A goaf identification method and system and a storage medium

Abstract

The application discloses a goaf identification method and system and a storage medium, which are used for identifying a goaf without drilling, thereby improving the detection accuracy and efficiency of the goaf. The method comprises the following steps: performing horizon division on offset data corresponding to seismic exploration; determining the frequency of target layer reflection waves and the layer velocity of the target layer; calculating the wavelength of the target layer reflection waves according to the frequency of the target layer reflection waves and the layer velocity of the target layer; calculating the continuation time of the reflection waves according to the wavelength of the target layer reflection waves and the layer velocity of the target layer; determining the continuation reflection waves corresponding to the target layer reflection waves according to the continuation time of the target layer reflection waves; and generating a plan view for identifying the goaf of the target layer according to the root mean square amplitude value of the target layer reflection waves and the root mean square amplitude value of the continuation reflection waves. According to the scheme provided in the application, the goaf can be identified without drilling, thereby improving the detection accuracy and efficiency of the goaf.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of goaf detection, and particularly relates to a goaf identification method and system and a storage medium. BACKGROUND

[0002] According to relevant data at home and abroad, at present, goaf exploration of old mines mainly adopts drilling and geophysical detection technology, supplemented by mining condition investigation, deformation observation, hydrological test and other means. The drilling method is relatively intuitive, but often needs to arrange dense drill holes, and a large amount of work is needed. Moreover, due to the irregularity of small mine exploitation, it is difficult to accurately explore the range of the goaf by drilling, and the drilling efficiency is low, the economic investment is large, and the goaf is easily drilled through.

[0003] Therefore, it is urgent to provide a goaf identification method for identifying the goaf without drilling, so as to improve the detection accuracy and detection efficiency of the goaf. SUMMARY

[0004] The present application provides a goaf identification method, system and storage medium for identifying the goaf without drilling, so as to improve the detection accuracy and detection efficiency of the goaf.

[0005] The present application provides a goaf identification method, comprising:

[0006] Performing horizon division on offset data corresponding to seismic exploration;

[0007] Determining the frequency of the target layer reflection wave and the layer velocity of the target layer;

[0008] Calculating the wavelength of the target layer reflection wave according to the frequency of the target layer reflection wave and the layer velocity of the target layer;

[0009] Calculating the continuation time of the reflection wave according to the wavelength of the target layer reflection wave and the layer velocity of the target layer;

[0010] Determining the continuation reflection wave corresponding to the target layer reflection wave according to the continuation time of the target layer reflection wave;

[0011] Generating a plan view for identifying the target layer goaf according to the root mean square amplitude value of the target layer reflection wave and the root mean square amplitude value of the continuation reflection wave.

[0012] The application has the beneficial effects that: the target layer reflection wave and the continuation reflection wave are determined through the corresponding migration data volume of the seismic exploration, and then the plan view for identifying the target layer goaf is generated according to the root mean square amplitude value of the target layer reflection wave and the root mean square amplitude value of the continuation reflection wave, so that the position of the goaf is determined through the change of the reflection wave energy, and the identification of the goaf is realized without drilling, thereby improving the detection accuracy and detection efficiency of the goaf.

[0013] In one embodiment, the frequency of the target layer reflection wave is determined according to the following manner:

[0014] The frequency spectrum curve of the target layer is generated through a seismic data processing program;

[0015] The frequency of the reflection wave is determined according to the frequency spectrum curve of the target layer.

[0016] In one embodiment, the layer velocity of the target layer reflection wave is determined according to the following manner:

[0017] Data recording the corresponding relationship between the reflection time of the reflection wave and the stacking velocity are obtained;

[0018] The reflection time of the target layer reflection wave is interpolated through the data recording the corresponding relationship between the reflection time of the reflection wave and the stacking velocity and the reflection time of the target layer reflection wave, so as to determine the stacking velocity corresponding to the reflection time of the target layer reflection wave;

[0019] The stacking velocity is substituted into the Dix formula to obtain the layer velocity of the target layer.

[0020] In one embodiment, the wavelength of the target layer reflection wave is calculated according to the frequency of the target layer reflection wave and the layer velocity of the target layer, including:

[0021] The frequency of the target layer reflection wave is substituted into the following formula to determine the period of the target layer reflection wave:

[0022] 1=Tf;

[0023] Wherein, f is the frequency of the target layer reflection wave, and T is the period of the target layer reflection wave;

[0024] The period of the reflection wave and the layer velocity of the target layer are substituted into the following formula to determine the wavelength of the target layer reflection wave:

[0025] λ=TV;

[0026] Wherein, V is the layer velocity of the target layer, and λ is the wavelength of the target layer reflection wave.

[0027] In one embodiment, the continuation time of the reflection wave is calculated according to the wavelength of the target layer reflection wave and the layer velocity of the target layer, including:

[0028] The wavelength of the target layer reflected wave and the layer velocity of the target layer are substituted into the following formula to determine the continuation time of the reflected wave:

[0029] t0 = λ / V;

[0030] Wherein, t0 is the continuation time of the reflected wave, λ is the wavelength of the target layer reflected wave, and V is the layer velocity of the target layer.

[0031] In one embodiment, the continuation reflected wave corresponding to the target layer reflected wave is determined according to the continuation time of the target layer reflected wave, comprising:

[0032] Extracting a seismic time section as a main section in the seismic exploration corresponding migration data volume;

[0033] Determining the continuation reflected wave in the main section according to the continuation time of the target layer reflected wave.

[0034] In one embodiment, the plan view for identifying the target layer goaf is generated according to the root mean square amplitude value of the target layer reflected wave and the root mean square amplitude value of the continuation reflected wave, comprising:

[0035] Determining the root mean square amplitude value of the target layer reflected wave;

[0036] Generating the root mean square amplitude value of the continuation reflected wave;

[0037] Generating the plan view according to the difference between the root mean square amplitude value of the target layer reflected wave and the root mean square amplitude value of the continuation reflected wave.

[0038] In one embodiment, different difference values correspond to different color areas in the plan view, and the method further comprises:

[0039] Determining that the area with the preset color on the generated plan view is the mining boundary of the goaf.

[0040] The present application provides a goaf identification system, comprising:

[0041] At least one processor; and,

[0042] The memory is in communication connection with the at least one processor; wherein,

[0043] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to implement the goaf identification method described in any one of the above embodiments.

[0044] The application provides a computer storage medium, when instructions in the computer storage medium are executed by a processor corresponding to a goaf identification system, the goaf identification system can implement the goaf identification method described in any one of the embodiments.

[0045] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the present application. The objects and other advantages of the present application will be realized and attained by means of the instrumentalities particularly pointed out in the written description and claims hereof as well as the appended drawings.

[0046] The technical solutions of the present application are described in detail below with the aid of the accompanying drawings and embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0047] The accompanying drawings are included to provide a further understanding of the present application, and constitute a part of the specification, illustrate the present application, and are used to explain the present application together with the embodiments of the present application, and do not constitute a limitation on the present application. In the drawings:

[0048] Figure 1 A flowchart of a goaf identification method in an embodiment of the present application;

[0049] Figure 2 A distribution diagram of a root mean square amplitude value of a target layer reflected wave in an embodiment of the present application;

[0050] Figure 3 A distribution diagram of a root mean square amplitude value of a continued reflected wave in an embodiment of the present application;

[0051] Figure 4 A plan view for identifying a goaf of a target layer in an embodiment of the present application;

[0052] Figure 5 A schematic diagram of a seismic time profile in an embodiment of the present application;

[0053] Figure 6 A hardware structure schematic diagram of a goaf identification system of the present application. DETAILED DESCRIPTION

[0054] The preferred embodiments of the present application are described below with the aid of the accompanying drawings, and it should be understood that the preferred embodiments described herein are only used to illustrate and explain the present application, and do not limit the present application.

[0055] Figure 1 A flowchart of a goaf identification method in an embodiment of the present application, as shown in Figure 1 The method can be implemented as the following steps S11-S16:

[0056] In step S11, horizon division is performed on the migration data volume corresponding to the seismic exploration.

[0057] In step S12, the frequency of the target layer reflection wave and the layer velocity of the target layer are determined.

[0058] In step S13, the wavelength of the target layer reflection wave is calculated according to the frequency of the target layer reflection wave and the layer velocity of the target layer.

[0059] In step S14, the continuation time of the reflection wave is calculated according to the wavelength of the target layer reflection wave and the layer velocity of the target layer.

[0060] In step S15, the continuation reflection wave corresponding to the target layer reflection wave is determined according to the continuation time of the target layer reflection wave.

[0061] In step S16, the plan view for identifying the goaf of the target layer is generated according to the root mean square amplitude value of the target layer reflection wave and the root mean square amplitude value of the continuation reflection wave.

[0062] In this application, horizon division is performed on the migration data volume corresponding to the seismic exploration. Specifically, first, horizon calibration is performed on the migration data volume provided by the seismic exploration data processing, and then horizon interpretation is performed according to a grid of 5m x 5m on the basis of the horizon calibration.

[0063] The frequency of the target layer reflection wave and the layer velocity of the target layer are determined. Specifically, Fourier transform is applied to obtain the frequency spectrum of the reflection wave of the target layer segment:

[0064]

[0065] Where f is the frequency, t is the time, and F(f) is called the frequency spectrum.

[0066] In addition, the layer velocity of the target layer is calculated by using the DIX formula:

[0067]

[0068] Where Vn is the layer velocity of the nth layer, tn is the reflection time of the nth layer, and Vn is the root mean square (stacked) velocity corresponding to the nth layer. n o，n R

[0069] The wavelength of the target layer reflection wave is calculated according to the frequency of the target layer reflection wave and the layer velocity of the target layer. Specifically, the frequency of the target layer reflection wave is substituted into the following formula to determine the period of the target layer reflection wave:

[0070] 1 = Tf; formula (3)

[0071] ​​​Wherein, f is the frequency of the target layer reflected wave, and T is the period of the target layer reflected wave.

[0072] The period of the reflected wave and the interval velocity of the target layer are substituted into the following formula to determine the wavelength of the target layer reflected wave:

[0073] λ=TV; formula (4)

[0074] Wherein, V is the interval velocity of the target layer, and λ is the wavelength of the target layer reflected wave.

[0075] The continuation time of the reflected wave is calculated according to the wavelength of the target layer reflected wave and the interval velocity of the target layer; specifically, the wavelength of the target layer reflected wave and the interval velocity of the target layer are substituted into the following formula to determine the continuation time of the reflected wave:

[0076] t0=λ / V; formula (5)

[0077] Wherein, t0 is the continuation time of the reflected wave, λ is the wavelength of the target layer reflected wave, and V is the interval velocity of the target layer.

[0078] The continuation reflected wave corresponding to the target layer reflected wave is determined according to the continuation time of the target layer reflected wave; specifically, in the migration data volume corresponding to the seismic exploration, a seismic time section is extracted as a main section; and the continuation reflected wave is determined in the main section according to the continuation time of the target layer reflected wave.

[0079] A plan view for identifying the mined-out area of the target layer is generated according to the root mean square amplitude value of the target layer reflected wave and the root mean square amplitude value of the continuation reflected wave; specifically, Figure 2 is a distribution diagram of the root mean square amplitude value of the target layer reflected wave, Figure 3 is a distribution diagram of the root mean square amplitude value of the continuation reflected wave, and the root mean square amplitude value of the target layer reflected wave is subtracted from the root mean square amplitude value of the continuation reflected wave to obtain a plan view for identifying the mined-out area of the target layer as shown in Figure 4 In Figure 2 and Figure 3 white represents a larger root mean square amplitude value area, and gray and gray-black represent smaller root mean square amplitude value areas. Figure 2 White in the figure represents a mined area, and gray and gray-black represent non-mined areas. Figure 3 Gray and gray-black represent possible mined areas, and white represents non-mined areas. Figure 4 White represents a larger root mean square amplitude difference value area, gray and gray-black represent smaller root mean square amplitude difference value areas, white represents a mined area, and the boundary between white and other colors is the boundary of the mined area.

[0080] Next, the overall scheme of the present application will be described by way of example in conjunction with specific examples, for example:

[0081] The horizon calibration is performed on the migration data volume provided by the seismic exploration data processing, specifically as T2 in Figure 5 corresponding to the reflection wave of No. 2 coal seam, and the horizon interpretation is performed according to the grid of 5m x 5m on the basis of the horizon calibration, and the spectrum of the reflection wave of the target layer is calculated; the spectrum curve of the target layer can be calculated by using the seismic data processing software, for example, the main frequency of the coal seam reflection wave is 45Hz-55Hz from the spectrum curve. In addition, the reflection time corresponding to the stacking velocity of the target layer reflection wave is determined by interpolation according to the data of the corresponding relationship between the reflection time and the stacking velocity of the reflection wave and the reflection time of the target layer reflection wave, and the Dix formula is used to calculate the interval velocity of the target layer.

[0082] The corresponding relationship between the reflection time and the stacking velocity of the reflection wave is that when the reflection time is 50ms, the stacking velocity is 900m / s; when the reflection time is 150ms, the stacking velocity is 1800m / s, and when the reflection time is 250ms, the stacking velocity is 3000m / s; and on the seismic time profile, the time of the coal seam reflection wave is 170ms, and by using the linear interpolation method, it can be known that the stacking velocity at the time of 170ms is 1800m / s+(3000m / s-1800m / s) / (250ms-150ms) x (170ms-150ms)=2040m / s. The corresponding interval velocity is substituted into the above formula (2), and the interval velocity of the coal seam is 2040m / s.

[0083] The wavelength of the target layer reflection wave is calculated according to the frequency of the target layer reflection wave and the interval velocity of the target layer; specifically,

[0084] 50Hz (frequency) is substituted into the above formula (3), and T=0.02

[0085] T=0.02, V=2040 is substituted into the above formula (4), and λ=40.8m

[0086] λ=40.8m, V=2040 is substituted into the above formula (5), and the reflection wave continuation time t0=20ms.

[0087] Secondly, in the interpreted seismic data volume, seismic time sections are extracted as main profiles according to the grid of 80m x 80m, and a continuation reflection (Tcont) is marked at the position about 20ms (calculated reflection wave continuation time) below the interpreted horizon (T2); on this basis, continuation reflection horizon interpretation is completed according to the grid of 5m x 5m; because the interpreted seismic data only contains time information, and cannot directly display the energy and frequency variation information of the reflection wave, it is necessary to calculate the root mean square amplitude value of the reflection wave and the continuation reflection wave of the interpreted target layer and output the corresponding data value;

[0088] The root mean square amplitude is the square root of the average of the square of the amplitude, and it is very sensitive to particularly large amplitudes because the amplitude value is squared before averaging.

[0089] Because the continuation phase reflection wave can be a reflection wave generated by a certain stratum, or it can be a pure overlying stratum continuation reflection wave. If it is a reflection wave generated by a certain stratum, its reflection wave amplitude and frequency will be affected by the overlying stratum reflection wave within the influence range of the overlying stratum reflection wave continuation phase; if it is only a pure continuation phase, the amplitude and frequency of this wave are certainly closely related to the amplitude and frequency of the overlying stratum reflection wave. After the coal seam is mined, the material density of the mined-out area decreases, and the energy transmitted downward will be weakened, which is reflected in the amplitude as a decrease in amplitude. Therefore, the root mean square amplitude value of the target layer reflection wave (A) is subtracted from the root mean square amplitude value of the continuation reflection wave (B), and the result is generated as a planar graph (C), on which the mining boundary of the room-and-pillar mined-out area can be determined. Figure 2 Figure 3 Figure 4

[0090] In Figure 2 and Figure 3 , white represents a larger root mean square amplitude value area, and gray and gray-black represent smaller root mean square amplitude value areas. Figure 2 White areas in the figures represent mined areas, and gray and gray-black areas represent unmined areas. Figure 3 Gray and gray-black represent possible mined areas, and white represents unmined areas. Figure 4 White represents a larger root mean square amplitude difference value area, gray and gray-black represent smaller root mean square amplitude difference value areas, white areas represent mined areas, and the boundary between white and other colors is the mined-out area boundary.

[0091] ​​​The application has the beneficial effects that: the target layer reflection wave and the continuation reflection wave are determined through the corresponding migration data volume of the seismic exploration, and then the plan view for identifying the target layer goaf is generated according to the root mean square amplitude value of the target layer reflection wave and the root mean square amplitude value of the continuation reflection wave, so that the position of the goaf is judged through the change of the reflection wave energy, and the identification of the goaf is realized without drilling, thereby improving the detection accuracy and detection efficiency of the goaf.

[0092] In one embodiment, the frequency of the target layer reflection wave is determined according to the following steps A1-A2:

[0093] In step A1, the frequency spectrum curve of the target layer is generated through a seismic data processing program.

[0094] In step A2, the frequency of the reflection wave is determined according to the frequency spectrum curve of the target layer.

[0095] In this embodiment, the frequency spectrum curve of the target layer is generated through a seismic data processing program; the seismic data processing program can apply Fourier transform to obtain the frequency spectrum of the reflection wave of the target layer segment:

[0096]

[0097] Wherein f is the frequency, t is the time, and F(f) is called the frequency spectrum.

[0098] After obtaining the frequency spectrum of the reflection wave of the target layer segment, the frequency spectrum curve of the target layer is generated; and then the frequency of the reflection wave is determined according to the frequency spectrum curve of the target layer.

[0099] In one embodiment, the layer velocity of the target layer reflection wave is determined according to the following steps B1-B3:

[0100] In step B1, data recording the corresponding relationship between the reflection time of the reflection wave and the stacking velocity is obtained.

[0101] In step B2, the reflection time of the target layer reflection wave is interpolated through the data recording the corresponding relationship between the reflection time of the reflection wave and the stacking velocity and the reflection time of the target layer reflection wave, to determine the stacking velocity corresponding to the reflection time of the target layer reflection wave.

[0102] In step B3, the stacking velocity is substituted into the Dix formula to obtain the layer velocity of the target layer.

[0103] In one embodiment, the above step S13 can be implemented as the following steps C1-C2:

[0104] In step C1, the frequency of the target layer reflection wave is substituted into the following formula to determine the period of the target layer reflection wave:

[0105] 1=Tf;

[0106] wherein f is the frequency of the target layer reflected wave, and T is the period of the target layer reflected wave;

[0107] In step C2, the period of the reflected wave and the interval velocity of the target layer are substituted into the following formula to determine the wavelength of the target layer reflected wave:

[0108] λ = TV;

[0109] wherein V is the interval velocity of the target layer, and λ is the wavelength of the target layer reflected wave.

[0110] For example, assuming that the frequency of the target layer reflected wave is 50 Hz, substituting 1 = Tf, T = 0.02 can be obtained; secondly, substituting T = 0.02 and V = 2040 into λ = TV, λ = 40.8 m can be obtained.

[0111] In one embodiment, the above step S14 can be implemented as the following steps:

[0112] The wavelength of the target layer reflected wave and the interval velocity of the target layer are substituted into the following formula to determine the duration time of the reflected wave:

[0113] t0 = λ / V;

[0114] wherein t0 is the duration time of the reflected wave, λ is the wavelength of the target layer reflected wave, and V is the interval velocity of the target layer.

[0115] In one embodiment, the above step S15 can be implemented as the following steps D1-D2:

[0116] In step D1, a seismic time section is extracted as a main section in the migration data volume corresponding to the seismic exploration;

[0117] In step D2, a duration reflected wave is determined in the main section according to the duration time of the target layer reflected wave.

[0118] In one embodiment, the above step S16 can be implemented as the following steps E1-E3:

[0119] In step E1, a root mean square amplitude value of the target layer reflected wave is determined;

[0120] In step E2, a root mean square amplitude value of the duration reflected wave is generated;

[0121] In step E3, a plan view is generated according to the difference between the root mean square amplitude value of the target layer reflected wave and the root mean square amplitude value of the duration reflected wave.

[0122] In one embodiment, different difference values correspond to different color areas in the plan view, and the method can be further implemented as the following steps:

[0123] determining a region of the preset color on the generated plan view as a goaf boundary.

[0124] Figure 6 A hardware structure schematic diagram of a goaf identification system of the present application comprises:

[0125] at least one processor 620; and,

[0126] a memory 604 in communication with the at least one processor; wherein,

[0127] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to implement the goaf identification method described in any one of the above embodiments.

[0128] Referring to Figure 6 The goaf identification system 600 can include one or more of the following components: a processing component 602, a memory 604, a power supply component 606, a multimedia component 608, an audio component 610, an input / output (I / O) interface 612, a sensor component 614, and a communication component 616.

[0129] The processing component 602 generally controls the overall operation of the goaf identification system 600, and can include one or more processors 620 to execute instructions to complete all or part of the steps of the above methods. In addition, the processing component 602 can include one or more modules to facilitate interaction between the processing component 602 and other components. For example, the processing component 602 can include a multimedia module to facilitate the interaction between the multimedia component 608 and the processing component 602.

[0130] The memory 604 is configured to store various types of data to support the operation of the goaf identification system 600. Examples of these data include instructions for any application or method operating on the goaf identification system 600, such as text, pictures, videos, etc. The memory 604 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0131] The power supply component 606 provides power for various components of the goaf identification system 600. The power supply component 606 can include a power management system, one or more power supplies, and other components associated with generating, managing and distributing power for the goaf identification system 600.

[0132] The multimedia component 608 includes a screen providing an output interface between the gob recognition system 600 and a user. In some embodiments, the screen includes a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen can be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, swiping, and gestures on the touch panel. The touch sensor can not only sense a boundary of a touching or swiping action, but also detect duration and pressure related to the touching or swiping action. In some embodiments, the multimedia component 608 can further include a front camera and / or a rear camera. When the gob recognition system 600 is in an operation mode, such as a shooting mode or a video mode, the front camera and / or the rear camera can receive external multimedia data. Each of the front camera and the rear camera can be a fixed optical lens system or have a focal length and optical zoom capability.

[0133] The audio component 610 is configured to output and / or input audio signals. For example, the audio component 610 includes a microphone (MIC) that is configured to receive external audio signals when the gob recognition system 600 is in an operation mode, such as a calling mode, a recording mode, and a voice recognition mode. The received audio signals can be further stored in the memory 604 or transmitted via the communication component 616. In some embodiments, the audio component 610 further includes a speaker for outputting audio signals.

[0134] The I / O interface 612 provides an interface between the processing component 602 and peripheral interface modules, which can be a keyboard, a click wheel, a button, and the like. The buttons can include, but are not limited to, a home button, a volume button, a start button, and a lock button.

[0135] The sensor assembly 614 includes one or more sensors for providing various aspects of state assessment for the gob identification system 600. For example, the sensor assembly 614 can include a sound sensor. In addition, the sensor assembly 614 can detect an on / off state of the gob identification system 600, a relative positioning of components, such as a display and a keypad of the gob identification system 600, a change in position of the gob identification system 600 or a component of the gob identification system 600, a presence or absence of user contact with the gob identification system 600, a change in orientation or acceleration / deceleration of the gob identification system 600, and a change in temperature of the gob identification system 600. The sensor assembly 614 can include a proximity sensor configured to detect presence of nearby objects without any physical contact. The sensor assembly 614 can also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 614 can also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0136] The communication assembly 616 is configured to enable the gob identification system 600 to provide communication capability with other devices and with a cloud platform in a wired or wireless manner. The gob identification system 600 can access a wireless network based on a communication standard, such as WiFi, 2G, or 3G, or a combination thereof. In an example embodiment, the communication assembly 616 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In an example embodiment, the communication assembly 616 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0137] In an example embodiment, the gob identification system 600 can be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, micro-controllers, microprocessors, or other electronic elements for performing the above-described gob identification method.

[0138] The present disclosure provides a computer storage medium storing instructions which, when executed by a processor of a gob identification system, enable the gob identification system to implement the gob identification method according to any one of the above-described embodiments.

[0139] Those skilled in the art will appreciate that embodiments of the present application can be readily used as software, hardware, or a combination of software and hardware. In one embodiment, the present application can be implemented in software and can be stored on a computer readable medium, which can include random access memory (RAM), read only memory (ROM), magnetic disk or optical disk, or the like. The software implementation of the present application files can further be transmitted or received over a modem or network connection.

[0140] The present application is described in reference to the flowchart illustrations and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams block or blocks.

[0141] These computer program instructions can 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 instructions which implement the flowchart illustrations and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams block or blocks.

[0142] These computer program instructions can 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 flowchart illustrations and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams block or blocks.

[0143] Obviously, numerous modifications and variations of the present application are possible in light of the above teachings. It is therefore to be understood that within the scope of the appended claims and their equivalents, the application can be practiced otherwise than as specifically described.

Claims

1. A goaf identification method characterized by, The method comprises the following steps: stratigraphic division is performed on the migration data volume corresponding to the seismic exploration; the frequency of the target layer reflection wave and the interval velocity of the target layer are determined; the wavelength of the target layer reflection wave is calculated according to the frequency of the target layer reflection wave and the interval velocity of the target layer; the continuation time of the reflection wave is calculated according to the wavelength of the target layer reflection wave and the interval velocity of the target layer; the continuation reflection wave corresponding to the target layer reflection wave is determined according to the continuation time of the target layer reflection wave; a plan view for identifying the mined-out area of the target layer is generated according to the root mean square amplitude value of the target layer reflection wave and the root mean square amplitude value of the continuation reflection wave. The method for determining the continuation reflection wave corresponding to the target layer reflection wave comprises the following steps: a seismic time profile is extracted as a main profile in the migration data volume corresponding to the seismic exploration; the continuation reflection wave is determined in the main profile according to the continuation time of the target layer reflection wave.

2. The method of claim 1, wherein, The frequency of the target layer reflection wave is determined in the following manner: a frequency spectrum curve of the target layer is generated by a seismic data processing program; the frequency of the reflection wave is determined according to the frequency spectrum curve of the target layer.

3. The method of claim 1, wherein, The interval velocity of the target layer reflection wave is determined in the following manner: data recording the corresponding relationship between the reflection time and the stacking velocity of the reflection wave are obtained; the stacking velocity corresponding to the reflection time of the target layer reflection wave is determined by interpolation according to the data recording the corresponding relationship between the reflection time and the stacking velocity of the reflection wave and the reflection time of the target layer reflection wave; the interval velocity of the target layer is calculated by substituting the stacking velocity into the Dix formula.

4. The method of claim 1, wherein, The wavelength of the target layer reflection wave is calculated in the following manner: the frequency of the target layer reflection wave is substituted into the following formula to determine the period of the target layer reflection wave: 1 = Tf; wherein f is the frequency of the target layer reflection wave and T is the period of the target layer reflection wave; the period of the reflection wave and the interval velocity of the target layer are substituted into the following formula to determine the wavelength of the target layer reflection wave: λ = TV; wherein V is the interval velocity of the target layer and λ is the wavelength of the target layer reflection wave.

5. The method of claim 1, wherein, The continuation time of the reflection wave is calculated in the following manner: the wavelength of the target layer reflection wave and the interval velocity of the target layer are substituted into the following formula to determine the continuation time of the reflection wave: t0 = λ / V; wherein t0 is the continuation time of the reflection wave, λ is the wavelength of the target layer reflection wave and V is the interval velocity of the target layer.

6. The method of claim 1, wherein, The continuation reflection wave corresponding to the target layer reflection wave is determined in the following manner: a seismic time profile is extracted as a main profile in the migration data volume corresponding to the seismic exploration; the continuation reflection wave is determined in the main profile according to the continuation time of the target layer reflection wave.

7. The method of claim 1, wherein, The plan view for identifying the mined-out area of the target layer is generated in the following manner: the root mean square amplitude value of the target layer reflection wave is determined; the root mean square amplitude value of the continuation reflection wave is generated; generate a plan view according to a difference between the root mean square amplitude value of the target layer reflected wave and the root mean square amplitude value of the continued reflected wave.

8. The method of claim 7, wherein, Different difference values correspond to different color areas in the plan view, and the method further comprises: determining that a preset color area on the generated plan view is a mining boundary of the goaf.

9. A goaf identification system characterized by, comprise: at least one processor; and a memory in communication with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to implement the goaf identification method according to any one of claims 1-8.

10. A computer storage medium, characterized in that, When the instructions in the storage medium are executed by the processor corresponding to the goaf identification system, the goaf identification system can implement the goaf identification method according to any one of claims 1-8.

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