A forward simulation method of seismic data with tunneling

Abstract

This disclosure proposes a forward modeling method for roadheader-as-you-go (TBM) seismic data. The method includes: acquiring a first number of Ricker wavelets; superimposing the first number of Ricker wavelets to obtain a TBM seismic source wavelet; determining the TBM seismic direct wave signal received by a geophone based on the TBM seismic source wavelet; determining the TBM seismic record data received by the geophone based on the TBM seismic direct wave signal; and extracting TBM seismic forward modeling data based on the TBM seismic record data. By implementing this method, rapid acquisition of TBM seismic forward modeling data can be achieved.

Description

Technical Field

[0001] This disclosure relates to the field of seismic detection technology for mine excavation, specifically to a forward modeling method for seismic data from tunnel boring machines. Background Technology

[0002] As a novel method for advanced geological structure detection in mines, seismic exploration during mining has been the subject of numerous theoretical studies by experts from various perspectives, including signal characteristic analysis, seismic signal synthesis, seismic signal pulse processing, and seismic data processing.

[0003] In related technologies, it is not possible to quickly perform forward modeling of seismic data from tunnel boring machines. Summary of the Invention

[0004] This disclosure aims to at least partially address one of the technical problems in the related art.

[0005] Therefore, the purpose of this disclosure is to propose a forward modeling method, device, computer equipment, and storage medium for roadheader-driven seismic data, which enables rapid acquisition of forward modeling data for roadheader-driven seismic data.

[0006] To achieve the above objectives, the forward modeling method for seismic data from roadheader during tunneling, as proposed in the first aspect of this disclosure, includes:

[0007] Obtain the first number of Rek subwaves;

[0008] The first number of said Reck wavelets are superimposed to obtain the seismic source wavelet of the tunnel boring machine during excavation;

[0009] Based on the seismic source wavelet of the tunnel boring machine, determine the direct wave signal of the seismic event received by the geophone during tunneling;

[0010] Based on the direct wave signal of the tunneling seismic event, determine the tunneling seismic record data received by the geophone;

[0011] Based on the seismic records obtained during tunneling, forward modeling data of seismic activity during tunneling by a roadheader was extracted.

[0012] To achieve the above objectives, the forward modeling device for seismic data generated during tunneling by a roadheader, as proposed in the second aspect of this disclosure, includes:

[0013] The acquisition module is used to acquire the first number of Reck subwaves;

[0014] The first processing module is used to superimpose the first number of the Reck wavelets to obtain the seismic source wavelet of the tunnel boring machine during excavation.

[0015] The first determining module is used to determine the direct wave signal of the seismic source received by the detector based on the seismic source wavelet of the tunneling machine.

[0016] The second determining module is used to determine the seismic record data received by the geophone based on the seismic direct wave signal during tunneling.

[0017] The second processing module is used to extract forward modeling data of tunneling machine seismic activity based on the seismic record data during tunneling.

[0018] The computer device proposed in the third aspect of this disclosure includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the forward modeling method for seismic data from tunnel boring machines as proposed in the first aspect of this disclosure.

[0019] The fourth aspect of this disclosure provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a forward modeling method for seismic data from tunnel boring machines as proposed in the first aspect of this disclosure.

[0020] The fifth aspect of this disclosure provides a computer program product that, when executed by a processor, performs a forward modeling method for seismic data generated during tunneling by a roadheader, as proposed in the first aspect of this disclosure.

[0021] The forward modeling method, apparatus, computer equipment, and storage medium for roadheader-as-you-go (TBM) seismic data provided in this disclosure involve: acquiring a first number of Ricker wavelets; superimposing the first number of Ricker wavelets to obtain the source wavelet of the TBM seismic data; determining the direct wave signal received by the geophone based on the source wavelet; determining the recorded data of the TBM seismic data received by the geophone based on the direct wave signal; and extracting forward modeling data of the TBM seismic data based on the recorded data. This enables the rapid acquisition of forward modeling data for TBM seismic data.

[0022] Additional aspects and advantages of this disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this disclosure. Attached Figure Description

[0023] The above and / or additional aspects and advantages of this disclosure will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, in which:

[0024] Figure 1 This is a schematic flowchart of a forward modeling method for seismic data generated during tunneling by a roadheader, as proposed in one embodiment of this disclosure.

[0025] Figure 2 This is a schematic diagram of the seismic source wavelet of a roadheader during excavation, as presented in this disclosure;

[0026] Figure 3 This is a schematic diagram of the forward geometric model proposed in this disclosure;

[0027] Figure 4 This is a schematic diagram of the simulated seismic reflection record received by the detector during tunneling, based on the present disclosure.

[0028] Figure 5 This is a schematic flowchart of a forward modeling method for seismic data generated during tunneling by a roadheader, proposed in another embodiment of this disclosure.

[0029] Figure 6 This is a schematic diagram of the volumetric reflection seismic record of non-zero seismic data during excavation, as presented in this disclosure;

[0030] Figure 7 This is a schematic diagram of the processing effect of the virtual shot collection during excavation based on the present disclosure;

[0031] Figure 8 This is a flowchart of the forward modeling technology for seismic data from tunnel boring machines, as proposed in this disclosure;

[0032] Figure 9 This is a schematic diagram of the structure of a forward modeling device for seismic data generated during tunneling by a roadheader, according to an embodiment of this disclosure.

[0033] Figure 10 A block diagram of an exemplary computer device suitable for implementing embodiments of the present disclosure is shown. Detailed Implementation

[0034] Embodiments of this disclosure are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are used only to explain this disclosure, and should not be construed as limiting this disclosure. Rather, embodiments of this disclosure include all variations, modifications, and equivalents falling within the spirit and scope of the appended claims.

[0035] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, data stored, data displayed, etc.) and signals involved in this disclosure are all authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0036] Figure 1This is a schematic flowchart of a forward modeling method for seismic data generated during tunneling by a roadheader, as proposed in one embodiment of this disclosure.

[0037] It should be noted that the main body executing the forward modeling simulation method of the tunneling machine seismic data in this embodiment is the forward modeling simulation device for the tunneling machine seismic data. This device can be implemented by software and / or hardware. The device can be configured in a computer device, which may include, but is not limited to, a terminal, a server, etc. For example, the terminal may be a mobile phone, a handheld computer, etc.

[0038] like Figure 1 As shown, the forward modeling method for seismic data during tunneling by the roadheader includes:

[0039] S101: Obtain the first number of Reck subwaves.

[0040] The first quantity can be flexibly configured according to the application scenario, and there are no restrictions on it. For example, it can be set to 5.

[0041] Among them, the Ricker wavelet, also known as the "Ricker wave," is a waveform commonly used in fields such as seismic exploration, signal processing, and geological exploration. It is a waveform with high-frequency components, and its shape resembles a symmetrical Gaussian pulse. It is often used to simulate seismic waves or other types of pulse signals.

[0042] Optionally, in some embodiments, when obtaining the first number of Ricker wavelets, a first number of analog frequencies may be determined, wherein the analog frequencies belong to a preset frequency range; and corresponding Ricker wavelets are generated using the analog frequencies as the dominant frequencies. Thus, the corresponding Ricker wavelets can be quickly generated by combining the analog frequencies, and the reliability and practicality of the obtained Ricker wavelets are ensured based on the preset frequency range.

[0043] The analog frequency can be the frequency used as the main frequency of the Rick wavelet in this embodiment of the disclosure.

[0044] The preset frequency range refers to a pre-defined range used to limit the value of the main frequency. For example, the preset frequency range can be 1-100Hz.

[0045] In this embodiment of the disclosure, when determining the analog frequency, a frequency value between 1 and 100 Hz can be randomly generated as the main frequency.

[0046] In this embodiment of the present disclosure, when generating the corresponding Lake wavelet using the analog frequency as the main frequency, the analog frequency can be substituted into a preset formula to obtain the corresponding Lake wavelet.

[0047] S102: Superimpose the first number of Reck wavelets to obtain the seismic source wavelet of the tunnel boring machine during excavation.

[0048] Among them, the seismic source wavelet generated during tunneling by a roadheader refers to the seismic waveform that serves as the source when a roadheader is used for seismic exploration. Roadheaders are typically used for tunneling operations in underground engineering, and their function is similar to that of a seismic source. The vibration waves generated during their operation serve as the source signal in seismic exploration, producing seismic waves for detecting underground structures.

[0049] In other words, in this embodiment of the present disclosure, after obtaining a first number of Reck wavelets, the first number of Reck wavelets can be superimposed to obtain the seismic source wavelet of the tunnel boring machine, thereby realizing the synthesis of the seismic source wavelet of the tunnel boring machine.

[0050] It is understandable that roadheaders generate vibration signals during coal cutting operations. These signals are complex, containing multiple frequency components, and serve as the source wavelet for simulating the seismic source generated by the roadheader's vibration. First, five frequencies (the first number mentioned above) are randomly simulated, all within the 1-100Hz range (the preset frequency range mentioned above). Then, using these five randomly simulated frequencies as the dominant frequencies of the Ricker wavelet, a Ricker wavelet is generated. Finally, by superimposing these five Ricker wavelets, the source wavelet of the roadheader's seismic source is synthesized. As shown below:

[0051] f r (i)=rand(1)*99+1,i=1,…,5

[0052]

[0053] Where f r (i) is the random dominant frequency of the Reich wavelet, R i (t) is the main frequency f r (i) corresponds to the Ricker wavelet, and R(t) is the wavelet of the seismic source during tunneling by the roadheader. t is the sampling time of the Ricker wavelet.

[0054] like Figure 2 As shown, Figure 2 This is a schematic diagram of the seismic source wavelet of the tunnel boring machine as presented in this disclosure.

[0055] S103: Determine the direct wave signal of the seismic source received by the detector based on the seismic source wavelet of the tunnel boring machine.

[0056] Among them, the direct-arrival seismic signal during underground excavation refers to the signal that, during underground excavation, the seismic waves generated by a vibration source (such as a roadheader or blasting) propagate directly to the ground receiving equipment (detector). These signals do not undergo reflection or refraction by the underground medium, but propagate directly from the source to the receiver, and are typically used to detect the direct wave propagation characteristics underground.

[0057] A seismic detector (also called a seismic receiver) is an instrument used to receive and record seismic wave signals (including P-waves and S-waves). It is an indispensable part of seismic exploration and monitoring, mainly used to capture the propagation information of underground seismic waves and convert this information into electrical signals for subsequent analysis and processing.

[0058] In this embodiment, multiple detectors can be configured simultaneously, and the first detector is placed at the vibration source location of the roadheader.

[0059] Optionally, in some embodiments, when determining the direct wave signal of the tunneling seismic source received by the geophone based on the wavelet of the tunneling seismic source, the following steps can be taken: determining the direct wave sampling time and the direct wave sampling time interval; determining a second number based on the direct wave sampling time and the direct wave sampling time interval; obtaining the second number of source excitation coefficients; performing convolution processing on the source excitation coefficients and the tunneling seismic source wavelet to simulate the continuous excitation data of the tunneling seismic source wavelet; and obtaining the tunneling seismic direct wave signal based on the relative position of the geophone and the source, the time interval of the offset distance between adjacent geophones, and the continuous excitation data. This can effectively improve the simulation effect of the obtained tunneling seismic direct wave signal.

[0060] In this embodiment of the disclosure, when determining the second quantity based on the direct wave sampling time and the direct wave sampling time interval, the direct wave sampling time can be divided by the direct wave sampling time interval, and the resulting quotient can be used as the second quantity.

[0061] The specific value of the source excitation coefficient can be flexibly configured according to the application scenario. For example, any random number within the interval (0.1) can be used as the source excitation coefficient.

[0062] Understandably, according to traditional seismic impulse response theory, the convolution of the seismic wavelet and the reflection sequence constitutes the seismic record. However, compared to traditional seismic detection, in tunneling-in-progress seismic exploration, the detector deployment method and detection direction are opposite, and the source wavelet is a continuous vibration. To simulate the direct wave signal of tunneling-in-progress seismic exploration (continuous vibration signal of the source wavelet), in this embodiment, the number of source excitation coefficients can first be determined based on the sampling time and sampling time interval. Then, the source excitation coefficients and the source wavelet are convolved to simulate the continuous excitation data of the source wavelet of tunneling-in-progress seismic exploration. Finally, the direct wave signal of tunneling-in-progress seismic exploration is simulated based on the relative positions of the detectors and the source. As shown below:

[0063] N = T / dt

[0064] k i = rand(1), i = 1, ..., N

[0065] S_t1(t)=R(t)*k(i)

[0066] S_t j (t)=S_t1[t+(j-1)×Δt],j=1,…,10

[0067] Where T is the direct wave sampling time, dt is the direct wave sampling time interval, N is the second quantity mentioned above, k is the source excitation coefficient, and S_t j (t) represents the seismic direct wave signal received by the j-th geophone during excavation, and Δt represents the time interval between the offset distances of adjacent geophones.

[0068] For example, in this invention, the first geophone is deployed at the seismic source location of the tunnel boring machine. The seismic record sampling time is T = 2s, and the sampling time interval is dt = 0.001s. A total of 10 geophones can be deployed, with the geophone interval sampling time being Δt = 0.01s. Figure 3 As shown, Figure 3 This is a schematic diagram of the forward geometric model proposed in this disclosure.

[0069] S104: Determine the seismic record data received by the geophone based on the direct wave signal of the seismic tunneling.

[0070] Among them, seismic record data during excavation refers to seismic wave data related to underground structures or construction activities recorded during seismic exploration or engineering survey.

[0071] In this embodiment of the disclosure, when determining the seismic record data received by the geophone based on the seismic direct wave signal during tunneling, the seismic direct wave signal can be input into a pre-trained machine learning model to obtain the corresponding seismic record data during tunneling. Alternatively, the seismic record data received by the geophone can be determined based on the seismic direct wave signal during tunneling using a third-party device. There are no limitations on this method.

[0072] Optionally, in some embodiments, when determining the seismic record data received by the geophone based on the direct wave signal of the seismic wave during tunneling, the distance of the single structure from the source and the propagation velocity of the seismic wave in the coal seam can be determined; based on the distance of the single structure from the source and the propagation velocity, the lag time of the reflected wave signal relative to the direct wave signal can be determined; based on the lag time and the direct wave signal, the reflected wave signal can be obtained; and the direct wave signal and the reflected wave signal can be superimposed to obtain the seismic record data received by the geophone when a single structure is detected ahead. Therefore, combining the direct wave signal and the reflected wave signal can effectively improve the indicative accuracy of the obtained seismic record data.

[0073] Among them, the seismic reflection wave signal during excavation refers to the signal formed when seismic waves propagate in the underground medium and encounter the interface of different geological layers during underground engineering (such as tunnels, mines, oil and gas exploration, etc.). Some of the seismic waves are reflected back to the surface, forming a reflection wave signal.

[0074] It is understandable that the direction of seismic exploration during tunneling is opposite to the direction of geophone deployment. Therefore, when there is a single structure ahead, the reflected seismic signal received by each geophone is only delayed in time compared to the direct wave signal. The relative delay between the reflected seismic signals received by different geophones is the same as the relative delay between their direct wave signals. Therefore, the seismic records received by each geophone during tunneling can be represented by superimposing their respective direct wave and reflected wave signals. This invention first calculates the lag time t′ of the reflected seismic signal relative to the direct wave signal based on the relative positions of the structure and the seismic source. Then, it calculates the reflected wave signal and finally superimposes the direct wave and reflected wave signals to realize the seismic signal received by the geophone when there is a single structure ahead. As shown below:

[0075]

[0076] S_t′ j (t)=S_t j [t+t′],j=1,…,10

[0077] S j (t)=S_t j (t)+S_t′ j (t), j = 1, ..., 10

[0078] Where D is the distance of a single structure from the seismic source (roadheader), v is the propagation velocity of seismic waves during tunneling in the coal seam, and S_t′ j (t) represents the seismic reflection signal received by the j-th detector during excavation, S j (t) represents the seismic record received by the j-th detector during excavation.

[0079] For example, in this embodiment of the disclosure, the single structural distance is D = 100m, and the propagation velocity of the seismic wave in the coal seam is v = 2000m / s.

[0080] like Figure 4 As shown, Figure 4 This is a schematic diagram of the simulated seismic reflection records received by the detector according to the present disclosure. That is, a schematic diagram of the aforementioned seismic record data received during tunneling.

[0081] S105: Seismic forward modeling data of tunnel boring machines is obtained by extracting seismic record data during tunneling.

[0082] In other words, in this embodiment of the present disclosure, after obtaining the seismic record data during tunneling, the forward modeling data of the tunneling machine during tunneling can be extracted based on the seismic record data during tunneling.

[0083] In this embodiment of the disclosure, when extracting forward modeling data of tunneling machine based on seismic record data, the seismic record data can be input into a pre-trained machine learning model to obtain the corresponding forward modeling data of tunneling machine. Alternatively, any other possible method can be used to extract forward modeling data of tunneling machine based on seismic record data. No limitation is imposed on this.

[0084] In this embodiment, a first number of Ricker wavelets are acquired; these first number of Ricker wavelets are superimposed to obtain the seismic source wavelet of the roadheader during tunneling; based on the seismic source wavelet of the roadheader during tunneling, the direct wave signal of the seismic event received by the geophone is determined; based on the direct wave signal of the seismic event received by the geophone, the seismic record data of the roadheader during tunneling is determined; and based on the seismic record data of the roadheader during tunneling, forward modeling data of the seismic event during tunneling is extracted. Thus, rapid acquisition of forward modeling data of the seismic event during tunneling by the roadheader can be achieved.

[0085] Figure 5 This is a flowchart illustrating the forward modeling method for seismic data generated during tunneling by a roadheader, as proposed in another embodiment of this disclosure.

[0086] like Figure 5 As shown, the forward modeling method for seismic data during tunneling by the roadheader includes:

[0087] S501: Obtain the first number of Reck subwaves.

[0088] S502: Superimpose the first number of Reck wavelets to obtain the seismic source wavelet of the tunnel boring machine during excavation.

[0089] S503: Determine the direct wave signal of the seismic source received by the detector based on the seismic source wavelet of the tunnel boring machine.

[0090] S504: Determine the seismic record data received by the geophone based on the direct wave signal of the seismic tunneling.

[0091] For a detailed description of S501-S504, please refer to the above embodiments, which will not be repeated here.

[0092] S505: Determine the data cut-off time based on the second quantity and the time interval between adjacent detector offset distances.

[0093] Understandably, in order to simulate the waveform record of the seismic data received by the detector at any given moment during tunneling as realistically as possible, it is necessary to first cut off the data from the simulated seismic reflection record during tunneling.

[0094] The data removal time refers to the point in time when data removal is required.

[0095] S506: Based on the data cut-off time and the direct wave sampling time, the target record data is obtained by cutting off the seismic record data during excavation. The target record data is non-zero data.

[0096] In other words, in this embodiment of the present disclosure, the time interval for data extraction can be determined based on the data cut-off time and the direct wave sampling time, and then non-zero data can be extracted from the seismic record data during tunneling as target record data for subsequent generation of forward modeling data of seismic tunneling machine during tunneling.

[0097] S507: Based on the target recorded data, obtain the seismic forward modeling data of the tunnel boring machine during tunneling.

[0098] Optionally, in some embodiments, when obtaining the tunneling machine's seismic forward modeling data based on the target recorded data, the seismic data received by the first geophone can be determined as reference data based on the target recorded data. Cross-correlation processing is then used based on the reference data to extract the virtual shot set of the tunneling machine's seismic data, thereby obtaining the tunneling machine's seismic forward modeling data. This effectively improves the practicality of the obtained tunneling machine's seismic forward modeling data.

[0099] Understandably, the seismic signals received by the geophones during actual tunneling are chaotic and the arrival time of the first wave cannot be identified. To simulate the waveform of the tunneling machine's seismic data received by the geophones at any given time as realistically as possible, the simulated seismic reflection record of the tunneling machine needs to be data-trimmed to extract the non-zero data volumes. Then, using the first trace (the seismic data received by the first geophone) as the reference trace, cross-correlation processing is used to extract the virtual shot gather of the tunneling machine's seismic data. As shown below:

[0100] t 切 =Δt(N-1)

[0101] S j =S j (t), t∈[t 切 ,T]

[0102]

[0103] Where S j To simulate the non-zero data in the excavated seismic records (i.e., the target record data mentioned above), such as Figure 6 As shown, Figure 6 This is a schematic diagram of the volumetric reflection seismic record based on the non-zero seismic data during excavation, as presented in this disclosure. Specifically, it is a schematic diagram of the target recorded data.

[0104] S′ j (τ) represents the forward seismic simulation data of the j-th tunnel boring machine after virtual shot collection extraction.

[0105] like Figure 7 As shown, Figure 7 This is a schematic diagram of the processing effect of the virtual shot collection during excavation based on the present disclosure.

[0106] In other words, in this embodiment of the present disclosure, after determining the seismic recording data received by the geophone, the data cut-off time can be determined based on the second quantity and the time interval between the offset distances of adjacent geophones; based on the data cut-off time and the direct wave sampling time, the seismic recording data is cut to obtain the target recording data, wherein the target recording data is non-zero data; based on the target recording data, the forward modeling data of the roadheader during tunneling is obtained. Therefore, the simulation effect of the obtained forward modeling data of the roadheader during tunneling can be effectively improved.

[0107] In this embodiment, the data cut-off time is determined based on the second quantity and the time interval between the offset distances of adjacent geophones. Based on the data cut-off time and the direct wave sampling time, the seismic recording data obtained during tunneling is cut off to obtain target recording data, which is non-zero data. Based on the target recording data, the tunneling machine's forward modeling data for seismic simulation is obtained. This effectively improves the simulation effect of the obtained forward modeling data for tunneling machine seismic simulation.

[0108] In summary, this disclosure describes a forward modeling method for seismic data generated during tunneling using a roadheader. It establishes a complete forward modeling method for seismic data generated during tunneling using a roadheader as the seismic source. First, it explains the source wavelet synthesis method generated by the vibration of the roadheader. Then, it obtains the direct wave signal of the seismic data received by the geophones during tunneling through simulation. For a forward modeling geometric model with one structural anomaly zone ahead of the probe, it solves for the reflection signal of the seismic data generated during tunneling. Using superposition and time-shift theory, it obtains the seismic record data received by 10 geophones during tunneling. Finally, it obtains usable forward modeling data of the seismic data generated during tunneling through virtual shot gather extraction technology. Figure 8 As shown, Figure 8 This is a flowchart of the forward modeling technology for seismic data from tunnel boring machines, as proposed in this disclosure.

[0109] Figure 9 This is a schematic diagram of the structure of a forward modeling device for seismic data generated during tunneling by a roadheader, as proposed in one embodiment of this disclosure.

[0110] like Figure 9 As shown, the forward modeling device 90 for seismic data collected during tunneling by the roadheader includes:

[0111] Module 901 is used to acquire the first number of Reck subwaves;

[0112] The first processing module 902 is used to superimpose a first number of Reck wavelets to obtain the seismic source wavelet of the tunnel boring machine during excavation.

[0113] The first determining module 903 is used to determine the seismic direct wave signal received by the detector based on the seismic source wavelet of the tunneling machine.

[0114] The second determining module 904 is used to determine the seismic record data received by the detector based on the seismic direct wave signal during tunneling.

[0115] The second processing module 905 is used to extract forward modeling data of tunnel boring machine seismic data based on the seismic record data during tunneling.

[0116] It should be noted that the aforementioned explanation of the forward modeling method for seismic data during tunneling by a roadheader also applies to the forward modeling device for seismic data during tunneling by a roadheader in this embodiment, and will not be repeated here.

[0117] In this embodiment, a first number of Ricker wavelets are acquired; these first number of Ricker wavelets are superimposed to obtain the seismic source wavelet of the roadheader during tunneling; based on the seismic source wavelet of the roadheader during tunneling, the direct wave signal of the seismic event received by the geophone is determined; based on the direct wave signal of the seismic event received by the geophone, the seismic record data of the roadheader during tunneling is determined; and based on the seismic record data of the roadheader during tunneling, forward modeling data of the seismic event during tunneling is extracted. Thus, rapid acquisition of forward modeling data of the seismic event during tunneling by the roadheader can be achieved.

[0118] Figure 10 A block diagram of an exemplary computer device suitable for implementing embodiments of the present disclosure is shown. Figure 10 The computer device 12 shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments disclosed herein.

[0119] like Figure 10 As shown, the computer device 12 is represented in the form of a general-purpose computing device. The components of the computer device 12 may include, but are not limited to: one or more processors or processing units 16, system memory 28, and a bus 18 connecting different system components (including system memory 28 and processing unit 16).

[0120] Bus 18 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. Examples of these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0121] Computer device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by computer device 12, including volatile and non-volatile media, removable and non-removable media.

[0122] Memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. Computer device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write non-removable, non-volatile magnetic media (…). Figure 10 Not shown; usually referred to as a "hard drive".

[0123] although Figure 10 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disc drive for reading and writing to a removable non-volatile optical disc (e.g., a Compact Disc Read-Only Memory (CD-ROM), a Digital Video Disc Read-Only Memory (DVD-ROM), or other optical media). In these cases, each drive may be connected to bus 18 via one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this disclosure.

[0124] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 42 typically perform the functions and / or methods described in the embodiments of this disclosure.

[0125] Computer device 12 can also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), and with one or more devices that enable human interaction with the computer device 12, and / or with any device that enables the computer device 12 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed via input / output (I / O) interface 22. Furthermore, computer device 12 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 20. As shown, network adapter 20 communicates with other modules of computer device 12 via bus 18. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with computer device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0126] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, such as implementing the forward modeling method for seismic data during tunneling mentioned in the foregoing embodiments.

[0127] To implement the above embodiments, this disclosure also proposes a non-transitory computer-readable storage medium storing a computer program that, when executed by a processor, implements the forward modeling method for seismic data generated during tunneling by a roadheader as proposed in the foregoing embodiments of this disclosure.

[0128] To implement the above embodiments, this disclosure also proposes a computer program product that, when executed by an instruction processor, performs a forward modeling method for seismic data generated during tunneling by a roadheader as proposed in the foregoing embodiments of this disclosure.

[0129] The collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in this disclosure all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0130] It should be noted that personal information collected from users should be used for legitimate and reasonable purposes and should not be shared or sold outside of these legitimate uses. Furthermore, such collection / sharing should only be conducted after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign an agreement / authorization that includes authorization of relevant user information before the user uses the function. In addition, any necessary steps must be taken to protect and safeguard access to such personal information data and ensure that others with access to personal information data comply with their privacy policies and procedures.

[0131] This disclosure is intended to provide implementation schemes for users to selectively prevent the use or access to their personal information data. Specifically, this disclosure is intended to provide hardware and / or software to prevent or block access to such personal information data. Once personal information data is no longer needed, risks can be minimized by restricting data collection and deleting data. Furthermore, where applicable, such personal information is de-identified to protect user privacy.

[0132] In the foregoing descriptions of the embodiments, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0133] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this disclosure, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0134] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of preferred embodiments of this disclosure includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of this disclosure pertain.

[0135] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0136] It should be understood that various parts of this disclosure can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0137] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0138] Furthermore, the functional units in the various embodiments of this disclosure can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0139] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of the present disclosure have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present disclosure. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present disclosure.

Claims

1. A forward modeling method for seismic data with mining, characterized in that, include: Obtain the first number of Rek subwaves; The first number of said Reck wavelets are superimposed to obtain the seismic source wavelet of the tunnel boring machine during excavation; Based on the seismic source wavelet of the tunnel boring machine, determine the direct wave signal of the seismic event received by the geophone during tunneling; Based on the direct wave signal of the tunneling seismic event, determine the tunneling seismic record data received by the geophone; Based on the aforementioned seismic recording data during tunneling, forward modeling data of seismic activity during tunneling by a roadheader is extracted; The step of determining the direct wave signal of the seismic source received by the geophone based on the seismic source wavelet of the tunnel boring machine includes: Determine the direct wave sampling time and the direct wave sampling time interval; The second quantity is determined based on the direct wave sampling time and the direct wave sampling time interval; Obtain the second number of source excitation coefficients; Based on the convolution processing of the source excitation coefficient and the source wavelet of the tunnel boring machine during tunneling, the continuous excitation data of the source wavelet of the tunnel boring machine during tunneling are simulated. The seismic direct wave signal obtained during excavation is obtained based on the relative position of the detector and the seismic source, the time interval of the offset distance between adjacent detectors, and the continuous excitation data. The method of extracting forward modeling data of tunnel boring machine seismic data based on the tunneling seismic record data includes: The data cut-off time is determined based on the second quantity and the time interval between the offset distances of the adjacent detectors; Based on the data cut-out time and the direct wave sampling time, the target record data is obtained by cutting out the seismic record data during excavation, wherein the target record data is non-zero data; Based on the target recorded data, the seismic forward modeling data of the tunnel boring machine during excavation is obtained.

2. The method of claim 1, wherein, The acquisition of the first number of Reich subwaves includes: Determine the first number of analog frequencies, wherein the analog frequencies belong to a preset frequency range; The corresponding Reck wavelets are generated using the simulated frequencies as the main frequencies.

3. The method of claim 1, wherein, The step of determining the seismic record data received by the geophone based on the seismic direct wave signal during tunneling includes: Determine the distance of a single structure from the seismic source, and the propagation speed of seismic waves during excavation within the coal seam; Based on the distance of the single structure from the seismic source and the propagation velocity, the lag time of the reflected wave signal relative to the direct wave signal during excavation is determined. The reflected wave signal of the seismic wave during excavation is obtained based on the lag time and the direct wave signal of the seismic wave during excavation. The direct wave signal and the reflected wave signal of the seismic tunneling are superimposed to obtain the seismic record data received by the detector when a single structure is detected ahead.

4. The method of claim 1, wherein, The step of obtaining the seismic forward modeling data of the roadheader during tunneling based on the target recorded data includes: Based on the target recorded data, the seismic data received by the first geophone during tunneling is determined as reference data; Based on the reference data, cross-correlation processing is used to extract the virtual shot set of the tunnel boring machine during tunneling, so as to obtain the forward modeling data of the tunnel boring machine during tunneling.

5. A forward modeling device for seismic data with mining, characterized in that, include: The acquisition module is used to acquire the first number of Reck subwaves; The first processing module is used to superimpose the first number of the Reck wavelets to obtain the seismic source wavelet of the tunnel boring machine during excavation. The first determining module is used to determine the direct wave signal of the seismic source received by the detector based on the seismic source wavelet of the tunneling machine. The second determining module is used to determine the seismic record data received by the geophone based on the seismic direct wave signal during tunneling. The second processing module is used to extract forward modeling data of tunneling machine seismic activity based on the tunneling seismic record data. The first determining module is specifically used for: Determine the direct wave sampling time and the direct wave sampling time interval; The second quantity is determined based on the direct wave sampling time and the direct wave sampling time interval; Obtain the second number of source excitation coefficients; Based on the convolution processing of the source excitation coefficient and the source wavelet of the tunnel boring machine during tunneling, the continuous excitation data of the source wavelet of the tunnel boring machine during tunneling are simulated. The seismic direct wave signal obtained during excavation is obtained based on the relative position of the detector and the seismic source, the time interval of the offset distance between adjacent detectors, and the continuous excitation data. The second processing module is specifically used for: The data cut-off time is determined based on the second quantity and the time interval between the offset distances of the adjacent detectors; Based on the data cut-out time and the direct wave sampling time, the target record data is obtained by cutting out the seismic record data during excavation, wherein the target record data is non-zero data; Based on the target recorded data, the seismic forward modeling data of the tunnel boring machine during excavation is obtained.

6. A computer device, comprising: include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-4.

7. A non-transitory computer readable storage medium having stored thereon computer instructions, wherein, in, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-4.

8. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the steps of the method according to any one of claims 1-4.

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