Coal rock identification method and device

Abstract

The invention provides a coal rock identification method and device, and relates to the technical field of coal mines. The method comprises the following steps: processing an inelastic scattering gamma-ray energy spectrum and a capture energy spectrum of a stratum to be detected to obtain a net inelastic scattering gamma-ray energy spectrum and a net capture gamma-ray energy spectrum; according to the net inelastic scattering gamma-ray energy spectrum, the first relative yield of the to-be-detected stratum target element is obtained, and according to the net capture gamma-ray energy spectrum, the second relative yield of the to-be-detected stratum target element is obtained. Determining the element content of the target element according to the first relative yield and the second relative yield; and determining whether the to-be-detected stratum is a coal seam based on the element content. According to the invention, active detection analysis is carried out on the element components of the stratum to be detected by using the chemical component difference of coal and rock, and high-precision, stable and reliable working face coal and rock identification is realized. The coal rock interface can be monitored and accurately positioned in real time, a coal mining machine roller height adjusting strategy can be conveniently formulated subsequently, and the coal mining efficiency of coal is greatly improved.

Description

Technical Field

[0001] The present disclosure relates to the field of coal mine technology, and in particular to a coal-rock identification method and device. Background Art

[0002] In traditional coal mining, miners often face safety risks such as gas explosions, collapses, and water damage. The introduction of intelligent technology allows miners to mine by remotely operating equipment at the ground control center, away from the direct underground operation risks. While reducing dependence on human resources, intelligent technology also provides miners with a safer working environment. This remote operation not only protects the lives of miners, but also improves the comfort and work efficiency of operations.

[0003] In the process of intelligent mining, the first challenge is to achieve accurate identification of coal and rock. This technical difficulty is a limiting factor in the development of current intelligent mining equipment. When the coal mining machine is operating, it should cut along the natural boundary between the coal seam and the rock stratum as much as possible to reduce the waste of resources and improve the quality of coal. When the drum of the coal mining machine contacts the rock stratum, its working height must be adjusted immediately to avoid unnecessary loss of resources or degradation of coal quality. Improper cutting operations will not only reduce the economic value of coal, but may also increase the difficulty and cost of subsequent processing. In mines with high gas content, improper operation may also cause safety accidents.

[0004] Traditional coal-rock identification technology uses the differences in physical or chemical properties between coal and rock to identify coal and rock. Most coal-rock identification technologies that require sensors require high modification costs. Taking the stress pick method as an example, it requires structural modification of the drum part of the coal mining machine to install specific force measuring picks and corresponding force measurement sensors. Similarly, in the vibration test method, it is also necessary to integrate a variety of sensors on the coal mining machine, such as accelerometers, torsional vibration sensors, and torque sensors, which require additional installation and configuration work. Coal-rock identification technology that uses images and signals is easily affected by the working environment. At the coal mining site, a large amount of dust and poor lighting conditions often lead to a decrease in the quality of the collected images. During mechanical operation, interference factors such as severe mechanical vibration, high heat generation, and deep electromagnetic signals will reduce the accuracy of the collected signals and reduce the accuracy of coal-rock identification. Summary of the invention

[0005] The present disclosure aims to solve one of the technical problems in the related art at least to some extent.

[0006] To this end, the first embodiment of the present disclosure proposes a coal-rock identification method, comprising:

[0007] Obtaining inelastic scattering gamma energy spectrum and captured gamma energy spectrum of the formation to be measured;

[0008] Determine the net inelasticity deduction factor and the net capture deduction factor;

[0009] Determining the target element in the stratum to be measured;

[0010] determining a net inelastic scattering gamma energy spectrum according to the inelastic scattering gamma energy spectrum and the net inelastic deduction factor;

[0011] determining a net captured gamma energy spectrum according to the captured gamma energy spectrum and the net captured deduction factor;

[0012] Decomposing the net inelastic scattering gamma energy spectrum to obtain a first relative yield of the target element;

[0013] Decoding the net captured gamma-ray spectrum to obtain a second relative yield of the target element;

[0014] determining an element content of the target element according to the first relative yield and the second relative yield;

[0015] Whether the stratum to be tested is a coal seam is determined based on the element content.

[0016] In some embodiments of the present disclosure, the obtaining of the inelastic scattering gamma energy spectrum and the captured gamma energy spectrum of the formation to be measured includes: obtaining the initial inelastic scattering gamma energy spectrum and the initial captured gamma energy spectrum of the formation to be measured; preprocessing the initial inelastic scattering gamma energy spectrum and the initial captured gamma energy spectrum, respectively, to obtain the inelastic scattering gamma energy spectrum and the captured gamma energy spectrum; wherein the preprocessing includes at least one of the following: smoothing filtering, spectral drift correction, and energy window selection.

[0017] In some embodiments of the present disclosure, the net inelastic scattering gamma energy spectrum is determined by the following formula according to the inelastic scattering gamma energy spectrum and the net inelastic deduction factor:

[0018] Net Ine=SBUR-α*SEAR

[0019] Wherein, Net Ine is the net inelastic scattering gamma energy spectrum, SBUR is the inelastic scattering gamma energy spectrum, α is the net inelastic deduction factor, and SEAR is the early captured gamma energy spectrum in the previous preset recording period in the captured gamma energy spectrum.

[0020] In some embodiments of the present disclosure, the net captured gamma spectrum is determined by the following formula according to the captured gamma spectrum and the net captured deduction factor, including:

[0021] Net Cap = CGS-β*SBKU

[0022] Wherein, Net Cap is the net captured gamma energy spectrum, CGS is the captured gamma energy spectrum, β is the net capture subtraction factor, and SBKU is the background gamma energy spectrum.

[0023] In some embodiments of the present disclosure, determining the elemental content of the target element based on the first relative yield and the second relative yield includes: using a least squares method or a stripping method based on machine learning to process the first relative yield and the second relative yield to obtain the elemental content of the target element.

[0024] In some embodiments of the present disclosure, determining the element content of the target element based on the first relative yield and the second relative yield includes: determining an average value of the first relative yield and the second relative yield; and determining the element content of the target element based on the average value.

[0025] In some embodiments of the present disclosure, the target element is carbon; determining whether the formation to be tested is a coal seam based on the element content includes: comparing the element content with a carbon content threshold of coal; in response to the element content being greater than or equal to the carbon content threshold, determining that the formation to be tested is a coal seam.

[0026] In some embodiments of the present disclosure, the target element is silicon; determining whether the formation to be tested is a coal seam based on the element content includes: comparing the element content with a silicon content threshold of coal; in response to the element content being less than or equal to the silicon content threshold, determining that the formation to be tested is a coal seam.

[0027] The second aspect of the present disclosure provides a coal-rock identification device, comprising:

[0028] An acquisition module, used for acquiring an inelastic scattering gamma energy spectrum and a captured gamma energy spectrum of a formation to be measured;

[0029] A first determination module is used to determine a net inelastic deduction factor and a net captured deduction factor;

[0030] A second determination module is used to determine the target element in the stratum to be measured;

[0031] A third determination module is used to determine a net inelastic scattering gamma energy spectrum according to the inelastic scattering gamma energy spectrum and the net inelastic deduction factor;

[0032] a fourth determination module, configured to determine a net captured gamma energy spectrum according to the captured gamma energy spectrum and the net captured deduction factor;

[0033] a fifth determination module, configured to decompose the net inelastic scattering gamma energy spectrum to obtain a first relative yield of the target element;

[0034] a sixth determination module, configured to despect the net captured gamma-ray energy spectrum to obtain a second relative yield of the target element;

[0035] a seventh determination module, configured to determine the element content of the target element according to the first relative yield and the second relative yield;

[0036] An eighth determination module is used to determine whether the stratum to be tested is a coal seam based on the element content.

[0037] The third aspect of the present disclosure provides an electronic device, including: a detector for collecting an inelastic scattered gamma energy spectrum and a captured gamma energy spectrum of a formation to be measured;

[0038] A processor, and a memory communicatively connected to the processor;

[0039] The memory stores computer-executable instructions;

[0040] The processor executes the computer-executable instructions stored in the memory to implement the method described in the first aspect.

[0041] The present invention provides a non-contact coal-rock identification method, which uses the chemical composition difference between coal and rock to actively detect and analyze the elemental composition of the stratum to be tested, and realizes high-precision, stable and reliable coal-rock identification of the working face. The present invention can monitor and accurately locate the coal-rock interface in real time, and facilitates the subsequent formulation of the coal mining machine drum height adjustment strategy, greatly improving the coal mining efficiency.

[0042] Additional aspects and advantages of the present disclosure will be given in part in the following description and in part will be obvious from the following description or learned through practice of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The above and / or additional aspects and advantages of the present disclosure will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0044] Figure 1 A schematic diagram of a flow chart of a coal-rock identification method provided in an embodiment of the present disclosure;

[0045] Figure 2 A schematic diagram of obtaining an inelastic scattering gamma energy spectrum and a captured gamma energy spectrum provided by an embodiment of the present disclosure;

[0046] Figure 3 A schematic diagram of a coal-rock identification device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0047] Embodiments of the present disclosure are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, and should not be construed as limiting the present disclosure.

[0048] Specifically, the coal-rock identification method and device according to the embodiments of the present disclosure are described below with reference to the accompanying drawings.

[0049] Figure 1 The following is a flow chart of a coal rock identification method provided by an embodiment of the present disclosure. Figure 1 As shown, the coal-rock identification method may include the following steps:

[0050] Step 101: Obtain an inelastic scattering gamma spectrum and a captured gamma spectrum of a formation to be measured.

[0051] Figure 2 A schematic diagram of obtaining an inelastic scattering gamma spectrum and a captured gamma spectrum provided by an embodiment of the present disclosure. In some embodiments of the present disclosure, Figure 2 As shown, the pulse neutron source generator is controlled by a neutron source controller. The pulse generator emits 14 MeV fast neutrons into the formation to be tested. The fast neutrons are inelastically scattered with some elements in the formation to be tested, and the energy is reduced. After several inelastic collisions, the fast neutrons become thermal neutrons and are eventually captured by the surrounding atoms. The elements return to the initial state by releasing gamma rays. The detector composed of lanthanum bromide crystals detects and obtains the inelastic scattered gamma energy spectrum and captured gamma energy spectrum of the formation to be tested. The signal acquisition module collects the electrical signal and transmits the electrical signal to the signal processing module through the signal transmission module. The coal-rock interface is determined through the analysis of the signal processing module.

[0052] Optionally, in order to improve the accuracy of coal-rock identification, in some embodiments of the present disclosure, the initial inelastic scattered gamma energy spectrum and the initial captured gamma energy spectrum obtained by detection can be preprocessed respectively. Among them, the preprocessing can include at least one of the following: smoothing filtering, spectral drift correction and energy window selection. Smoothing filtering is mainly aimed at the situation where many burrs are introduced into the noise. Smoothing filtering can adopt arithmetic average filtering, Savitzky-Golay filtering method, etc., and replace the original gamma energy spectrum data with the fitting data through polynomial fitting means, which is the result of filtering processing. Affected by temperature and pressure, the measured gamma energy spectrum recorded by the scintillation crystal will drift. Generally, the positions of H peak and O peak are relatively obvious and relatively stable. Therefore, the H peak and O peak are used as standards to perform drift correction on the measured inelastic gamma energy spectrum, that is, spectral drift correction. Most of the gamma rays obtained are in the energy range of 1 to 10 MeV. Due to the interference of factors such as the Compton platform effect and instrument noise, gamma rays measured in the low energy range are prone to false counts, making the counts in the low energy range very high. These interference information will have a great impact on the measurement of the capture yield of gamma spectrum analysis. In addition, the energy spectrum in the high energy range has a low role in solving the element yield due to the low counts. Invalid data in the low and high energy ranges will affect the spectrum analysis, but too much lost data may cause the loss of some valuable information, which will also interfere with the spectrum analysis of gamma energy spectrum. By comparing with the data published by the International Atomic Energy Agency, the characteristic peak position is obtained for energy window selection, and the same channel address interval as in the captured gamma energy spectrum is selected to complete the preprocessing of the energy spectrum.

[0053] Step 102, determining the net inelastic deduction factor and the net capture deduction factor.

[0054] Among them, the ratio of the H peak area in the inelastic scattering gamma energy spectrum SBUR and the capture gamma energy spectrum SEAR can be determined as the net inelastic deduction factor α, and the net capture deduction factor β can be a preset empirical value. Optionally, the net capture deduction factor β can take a value of 10 or about 10.

[0055] Step 103: determine the target element in the stratum to be measured.

[0056] It should be noted that the target element is the difference in chemical composition between coal and rock, which is used to judge or distinguish coal and rock. Coal is mainly composed of carbon, which is formed by the ancient plant remains in the ground after a long period of geological action, while rock is mainly composed of silicate minerals, which is the most common mineral group in the earth's crust. Using this essential difference, the target element can be selected as carbon or silicon.

[0057] Step 104 : determining a net inelastic scattering gamma energy spectrum according to the inelastic scattering gamma energy spectrum and the net inelastic deduction factor.

[0058] It should be noted that the collected inelastic scattering gamma energy spectrum will inevitably be disturbed by the capture reaction, resulting in the mixing of captured gamma ray signals in the spectrum. This phenomenon occurs within the measurement time window. Since the neutrons released early may undergo capture reactions with atomic nuclei after the slowing down process, the generated captured gamma rays are recorded simultaneously with the inelastic scattering signals. Therefore, before using the inelastic scattering gamma energy spectrum for coal rock identification, the influence of these captured gamma rays must be eliminated to ensure the accuracy of the energy spectrum data. In one implementation, the net inelastic scattering gamma energy spectrum can be determined by the following formula:

[0059] Net Ine=SBUR-α*SEAR

[0060] Among them, Net Ine is the net inelastic scattering gamma energy spectrum, SBUR is the inelastic scattering gamma energy spectrum, α is the net inelastic deduction factor, and SEAR is the early captured gamma energy spectrum in the preset recording period before the captured gamma energy spectrum. SEAR records the gamma rays emitted by the formation elements capturing neutrons in the early stage after the neutron emission and is a part of the capture gamma energy spectrum acquisition process. Since the inelastic scattering cross section of the H peak is very small compared with other nuclides, the peak corresponding to 2.223MeV in the energy spectrum can be considered to be completely caused by the radiation capture reaction of the H peak. Therefore, deducting the H peak from the inelastic scattering gamma energy spectrum SBUR can be considered as the net inelastic scattering gamma energy spectrum.

[0061] Step 105 : determining a net captured gamma energy spectrum according to the captured gamma energy spectrum and the net captured deduction factor.

[0062] When processing the captured gamma-ray spectrum, it is also necessary to consider the background factor of natural gamma radioactivity, which is always present during the entire measurement process. This means that in the acquired captured gamma-ray spectrum, a certain processing method is needed to subtract this background signal (i.e., the background gamma-ray spectrum) in order to extract the pure captured gamma-ray signal. The acquisition of this net captured gamma-ray spectrum is crucial for subsequent data analysis because it provides clearer and more accurate data, which helps to more accurately understand the nature of the interaction between gamma-rays and matter. The background gamma-ray spectrum can refer to public data.

[0063] In one implementation, the net captured gamma spectrum may be determined by the following formula:

[0064] Net Cap = CGS-β*SBKU

[0065] Among them, Net Cap is the net captured gamma energy spectrum, CGS is the captured gamma energy spectrum, β is the net capture subtraction factor, and SBKU is the background gamma energy spectrum.

[0066] Step 106: Deconstruct the net inelastic scattering gamma energy spectrum to obtain a first relative yield of the target element.

[0067] In some embodiments of the present disclosure, the net inelastic scattering gamma energy spectrum can be compared with a single element gamma energy spectrum library of the target element (which can be a standard gamma energy spectrum library or a spectrum library obtained using a Monte Carlo simulation method) to obtain a first relative yield of the target element in the net inelastic scattering gamma energy spectrum.

[0068] Step 107: Deconvolution of the net captured gamma-ray spectrum to obtain a second relative yield of the target element.

[0069] In some embodiments of the present disclosure, the net captured gamma energy spectrum can be compared with a single element gamma energy spectrum library of the target element (which can be a standard gamma energy spectrum library or a spectrum library obtained using a Monte Carlo simulation method) to obtain a second relative yield of the target element in the net captured gamma energy spectrum.

[0070] Step 108: determining the element content of the target element according to the first relative yield and the second relative yield.

[0071] Optionally, in one implementation, the first relative yield and the second relative yield are processed using a least squares method or a stripping method based on machine learning to obtain the element content of the target element.

[0072] In another implementation, an average value of the first relative yield and the second relative yield may be determined, and the element content of the target element may be determined based on the average value.

[0073] Step 109, determining whether the stratum to be tested is a coal seam based on the element content.

[0074] When the target element is carbon, the element content of carbon is compared with the carbon content threshold of coal. If the carbon content obtained by the energy spectrum is greater than or equal to the carbon content threshold, it can be determined that the stratum to be tested is a coal seam. On the contrary, if the carbon content obtained by the energy spectrum is less than the carbon content threshold, it can be determined that the stratum to be tested is a rock stratum. Based on the coal-rock identification results of the stratum to be tested, the optimal drum height adjustment strategy can be formulated, and detailed information on the coal-rock interface can be provided. There is no need to structurally modify the drum of the coal mining machine, and it is less affected by harsh environmental factors, which will greatly increase the output rate of coal and promote the development of intelligent unmanned mining technology to a higher level.

[0075] When the target element is silicon, the element content of silicon is compared with the silicon content threshold of coal. If the silicon content obtained by the energy spectrum is less than or equal to the silicon content threshold, it can be determined that the stratum to be tested is a coal seam. On the contrary, if the silicon content obtained by the energy spectrum is greater than the silicon content threshold, it can be determined that the stratum to be tested is a rock stratum.

[0076] The present disclosure provides a non-contact coal-rock identification method. By implementing the embodiments of the present disclosure, the chemical composition difference between coal and rock is utilized to actively detect and analyze the elemental composition of the stratum to be tested, thereby realizing high-precision, stable and reliable coal-rock identification of the working face. The present disclosure can monitor and accurately locate the coal-rock interface in real time, and facilitates the subsequent formulation of the coal mining machine drum height adjustment strategy, greatly improving the coal mining efficiency.

[0077] Figure 3 Schematic diagram of a coal rock identification device provided by an embodiment of the present disclosure. Figure 3 As shown, the coal rock identification device may include: an acquisition module 301, a first determination module 302, a second determination module 303, a third determination module 304, a fourth determination module 305, a fifth determination module 306, a sixth determination module 307, a seventh determination module 308 and an eighth determination module 309.

[0078] The acquisition module 301 is used to acquire the inelastic scattering gamma spectrum and the captured gamma spectrum of the formation to be measured.

[0079] The first determination module 302 is used to determine a net inelastic deduction factor and a net captured deduction factor.

[0080] The second determination module 303 is used to determine the target element in the stratum to be measured.

[0081] The third determination module 304 is configured to determine the net inelastic scattering gamma energy spectrum according to the inelastic scattering gamma energy spectrum and the net inelastic deduction factor.

[0082] The fourth determination module 305 is configured to determine the net captured gamma energy spectrum according to the captured gamma energy spectrum and the net captured deduction factor.

[0083] The fifth determination module 306 is used to decompose the net inelastic scattering gamma energy spectrum to obtain a first relative yield of the target element.

[0084] The sixth determination module 307 is used to despectrify the net captured gamma-ray energy spectrum to obtain a second relative yield of the target element.

[0085] The seventh determination module 308 is configured to determine the element content of the target element according to the first relative yield and the second relative yield.

[0086] The eighth determination module 309 is used to determine whether the stratum to be tested is a coal seam based on the element content.

[0087] In some embodiments of the present disclosure, the acquisition module 301 is specifically used to: acquire the initial inelastic scattering gamma energy spectrum and the initial captured gamma energy spectrum of the formation to be measured; preprocess the initial inelastic scattering gamma energy spectrum and the initial captured gamma energy spectrum, respectively, to obtain the inelastic scattering gamma energy spectrum and the captured gamma energy spectrum; wherein the preprocessing includes at least one of the following: smoothing filtering, spectral drift correction, and energy window selection.

[0088] In some embodiments of the present disclosure, the third determination module 304 determines the net inelastic scattering gamma energy spectrum according to the inelastic scattering gamma energy spectrum and the net inelastic deduction factor by using the following formula:

[0089] Net Ine=SBUR-α*SEAR

[0090] Wherein, Net Ine is the net inelastic scattered gamma energy spectrum, SBUR is the inelastic scattered gamma energy spectrum, α is the net inelastic subtraction factor, and SEAR is the early captured gamma energy spectrum in the captured gamma energy spectrum in the previous preset recording period.

[0091] In some embodiments of the present disclosure, the fourth determination module 305 determines the net captured gamma spectrum according to the captured gamma spectrum and the net captured deduction factor by using the following formula, including:

[0092] Net Cap = CGS-β*SBKU

[0093] Among them, Net Cap is the net captured gamma energy spectrum, CGS is the captured gamma energy spectrum, β is the net capture subtraction factor, and SBKU is the background gamma energy spectrum.

[0094] In some embodiments of the present disclosure, the seventh determination module 308 is specifically used to: process the first relative yield and the second relative yield using a least squares method or a stripping method based on machine learning to obtain the element content of the target element.

[0095] In some embodiments of the present disclosure, the seventh determination module 308 is specifically used to: determine an average value of the first relative yield and the second relative yield; and determine the element content of the target element according to the average value.

[0096] In some embodiments of the present disclosure, the target element is carbon; the eighth determination module 309 is specifically used to: compare the element content with the carbon content threshold of coal; in response to the element content being greater than or equal to the carbon content threshold, determine that the stratum to be tested is a coal seam.

[0097] In some embodiments of the present disclosure, the target element is silicon; the eighth determination module 309 is specifically used to: compare the element content with the silicon content threshold of coal; in response to the element content being less than or equal to the silicon content threshold, determine that the stratum to be measured is a coal seam.

[0098] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0099] In order to implement the above embodiments, the present disclosure also proposes an electronic device, including: a detector, which may include a lanthanum bromide crystal, for collecting the inelastic scattering gamma energy spectrum and the captured gamma energy spectrum of the formation to be measured; a processor, and a memory communicatively connected to the processor; the memory stores computer execution instructions; the processor executes the computer execution instructions stored in the memory to implement the method provided by the above embodiments.

[0100] In order to implement the above embodiments, the present disclosure further proposes a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the methods provided by the above embodiments.

[0101] In order to implement the above embodiments, the present disclosure also proposes a computer program product, including a computer program, which implements the methods provided by the above embodiments when executed by a processor.

[0102] In the description of the aforementioned embodiments, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0103] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of the present disclosure, "plurality" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0104] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present disclosure includes additional implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present disclosure belong.

[0105] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0106] It should be understood that the various parts of the present disclosure can be implemented in hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0107] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0108] In addition, each functional unit in each embodiment of the present disclosure may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0109] The storage medium mentioned above may be a read-only memory, a disk or an optical disk, etc. Although the embodiments of the present disclosure have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations of the present disclosure. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present disclosure.

Claims

1. A coal-rock identification method, characterized in that: The following steps are involved: Obtaining inelastic scattering gamma energy spectrum and captured gamma energy spectrum of the formation to be measured; Determine the net inelasticity deduction factor and the net capture deduction factor; Determining the target element in the stratum to be measured; determining a net inelastic scattering gamma energy spectrum according to the inelastic scattering gamma energy spectrum and the net inelastic deduction factor; determining a net captured gamma energy spectrum according to the captured gamma energy spectrum and the net captured deduction factor; Decomposing the net inelastic scattering gamma energy spectrum to obtain a first relative yield of the target element; Decoding the net captured gamma-ray spectrum to obtain a second relative yield of the target element; determining an element content of the target element according to the first relative yield and the second relative yield; Whether the stratum to be tested is a coal seam is determined based on the element content.

2. The method according to claim 1, characterized in that: The method of obtaining the inelastic scattering gamma spectrum and the captured gamma spectrum of the formation to be measured comprises: Acquiring an initial inelastic scattering gamma energy spectrum and an initial captured gamma energy spectrum of the formation to be measured; The initial inelastic scattering gamma energy spectrum and the initial captured gamma energy spectrum are preprocessed respectively to obtain the inelastic scattering gamma energy spectrum and the captured gamma energy spectrum; wherein the preprocessing includes at least one of the following: smoothing filtering, spectral drift correction and energy window selection.

3. The method according to claim 1, characterized in that: According to the inelastic scattering gamma energy spectrum and the net inelastic deduction factor, the net inelastic scattering gamma energy spectrum is determined by the following formula: NetIne=SBUR-α*SEAR Wherein, NetIne is the net inelastic scattered gamma energy spectrum, SBUR is the inelastic scattered gamma energy spectrum, α is the net inelastic subtraction factor, and SEAR is the early captured gamma energy spectrum in the previous preset recording period in the captured gamma energy spectrum.

4. The method according to claim 1, characterized in that According to the captured gamma spectrum and the net capture deduction factor, the net captured gamma spectrum is determined by the following formula, including: Net Cap = CGS-β*SBKU Wherein, Net Cap is the net captured gamma energy spectrum, CGS is the captured gamma energy spectrum, β is the net capture subtraction factor, and SBKU is the background gamma energy spectrum.

5. The method according to claim 1, characterized in that The determining the element content of the target element according to the first relative yield and the second relative yield comprises: The first relative yield and the second relative yield are processed by a least square method or a stripping method based on machine learning to obtain the element content of the target element.

6. The method according to claim 1, characterized in that The determining the element content of the target element according to the first relative yield and the second relative yield comprises: determining an average of the first relative yield and the second relative yield; The element content of the target element is determined based on the average value.

7. The method according to claim 1, characterized in that The target element is carbon; and determining whether the stratum to be tested is a coal seam based on the element content includes: comparing the element content with a threshold value of carbon element content of coal; In response to the element content being greater than or equal to the carbon element content threshold, it is determined that the stratum to be measured is a coal seam.

8. The method according to claim 1, characterized in that The target element is silicon; and determining whether the stratum to be measured is a coal seam based on the element content includes: comparing the element content with a threshold value of silicon content of coal; In response to the element content being less than or equal to the silicon element content threshold, it is determined that the stratum to be measured is a coal seam.

9. A coal-rock identification device, characterized in that: include: An acquisition module, used for acquiring an inelastic scattering gamma energy spectrum and a captured gamma energy spectrum of a formation to be measured; A first determination module is used to determine a net inelastic deduction factor and a net captured deduction factor; A second determination module is used to determine the target element in the stratum to be measured; A third determination module is used to determine a net inelastic scattering gamma energy spectrum according to the inelastic scattering gamma energy spectrum and the net inelastic deduction factor; a fourth determination module, configured to determine a net captured gamma energy spectrum according to the captured gamma energy spectrum and the net captured deduction factor; a fifth determination module, configured to decompose the net inelastic scattering gamma energy spectrum to obtain a first relative yield of the target element; a sixth determination module, configured to despect the net captured gamma-ray energy spectrum to obtain a second relative yield of the target element; a seventh determination module, configured to determine the element content of the target element according to the first relative yield and the second relative yield; An eighth determination module is used to determine whether the stratum to be tested is a coal seam based on the element content.

10. An electronic device, characterized in that: include: A detector, used for collecting inelastic scattering gamma energy spectrum and captured gamma energy spectrum of the formation to be tested; A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 8.

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