle parameter indicates that the muon is incident on the muon detector in an oblique direction. The density sequence is determined based on the first flux of the first muon at each of the aforementioned depths and the preset density determination model, wherein the first muon is the muon incident with the first angle parameter; The density distribution is determined based on the second flux of the second muon at each of the stated depths, the trajectory of the second muon, and the preset density determination model, wherein the second muon is the muon incident with the second angle parameter.

5. The geological exploration method based on muon detection according to claim 1, characterized in that, The edge extraction algorithm for determining the closed contour of the formation interface in the density distribution includes: The density distribution is subjected to gradient enhancement processing to obtain the processed target density distribution; The closed contour of the formation interface is determined based on the edge extraction algorithm in the target density distribution.

6. The geological exploration method based on muon detection according to claim 1, characterized in that, The step of determining a layered geological model of the strata based on the density sequence and the location information of the corresponding closed contour of each of the multiple target boreholes, and completing the geological exploration of the strata, includes: Based on the density sequence and the position information of the closed contour corresponding to each of the multiple target boreholes, an objective function is constructed, which includes a data fitting term, a total variation regularization term, and a boundary constraint term. Determine the three-dimensional density field based on the objective function; Based on the three-dimensional density field, multiple voxels belonging to the same stratigraphic unit are determined. The multiple voxels belonging to the same stratigraphic unit are spatially connected and the difference in the third density value between two adjacent voxels is less than a preset difference. The three-dimensional density field includes the third density value corresponding to each of the multiple voxels. The stratigraphic units are calibrated according to a preset density range to determine the attribute information of the stratigraphic units; Based on the three-dimensional density field and the attribute information of the stratigraphic unit, a layered geological model characterizing the stratigraphic interface is obtained, and the geological exploration of the stratigraphy is completed.

7. A geological exploration device based on muon detection, characterized in that, The device includes: A muon detection unit is configured to acquire the muon flux and angle parameters of muons at various depths for each of a plurality of target boreholes, wherein the number of muons is at least one, the muon flux is determined by the outer fiber layer of the muon detector, and the angle parameters are determined by the inner fiber layer of the muon detector. The first determining unit is configured to determine, based on the muon flux, the angle parameter, and a preset density determining model, a density sequence along the axial direction and a density distribution along the radial direction of the target borehole; the density sequence includes a first density value corresponding to each depth along the axial direction of the target borehole; the density distribution is used to characterize a second density value of the target position in the plane containing the radial direction of the target borehole at different depths, which has a different radial distance from the geometric center of the plane. The second determining unit is configured to determine a density gradient based on a plurality of first density values ​​in the density sequence, and if the density gradient corresponding to the target depth is greater than a preset gradient threshold, determine that a stratigraphic interface exists at the target depth. The third determining unit is configured to determine the closed contour of the formation interface in the density distribution based on an edge extraction algorithm; The fourth determining unit is configured to determine the layered geological model of the strata based on the density sequence and the position information of the corresponding closed contour of each of the plurality of target boreholes, and to complete the geological exploration of the strata.

8. A muon detector, characterized in that, The muon detector includes: Shell, outer fiber layer, isolation layer, inner fiber layer; In this structure, the first optical fiber in the outer optical fiber layer is wound with a first pitch, and the second optical fiber in the inner optical fiber layer is wound with a second pitch. The winding direction of the optical fiber in the outer optical fiber layer is opposite to that of the optical fiber in the inner optical fiber layer. A first detection module is configured at both ends of each first optical fiber in the outer optical fiber layer, and a second detection module is configured at both ends of each second optical fiber in the inner optical fiber layer. The muon flux is determined by the signal output by the first detection module, and the angle parameter is determined by the signal output by the second detection module.

9. The muon detector according to claim 8, characterized in that, The internal cavity of the inner fiber layer also includes: The sensing unit is configured to detect parameters inside the formation and output sensing signals, which are used to correct the parameters of the first detection module and the second detection module inside the muon detector. The preprocessing unit is configured to perform parameter correction on the first detection module to determine a first valid signal corresponding to the first signal output by the first detection module and a first correction gain of the first detection module; and to perform parameter correction on the second detection module to determine a second valid signal corresponding to the second signal output by the second detection module and a second correction gain of the second detection module. The storage unit is configured to store the muon flux and the angle parameters.

10. A computer device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to perform the geological exploration method based on muon detection as described in any one of claims 1-6.

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