CN114509755A - Coal monitoring method, equipment and storage medium based on reverse time migration imaging algorithm - Google Patents
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Abstract
The application discloses a coal monitoring method, equipment and a storage medium based on a reverse time migration imaging algorithm, which comprises the following steps: acquiring source wave field data transmitted at each moment of a target area, and receiving reflected echo data corresponding to the source wave field data; determining target receiving data based on the reflected echo data and the pre-detected receiving data, and performing reverse continuation on the target receiving data based on a preset pseudo-spectrum time domain algorithm to obtain receiving wave field data of each moment in a target area; performing wave field decomposition on the source wave field data and the receiving wave field data to obtain decomposed source wave field data and decomposed receiving wave field data; performing cross-correlation imaging on the decomposed source wave field data and the decomposed receiving wave field data at the same moment to obtain cross-correlation imaging results; and performing filtering processing on each cross-correlation imaging result to determine a final reconstructed image of the target area. The application solves the technical problem of low monitoring accuracy of the complex geological structure.
Description
Technical Field
The application relates to the technical field of physical exploration, in particular to a coal monitoring method and device based on a reverse time migration imaging algorithm and a storage medium.
Background
The underground coal environment is complex, in order to avoid coal accidents, an underground disease body needs to be detected in advance, namely, the position and the shape of the disease body need to be detected in advance, at present, a ground penetrating radar detection method is generally used for detecting the underground disease body, and a traditional ground penetrating radar migration method mainly comprises Kirchhoff migration and one-way wave equation migration, wherein the one-way wave equation is used for performing one-way wave decomposition on the basis of a two-way wave equation, and is only accurately established under the condition of constant speed, high steep dip angle cannot be well imaged in a complex geological abnormal body, and Kirchhoff migration can be imaged in a steep dip angle stratum, but the complex structure is usually accompanied with strong transverse speed change, and the imaging homing error is large, so that the monitoring accuracy is low.
Disclosure of Invention
The application mainly aims to provide a coal monitoring method, equipment and a storage medium based on a reverse time migration imaging algorithm, and aims to solve the technical problem that in the prior art, the monitoring accuracy of a complex geological structure is low.
In order to achieve the above object, the present application provides a coal monitoring method based on a reverse time migration imaging algorithm, where the coal monitoring method based on the reverse time migration imaging algorithm includes:
acquiring source wave field data transmitted at each moment of a target area, and receiving reflected echo data corresponding to the source wave field data;
determining target receiving data based on the reflected echo data and the receiving data detected in advance, and performing reverse continuation on the target receiving data based on a preset pseudo-spectrum time domain algorithm to obtain receiving wave field data of each moment in the target area;
performing wave field decomposition on the source wave field data and the receiving wave field data to obtain decomposed source wave field data and decomposed receiving wave field data;
performing cross-correlation imaging on the decomposed source wave field data and the decomposed receiving wave field data at the same moment to obtain cross-correlation imaging results;
and performing filtering processing on each cross-correlation imaging result to determine a final reconstructed image of the target area.
The application also provides a coal monitoring system based on reverse time migration imaging algorithm, coal monitoring system based on reverse time migration imaging algorithm is virtual system, coal monitoring system based on reverse time migration imaging algorithm includes:
the acquisition module is used for acquiring source wave field data transmitted at each moment of a target area and receiving reflected echo data corresponding to the source wave field data;
the reverse continuation module is used for determining target receiving data based on the reflected echo data and the receiving data detected in advance, and performing reverse continuation on the target receiving data based on a preset pseudo-spectrum time domain algorithm to obtain receiving wave field data of each moment in the target area;
the wave field decomposition module is used for carrying out wave field decomposition on the source wave field data and the receiving wave field data to obtain each decomposed source wave field data and each decomposed receiving wave field data;
the cross-correlation imaging module is used for carrying out cross-correlation imaging on the decomposed source wave field data and the decomposed receiving wave field data at the same moment to obtain cross-correlation imaging results;
and the filtering module is used for carrying out filtering processing on each cross-correlation imaging result to determine a final reconstructed image of the target area.
The application also provides a coal monitoring equipment based on reverse time migration imaging algorithm, coal monitoring equipment based on reverse time migration imaging algorithm is entity equipment, coal monitoring equipment based on reverse time migration imaging algorithm includes: the coal monitoring method comprises a memory, a processor and a coal monitoring program based on a reverse time migration imaging algorithm, wherein the coal monitoring program based on the reverse time migration imaging algorithm is stored on the memory and is executed by the processor to realize the steps of the coal monitoring method based on the reverse time migration imaging algorithm.
The application also provides a storage medium which is a computer readable storage medium, wherein a coal monitoring program based on the reverse time migration imaging algorithm is stored on the computer readable storage medium, and the coal monitoring program based on the reverse time migration imaging algorithm is executed by a processor to realize the steps of the coal monitoring method based on the reverse time migration imaging algorithm.
The application provides a coal monitoring method, equipment and a storage medium based on a reverse time migration imaging algorithm, the method comprises the steps of firstly obtaining source wave field data emitted at each moment of a target area, receiving reflected echo data corresponding to the source wave field data, further determining target receiving data based on the reflected echo data and pre-detected receiving data, carrying out reverse continuation on the target receiving data based on a preset pseudo-spectrum time domain algorithm, obtaining receiving wave field data at each moment in the target area, further carrying out wave field decomposition on the source wave field data and the receiving wave field data, obtaining decomposition source wave field data and decomposition receiving wave field data, further carrying out cross-correlation wave field imaging on the decomposition source wave field data and the decomposition receiving wave field data at the same moment, obtaining cross-correlation imaging results, and further, and filtering each cross-correlation imaging result to determine a final reconstructed image of a target area, so that low-frequency noise generated in the reverse time migration process is eliminated by decomposing the source wave field data and the received wave field data, the identification capability of the steep dip angle structure is improved, the cross-correlation imaging is further filtered, the imaging resolution is improved, and the monitoring accuracy of a complex geological structure is improved.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious to those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
FIG. 1 is a schematic flow chart of a first embodiment of a coal monitoring method based on a reverse time migration imaging algorithm according to the present application;
FIG. 2 is a schematic diagram illustrating propagation of an excitation source wave field and a receiving wave field in the coal monitoring method based on the reverse time migration imaging algorithm;
FIG. 3 is a flowchart of a reverse time migration imaging method based on pseudo-spectral time domain algorithm according to the present application;
FIG. 4 is a schematic flow chart of a coal monitoring method based on a reverse time migration imaging algorithm according to a second embodiment of the present application;
FIG. 5 is a schematic flow chart of a coal monitoring method based on a reverse time migration imaging algorithm according to a third embodiment of the present application;
FIG. 6 is a schematic structural diagram of a coal monitoring device based on a reverse time migration imaging algorithm in a hardware operating environment according to an embodiment of the present application;
FIG. 7 is a schematic diagram of functional modules of a coal monitoring device based on a reverse time migration imaging algorithm according to the present application.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
In a first embodiment of the coal monitoring method based on the reverse time migration imaging algorithm, referring to fig. 1, the coal monitoring method based on the reverse time migration imaging algorithm includes:
step S10, acquiring source wave field data emitted at each moment of a target area, and receiving reflected echo data corresponding to the source wave field data;
in this embodiment, it should be noted that the target area may be the entire exploration area or a partial area in the entire exploration area, and a plurality of excitation sources and a plurality of detectors are disposed on a surface of the target area, where the excitation sources are configured to emit source wave signals, the detectors are configured to receive signal data reflected by the emitted signals, and the reflected echo data includes reflection information of a target body in the current target area, information of direct waves propagated by the excitation sources, information data of interface layered reflection, and the like.
The method comprises the steps of obtaining source wave field data transmitted at each moment of a target area, receiving reflected echo data corresponding to the source wave field data, specifically, based on a preset pseudo-spectrum time domain algorithm, extending the source wave field data transmitted by an excitation source from a minimum time to a preset maximum time according to a preset time step forward, so that the source wave field data are discretely transmitted in different directions along with time in a wave field extending process, and storing the source wave field data at each moment, wherein the preset time step is a time slice for transmission, and understandably, the larger the time step is, the faster the transmission speed is in the same total transmission time.
Step S20, determining target receiving data based on the reflected echo data and the receiving data detected in advance, and performing reverse continuation on the target receiving data based on a preset pseudo-spectrum time domain algorithm to obtain receiving wave field data of each moment in the target area;
in this embodiment, it should be noted that the received data detected in advance is reflection data received by detecting a target area not including an abnormal body in advance.
The method comprises the steps of determining target receiving data based on the reflected echo data and the pre-detected receiving data, conducting reverse continuation on the target receiving data based on a pre-set pseudo-spectrum time domain algorithm to obtain receiving wave field data of each moment in a target area, specifically, calculating a difference value between the reflected echo data and the pre-detected receiving data to obtain the target receiving data, namely, obtaining data only containing reflection information of a target body, reducing influence of clutter and enabling imaging effect of the target body to be clearer, and then conducting reverse continuation on the target receiving data from a preset maximum time to a preset minimum time based on the pre-set pseudo-spectrum time domain algorithm to obtain the receiving wave field data of each moment in the target area.
Step S30, performing wave field decomposition on the source wave field data and the receiving wave field data to obtain each decomposed source wave field data and each decomposed receiving wave field data;
in this embodiment, it should be noted that the method for performing wavefield decomposition includes a Hilbert transform method, a fourier transform decomposition method, and the like, and preferably, the Hilbert transform method is selected, and the source wavefield data and the received wavefield data have the same direction, as shown in fig. 2, FIG. 2 is a schematic diagram showing the propagation of the excitation source wave field and the receiving wave field in the coal monitoring method based on the reverse time migration imaging algorithm, wherein, FIG. 2(a) is a schematic diagram of excitation source wavelength propagation, FIG. 2(b) is a schematic diagram of receiving wave field propagation, it will be appreciated that the source wavefield data and the receive wavefield data have opposite projections in a direction normal to the reflection interface, each said decomposed source wavefield data comprising an up-going source wavefield data and a down-going source wavelength data, each said decomposed receive wavefield data comprising an up-going receive wavefield data and a down-going receive wavefield data.
And performing wave field decomposition on the source wave field data and the receiving wave field data to obtain decomposed source wave field data and decomposed receiving wave field data, specifically, performing up-down wave decomposition on the source wave field data and the receiving wave field data according to the propagation directions of the source wave field data and the receiving wave field data, and further obtaining up-going source wave field data, down-going source wavelength data, up-going wave field receiving data and down-going receiving wave field data.
Step S40, performing cross-correlation imaging on the decomposed source wave field data and the decomposed receiving wave field data at the same moment to obtain cross-correlation imaging results;
in this embodiment, it should be noted that, in the process of forward extending the source wave field data, the dispersion in the source wave field data gradually increases, in the process of backward extending the received wave field data, the dispersion in the received wave field data gradually decreases, and when the source wave field data and the received wave field data are cross-correlated at each time, the dispersion in the source wave field data and the dispersion in the received wave field data cancel each other, so that the dispersion phenomenon does not occur in the obtained offset image.
It should be further noted that the correlation between the source wavefield data and the received wavefield data after performing wavefield separation is related to the normal projection of the propagation direction, and when the decomposed source wavefield data and the decomposed received wavefield data have opposite projection directions, the two wavefields will produce imaging at the reflection point and will not produce imaging at the non-reflection point.
Performing cross-correlation imaging on decomposed source wave field data and decomposed receiving wave field data at the same moment to obtain cross-correlation imaging results, specifically, performing cross-correlation imaging on decomposed source wave field data and decomposed receiving wave field data with opposite propagation directions at the same moment, namely performing cross-correlation imaging on uplink source wave field data and downlink receiving wave field data at the same moment, and performing cross-correlation imaging on downlink source wavelength data and uplink receiving wave field data to obtain cross-correlation imaging results, wherein the cross-correlation imaging formula is as follows:
IMAGE(x,z)＝∑tS(x,z,t)R(x,z,t)
where IMAGE (x, z) is the single excitation source final imaging value, S (x, z, t), R (x, z, t) are the source wavefield data and the receive wavefield data, respectively, x, z are two-dimensional computed spatial coordinates, and t is the wavefield value travel time.
Step S50, performing filtering processing on each of the cross-correlation imaging results to determine a final reconstructed image of the target region.
In this embodiment, it should be noted that the filtering process is performed to reduce low-frequency noise so as to obtain a high-resolution reconstructed image.
Wherein the step of performing filtering processing on each cross-correlation imaging result to determine a final reconstructed image of the target region includes:
and step S51, based on a preset filtering algorithm, performing square operation on each cross-correlation imaging result, and overlapping the cross-correlation imaging results after filtering along a time axis to obtain a final reconstructed image of the target area.
In this embodiment, it should be noted that the preset filtering algorithm includes filtering algorithms such as a functional method filtering method, a median filtering method, an arithmetic mean filtering method, and the like, and preferably, the functional method filtering algorithm is selected, because the cross-correlation imaging result in the present application is discretized, the correlation at the imaging position based on the cross-correlation imaging result is 1, the correlation at the non-imaging point is zero, and the low-frequency noise is based between 0 and 1, and then the low-frequency noise becomes smaller by performing a value after squaring through the functional method filtering algorithm, and then the final reconstructed image with high resolution is obtained by screening, so that the influence of the low-frequency noise is reduced, and the imaging resolution is improved.
Further, referring to fig. 3, fig. 3 is a flowchart of a reverse time migration imaging based on a pseudo-spectral time domain algorithm according to the present application, where a signal source is source wave field data transmitted by the excitation source, a source wave field is evolved into the source wave field data for forward continuation, t is 0 as a minimum time, tmax is a maximum time, Δ t is the preset time step, receiving point data 2 is the reflected echo data, receiving point data 1 is the pre-detected receiving data, receiving data is the target receiving data, a receiving wavelength is reversely extrapolated to the target receiving data for reverse continuation, an imaging condition is applied at the same time to perform cross-correlation imaging on decomposed source wave field data and decomposed receiving wave field data at the same time, and the cross-correlation imaging result after filtering is superimposed along a time axis, specifically, based on a wipu time domain algorithm, the method comprises the steps of forward extending source wave field data transmitted by an excitation source from minimum time to preset maximum time according to a preset time step, storing the source wave field data at each moment, performing difference on reflected callback data received by a detector and pre-detected received data without a target abnormal body to obtain target received data, further forward extending the target received data from maximum time to minimum time according to the preset time step, storing the received wave field data at each moment, performing wave field decomposition on the source wave field data and the received wave field data, performing cross-correlation imaging on the decomposed source wave field data and the decomposed received wave field data at the same moment, and superposing the filtered cross-correlation imaging results according to a time axis to determine a final reconstructed image of a target area.
After the step of performing filtering processing on each cross-correlation imaging result to determine a final reconstructed image of the target region, the method further includes:
step A10, performing three-dimensional reconstruction on the final reconstructed image to obtain abnormal information of a target area;
and step A20, analyzing the abnormal information, and counting and displaying the analysis result on the target web platform.
In the present embodiment, the abnormality information includes information such as a specific position of an abnormal body in the target region and morphological size information.
Specifically, the final reconstruction result is subjected to three-dimensional reconstruction and three-dimensional display, so that abnormal information such as specific positions and shapes of regions where water inrush and mud inrush may occur is detected, qualitative and/or quantitative analysis can be performed by coal mine safety detection personnel based on the abnormal information, an analysis result is obtained, the analysis result is displayed on a web platform, visualization of a coal structure is achieved, and the occurrence of underground water inrush disasters is prevented.
The embodiment of the application provides a coal monitoring method based on a reverse time migration imaging algorithm, firstly, source wave field data transmitted at each moment of a target area is obtained, reflected echo data corresponding to the source wave field data are received, then, target receiving data are determined based on the reflected echo data and pre-detected receiving data, reverse continuation is carried out on the target receiving data based on a preset pseudo-spectrum time domain algorithm, receiving wave field data at each moment in the target area are obtained, further, wave field decomposition is carried out on the source wave field data and the receiving wave field data, decomposition source wave field data and decomposition receiving wave field data are obtained, further, cross-correlation imaging is carried out on the decomposition source wave field data and the decomposition receiving wave field data at the same moment, cross-correlation imaging results are obtained, and further, and filtering each cross-correlation imaging result to determine a final reconstructed image of a target area, so that low-frequency noise generated in the reverse time migration process is eliminated by decomposing the source wave field data and the received wave field data, the identification capability of the steep dip angle structure is improved, the cross-correlation imaging is further filtered, the imaging resolution is improved, and the monitoring accuracy of a complex geological structure is improved.
Further, referring to fig. 4, based on the first embodiment of the present application, in another embodiment of the present application, the step of acquiring source wave field data transmitted at each time of the target area includes:
and step B10, based on a preset pseudo-spectrum time domain algorithm, extending the source wave field data transmitted by the excitation source from the minimum time to the preset maximum time from the preset time step forward, and storing the source wave field data at each moment.
In this embodiment, it should be noted that, in the present application, a propagation rate of an inverse time migration algorithm is improved, a finite time domain difference algorithm is replaced with a pseudo-spectrum time domain algorithm in a propagation continuation process, a main idea of the pseudo-spectrum time domain algorithm is to use fourier transform and inverse transform to replace differentiation in a spatial domain, first, a relationship between triangular interpolation and a fast fourier transform algorithm is derived, and a calculation period T ═ x is setmax-xminX for sampling pointseIndicates that there is
xe＝xmin+(t-1)Δx,t＝0,...,N-1
And Δ x ═ T/N, where periodic functions are represented by Fourier sequences, then
Fourier coefficient fnIt can be calculated from the following equation:
thus, it is possible to obtain:
namely, a form of replacing differentiation by Fourier transformation and inverse transformation is adopted, numerical simulation is further carried out on the basis of Maxwell rotation equation, in a pseudo-spectrum time domain algorithm, spatial derivatives are represented by Fourier transformation, and a staggered differential grid consistent with a finite time domain differential algorithm is still adopted for time differentiation:
to eliminate the periodic effects implicit in the FFT algorithm, perfect matching layer absorption boundary condition PML is used, while to match the setup of the medium PML layer, an alternative formula with stretched coordinates is used:
wherein, aηIs a scale factor, ωηRepresenting loss in PML, operator
In the frequency domain, replace:
and further deducing a Maxwell splitting equation with PML:
since only E exists in TM modex、Ey、HzThese three components, and
substituting the spatial Fourier transform for the differential into the derivation, and obtaining a two-dimensional Fourier pseudo-spectrum time domain algorithm, wherein the update equation is as follows:
where (i, j) is a spatial index, n is a temporal index, fx，fx -1The fast fourier transform and the inverse fourier transform are respectively shown for the x direction, and the other directions are similar.
The finite time domain algorithm only needs to sample 8 field values in the minimum wavelength to replace differentiation (Maxwell differential equation) to carry out wave field propagation, only 2 field values are sampled in the minimum wavelength of the pseudo-spectrum method, namely the pseudo-spectrum method has low sampling density, and the sampling density is in direct proportion to the sampling time, namely the pseudo-spectrum method has longer time step, so that the wave field propagation speed is improved, and the calculation efficiency of reverse time migration is greatly improved.
Further, referring to fig. 5, in another embodiment of the present application, based on the first embodiment of the present application, the step of performing cross-correlation imaging on the decomposed source wavefield data and the decomposed receive wavefield data at the same time to obtain each cross-correlation imaging result includes:
step C10, performing cross-correlation imaging on the decomposed source wave field data subjected to the 1 st forward continuation and the decomposed received wave field data subjected to the Mth backward continuation to obtain a first cross-correlation imaging result;
and step C20, performing cross-correlation imaging on the decomposed source wave field data subjected to the 2 nd forward continuation and the decomposed receiving wave field data subjected to the M-1 st reverse continuation to obtain a second cross-correlation imaging result until the decomposed source wave field data subjected to the M-th forward continuation and the decomposed receiving wave field data subjected to the 1 st reverse continuation are subjected to cross-correlation imaging to obtain each cross-correlation imaging result, wherein M is the time step number for performing reverse time migration imaging.
In this embodiment, it should be noted that, during the forward continuation, the minimum time is extended forward to the preset maximum time according to the preset time step, and during the reverse continuation, the maximum time is extended backward to the preset minimum time according to the preset time step, so that the time of the step 1 of performing the forward continuation is the same as the time of the last step of performing the reverse continuation, M is the number of time steps of performing the reverse time shift imaging, and the number of time steps is obtained by dividing the difference between the maximum time and the minimum time by the preset time step.
Specifically, based on the decomposed source wave field data subjected to the 1 st forward continuation and the decomposed receiving wave field data subjected to the M th backward continuation, cross-correlation is performed on the uplink source wave field data and the downlink receiving wave field data in opposite directions, cross-correlation imaging is performed on the downlink source wave field data and the uplink receiving wave field data, so that a first cross-correlation imaging result is obtained, further, cross-correlation imaging is performed on the decomposed source wave field data subjected to the 2 nd forward continuation and the decomposed receiving wave field data subjected to the M-1 st backward continuation, a second cross-correlation imaging result is obtained, and until the decomposed source wave field data subjected to the M th forward continuation and the decomposed receiving wave field data subjected to the 1 st backward continuation are subjected to cross-correlation imaging, so that each cross-correlation imaging result is obtained.
Performing cross-correlation imaging on the decomposed source wave field data subjected to the forward continuation in the step 1 and the decomposed received wave field data subjected to the reverse continuation in the step M, and obtaining a first cross-correlation imaging result, wherein the step B comprises the following steps of:
step C11, performing cross-correlation imaging on the uplink source wave field data of the step 1 and the downlink receiving wave field data of the step M to obtain a first opposite cross-correlation result, and performing cross-correlation imaging on the downlink source wave field data of the step 1 and the uplink receiving wave field data of the step M to obtain a second opposite cross-correlation result;
step C12, accumulating the first inverse cross-correlation result and the second imaging result to obtain the cross-correlation imaging result.
In this embodiment, it should be noted that, according to the ray theory, at some reflection points, an incident wave is a down wave, a reflected wave is an up wave, and at some reflection points, an incident wave is an up wave, and a reflected wave is a down wave, and therefore, in the cross-correlation imaging process, the method includes: the method comprises the steps of performing cross-correlation imaging on a downlink wave field of source wave field data and an uplink wave field of receiving wave field data, performing cross-correlation imaging on an uplink wave field of the source wave field data and a downlink wave field of the receiving wave field data, performing cross-correlation imaging on a downlink wave field of the source wave field data and a downlink wave field of the receiving wave field data, performing cross-correlation imaging on an uplink wave field of the source wave field data and an uplink wave field of the receiving wave field data, and generating low-frequency noise by performing cross-correlation imaging on the downlink wave field of the source wave field data and the uplink wave field of the receiving wave field data and performing cross-correlation imaging on the uplink wave field of the receiving wave field data.
Referring to fig. 6, fig. 6 is a schematic structural diagram of a coal monitoring device based on a reverse time migration imaging algorithm in a hardware operating environment according to an embodiment of the present application.
As shown in FIG. 6, the coal monitoring device based on the reverse time migration imaging algorithm can comprise: a processor 1001, such as a CPU, a memory 1005, and a communication bus 1002. The communication bus 1002 is used to realize connection and communication between the processor 1001 and the memory 1005. The memory 1005 may be a high-speed RAM memory or a non-volatile memory (e.g., a magnetic disk memory). The memory 1005 may alternatively be a memory device separate from the processor 1001 described above.
Optionally, the coal monitoring device based on the reverse time migration imaging algorithm may further include a rectangular user interface, a network interface, a camera, a Radio Frequency (RF) circuit, a sensor, an audio circuit, a WiFi module, and the like. The rectangular user interface may comprise a Display screen (Display), an input sub-module such as a Keyboard (Keyboard), and the optional rectangular user interface may also comprise a standard wired interface, a wireless interface. The network interface may optionally include a standard wired interface, a wireless interface (e.g., WIFI interface).
Those skilled in the art will appreciate that the reverse time migration imaging algorithm based coal monitoring equipment configuration shown in FIG. 6 does not constitute a limitation of reverse time migration imaging algorithm based coal monitoring equipment, and may include more or fewer components than shown, or some components in combination, or a different arrangement of components.
As shown in fig. 6, a memory 1005, which is a kind of computer storage medium, may include an operating system, a network communication module, and a coal monitoring program based on a reverse time migration imaging algorithm. The operating system is a program for managing and controlling hardware and software resources of the coal monitoring equipment based on the reverse time migration imaging algorithm, and supports the running of the coal monitoring program based on the reverse time migration imaging algorithm and other software and/or programs. The network communication module is used for realizing communication among components in the memory 1005 and communication with other hardware and software in the coal monitoring system based on the reverse time migration imaging algorithm.
In the coal monitoring device based on the reverse time migration imaging algorithm shown in fig. 6, the processor 1001 is configured to execute the coal monitoring program based on the reverse time migration imaging algorithm stored in the memory 1005, and implement any of the steps of the coal monitoring method based on the reverse time migration imaging algorithm described above.
The specific implementation manner of the coal monitoring equipment based on the reverse time migration imaging algorithm is basically the same as that of each embodiment of the coal monitoring method based on the reverse time migration imaging algorithm, and is not described herein again.
In addition, referring to fig. 7, fig. 7 is a schematic functional module diagram of the coal monitoring device based on the reverse time migration imaging algorithm according to the present application, and the present application further provides a coal monitoring system based on the reverse time migration imaging algorithm, where the coal monitoring system based on the reverse time migration imaging algorithm includes:
the acquisition module is used for acquiring source wave field data transmitted at each moment of a target area and receiving reflected echo data corresponding to the source wave field data;
the reverse continuation module is used for determining target receiving data based on the reflected echo data and the receiving data detected in advance, and performing reverse continuation on the target receiving data based on a preset pseudo-spectrum time domain algorithm to obtain receiving wave field data of each moment in the target area;
the wave field decomposition module is used for carrying out wave field decomposition on the source wave field data and the receiving wave field data to obtain each decomposed source wave field data and each decomposed receiving wave field data;
the cross-correlation imaging module is used for carrying out cross-correlation imaging on the decomposed source wave field data and the decomposed receiving wave field data at the same moment to obtain cross-correlation imaging results;
and the filtering module is used for carrying out filtering processing on each cross-correlation imaging result to determine a final reconstructed image of the target area.
Optionally, the obtaining module is further configured to:
based on a preset pseudo-spectrum time domain algorithm, the source wave field data transmitted by the excitation source is extended forward from the minimum time to the preset maximum time according to the preset time step, and the source wave field data at each moment are stored.
Optionally, the reverse continuation module is further configured to:
and reversely extending the target receiving data from the preset maximum time to the minimum time based on a preset pseudo-spectrum time domain algorithm, and storing the receiving wave field data at each moment.
Optionally, the cross-correlation imaging module is further configured to:
performing cross-correlation imaging on the decomposed source wave field data subjected to the forward continuation in the step 1 and the decomposed receiving wave field data subjected to the reverse continuation in the step M to obtain a first cross-correlation imaging result;
and performing cross-correlation imaging on the decomposed source wave field data subjected to the 2 nd forward continuation and the decomposed receiving wave field data subjected to the M-1 st reverse continuation to obtain a second cross-correlation imaging result until the decomposed source wave field data subjected to the M-th forward continuation and the decomposed receiving wave field data subjected to the 1 st reverse continuation are subjected to cross-correlation imaging to obtain each cross-correlation imaging result, wherein M is the time step number for performing reverse time migration imaging.
Optionally, the cross-correlation imaging module is further configured to:
performing cross-correlation imaging on the uplink source wave field data of the step 1 and the downlink receiving wave field data of the step M to obtain a first opposite cross-correlation result, and performing cross-correlation imaging on the downlink source wave field data of the step 1 and the uplink receiving wave field data of the step M to obtain a second opposite cross-correlation result;
and accumulating the first and second inverse cross-correlation results to obtain the first cross-correlation imaging result.
Optionally, the filtering module is further configured to:
and based on a preset filtering algorithm, performing square operation on each cross-correlation imaging result to obtain a final reconstructed image of the target area.
Optionally, the coal monitoring system based on the reverse time migration imaging algorithm is further configured to:
performing three-dimensional reconstruction on the final reconstructed image to obtain abnormal information of a target area;
and analyzing the abnormal information, and counting and displaying an analysis result on a target web platform.
The specific implementation of the coal monitoring system based on the reverse time migration imaging algorithm is basically the same as that of each embodiment of the coal monitoring method based on the reverse time migration imaging algorithm, and is not described herein again.
The present application provides a storage medium, which is a computer-readable storage medium, and the computer-readable storage medium stores one or more programs, which are further executable by one or more processors for implementing the steps of any one of the above-mentioned coal monitoring methods based on the reverse time migration imaging algorithm.
The specific implementation of the computer-readable storage medium of the present application is substantially the same as the above-mentioned embodiments of the coal monitoring method based on the reverse time migration imaging algorithm, and is not described herein again.
The above description is only a preferred embodiment of the present application, and not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings, or which are directly or indirectly applied to other related technical fields, are included in the scope of the present application.
Claims (10)
1. A coal monitoring method based on a reverse time migration imaging algorithm is characterized by comprising the following steps:
acquiring source wave field data transmitted at each moment of a target area, and receiving reflected echo data corresponding to the source wave field data;
determining target receiving data based on the reflected echo data and the receiving data detected in advance, and performing reverse continuation on the target receiving data based on a preset pseudo-spectrum time domain algorithm to obtain receiving wave field data of each moment in the target area;
performing wave field decomposition on the source wave field data and the receiving wave field data to obtain decomposed source wave field data and decomposed receiving wave field data;
performing cross-correlation imaging on the decomposed source wave field data and the decomposed receiving wave field data at the same moment to obtain cross-correlation imaging results;
and performing filtering processing on each cross-correlation imaging result to determine a final reconstructed image of the target area.
2. The coal monitoring method based on the reverse time migration imaging algorithm according to claim 1, wherein the step of obtaining the source wave field data transmitted at each moment of the target area comprises:
based on a preset pseudo-spectrum time domain algorithm, the source wave field data transmitted by the excitation source is extended forward from the minimum time to the preset maximum time according to the preset time step, and the source wave field data at each moment are stored.
3. The coal monitoring method based on the reverse time migration imaging algorithm as claimed in claim 1, wherein the step of performing reverse continuation on the target received data to obtain the received wave field data at each moment comprises:
and reversely extending the target receiving data from the preset maximum time to the minimum time based on a preset pseudo-spectrum time domain algorithm, and storing the receiving wave field data at each moment.
4. The method for coal monitoring based on reverse time migration imaging algorithm as claimed in any one of claims 1 to 3, wherein said step of performing cross-correlation imaging on decomposed source wave field data and decomposed received wave field data at the same time, and obtaining each cross-correlation imaging result comprises:
performing cross-correlation imaging on the decomposed source wave field data subjected to the forward continuation in the step 1 and the decomposed receiving wave field data subjected to the reverse continuation in the step M to obtain a first cross-correlation imaging result;
and performing cross-correlation imaging on the decomposed source wave field data subjected to the 2 nd forward continuation and the decomposed receiving wave field data subjected to the M-1 st reverse continuation to obtain a second cross-correlation imaging result until the decomposed source wave field data subjected to the M-th forward continuation and the decomposed receiving wave field data subjected to the 1 st reverse continuation are subjected to cross-correlation imaging to obtain each cross-correlation imaging result, wherein M is the time step number for performing reverse time migration imaging.
5. The reverse time migration imaging algorithm-based coal monitoring method of claim 4, wherein the decomposed source wavefield data comprises up-going source wavefield data and down-going source wavelength data, and the decomposed receive wavefield data comprises up-going receive wavefield data and down-going receive wavefield data.
6. The coal monitoring method based on the reverse-time migration imaging algorithm as claimed in claim 5, wherein the step of performing cross-correlation imaging on the decomposed source wave field data subjected to the 1 st forward continuation and the decomposed received wave field data subjected to the M-th reverse continuation comprises the steps of:
performing cross-correlation imaging on the uplink source wave field data of the step 1 and the downlink receiving wave field data of the step M to obtain a first opposite cross-correlation result, and performing cross-correlation imaging on the downlink source wave field data of the step 1 and the uplink receiving wave field data of the step M to obtain a second opposite cross-correlation result;
and accumulating the first and second inverse cross-correlation results to obtain the first cross-correlation imaging result.
7. The coal monitoring method based on reverse-time migration imaging algorithm as claimed in claim 1, wherein said step of filtering each of said cross-correlation imaging results to determine a final reconstructed image of the target region comprises:
and based on a preset filtering algorithm, performing square operation on each cross-correlation imaging result to obtain a final reconstructed image of the target area.
8. The coal monitoring method based on reverse time migration imaging algorithm as claimed in claim 1, wherein after said step of filtering each said cross-correlation imaging result to determine a final reconstructed image of the target region, further comprising:
performing three-dimensional reconstruction on the final reconstructed image to obtain abnormal information of a target area;
and analyzing the abnormal information, and counting and displaying an analysis result on a target web platform.
9. A coal monitoring device based on a reverse time migration imaging algorithm is characterized by comprising: a memory, a processor and a coal monitoring program based on a reverse time migration imaging algorithm stored on the memory,
the coal monitoring program based on the reverse time migration imaging algorithm is executed by the processor to realize the coal monitoring method based on the reverse time migration imaging algorithm according to any one of claims 1 to 8.
10. A storage medium which is a computer readable storage medium, wherein the computer readable storage medium stores thereon a coal monitoring program based on a reverse time migration imaging algorithm, and the coal monitoring program based on the reverse time migration imaging algorithm is executed by a processor to implement the coal monitoring method based on the reverse time migration imaging algorithm according to any one of claims 1 to 8.
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