CN116738514A

CN116738514A - Underground kilometer grade Miao Ziliu strong prediction method

Info

Publication number: CN116738514A
Application number: CN202310678436.5A
Authority: CN
Inventors: 陈少敏; 张彬; 王喆
Original assignee: Tsinghua University
Current assignee: Tsinghua University
Priority date: 2023-06-08
Filing date: 2023-06-08
Publication date: 2023-09-12

Abstract

The disclosure provides a strong prediction method for underground kilometer grade Miao Ziliu, and relates to the field of geological exploration. The method comprises the following steps: acquiring an initial cosmic ray model; acquiring a contour parameterized model of a target geologic body and acquiring a reference density of the target geologic body; determining a target geologic body model according to the reference density and the outline parameterized model of the target geologic body; simulating according to the initial cosmic ray model and the target geologic body model, and determining a muon instance reaching the detection device; according to the type of the detection device, response correction is carried out on the muon case based on a corresponding direction reconstruction algorithm, so as to obtain muon direction prediction distribution; obtaining a muon differential flow intensity predicted value of each direction interval according to the muon direction predicted distribution; acquiring a muon differential flow intensity measured value of each direction interval; and determining the position of a density change zone of the target geologic body according to the difference between the muon differential flow intensity measured value and the muon differential flow intensity predicted value in each direction interval.

Description

Underground kilometer grade Miao Ziliu strong prediction method

Technical Field

The present disclosure relates to the field of geological exploration, and in particular to a method for strongly predicting the underground kilometer grade Miao Ziliu.

Background

The cosmic ray muon (hereinafter referred to as muon) is generated by the interaction of the original cosmic ray and the atmospheric substances at the altitude of 30 km and above, and the energy thereof can be extended to TeV and above. The high-energy muon has strong penetrability in substances, and the track of the high-energy muon can be approximately straight. Energy is lost as it passes through the material, primarily through ionization energy losses, which are strongly correlated to different material densities and are thus naturally suitable for geological tomography.

The muon imaging technique has the following advantages: because the penetrability of the high-energy muon is strong, the sensitive detection area is buried deeply and can be as deep as 1 km or more; the measuring result does not depend on the property of the objects around the detected object, but mainly depends on the single property of the density of the detected object, so that the anti-interference capability is strong; in addition, the muon is a naturally-occurring free particle source, and special radiation protection is not needed, so that the cost is reduced.

The strong attenuation of Miao Ziliu during the transit of a muon through a detected object is related to its track length in the object and the density of matter along the track. Thus, by measuring Miao Ziliu intensity and calculating the track length from the material profile, the material density can be calculated for geologic tomography.

Miao Ziliu is an important ring in geological tomography, and in the related art, a kilometer-level geological tomography method suitable for various types of detection devices is not found, and the method is suitable for a kilometer-level geological tomography method of a spherical full-liquid flash 4 pi solid angle detection device.

Disclosure of Invention

In view of the above, embodiments of the present disclosure provide a strong prediction method in the subsurface kilometer scale Miao Ziliu to overcome or at least partially solve the above-described problems.

The embodiment of the disclosure provides a strong prediction method for underground kilometer grade Miao Ziliu, which comprises the following steps:

acquiring an initial cosmic ray model, the initial cosmic ray model comprising: distribution of energy and direction followed by the first muon before entering the body;

acquiring a profile parameterized model of a target geologic body, and acquiring reference density of the target geologic body, wherein the burial depth of the target geologic body reaches the underground kilometer level;

determining a target geologic body model according to the reference density of the target geologic body and the outline parameterized model;

simulating according to the initial cosmic ray model and the target geologic body model, and determining a muon instance reaching a detection device, wherein the detection device is as follows: spherical full liquid flashing 4 pi solid angle detection device or other detection devices;

According to the type of the detection device, based on a corresponding direction reconstruction algorithm, performing response correction on the muon instance to obtain a muon direction prediction distribution, including: under the condition that the detection device is a spherical full liquid flashing 4 pi solid angle detection device, according to a time sequence and/or an energy sequence corresponding to each photomultiplier in the spherical full liquid flashing 4 pi solid angle detection device, carrying out response correction on the muon instance to obtain the muon direction prediction distribution;

and obtaining the differential flow intensity predicted value of the muon in each direction interval according to the muon direction prediction distribution.

Optionally, in the case that the detection device is the spherical liquid-full flash 4 pi solid angle detection device, the method further includes:

according to the electric signals output by each photomultiplier of the spherical whole liquid flashing 4 pi solid angle detection device, determining the corresponding time sequence and energy sequence of each photomultiplier, wherein the electric signals are as follows: generating based on a second muon passing through the target geologic volume and reaching the spherical all-liquid flash 4 pi solid angle detection device;

removing background electric signals from the electric signals according to the sum of energy sequences of the electric signals to obtain electric signals of the second muon;

Acquiring a time sequence and an energy sequence of the electric signals of the second muon output by each photomultiplier tube;

obtaining a reconstruction direction of the second muon according to the time sequence and/or the energy sequence of the electric signal of the second muon;

acquiring the detection efficiency and the effective sectional area of the spherical full liquid flashing 4 pi solid angle detection device;

and obtaining the differential flow intensity measurement values of the muon in each direction interval according to the reconstruction direction of the second muon and the detection efficiency and the effective sectional area of the spherical full-liquid flash 4 pi solid angle detection device.

Optionally, in the case that the detecting device is the other detecting device, the detecting device further includes:

acquiring electrical signals output by the other detection devices, wherein the electrical signals are as follows: generated based on third muon passing through the target geologic volume and reaching the other detection device;

acquiring a direction reconstruction algorithm corresponding to the other detection devices;

obtaining the reconstruction direction of the third muon according to a direction reconstruction algorithm corresponding to the other detection devices and the electric signals output by the other detection devices;

acquiring the detection efficiency and the effective sectional area of the other detection devices;

and obtaining the muon differential flow intensity measurement values of all the direction intervals according to the reconstruction direction of the third muon, the detection efficiency and the effective sectional area of the other detection devices.

Optionally, the method further comprises:

determining standard deviation of each direction interval according to the predicted value of the differential mu flow intensity and the measured value of the differential mu flow intensity of each direction interval;

determining whether the measured density of each direction interval deviates from the reference density according to the standard deviation of each direction interval;

and determining the position of a density change area according to whether the measured density of each direction interval deviates from the reference density, wherein the measured density of the direction interval in which the position of the density change area is located deviates from the reference density.

Optionally, the simulating according to the initial cosmic ray model and the target geologic body model determines a muon instance reaching the detecting device, including:

introducing the target geologic body model into a Monte Carlo simulation program based on a physical process of interaction between muon and a substance;

sampling the initial cosmic ray model to obtain the energy and directions of a plurality of first muons;

setting initial positions of the first muons as follows: a point on a ray passing through a point where the center of the detection device is located and having a direction of each of the first muons, the point being outside the target geologic body;

In the monte carlo simulation program, simulating an interaction process of the plurality of first muon with the target geologic body, and determining muon instances reaching the detection device.

Optionally, the performing response correction on the muon case based on a corresponding direction reconstruction algorithm according to the type of the detecting device to obtain a muon direction prediction distribution includes:

the structure of the detection device is imported into an analog response program;

in the simulation response program, taking the direction of the muon instance as an initial direction, simulating the response process of the muon instance in the detection device, and obtaining a simulation output signal;

according to the structure of the detection device, a corresponding direction reconstruction algorithm is adopted to reconstruct the direction of the analog output signal, so as to obtain a predicted direction, and the structure of the detection device comprises: a detection device structure with direction resolution and/or detection efficiency related to the muon incidence direction, and a detection device structure with direction resolution and detection efficiency independent of the muon incidence direction;

and obtaining the prediction distribution of the muon direction according to the prediction directions corresponding to the muon instances.

Alternatively, in the case that the structure of the detecting device is a detecting device structure in which the direction resolution and the detecting efficiency are independent of the incident direction of the muon,

in the simulation response procedure, the direction of the muon instance is taken as an initial direction, and a response process of the muon instance in the detection device is simulated to obtain a simulation output signal, which includes:

in the simulation response program, randomly and uniformly extracting the direction of part of the muon cases as an initial direction, and simulating the response process of the part of the muon cases in the detection device to obtain the simulation output signal;

the method for reconstructing the direction of the analog output signal by adopting a corresponding direction reconstruction algorithm according to the structure of the detection device to obtain a predicted direction comprises the following steps:

reconstructing the directions of the part of the muon cases by adopting a corresponding direction reconstruction algorithm to obtain the predicted directions of the part of the muon cases and obtain the distribution of the included angles between the predicted directions and the initial directions;

and acquiring an included angle from the distribution of the included angles for each of the other muon examples except for the part of muon examples, and obtaining the prediction direction of each of the other muon examples according to the included angle and the initial direction of each of the other muon examples.

Optionally, the obtaining the predicted value of the differential flow intensity of the muon in each direction interval according to the predicted distribution of the muon direction includes:

counting the number of the muon cases with the predicted direction falling in each direction interval according to the muon direction prediction distribution;

and determining the predicted value of the differential flow intensity of the muon in each direction section according to the number of the muon instances falling in each direction section.

Optionally, removing the background electric signal from the electric signals according to the sum of the energy sequences of the electric signals to obtain the electric signal of the second muon, including:

acquiring an energy threshold;

determining an electrical signal corresponding to energy less than the energy threshold in the energy sequence as the electrical signal of the background;

and removing the electric signals of the background from the electric signals to obtain the electric signals of the second muon.

Optionally, obtaining a reconstruction direction of the second muon according to a time sequence of the electrical signals of the second muon includes:

removing time information of the second muon incident to the detection device from the time sequence of the electric signals of the second muon;

simulating a response caused by energy deposition of charged particles in a target substance by a Monte Carlo simulation method to obtain the time for each photomultiplier tube to receive optical photons in each simulation process;

Obtaining a plurality of templates according to the time of receiving optical photons by each photomultiplier in each simulation process;

obtaining the distances between the templates and the second muon respectively, and determining a plurality of target templates from the templates according to the distances, wherein the distance between the target templates and the second muon is smaller than the distance between a non-target template and the second muon;

determining the weight of each target template according to the distance between each target template and the second muon;

and determining the reconstruction direction of the second muon according to the weight of each target template and the incidence direction of the muon in each target template.

Optionally, said determining a standard deviation of said respective direction intervals from said predicted values of said differential mu flow intensities and said measured values of differential mu flow intensities for said respective direction intervals comprises:

determining the dimensionless number of each direction interval according to the predicted value of the differential flow intensity of the muon and the measured value of the differential flow intensity of the muon in each direction interval, wherein the dimensionless number of one direction interval is represented by: a difference between a mass density of the target geologic volume whose reconstruction direction falls on a second muon track of the direction interval and the reference density;

Determining uncertainty of dimensionless numbers of the respective direction intervals according to the number of the second muon in the respective direction intervals;

determining standard deviation of each direction interval according to the uncertainty of each direction interval and the dimensionless number of each direction interval;

the determining whether the measured density of each direction interval deviates from the reference density according to the standard deviation of each direction interval comprises:

under the condition that the standard deviation of one direction interval is larger than a preset value and the dimensionless number of the direction interval is larger than 1, determining that the measured density of the target geologic body in the direction interval is smaller than the reference density;

and under the condition that the standard deviation of one direction interval is larger than a preset value and the dimensionless number of the direction interval is smaller than 1, the measured density of the target geologic body in the direction interval is larger than the reference density.

Embodiments of the present disclosure include the following advantages:

in the embodiment of the disclosure, the reference density and contour parameterized model of the target geologic body can be used for determining the target geologic body model, and the burial depth of the target geologic body reaches the underground kilometer level. And then the model can be simulated according to the initial cosmic ray model and the target geologic body model to determine the muon instance reaching the detection device, wherein the detection device can be a spherical full-liquid flashing 4 pi solid angle detection device or other detection devices. According to the type of the detection device, the direction of the muon instance can be corrected to obtain the muon direction prediction distribution, and further, the muon differential flow intensity prediction value of each direction interval is obtained. Under the condition that the detection device is a spherical full liquid flashing 4 pi solid angle detection device, according to the time sequence and/or the energy sequence corresponding to each photomultiplier in the spherical full liquid flashing 4 pi solid angle detection device, response correction can be carried out on the muon case, and the muon direction prediction distribution can be obtained. Thus, the embodiment of the disclosure provides an underground kilometer level Miao Ziliu strong prediction method suitable for a spherical all-liquid flashing 4 pi solid angle detection device, and an underground kilometer level Miao Ziliu strong prediction method suitable for other various types of detection devices. And determining the position of a density change area of the target geologic body according to the muon differential flow intensity predicted value.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings that are needed in the description of the embodiments of the present disclosure will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and other drawings may be obtained according to these drawings without inventive effort to a person of ordinary skill in the art.

FIG. 1 is a flow chart of steps of a method of strong prediction of underground kilometers scale Miao Ziliu in an embodiment of the present disclosure;

fig. 2 is a schematic view of the direction of the muon in an embodiment of the present disclosure;

FIG. 3 is a flowchart of steps for determining the location of a density change zone in an embodiment of the present disclosure.

Detailed Description

In order that the above-recited objects, features and advantages of the present disclosure will become more readily apparent, a more particular description of the disclosure will be rendered by reference to the appended drawings and appended detailed description.

Referring to fig. 1, a step flow diagram of an underground kilometer level Miao Ziliu strong prediction method in an embodiment of the disclosure is shown, and as shown in fig. 1, the underground kilometer level Miao Ziliu strong prediction method may specifically include steps S11 to S16.

Step S11: acquiring an initial cosmic ray model, the initial cosmic ray model comprising: distribution of energy and direction followed by the first muon before entering the body.

The initial cosmic ray model may be an empirical model, a monte carlo simulation model determined based on physical mechanisms of generation and propagation of atmospheric material parameters and muon, a fitting model based on measurement data, or the like. The first muon total flux in the initial cosmic ray model may be set to F ₀ . Alternatively, the empirical model may be a model determined based on a gaiser (gaussian law) formula. The first muon refers to the simulated cosmic ray muon. The initial cosmic ray model comprises a plurality of distributions of energy and directions followed by the first muon before entering the geologic body, and the initial cosmic ray model is extractedIn this way, the energy and direction followed by the first muon before it enters the body can be obtained.

For convenience of description, the direction in which the muon track is located is defined as the muon direction, represented by azimuth angle phi and zenith angle theta, and the definition of phi and theta is shown in fig. 2. Wherein the Z axis is forward along the vertical upward direction, and the X axis and the Y axis are along the horizontal direction. Because the flying direction of the muon is from top to bottom, the detection device should be placed below the lowest level of the target geologic body region of interest (hereinafter referred to as ROI), and the target geologic body region of interest may be the whole target geologic body or a part of the target geologic body.

The present disclosure utilizes high energy muon (energy in several gevs and above), which are extremely relativistic particles, and can be considered as interactions with target substances independent of kinetic energy, i.e., the kinetic energy cannot be measured by the detection device for which the present disclosure is directed. The present disclosure utilizes the integration of the muon flux over kinetic energy, referred to as the streaming intensity. Further, the present disclosure utilizes flow strength, i.e., micro-flow strength, within a directional interval.

Alternatively, the division of the direction section may be performed as follows: the value interval [0,1 ] of cos theta]Divided into n _cosθ The part of the total number is that the value interval of phi (0, 2 pi)]Divided into n _φ And defining the ith cos theta interval x the jth phi interval as the (i, j) th direction interval, where 1.ltoreq.i.ltoreq.n _cosθ ，1≤j≤n _φ 。

Step S12: and acquiring a profile parameterized model of the target geologic body, and acquiring the reference density of the target geologic body, wherein the burial depth of the target geologic body reaches the underground kilometer level.

The target geologic body may be any geologic body to be predicted, the burial depth of the target geologic body according to the embodiment of the disclosure is in the underground kilometer level, for example, the target geologic body is a mountain with a height of 1.5 kilometers, the detecting device is placed at a position of 0.5 kilometers, and then the detecting device can detect a mountain with a height of 0.5 kilometers to 1.5 kilometers, and the burial depth of the mountain reaches 1 kilometer. Wherein, the burial depth of the target geologic body refers to: the vertical distance from the highest position of the target geologic body to the detection device. The position of the detection device is lower than the interested area of the target geologic body.

The contour parameterized model of the target geologic volume is generated from contour information of the target geologic volume. The sampling points of the contour information of the target geologic body can be obtained by means including but not limited to remote sensing, satellite measurement and the like, and the three-dimensional closed geometric body formed by the collection of the sampling points is the contour parameterized model of the target geologic body. Alternatively, the collection of sampling points may be expanded into a three-dimensional closed geometry using the delaunay triangulation method.

The present disclosure considers only the effect of the change in the bulk density of the targeted geologic volume on the muon flux or flux intensity. Alternatively, it may be assumed that the composition percentage of the material elements inside the target geologic volume is the same as the composition percentage of the crust elements. Prior to making the measurement of the internal density of the target geologic volume, a priori assumption can be made on the internal density of the target geologic volume based on a priori information to obtain a reference density of the target geologic volume. Alternatively, it may be assumed that the density of the substance within the body is the same, and the density value may be a density value measured by sampling the substance of the body, thereby obtaining the reference density of the target body.

Step S13: and determining a target geologic body model according to the reference density of the target geologic body and the outline parameterized model.

And setting the density of the outline parameterized model of the target geologic body as the assumed reference density of the target geologic body, thereby obtaining the target geologic body model.

Step S14: simulating according to the initial cosmic ray model and the target geologic body model, and determining a muon instance reaching a detection device, wherein the detection device is as follows: spherical full liquid flash 4 pi solid angle detection device or other detection devices.

The target geologic volume model is imported into a monte carlo simulation program based on physical processes of interactions of muon with matter, optionally based on open source tool software Geant4 (GEometry ANd Tracking ) commonly used in high energy physics and medical physics.

Sampling the initial cosmic ray model to obtain the energy and the direction of the first muon, and setting the initial positions of the first muons as follows: outside the target geologic body, and passing through a point where the center of the detection device is located and a point on a ray whose direction is the direction of each first muon.

In the Monte Carlo simulation program, the interaction process of a plurality of first muon and substances in the target geologic body is simulated, whether each first muon is blocked by the target geologic body and cannot reach the detection device is judged, the muon instance which can pass through the target geologic body to reach the detection device (namely, not blocked by the target geologic body) is reserved, and the zenith angle and the azimuth angle of the actual direction of the muon instance which reaches the detection device are recorded. Repeating the above simulation N _MC And twice. To reduce the statistical uncertainty introduced during simulation, N _MC It should be large that the number of analog instances that can reach the detection means is much larger than the number of measurement instances. The detection device can be a spherical full liquid flashing 4 pi solid angle detection device or other detection devices; the spherical all-liquid flashing 4 pi solid angle detection device is a detection device with the direction resolution and detection efficiency irrelevant to the incident direction of muon; other detection devices are detection devices except the spherical full liquid flashing 4 pi solid angle detection device. Other detection means may include: the direction resolution and/or the detection efficiency are/is related to the muon incidence direction, and the direction resolution and the detection efficiency are/is independent of the muon incidence direction except for the spherical all-liquid flash 4 pi solid angle detection device.

Step S15: according to the type of the detection device, based on a corresponding direction reconstruction algorithm, performing response correction on the muon instance to obtain a muon direction prediction distribution, including: and under the condition that the detection device is a spherical full liquid flashing 4 pi solid angle detection device, according to a time sequence and/or an energy sequence corresponding to each photomultiplier in the spherical full liquid flashing 4 pi solid angle detection device, carrying out response correction on the muon instance to obtain the muon direction prediction distribution.

When the types of the detection devices are different, the direction reconstruction algorithms for performing response correction on the muon cases are also different. And a proper direction reconstruction algorithm can be selected according to the type of the detection device to carry out response correction on the muon case.

Alternatively, for a detection device whose direction resolution and/or detection efficiency are related to the direction of the incident muon, the response correction may be performed on the muon case according to the method described below. Step a: the structure of the detection device is imported into a simulation program, alternatively the simulation program may be based on open source tool software Geant4 in high energy physics and medical physics research. Step b: taking the actual direction of the muon instance as the initial direction of the muon in the simulation process, and simulating the response process of the muon instance in the detection device by using the simulation program in the step a to obtain an output signal. Step c: and c, carrying out direction reconstruction on the output signal of the step b by using a direction reconstruction algorithm corresponding to the detection device, wherein the obtained reconstruction direction is the prediction direction. And c, repeating the step b and the step c, wherein the obtained distribution of the predicted direction composition is the predicted distribution of the muon direction. The corresponding direction reconstruction algorithm may refer to a direction reconstruction algorithm proposed by the related art.

Optionally, for a detection device (such as a spherical full liquid flash 4 pi solid angle detection device) with both the direction resolution and the detection efficiency independent of the incident direction of the muon, an equivalent direction reconstruction algorithm may be used to perform response correction on the muon case. According to the structure of the detection device, an equivalent direction reconstruction algorithm is adopted, and the distribution of included angles between the reconstruction direction and the initial direction is obtained by reconstructing the directions of part of muon cases; for each other muon instance except the part of muon instance, acquiring an included angle from the distribution of the included angles, and obtaining the prediction direction of each other muon instance according to the included angle and the initial direction; and obtaining the prediction distribution of the muon direction according to the prediction direction corresponding to each muon instance.

Alternatively, the response correction of the detection device with the direction resolution and the detection efficiency independent of the incident direction of the muon may comprise the following steps. Step a: the structure of the detection device is imported into a simulation program, alternatively the simulation program may be based on open source tool software Geant4 in high energy physics and medical physics research. Step b: and c, randomly and uniformly sampling the direction of the muon instance, and using the simulation program in the step a to simulate the response process of the muon in the detection device as the initial direction of the muon in the simulation process, so as to obtain an output signal. Step c: and c, carrying out direction reconstruction on the output signal of the step b by using a direction reconstruction algorithm corresponding to the detection device, obtaining a reconstruction direction which is a predicted direction, and calculating an included angle between the reconstruction direction and the initial direction. Step d: repeating the step b and the step c, and counting the distribution of the included angles in the step c. Step e: and d, obtaining an included angle by the included angle distribution in the sampling step, and rotating the actual direction of the simulated muon instance obtained in the previous section by the included angle to obtain a predicted direction. And e, repeating the step, wherein the obtained distribution of the predicted direction composition is the predicted distribution of the muon direction.

The direction reconstruction algorithm corresponding to the spherical full liquid flash 4 pi solid angle detection device can be as follows: and according to the time sequence and/or the energy sequence corresponding to each photomultiplier in the spherical full-liquid flash 4 pi solid angle detection device, carrying out response correction on the muon instance to obtain the muon direction prediction distribution.

The spherical all-liquid flash 4 pi solid angle detection device refers to a cosmic ray muon detection device in which a target substance is a liquid scintillator, the target substance is spherical in shape, and all regions inside the device except a photomultiplier tube (PMT) and a support structure are occupied by the liquid scintillator. Such a device has the advantage that the detection efficiency and the direction resolution are independent of the direction of the incident muon for the whole 4 pi solid angle muon direction detection range. For such detection devices, the incident muon deposits energy in the target material, emitting optical photons. The optical photons are incident on the photocathode of the PMT in the detection device, exciting electrons (called photoelectrons) with a certain probability (about 20-30%). The photoelectrons undergo a subsequent multiplication process to form an output electrical signal of the PMT. For the ith PMT, the time T of its output electric signal can be calculated _i And amplitude (optionally expressed by the number of photoelectrons) nPE _i Wherein i is more than or equal to 1 and N is more than or equal to _PMT ，N _PMT Is the number of PMTs. T (T) _i (1≤i≤N _PMT ) The sequence formed is called time sequence, nPE _i (1≤i≤N _PMT ) The sequence formed is called an energy sequence. The time sequence and the energy sequence are inputs of an analysis method of the spherical whole liquid flashing 4 pi solid angle detection device.

The spherical full liquid flashing 4 pi solid angle detection device has spherical symmetry, so that the included angle distribution (hereinafter referred to as included angle distribution) between the muon measuring direction and the Miao Zishi inter-direction is irrelevant to the Miao Zishi inter-direction. The mean value of the angular distribution (defined as angular resolution) is also independent of the Miao Zishi orientation, i.e. the angular resolution is uniform. While the muon detection efficiency is also uniform. The two advantages can be used for the internal density imaging analysis of the geologic body without correcting the non-uniformity of the angular resolution and the non-uniformity of the detection efficiency.

One of the incoming muon (which may be the first muon, or the second muon described later) corresponds to one track within the target of the detection device. The time at which the optical photons are received at the PMT and the number of optical photons are related to the relative position of the PMT and the track. Then T is on PMT for the muon instance _i And nPE _i (1≤i≤N _PMT ) The track direction, i.e. muon direction information, is contained. The direction of the incident muon may be reconstructed from the time series and the energy series (either from the time series alone or from the energy series alone).

The following describes a method of reconstructing the direction of the incident muon solely from the time series: removing time information of the second muon incident to the detection device from the time sequence of the electric signals of the second muon; simulating a response caused by energy deposition of charged particles in a target substance by a Monte Carlo simulation method to obtain the time for each photomultiplier tube to receive optical photons in each simulation process; obtaining a plurality of templates according to the time of receiving optical photons by each photomultiplier in each simulation process; obtaining the distances between the templates and the second muon respectively, and determining a plurality of target templates from the templates according to the distances, wherein the distance between the target templates and the second muon is smaller than the distance between a non-target template and the second muon; determining the weight of each target template according to the distance between each target template and the second muon; and determining the reconstruction direction of the second muon according to the weight of each target template and the incidence direction of the muon in each target template.

T _i (1≤i≤N _PMT ) The information of when the muon is incident into the detecting device is recorded, the information does not contain muon direction information, cannot be utilized by a muon direction reconstruction algorithm, and can be removed by the following method. Definition:

Wherein t is _i Time series representing information corresponding to the ith PMT, from which when muon is incident in the detection device is removed, i=1, 2, … N _PMT The method comprises the steps of carrying out a first treatment on the surface of the The meaning of the remaining individual characters may be referred to above.

By means of the Monte Carlo simulation method based on the structure of the detection device, the response caused by the deposition energy of the charged particles in the target material can be obtained. Wherein the response indicators of interest in the present disclosure areI.e., the time at which an optical photon is received at the ith PMT in the jth monte carlo simulation muon instance. Wherein j is more than or equal to 1 and less than or equal to M, and M is the simulation times. In order to reduce the effect of statistical uncertainty, M needs to be large. Similarly, define:

wherein, the liquid crystal display device comprises a liquid crystal display device,characterizing a time sequence of information corresponding to an ith PMT in the jth monte carlo simulated muon instance, from which when muons are incident into the detection device are removed; />Characterization of the time series corresponding to the kth PMT in the jth monte carlo simulation muon instance, k=1, 2, … N _PMT The method comprises the steps of carrying out a first treatment on the surface of the The meaning of the remaining individual characters may be referred to above.

For the followingThe j-th Monte Carlo simulation muon instance with the incident direction P _j Is set by man, i.e. known. P (P) _j Is uniform, i.e. the zenith angle theta _j And azimuth angle phi _j Is P _j Cos θ at the zenith and azimuth angles corresponding thereto _j Obeys [ -1,1]Is of uniform distribution phi _j Obeys (0, 2 pi)]Is a uniform distribution of (c). And the incident position of the muon on the outer surface of the spherical container is facing the P _j And the hemispherical surfaces of the directions are uniformly distributed. A simulation instance is called a template and definition j is the number of the template.

Reconstruction direction P of the incident muon instance _in Can be obtained by the following method:

wherein l is the first template closest to the incident muon instance, j _l Is the number of the template, P _jl Is the incident direction of the muon in the template, W _jl Is P _jl L is a constant to be determined.

Wherein d is _j The smaller is defined as the closer the jth template is to the incident muon instance, wherein:

wherein the meaning of the individual characters may be referred to above.

Optionally, the weight is defined as: w (W) _jl ＝1/d _jl 。

The angular distribution and angular resolution can be used to verify the performance of the direction reconstruction algorithm, alternatively, one calculation method is as follows: the test case is generated in the same way as the template, the direction of the test case is reconstructed by using the direction reconstruction algorithm, and the included angle between the reconstruction direction and the actual direction is calculated. And repeating the step, and counting the distribution of the included angles to obtain the distribution of the included angles, wherein the average value of the distribution of the included angles is the angular resolution.

Step S16: and obtaining the differential flow intensity predicted value of the muon in each direction interval according to the muon direction prediction distribution.

Counting the number of the muon cases with the predicted direction falling in each direction interval according to the muon direction prediction distribution; and determining the predicted value of the differential mu flow intensity of each direction section according to the number of the muon instances falling in each direction section.

The number of the muon cases falling in the (i, j) th direction interval is nEvents _MC (I, j), then the muon differential flow intensity prediction value I for that direction interval _MC (i,j)＝nEvents _MC (i,j)F ₀ /N _MC 。

By adopting the technical scheme of the embodiment of the disclosure, the target geologic body model can be determined through the reference density and contour parameterized model of the target geologic body. And then the simulation can be carried out according to the initial cosmic ray model and the target geologic body model so as to determine the muon instance reaching the detection device, wherein the detection device can be a spherical full-liquid flashing 4 pi solid angle detection device or other detection devices. Further, according to the type of the detection device, the direction of the muon instance can be corrected to obtain the muon direction prediction distribution, and further, the muon differential flow intensity prediction value of each direction interval can be obtained.

Under the condition that the detection device is a spherical full liquid flash 4 pi solid angle detection device, according to the time sequence and/or the energy sequence corresponding to each photomultiplier in the spherical full liquid flash 4 pi solid angle detection device, the direction of the muon instance can be corrected, and the prediction distribution of the muon direction can be obtained.

Thus, the embodiment of the disclosure provides an underground kilometer level Miao Ziliu strong prediction method suitable for a spherical all-liquid flashing 4 pi solid angle detection device, and an underground kilometer level Miao Ziliu strong prediction method suitable for other various types of detection devices.

As an example, the differential flow intensity measurements of the muon in each directional interval may also be obtained. In the case that the detecting device is a spherical full liquid flashing 4 pi solid angle detecting device, the obtaining of the muon differential flow intensity measurement value of each direction interval may include steps S21 to S26:

step S21: according to the electric signals output by each photomultiplier of the spherical whole liquid flashing 4 pi solid angle detection device, determining the corresponding time sequence and energy sequence of each photomultiplier, wherein the electric signals are as follows: based on a second muon passing through the target geologic volume and reaching the spherical all-liquid flash 4 pi solid angle detection device.

The second muon is the muon which is actually present and is incident on the spherical full liquid flash 4 pi solid angle detection device to cause response. The detection device is placed below the lowest horizontal plane of the target geologic body region of interest to detect the target geologic body region of interest and obtain a muon differential flow intensity measurement value.

The specific method for determining the time sequence and the energy sequence corresponding to each photomultiplier tube may be referred to the foregoing, and will not be described herein.

Step S22: and removing the background electric signal from the electric signals according to the sum of the energy sequences of the electric signals to obtain the electric signal of the second muon.

Optionally, acquiring an energy threshold; determining an electrical signal corresponding to energy less than the energy threshold in the energy sequence as the electrical signal of the background; and removing the electric signals of the background from the electric signals to obtain the electric signals of the second muon.

Specifically, the difference in the distribution of the total energy nptotal is used to distinguish the muon signal from the background (background noise). Wherein:

wherein the meaning of the individual characters may be referred to above.

For a detection device with an effective target volume sphere diameter of about 1 meter, the average deposition energy of the muon in the target material is about 100 MeV. From an instance rate perspective, the background caused by the deposition energy of charged particles generated by the decay of natural radionuclides in the target material dominates the muon background. Due to natural radiationThe deposition energy of this background is typically less than 5MeV, characteristic of sexual decay. I.e., the deposition energy of the muon signal is more than an order of magnitude greater than the background deposition energy. In the target material, the deposition energy of the charged particles is converted to the total energy of all optical photons, which is proportional to the number of optical photons, and thus to the number of photoelectrons, and thus to nPEtotal. Wherein, the optical photon is a scintillation photon generated by the energy of the deposition of the muon in the target substance and the cerenkov photon, and the photoelectron is an electron which is emitted by the optical photon on the photocathode of the photomultiplier. Due to the large light yield (number of optical photons excited per unit of deposition energy) of liquid flash, the deposition energy of MeV and above corresponds to a large npetoal, i.e. a relatively statistical uncertainty of npetoal Very small, i.e. nptotal can be measured very accurately. By setting the nptotal threshold, the background number can be reduced to a negligible level while ensuring the muon detection efficiency only by keeping the cases above the threshold. The muon detection efficiency is defined as the number of entrance muons that cross the target material per threshold muon number/track. For simplicity, the following cases above the nptotal threshold are considered to be caused by muons.

Step S23: and acquiring a time sequence and an energy sequence of the electric signals of the second muon output by each photomultiplier tube.

After the electrical signal of the second muon and the electrical signal of the background are distinguished, a time sequence and an energy sequence of the electrical signal of the second muon may be obtained according to the electrical signal of the second muon.

Step S24: and obtaining the reconstruction direction of the second muon according to the time sequence and/or the energy sequence of the electric signal of the second muon.

Under the condition that the detection device is a spherical full liquid flashing 4 pi solid angle detection device, the reconstruction direction of the second muon can be obtained according to the time sequence and/or the energy sequence of the electric signal of the second muon. The specific method for obtaining the reconstruction direction of the second muon according to the time sequence of the electric signal of the second muon may refer to the method for reconstructing the direction of the incident muon described above, which is not described herein again.

Step S25: and obtaining the detection efficiency and the effective sectional area of the spherical full liquid flashing 4 pi solid angle detection device.

For a detection device with detection efficiency independent of the incident direction (such as a spherical full liquid flash 4 pi solid angle detection device), one definition and calculation method of the detection efficiency ζ and the effective cross-sectional area S are as follows: set C ₁ Is the cross section of the target volume of the detection device with the largest area perpendicular to the vertical direction. Wherein the target volume refers to the volume occupied by the target substance. S is S ₁ Is C ₁ Is a part of the area of the substrate. At C ₁ 1 point was randomly extracted uniformly and simulated in the detection apparatus the response of a muon with a direction of motion vertically downward and tracking past that point. Repeating the simulation Q ₀ The number of muon satisfying the screening condition is counted as Q ₁ Then when Q ₀ When large, there is ζ=q ₁ /Q ₀ And s=s ₁ ξ。

Step S26: and obtaining the differential flow intensity measurement values of the muon in each direction interval according to the reconstruction direction of the second muon and the detection efficiency and the effective sectional area of the spherical full-liquid flash 4 pi solid angle detection device.

Because the zenith angle theta of the muir fruiting body in the direction meets the costheta not less than 0, the zenith angle cosine value of the reconstruction direction is not considered in the method<A muon instance of 0. The number of muon instances nEvents (i, j) whose measurement direction falls within the (i, j) th direction section is counted. Assuming that the fetch time is T, the Miao Ziwei shunt strength measurement value in the (i, j) th direction interval is: i _mea (i,j)＝nEvents(i,j)/(S(i,j)T)。

In the case that the detection device is not a spherical full liquid flashing 4 pi solid angle detection device and the detection device is other detection devices, acquiring the muon differential flow intensity measurement value of each direction interval may include steps S31 to S35:

step S31: acquiring electrical signals output by the other detection devices, wherein the electrical signals are as follows: based on third muon passing through the target geologic volume and reaching the other detection devices.

The third muon is the muon that is actually present and is incident on the other detecting means to cause a response. After the third muon is incident on the other detecting means, the other detecting means may output an electric signal caused by the third muon.

Step S32: and acquiring a direction reconstruction algorithm corresponding to the other detection devices.

The direction reconstruction algorithms corresponding to different types of detection devices are different, and the direction reconstruction algorithm corresponding to other detection devices in the related technology can be obtained for other detection devices which are not spherical all-liquid flashing 4 pi solid angle detection devices. The invention does not limit the direction reconstruction algorithm corresponding to other detection devices.

Step S33: and obtaining the reconstruction direction of the third muon according to the direction reconstruction algorithm corresponding to the other detection devices and the electric signals output by the other detection devices.

And the electric signals output by other detection devices can be subjected to response correction by using a direction reconstruction algorithm corresponding to the other detection devices, so that the reconstruction direction of the third muon is determined according to the corrected electric signals.

Step S34: and acquiring the detection efficiency and the effective sectional area of the other detection devices.

For a detection device whose detection efficiency is dependent on the incident direction, it is necessary to define and calculate the detection efficiency ζ (i, j) and the effective sectional area S (i, j) for each (i, j) direction section in a similar manner to ζ and S.

For convenience of description, the detection efficiency ζ and the effective cross-sectional area S of the detection device whose detection efficiency is independent of the incident direction are also denoted as ζ (i, j) and S (i, j) below. Note that, for a detection device whose detection efficiency is independent of the incident direction, for any of i and j (1.ltoreq.i.ltoreq.n) _cosθ ，1≤j≤n _φ ) All have: ζ (i, j) =ζ, S (i, j) =s.

Step S35: and obtaining the muon differential flow intensity measurement values of all the direction intervals according to the reconstruction direction of the third muon, the detection efficiency and the effective sectional area of the other detection devices.

Because the zenith angle theta of the muir fruiting body direction is fullThe cosθ of the foot is more than or equal to 0, and the method does not consider the zenith angle cosine value of the reconstruction direction <A muon instance of 0. The number of muon instances nEvents (i, j) whose measurement direction falls within the (i, j) th direction section is counted. Assuming that the fetch time is T, the Miao Ziwei shunt strength measurement value in the (i, j) th direction interval is: i _mea (i,j)＝nEvents(i,j)/(S(i,j)T)。

As an embodiment, on the basis of the foregoing technical solution, as shown in fig. 3, the position of the density change zone may be determined according to the predicted value of the differential mu flow intensity and the measured value of the differential mu flow intensity in each direction interval, and specifically includes steps S41 to S43:

step S41: and determining standard deviation of each direction interval according to the predicted value of the differential mu flow intensity of each direction interval and the measured value of the differential mu flow intensity.

According to the differential flow intensity prediction value I of the muon in each direction interval _MC (I, j) and said Miao Ziwei shunt strength measurement I _mea (i, j) determining a dimensionless number η of the respective directional intervals _i,j ＝I _mea (i,j)/I _MC (i, j) said dimensionless number representation: the reconstruction direction falls within each direction interval _i,j ) A difference in the mass density of the resulting target geologic volume on the second or third muon track from said reference density. And determining the uncertainty of the dimensionless number of each direction interval according to the number of the second muon or the third muon in each direction interval. Let Deltaeta _i,j Is eta _i,j Including statistical uncertainty and system uncertainty. At n much greater than 1, the statistical uncertainty can be considered to be

Statistical uncertainty equal to nEvents (i, j)The system uncertainty can be obtained from monte carlo simulations. Determining the standard deviation delta of each direction section according to the uncertainty of each direction section and the dimensionless number of each direction section _i,j 。

Step S42: and determining whether the measured density of each direction interval deviates from the reference density according to the standard deviation of each direction interval.

According to the standard deviation of each direction interval and the reference density of the target geologic body, determining whether the measured density of each direction interval deviates from the reference density comprises: when the standard deviation of one direction interval is larger than a preset value and the dimensionless number of the direction interval is larger than 1, the measured density of the target geologic body in the direction interval is smaller than the reference density; and under the condition that the standard deviation of one direction interval is larger than a preset value and the dimensionless number of the direction interval is smaller than 1, the measured density of the target geologic body in the direction interval is larger than the reference density.

If the standard deviation delta _i,j The following conditions are satisfied:

characterization of the first% _i,j ) The internal density of the target geologic body in the direction included in each direction interval is different from the reference density in m standard deviations and more, and eta _i,j -1 > (<) 0 indicates that the internal density of the target geologic volume is less than (greater than) the reference density. Alternatively, m takes 3, 5 or other positive numbers.

Step S43: and determining the position of a density change area according to whether the measured density of each direction interval deviates from the reference density, wherein the measured density of the direction interval in which the position of the density change area is located deviates from the reference density.

And if the measured density of one direction interval deviates from the reference density, the target geologic body positioned in the direction interval is the position of the density change area.

The technical scheme is suitable for various types of detection devices, and the position of a density change area can be determined. The background subtraction algorithm and the direction reconstruction algorithm applied to the spherical all-liquid-flash 4 pi solid angle detection device are provided, and the geological tomography method applicable to various types of detection devices is provided by combining the background subtraction algorithm and the direction reconstruction algorithm, so that the application of the spherical all-liquid-flash 4 pi solid angle detection device to the geological exploration field becomes possible.

It will be appreciated by those skilled in the art that the disclosed embodiments are not limited by the illustrated ordering of acts, as some steps may be performed in other order or concurrently in accordance with the disclosed embodiments. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred embodiments, and that the acts are not necessarily required by the disclosed embodiments.

In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described by differences from other embodiments, and identical and similar parts between the embodiments are all enough to be referred to each other.

It will be apparent to those skilled in the art that embodiments of the present disclosure may be provided as a method, apparatus, or computer program product. Accordingly, the disclosed embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present disclosure may take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

Embodiments of the present disclosure are described with reference to flowchart illustrations and/or block diagrams of methods, apparatus, electronic devices, and computer program products according to embodiments of the disclosure. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

While the preferred embodiments of the disclosed embodiments have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiment and all such alterations and modifications as fall within the scope of the disclosed embodiments.

Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or terminal device comprising the element.

The foregoing has outlined a detailed description of a method for predicting the strength of a subterranean kilometer Miao Ziliu provided by the present disclosure, and the detailed description has been given herein with respect to the principles and embodiments of the present disclosure, the foregoing examples being provided only to assist in understanding the method of the present disclosure and its core ideas; meanwhile, as one of ordinary skill in the art will have modifications in the specific embodiments and application scope in accordance with the ideas of the present disclosure, the contents of the present specification should not be construed as limiting the present disclosure in summary.

Claims

1. A method of strong prediction at the underground kilometer level Miao Ziliu, comprising:

2. The method of claim 1, wherein if the detection device is the spherical all-liquid flash 4 pi solid angle detection device, further comprising:

3. The method of claim 1, wherein if the detection device is the other detection device, further comprising:

4. A method of strong prediction of the underground kilometer scale Miao Ziliu as claimed in claim 2 or claim 3, further comprising:

5. The method of claim 1, wherein said simulating from said initial cosmic ray model and said target geologic volume model to determine the muon instance that arrived at the detection device comprises:

6. The underground kilometer level Miao Ziliu strong prediction method according to claim 1, wherein said performing a response correction on said muon instances based on a corresponding direction reconstruction algorithm according to a type of said detection device, to obtain a muon direction prediction distribution, comprises:

7. The method for strongly predicting the underground kilometer level Miao Ziliu of claim 6, wherein, in the case where the structure of the detecting means is a detecting means structure having a directional resolution and a detecting efficiency independent of the incident direction of the muon,

8. The underground kilometer level Miao Ziliu intensity prediction method according to claim 1, wherein said obtaining the muon differential flow intensity prediction value for each directional interval from the muon directional prediction distribution comprises:

9. A method for strongly predicting the power of a sub-surface kilometer scale Miao Ziliu as claimed in claim 2 wherein said removing a background electrical signal from said electrical signal based on the sum of the energy sequences of each of said electrical signals, results in an electrical signal of said second muon, comprises:

acquiring an energy threshold;

10. A method for strongly predicting the underground kilometer level Miao Ziliu of claim 2, wherein deriving the reconstruction direction of said second muon from a time sequence of electrical signals of said second muon comprises:

11. The underground kilometer grade Miao Ziliu intensity prediction method of claim 4, wherein said determining a standard deviation for said respective directional intervals from said muon differential flow intensity predictions and said muon differential flow intensity measurements for said respective directional intervals comprises: