WO2017133361A1 - 一种煤炭陷落柱识别方法和装置 - Google Patents

一种煤炭陷落柱识别方法和装置 Download PDF

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WO2017133361A1
WO2017133361A1 PCT/CN2016/113064 CN2016113064W WO2017133361A1 WO 2017133361 A1 WO2017133361 A1 WO 2017133361A1 CN 2016113064 W CN2016113064 W CN 2016113064W WO 2017133361 A1 WO2017133361 A1 WO 2017133361A1
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amplitude value
single shot
seismic
shot data
data
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PCT/CN2016/113064
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English (en)
French (fr)
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赵惊涛
彭苏萍
杜文凤
李晓婷
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中国矿业大学(北京)
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Priority to US15/758,559 priority Critical patent/US10302788B2/en
Publication of WO2017133361A1 publication Critical patent/WO2017133361A1/zh

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
    • G01V1/30—Analysis
    • G01V1/307—Analysis for determining seismic attributes, e.g. amplitude, instantaneous phase or frequency, reflection strength or polarity
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
    • G01V1/30—Analysis
    • G01V1/301—Analysis for determining seismic cross-sections or geostructures
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
    • G01V1/36—Effecting static or dynamic corrections on records, e.g. correcting spread; Correlating seismic signals; Eliminating effects of unwanted energy
    • G01V1/362—Effecting static or dynamic corrections; Stacking
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
    • G01V1/30—Analysis
    • G01V1/303—Analysis for determining velocity profiles or travel times
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/50—Corrections or adjustments related to wave propagation
    • G01V2210/51—Migration
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/60—Analysis
    • G01V2210/62—Physical property of subsurface
    • G01V2210/622—Velocity, density or impedance
    • G01V2210/6222—Velocity; travel time
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/60—Analysis
    • G01V2210/64—Geostructures, e.g. in 3D data cubes
    • G01V2210/641—Continuity of geobodies
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/60—Analysis
    • G01V2210/64—Geostructures, e.g. in 3D data cubes
    • G01V2210/642—Faults
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/60—Analysis
    • G01V2210/64—Geostructures, e.g. in 3D data cubes
    • G01V2210/646—Fractures

Definitions

  • the invention relates to the field of seismic data imaging, in particular to a method and a device for identifying a coal collapse column based on seismic diffraction wave imaging.
  • discontinuous geological bodies such as faults, faults and subsided columns will destroy the continuity of the coal seam, and it is easy to induce accidents such as water seepage and gas outburst, which seriously threaten the safety of coal mine operations. Therefore, it effectively identifies faults, faults and subsided columns. Waiting for discontinuous geological bodies is of great significance.
  • the related technology adopts seismic wave imaging technology to identify the collapse column.
  • the specific method includes collecting seismic gun data, and imaging the reflected wave in the seismic gun data through the existing seismic wave imaging technology, and adopting the reflected wave
  • the imaging processing results are identified by the coal collapse column.
  • the reflected wave is a macroscopic geological unit response, and usually only solves the geological body exploration problem in which the spatial distribution is larger than one seismic wavelength, and the small-scale geological information such as the coal collapse column The recognition effect is poor. Therefore, the seismic wave imaging technology developed for reflected waves is not effective for small-scale geological information imaging such as collapse columns, and can not identify small-scale geological information such as coal collapse columns.
  • the object of the present invention is to provide a method and a device for identifying a coal collapse column, which can extract the diffraction wave of the smaller-scale geological information carried in the acquired seismic gun data, and accurately evaluate the collapse column by the imaged diffraction wave.
  • the geological body so as to accurately identify the coal collapse column in the target area.
  • an embodiment of the present invention provides a method for identifying a coal collapse column, including:
  • the seismic wave detection point of each single shot data position of each single shot data is calculated through the position of any imaging point in the underground imaging space to each single shot data.
  • the Mahalanobis distance calculation process is performed on the diffraction wave travel time of each single shot data and the single shot data, and the diffraction wave amplitude value sample of each single shot data is obtained; wherein the diffraction wave amplitude value sample carries a small scale Geological information; small-scale geological information includes at least: stratigraphic horizon information, fault information, and collapse column information;
  • the diffraction wave amplitude value samples of each single shot data are respectively imaged, and the diffraction wave imaging result of each single shot data is obtained;
  • the diffraction wave imaging results of all the single shot data corresponding to the seismic gun set data are superimposed to obtain the diffraction wave imaging result of the seismic gun set data, so as to obtain the coal collapse column identification based on the diffraction wave imaging result of the seismic shot data. .
  • each of the single shots is calculated according to the spatial position of the detector corresponding to each single shot data and the seismic wave offset speed file.
  • the diffraction wave travel time of each single shot data of the seismic wave position of the data through the position of any one of the imaging points of the underground imaging space to the position of the seismic wave detection point of each single shot data includes:
  • T R & lt summing separately for each processing take a single shot data and the travel time t S t S corresponding to the travel time, obtained when the single shot data corresponding to each of the plurality of diffraction traveltime.
  • the embodiment of the present invention provides the second possible implementation manner of the first aspect, wherein the diffraction wave travel time of each single shot data and single shot data is performed.
  • the distance calculation process, the sample of the diffraction wave amplitude value obtained for each single shot data includes:
  • the amplitude value sample sequence of each imaging point in the underground imaging space corresponding to the single shot data is calculated, and the corresponding single shot data is obtained.
  • the amplitude value samples in each of the obtained amplitude value sample sequences are respectively sorted;
  • the amplitude value samples corresponding to the preset conditions in each sequence of the amplitude value samples corresponding to each amplitude value are extracted, and corresponding to each Multiple sets of diffraction wave amplitude value samples of single shot data.
  • the embodiment of the present invention provides a third possible implementation manner of the first aspect, wherein each of the amplitude value sample sequences after the sorting process is respectively subjected to a Mahalanobis distance
  • the calculation process obtains the Mahalanobis distances of all amplitude value samples arranged in a predetermined order in each amplitude value sample sequence including:
  • the embodiment of the present invention provides a fourth possible implementation of the first aspect, wherein all amplitudes are arranged in a predetermined order according to each amplitude value sample sequence
  • the Mahalanobis distance of the sample point, and the corresponding amplitude value samples in the sequence of each amplitude value sample corresponding to the preset condition are extracted:
  • the amplitude is calculated according to the Mahalanobis distance of each amplitude value sample arranged in a preset order in the amplitude value sample sequence and the preset Mahalanobis distance threshold of the reflected wave amplitude value sample. The number of sample removals in the sample sequence;
  • sample removal processing is performed on the amplitude value samples arranged in a preset order in the amplitude value sample sequence, and the remaining amplitude value samples in the amplitude value sample sequence are obtained;
  • the remaining amplitude value samples are extracted as amplitude value samples in the sequence of amplitude value samples that meet the preset conditions.
  • the embodiment of the present invention provides a fifth possible implementation manner of the first aspect, wherein the diffraction wave amplitude value samples of each single shot data are respectively imaged Processing, obtaining diffraction wave imaging results for each single shot data includes:
  • the amplitude value samples corresponding to the preset conditions extracted in each amplitude value sample sequence are respectively subjected to a summation process to obtain a diffraction wave imaging result corresponding to each amplitude value sample sequence;
  • the diffraction wave imaging results of all amplitude value sample sequences corresponding to the single shot data are superimposed to obtain a diffraction wave imaging result corresponding to the single shot data.
  • the embodiment of the present invention provides a sixth possible implementation manner of the first aspect, wherein the preset condition is extracted in each amplitude value sample sequence
  • the amplitude value samples are respectively subjected to summation processing, and the diffraction wave imaging results corresponding to each amplitude value sample sequence are obtained:
  • the embodiment of the present invention provides the seventh possible implementation manner of the first aspect, wherein acquiring the seismic wave offset velocity file corresponding to the seismic cannon set data and the seismic cannon set data of the target region includes:
  • Seismic preprocessing of seismic gun data is performed to obtain seismic gun data that can be used for offset imaging; wherein the seismic preprocessing includes at least: noise removal processing and static correction processing;
  • the seismic velocity data is subjected to an offset velocity analysis process to obtain a seismic wave offset velocity file corresponding to the seismic cannon data.
  • an embodiment of the present invention further provides a coal collapse column identification device, the device comprising:
  • the acquiring module is configured to acquire seismic seismic wave offset data corresponding to the seismic cannon data and the seismic cannon data of the target region; wherein the seismic cannon data includes a plurality of single-shot data; each single-shot data includes a subsurface rock interface reflection Or multiple seismic waves that are refracted; seismic waves carry geological information;
  • the diffraction wave travel time calculation module is configured to calculate the position of the seismic wave shot point of each single shot data through the position of any one of the imaging points of the underground imaging space according to the spatial position of the detector corresponding to each single shot data and the seismic wave offset speed file The diffraction wave travel time of each single shot data of the seismic wave detection point position of each single shot data;
  • the Mahalanobis distance calculation processing module is configured to perform a Mahalanobis distance calculation process on the diffraction wave travel time of each single shot data and single shot data, and obtain a diffraction wave amplitude value sample of each single shot data; wherein, the diffraction Wave amplitude value samples carry small-scale geological information; small-scale geological information includes at least: stratigraphic horizon information, fault information, and collapse column information;
  • An imaging processing module is configured to respectively perform imaging processing on the diffraction wave amplitude value samples of each single shot data to obtain a diffraction wave imaging result of each single shot data;
  • the superposition processing module is configured to superimpose the diffraction wave imaging results of all the single shot data corresponding to the seismic gun set data, and obtain the diffraction wave imaging result of the seismic gun set data, so as to obtain the diffraction wave imaging according to the seismic shot data. As a result, the coal collapse column was identified.
  • the embodiment of the present invention provides the first possible implementation manner of the second aspect, wherein the diffraction wave travel time calculation module includes:
  • the first travel time calculation unit is configured to calculate the travel time of the seismic wave shot position of each single shot data to the position of each underground imaging space imaging point according to the geophone space position and the seismic wave offset speed file corresponding to each single shot data.
  • a second travel time calculation unit is configured to calculate travel time of the seismic wave detection point position from each underground imaging space imaging point position to each single shot data according to the detector spatial position and the seismic wave offset speed file corresponding to each single shot data t R ; wherein the travel time t R of each single shot data at one imaging point corresponds to the travel time t S ;
  • the first summation processing unit is configured to respectively perform a summation process on the travel time t S of each single shot data and the travel time t R corresponding to the travel time t S to obtain a plurality of diffraction wave travel times corresponding to each single shot data. .
  • a method and device for identifying a coal collapse column includes: acquiring seismic gun data and a seismic wave offset speed file of a target area; and spatial position of the detector corresponding to each single shot data and a seismic wave offset speed a file, calculating a diffraction wave travel time of each single shot data corresponding to different imaging points; performing a Mahalanobis distance calculation process on the diffraction wave travel time of each single shot data and single shot data, and obtaining a diffraction of each single shot data Wave; respectively, imaging the diffraction wave of each single shot data to obtain the diffraction wave imaging result of each single shot data; superimposing the diffraction wave imaging results of all the single shot data corresponding to the seismic shot data The diffraction wave imaging result of the seismic gun set data is obtained, and the coal collapse column is identified according to the diffraction wave imaging result of the seismic gun data.
  • the effect of the collapse column identification is not good, firstly calculating the diffraction wave travel time corresponding to each single shot data in the seismic gun data, and passing the Mahalanobis distance Calculate and process the diffraction wave travel time of single shot data and single shot data, obtain the diffraction wave corresponding to all single shot data, and image the diffraction wave; because the diffraction wave carries smaller scale geological information Therefore, the imaged diffraction wave can accurately evaluate the collapse column geological body, that is, the identification of the coal collapse column in the target area can be accurately performed, thereby reducing the water inrush and gas leakage caused by the collapse column in coal mining. Risk of accidents, reducing unnecessary casualties and economic losses.
  • FIG. 1 is a flow chart showing a method for identifying a coal collapse column according to an embodiment of the present invention
  • FIG. 2 is a flow chart showing another method for identifying a coal collapse column according to an embodiment of the present invention
  • FIG. 3 is a flow chart showing another method for identifying a coal collapse column according to an embodiment of the present invention.
  • FIG. 4 is a flow chart showing another method for identifying a coal collapse column according to an embodiment of the present invention.
  • FIG. 5 is a flow chart showing another method for identifying a coal collapse column according to an embodiment of the present invention.
  • FIG. 6 is a schematic diagram showing the results of conventional reflected wave imaging provided by an embodiment of the present invention.
  • FIG. 7 is a schematic diagram showing a diffraction wave imaging result obtained by applying a coal collapse column identification method provided by an embodiment of the present invention; wherein a section and a slice diagram of a corresponding position are included;
  • FIG. 8 is a schematic structural diagram of a coal collapse column identification device according to an embodiment of the present invention.
  • FIG. 9 is a schematic structural diagram of a diffraction wave travel time calculation module and a Mahalanobis distance calculation processing module in a coal collapse column identification device according to an embodiment of the present invention.
  • the method of using seismic wave imaging technology to identify the collapse column mainly depends on the imaging result of the reflected wave, so that the small-scale geological information such as the coal collapse column cannot be well recognized.
  • the diffraction wave carries small-scale geological information, so it can detect geological information with spatial distribution less than one seismic wavelength.
  • the waves are directly imaged to detect small-scale geological bodies such as karst caves and cracks.
  • a method and device for identifying a coal collapse column provided by an embodiment of the present invention belongs to the third type of diffraction wave imaging technology described above, and is based on the principle.
  • the difference between the diffracted wave and the reflected wave amplitude characteristics is achieved by the statistical Mahalanobis distance criterion. Since the diffraction wave carries a smaller scale of geological information, the diffraction wave imaging is directly applied to evaluate the coal collapse column. More realistic reflection of underground geological conditions, so as to accurately identify the coal collapse column in the target area, without the need to remove reflected waves in advance, with efficient computing power.
  • an embodiment of the present invention provides a method for identifying a coal collapse column, the method comprising:
  • S101 acquiring seismic gun data of the target area and a seismic wave offset speed file corresponding to the seismic gun set data; wherein the seismic gun set data includes multiple single shot data; each single shot data includes a subsurface rock layer interface reflection or Multiple seismic waves that are refracted; seismic waves carry geological information.
  • the target area is a pre-selected area to be identified by the coal collapse column
  • the target area includes a plurality of seismic sources and a plurality of detectors, and one source corresponds to the plurality of detectors; wherein the plurality of seismic sources correspond to the plurality of seismic wave guns Point position, multiple detectors correspond to multiple seismic wave detection point positions; among them, multiple sources can transmit multiple single shot data, and multiple single shot data constitutes seismic gun set data; correspondingly, multiple detectors can collect multiple Single shot data, multiple single shot data constitutes seismic gun set data.
  • the seismic wave offset velocity file is obtained by performing an offset velocity analysis process on the seismic gun set data; wherein the seismic wave offset velocity file corresponds to a formation velocity parameter, and is used for calculating a seismic wave propagation travel time (ie, time) ), which is obtained by performing an offset velocity analysis on the seismic gun set data; and the above-described offset velocity analysis is a speed modeling technique that determines the velocity parameter by analyzing the seismic wave focus in the seismic acquisition data. That is, the seismic wave offset velocity file is obtained.
  • the geological information carried in the seismic wave includes small-scale geological information, and also includes large-scale geological information; wherein the small-scale address information includes: stratigraphic layer information, fault information, and collapse column information.
  • the diffraction wave travel time calculation is performed according to the spatial position of the detector corresponding to each single shot data and the seismic wave offset speed file corresponding to the single shot data, that is, the diffraction wave travel time calculation is performed according to the ray tracing function equation, and the ray is calculated.
  • the tracking function equation is the velocity (ie, the seismic wave offset velocity file corresponding to the single shot data), the spatial position (ie, the spatial position of the detector corresponding to the single shot data) and the differential relationship of the seismic wave travel time.
  • the seismic wave shot position of the single shot data is first calculated.
  • the travel time corresponding to any imaging point position in the underground imaging space in order to distinguish the following travel time, this becomes the first travel time
  • the underground imaging space includes multiple Image point
  • the calculated position of the seismic wave of the single shot data to the position of any one of the imaging points in the underground imaging space is also a plurality of first walking times;
  • second travel time a plurality of diffraction wave travels (herein referred to as second travel time) of any one of the imaging point positions of the underground imaging space to the position of the seismic wave detection point of the single shot data, wherein a second travel time at each imaging point Both correspond to a first walk.
  • first travel time and the second travel time corresponding to the first travel time are added to obtain a diffraction wave travel time corresponding to the single shot data.
  • each single shot data in the seismic gun data it can be calculated according to the diffraction wave travel time of the single shot data, and the diffraction wave travel time of each single shot data in the seismic gun data is obtained.
  • the jet amplitude value sample carries small-scale geological information; the small-scale geological information includes at least: stratum horizon information, fault information, and collapse column information.
  • a plurality of amplitude value sample sequences of each imaging point corresponding to the single shot data can be calculated. (ie multiple imaging amplitude value sample sequences);
  • the Mahalanobis distance of each amplitude value sample in the amplitude value sample sequence For each amplitude value sample sequence, calculate the Mahalanobis distance of each amplitude value sample in the amplitude value sample sequence, and finally remove the corresponding amplitude value of the reflected wave in the amplitude value sample sequence according to the calculated Mahalanobis distance. Point, the obtained remaining amplitude value sample point is the corresponding diffraction wave amplitude value sample point in the amplitude value sample sequence.
  • a single shot data includes a plurality of amplitude value sample sequences, so that a plurality of sets of amplitude values corresponding to one single shot data can be calculated according to the calculation method of the diffraction wave amplitude value sample points in the above one amplitude value sample sequence. Point sequence.
  • the amplitude value sample sequence corresponding to all the single shot data in the seismic shot data can be calculated.
  • S104 Perform imaging processing on the diffraction wave amplitude value samples of each of the single shot data, respectively, to obtain a diffraction wave imaging result of each of the single shot data.
  • each single shot data includes a plurality of amplitude value sample sequences; and all the diffraction wave amplitude value samples in each amplitude value sample sequence are summed to obtain a diffraction wave imaging result corresponding to each amplitude value sample sequence;
  • the diffraction wave imaging result corresponding to each amplitude value sample sequence included in the single shot data is superimposed, and the diffraction wave imaging result corresponding to the single shot data is obtained.
  • S105 Perform superimposition processing on the diffraction wave imaging results of all the single shot data corresponding to the seismic gun set data, to obtain a diffraction wave imaging result of the seismic shot data, so as to be diffracted according to the seismic gun data.
  • Wave imaging results were identified by coal collapse columns.
  • each single shot data in the seismic gun data is only a part of the geological information carrying the reaction target area; in the actual calculation process, it is targeted
  • Each single shot data is separately processed and imaged. Finally, the imaging processing results of each single shot data are superimposed to obtain the diffraction wave imaging result of the seismic shot data.
  • the coal collapse column identification can accurately evaluate the collapse column geological body, so as to accurately identify the coal collapse column in the target area.
  • the method for identifying a coal collapse column provided by the embodiment of the present invention is compared with the prior art for the seismic wave imaging technology developed by the reflected wave to perform the identification of the collapse column, and firstly calculating each of the seismic gun set data.
  • the diffraction wave corresponding to the gun data travels, and the diffraction wave travel time of the single shot data and the single shot data is calculated by the Mahalanobis distance, the diffraction wave corresponding to all the single shot data is obtained, and the diffraction wave is imaged.
  • the diffraction wave carries a smaller scale of geological information
  • the diffraction wave after imaging can accurately evaluate the collapse column geological body, that is, the identification of the coal collapse column in the target area can be accurately performed, thereby reducing coal Risk of accidents such as water inrush and gas leakage caused by the collapse column during mining, reducing unnecessary casualties and economic losses.
  • the acquired seismic gun data of the target area includes a plurality of single shot data
  • the diffraction wave corresponding to each single shot data is used, wherein each single shot data corresponds to the underground imaging space.
  • a plurality of imaging points are included, so that each single shot data corresponds to a plurality of diffraction wave travel times.
  • a specific method for calculating a plurality of diffraction wave travel times corresponding to each single shot data includes the following steps:
  • t S also corresponds to a plurality; t S different walking i.e. different subsurface imaging space corresponding to the imaging position.
  • each subsurface imaging space is imaged to the seismic wave of each single shot data.
  • t R of the position of the detection point that is, different imaging positions of the underground imaging space correspond to different travel times t R .
  • the travel time t R of each of the single shot data at one imaging point corresponds to the travel time t S .
  • a Mahalanobis distance calculation process is performed on each of the single shot data and the diffraction wave travel time of the single shot data, and the winding of each of the single shot data is obtained.
  • the specific steps of the jet amplitude value sample include:
  • each single shot data corresponds to different diffraction wave travel times of the imaging points in different underground imaging spaces; for any single shot data, according to the single shot data and the single shot data at one imaging point When a diffracted wave goes, you can calculate the single shot.
  • a sequence of amplitude value samples of the data at the imaging point, and for each imaging point of the single shot data, a corresponding amplitude value sample sequence can be calculated, and a single shot data corresponds to a plurality of imaging points, so A plurality of amplitude value sample sequences corresponding to the single shot data can be calculated.
  • each single shot data includes a plurality of amplitude value sample sequences
  • each amplitude value sample sequence includes a plurality of amplitude value samples; due to the energy corresponding to each amplitude value sample point Different sizes, so according to the energy value of the amplitude value sample, the amplitude value samples in each of the obtained amplitude value sample sequences can be sorted separately, so that the amplitude value samples in each amplitude value sample sequence are Arrange in the preset order.
  • each single shot data includes a plurality of amplitude value sample sequences, and for each amplitude value sample sequence, each amplitude value sample in the preset sequence is arranged in the amplitude value sample sequence. Markov distance.
  • each single shot data including a plurality of amplitude value sample sequences are calculated according to the above-described Mahalanobis distance calculation method, and each amplitude value arranged in a preset order in each amplitude value sample sequence is obtained.
  • the Markov distance of the sample is calculated according to the above-described Mahalanobis distance calculation method, and each amplitude value arranged in a preset order in each amplitude value sample sequence is obtained. The Markov distance of the sample.
  • the Mahalanobis distance of the amplitude value of the diffracted wave and the Mahalanobis distance of the amplitude value of the reflected wave have a preset threshold, and the preset threshold is calculated according to a plurality of experiments, and therefore, And extracting, according to the first preset Mahalanobis distance of the amplitude value sample of the diffraction wave, the amplitude value sample corresponding to the first preset Mahalanobis distance in each sequence of the amplitude value samples, and extracting the amplitude value sample
  • the point is used as a sample of the diffraction wave amplitude value; and the second preset Mahalanobis distance of the sample of the amplitude value of the reflected wave may be eliminated, and the corresponding sequence of each amplitude value sample corresponding to the second preset Mahalanobis distance is excluded.
  • the amplitude value sample, the obtained remaining amplitude value sample point is the amplitude value sample that meets the preset condition, and the extracted amplitude value sample point is also used as the
  • each single shot data corresponds to a plurality of amplitude value sample sequences
  • each single shot data also corresponds to a plurality of sets of diffraction wave amplitude value samples. That is, the number of sets of the diffraction wave amplitude value samples of each single shot data is the same as the number of the amplitude value sample sequences of the single shot data.
  • the method for calculating the Mahalanobis distance of the amplitude value sample in the above step 303 is as follows:
  • a specific method for calculating a plurality of sets of diffraction wave amplitude value samples of each single shot data includes:
  • the calculated Mahalanobis distance is compared with a preset Mahalanobis distance threshold of the reflected wave amplitude value sample (ie, the second predetermined Mahalanobis distance), and the reflected wave is matched.
  • the calculation result of the preset Mahalanobis distance threshold of the amplitude value sample is used as the culling processing object, and the number of sample removals in the amplitude value sample sequence can be calculated according to this method.
  • S402. Perform sample removal processing on the amplitude value samples arranged in a preset order in the amplitude value sample sequence according to the preset Mahalanobis distance threshold and the number of sample removals, to obtain the amplitude value sample. The remaining amplitude value samples in the point sequence.
  • the diffracted wave amplitude value sample is extracted from the sorted amplitude value sample sequence according to the sample removal amount, that is, the sequenced amplitude value sample sequence is removed, and the non-equivalent condition corresponding to the first segment and the end of the sequence is removed.
  • the amplitude value sample, the number of removal is determined according to the number of sample removals calculated above.
  • the remaining amplitude value samples are the first preset Mahalanobis distances satisfying the amplitude value samples of the diffracted waves, so the remaining amplitude value samples are extracted as the amplitude value samples meeting the preset conditions.
  • step 104 the diffraction wave amplitude value samples of each of the single shot data are respectively subjected to imaging processing, and the specific method for obtaining the diffraction wave imaging result of each of the single shot data includes:
  • the amplitude value samples corresponding to the preset conditions extracted in each amplitude value sample sequence are respectively subjected to a summation process to obtain a diffraction wave imaging result corresponding to each amplitude value sample sequence;
  • the diffraction wave imaging results of all amplitude value sample sequences corresponding to the single shot data are superimposed to obtain a diffraction wave imaging result corresponding to the single shot data.
  • each single shot data includes a plurality of amplitude value sample sequences
  • amplitude value samples ie, amplitude value samples of the diffraction wave
  • the diffraction wave imaging results are superimposed to obtain a diffraction corresponding to the single shot data. Wave imaging results.
  • the diffraction wave imaging result of each single shot data included in the same is superimposed and processed, and the diffraction wave imaging result of the seismic gun set data can be obtained.
  • the imaging result of the diffraction wave corresponding to each of the amplitude value sample sequences is as follows:
  • the acquired seismic gun set data needs to be processed.
  • the specific method includes:
  • S502 Perform seismic preprocessing on the seismic gun set data to obtain seismic gun set data that can be used for offset imaging; wherein the seismic preprocessing includes at least: removing noise processing and static correction processing.
  • the detector receives the data of the shot set (ie, the seismic wave), loads the observation system, and sends the acquired shot data to the observation system, and the observation system performs seismic preprocessing on the seismic gun data, that is, the observation The system loads the received seismic gun set data, and performs noise removal, velocity analysis and offset on the loaded seismic wave to obtain seismic wave offset velocity files corresponding to the seismic gun set data and the seismic shot data.
  • the data of the shot set ie, the seismic wave
  • loads the observation system loads the received seismic gun set data, and performs noise removal, velocity analysis and offset on the loaded seismic wave to obtain seismic wave offset velocity files corresponding to the seismic gun set data and the seismic shot data.
  • the present embodiment provides a comparison chart of the two, as shown in FIG. 6 is a conventional reflected wave imaging result, including the main measurement line.
  • the direction and tie line direction profile and the 0.24 second slice view, as shown in Figure 7, are the diffraction wave imaging results, including the corresponding position profile and slice view.
  • the diffraction wave imaging results show the shape of the verified collapse column at the intersection of the main line and the tie line of the slice, and the collapse column does not appear on the reflected wave imaging slice.
  • the method and device for identifying a coal collapse column provided by the embodiment of the present invention are compared with the prior art for the seismic wave imaging technology developed by the reflected wave to perform the identification of the collapse column.
  • the diffraction wave corresponding to the single shot data travels, and the diffraction wave travel time of the single shot data and the single shot data is calculated by the Mahalanobis distance, and the diffraction wave corresponding to all the single shot data is obtained, and the diffraction wave is performed.
  • the diffraction wave since the diffraction wave carries a smaller scale of geological information, the diffraction wave after imaging It can accurately evaluate the collapse column geological body, which can accurately identify the coal collapse column in the target area, thereby reducing the risk of accidents such as water inrush and gas leakage caused by the collapse column in coal mining, and reducing unnecessary personnel. Casualties and economic losses.
  • An embodiment of the present invention further provides a coal collapse column identification device.
  • the device includes:
  • the acquiring module 11 is configured to acquire the seismic gun set data of the target area and the seismic wave offset speed file corresponding to the seismic shot data; wherein the seismic shot data includes a plurality of single shot data; each of the single shots The data includes a plurality of seismic waves reflected or refracted by the interface of the subterranean formation; the seismic waves carry geological information;
  • the diffraction wave travel time calculation module 12 is configured to calculate a seismic wave shot position of each single shot data through an arbitrary imaging point position of the underground imaging space according to the geophone space position and the seismic wave offset speed file corresponding to each single shot data. a diffraction wave of each single shot data to the position of the seismic wave detection point of each single shot data;
  • the Mahalanobis distance calculation processing module 13 is configured to perform a Mahalanobis distance calculation process on the diffraction wave travel time of each single shot data and the single shot data, and obtain a diffraction wave amplitude value sample of each single shot data;
  • the jet amplitude value sample carries small-scale geological information;
  • the small-scale geological information includes at least: stratigraphic horizon information, fault information, and collapse column information;
  • the imaging processing module 14 is configured to respectively perform imaging processing on the diffraction wave amplitude value samples of each single shot data to obtain a diffraction wave imaging result of each single shot data;
  • the superposition processing module 15 is configured to superimpose the diffraction wave imaging results of all the single shot data corresponding to the seismic gun set data to obtain a diffraction wave imaging result of the seismic shot data, so as to obtain a diffraction wave according to the seismic gun data.
  • the imaging results were identified by coal collapse columns.
  • the diffraction wave travel time calculation module 12 includes:
  • the first travel time calculation unit 121 is configured to calculate a seismic wave shot position of each single shot data to a position of each underground imaging space imaging point according to the geophone space position and the seismic wave offset speed file corresponding to each single shot data. Walking time t S ;
  • the second travel time calculation unit 122 is configured to calculate the position of the seismic wave detection point from each of the underground imaging space imaging point positions to each single shot data according to the detector spatial position and the seismic wave offset speed file corresponding to each single shot data. Travel time t R ; wherein each single shot data corresponds to travel time t R at one imaging point and travel time t S ;
  • the first summation processing unit 123 is configured to perform a summation process on the travel time t S of each single shot data and the travel time t R corresponding to the travel time t S to obtain a plurality of diffraction waves corresponding to each single shot data. When to go.
  • the Mahalanobis distance calculation processing module 13 includes:
  • the amplitude value sample sequence calculation unit 131 is configured to calculate the amplitude of each imaging point in the underground imaging space corresponding to the single shot data according to the plurality of diffraction wave travel times of the single shot data and the single shot data for any single shot data. a sequence of value samples to obtain a plurality of amplitude value sample sequences corresponding to the single shot data;
  • the sorting processing unit 132 is configured to sort the amplitude value samples in each of the obtained amplitude value sample sequences according to the energy magnitude of the amplitude value samples;
  • the Mahalanobis distance calculation unit 133 is configured to perform a Mahalanobis distance calculation process on each of the amplitude value sample sequences after the sorting process, and obtain all amplitude value samples arranged in a preset order in each amplitude value sample sequence. Markov distance
  • the extracting unit 134 is configured to extract, according to a Mahalanobis distance of all the amplitude value samples arranged in a preset order in each amplitude value sample sequence, an amplitude value value corresponding to the preset condition in each sequence of the amplitude value samples corresponding to each amplitude value sample Point, a plurality of sets of diffraction wave amplitude value samples corresponding to each of the single shot data are obtained.
  • the Mahalanobis distance calculation unit 133 includes:
  • a first calculation subunit for calculating a median value of each sequence of amplitude value samples arranged in a predetermined order by the following formula
  • ⁇ x is the median of the sequence of amplitude value samples
  • median is the median operation.
  • N is the number of samples in the sequence of amplitude value samples
  • a second calculation word unit for calculating a median absolute value deviation square of each sequence of amplitude value samples arranged in a predetermined order by the following formula among them, The median absolute value deviation squared;
  • a third calculating sub-unit for calculating a Mahalanobis distance of all amplitude value samples arranged in a predetermined order in each amplitude value sample sequence according to a median and median absolute value deviation squared among them, It is the Markov distance.
  • the extracting unit 334 includes:
  • a fourth calculating subunit for presetting the Markov distance of each amplitude value sample and the reflected wave amplitude value sample according to a preset sequence in the amplitude value sample sequence for any one amplitude value sample sequence
  • the Mahalanobis distance threshold is used to calculate the number of sample removals in the amplitude value sample sequence
  • a sample removal processing subunit configured to perform sample removal processing on amplitude value samples arranged in a preset order in the sequence of amplitude value samples according to the preset Mahalanobis distance threshold and the number of sample removals Obtaining a sample of the amplitude value remaining in the sequence of the amplitude value samples;
  • the imaging processing module 14 includes:
  • a second summation processing unit is configured to perform summation processing on the amplitude value samples corresponding to the preset conditions extracted in each amplitude value sample sequence, to obtain diffraction wave imaging corresponding to each amplitude value sample sequence result;
  • the superimposition processing unit is configured to superimpose the diffraction wave imaging results of all the amplitude value sample sequences corresponding to the single shot data to obtain a diffraction wave imaging result corresponding to the single shot data.
  • r (x, y, z) is the position of the detection point
  • t S , t R are the travel time from the shot position to the imaging point position and the position from the imaging point to the detector position, respectively
  • A is the offset imaging aperture
  • w(m,r) is the geometric diffusion factor
  • Tri M is the diffraction wave imaging operator based on the Mahalanobis distance and reflected wave amplitude removal.
  • the obtaining module 11 includes:
  • An acquiring unit configured to acquire seismic gun data of the target area
  • the earthquake pre-processing unit is configured to perform seismic pre-processing on the seismic gun set data to obtain seismic gun set data that can be used for offset imaging; wherein the seismic pre-processing includes at least: removing noise processing and static correction processing;
  • the offset velocity analysis processing unit is configured to perform an offset velocity analysis process on the seismic gun set data to obtain a seismic wave offset velocity file corresponding to the seismic cannon set data.
  • the coal collapse column identification device compares the effect of the collapse column identification with the seismic wave imaging technology developed by the reflected wave in the prior art, and first calculates each single in the seismic gun set data.
  • the diffraction wave corresponding to the gun data travels, and the diffraction wave travel time of the single shot data and the single shot data is calculated by the Mahalanobis distance, the diffraction wave corresponding to all the single shot data is obtained, and the diffraction wave is imaged.
  • the diffraction wave carries a smaller scale of geological information
  • the diffraction wave after imaging can accurately evaluate the collapse column geological body, that is, the identification of the coal collapse column in the target area can be accurately performed, thereby reducing coal Risk of accidents such as water inrush and gas leakage caused by the collapse column during mining, reducing unnecessary casualties and economic losses.
  • the disclosed systems, devices, and methods may be implemented in other manners.
  • the device embodiments described above are merely illustrative.
  • the division of the unit is only a logical function division.
  • multiple units or components may be combined or Can be integrated into another system, or some features can be ignored or not executed.
  • the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some communication interface, device or unit, and may be electrical, mechanical or otherwise.
  • the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of the embodiment.
  • each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.
  • the functions may be stored in a computer readable storage medium if implemented in the form of a software functional unit and sold or used as a standalone product.
  • the technical solution of the present invention which is essential or contributes to the prior art, or a part of the technical solution, may be embodied in the form of a software product, which is stored in a storage medium, including
  • the instructions are used to cause a computer device (which may be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention.
  • the foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, and the like. .

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Abstract

一种煤炭陷落柱识别方法和装置，包括：获取目标区域的地震炮集数据和地震波偏移速度文件（S101）；根据上述数据计算每个单炮数据在不同成像点的绕射波走时（S102）；对每个单炮数据及其绕射波走时进行马氏距离计算处理，获取每个单炮数据的绕射波振幅值样点（S103）；分别对每个单炮数据的绕射波进行成像处理（S104）；将地震炮集数据对应的所有单炮数据的成像处理结果进行叠加处理，得到地震炮集数据的绕射波成像结果，以便根据该绕射波成像结果进行煤炭陷落柱识别（S105）；其通过马氏距离提取地震炮集数据对应的绕射波，由于绕射波携带的是更小尺度的地质信息，故通过成像后的绕射波能够准确的识别陷落柱地质体，从而能够降低煤炭开采中由陷落柱而引发的突水、瓦斯泄露等事故的发生风险。

Description

一种煤炭陷落柱识别方法和装置
本申请要求申请日为2016年2月2日，申请号为201610073638.7，发明名称为“一种煤炭陷落柱识别方法和装置”的中国专利申请的优先权，在此通过引用将该申请的全部内容包括在内。
技术领域
本发明涉及地震数据成像领域，具体而言，涉及一种基于地震绕射波成像的煤炭陷落柱识别方法和装置。
背景技术
在煤炭开采过程中，断层、断裂、陷落柱等不连续地质体会破坏煤层的连续性，易于诱发透水、瓦斯突出等事故，严重威胁着煤矿作业的安全，因此，有效识别断层、断裂和陷落柱等不连续地质体具有重要的意义。
目前，用于识别断层、断裂的方法有很多种方法，包括地震相干体技术(Bahorich and Farmer,1995；Marfurt,et al.,1998,1999；Gersztenkorn and Marfurt,1999)，匹配追踪算法(Mallat and Zhang,1993；Castagna et al.,2003；Liu and Marfurt,2005)和谱分解算法(Partyka et al.,1999；Puryear et al.,2012；Gao et al.,2013)等。但是，由于陷落柱本身的大小不等、分布规律差等特点，而使得上述的几种方法均不能有效识别陷落柱，因此，陷落柱识别一直都是地震勘探的难题。
为了解决上述难题，相关技术采取地震波成像技术对陷落柱进行识别，具体方法包括，采集地震炮集数据，通过现有地震波成像技术针对地震炮集数据中的反射波进行成像处理，通过对反射波的成像处理结果进行煤炭陷落柱识别，但是，反射波为宏观尺度地质单元响应，通常只能解决空间展布大于一个地震波长的地质体勘探问题，其对于煤炭陷落柱等小尺度的地质信息的识别效果较差。因此针对反射波研发的地震波成像技术针对陷落柱等小尺度的地质信息成像上效果不佳，不能很好的对煤炭陷落柱等小尺度的地质信息进行识别。
发明内容
本发明的目的在于提供一种煤炭陷落柱识别方法和装置，能够提取获取的地震炮集数据中携带的更小尺度的地质信息的绕射波，通过成像后的绕射波准确的评价陷落柱地质体，从而准确的进行目标区域中煤炭陷落柱的识别。
第一方面，本发明实施例提供了一种煤炭陷落柱识别方法，包括：
获取目标区域的地震炮集数据和地震炮集数据对应的地震波偏移速度文件；其中，地震炮集数据包括多个单炮数据；每个单炮数据均包括地下岩层界面反射或者折射的多个地震波；地震波携带有地质信息；
根据每个单炮数据对应的检波器空间位置和地震波偏移速度文件，计算由每个单炮数据的地震波炮点位置经地下成像空间任意一个成像点位置到每个单炮数据的地震波检波点位置的每个单炮数据的绕射波走时；
对每个单炮数据及单炮数据的绕射波走时进行马氏距离计算处理，获取每个单炮数据的绕射波振幅值样点；其中，绕射波振幅值样点携带有小尺度地质信息；小尺度地质信息至少包括：地层层位信息、断层信息和陷落柱信息；
分别对每个单炮数据的绕射波振幅值样点进行成像处理，得到每个单炮数据的绕射波成像结果；
将地震炮集数据对应的所有单炮数据的绕射波成像结果进行叠加处理，得到地震炮集数据的绕射波成像结果，以便根据地震炮集数据的绕射波成像结果进行煤炭陷落柱识别。
结合第一方面，本发明实施例提供了第一方面的第一种可能的实施方式，其中，根据每个单炮数据对应的检波器空间位置和地震波偏移速度文件，计算由每个单炮数据的地震波炮点位置经地下成像空间任意一个成像点位置到每个单炮数据的地震波检波点位置的每个单炮数据的绕射波走时包括：
根据每个单炮数据对应的检波器空间位置和地震波偏移速度文件，计算由每个单炮数据的地震波炮点位置到每一个地下成像空间成像点位置的走时tS；
根据每个单炮数据对应的检波器空间位置和地震波偏移速度文件，计算由每一个地下成像空间成像点位置到每个单炮数据的地震波检波点位置的走时tR；其中，每个单炮数据在一个成像点的走时tR与走时tS相对应；
分别对每个单炮数据的走时tS以及与走时tS对应的走时tR进行求和处理，得到对应于每个单炮数据的多个绕射波走时。
结合第一方面的第一种可能的实施方式，本发明实施例提供了第一方面的第二种可能的实施方式，其中，对每个单炮数据及单炮数据的绕射波走时进行马氏距离计算处理，获取每个单炮数据的绕射波振幅值样点包括：
对于任意一个单炮数据，根据单炮数据和单炮数据的多个绕射波走时，计算单炮数据对应的地下成像空间中每个成像点的振幅值样点序列，得到对应于单炮数据的多个振幅值样点序列；
根据振幅值样点的能量大小，分别对得到的每一个振幅值样点序列中的振幅值样点进行排序处理；
分别对排序处理后的每一个振幅值样点序列进行马氏距离计算处理，得到每一个振幅值样点序列中按预设顺序排列的所有振幅值样点的马氏距离；
根据每个振幅值样点序列中按预设顺序排列的所有振幅值样点的马氏距离，提取对应的每个振幅值样点序列中符合预设条件的振幅值样点，得到对应于每个单炮数据的多组绕射波振幅值样点。
结合第一方面的第二种可能的实施方式，本发明实施例提供了第一方面的第三种可能的实施方式，其中，分别对排序处理后的每一个振幅值样点序列进行马氏距离计算处理，得到每一个振幅值样点序列中按预设顺序排列的所有振幅值样点的马氏距离包括：
通过以下公式计算每一个按预设顺序排列的振幅值样点序列的中值
Figure PCTCN2016113064-appb-000001
其中，μx为振幅值样点序列的中值，median为中值运算，
Figure PCTCN2016113064-appb-000002
为按预设顺序排序后的振幅样点序列，N为振幅值样点序列中样点个数；
通过以下公式计算每一个按预设顺序排列的振幅值样点序列的中值绝对值偏差平方
Figure PCTCN2016113064-appb-000003
其中，
Figure PCTCN2016113064-appb-000004
为中值绝对值偏差平方；
根据中值和中值绝对值偏差平方，计算每一个振幅值样点序列中按预设顺序排列的所有振幅值样点的马氏距离
Figure PCTCN2016113064-appb-000005
其中，
Figure PCTCN2016113064-appb-000006
为马氏距离。
结合第一方面的第三种可能的实施方式，本发明实施例提供了第一方面的第四种可能的实施方式，其中，根据每个振幅值样点序列中按预设顺序排列的所有振幅值样点的马氏距离，提取对应的每个振幅值样点序列中符合预设条件的振幅值样点包括：
对于任意一个振幅值样点序列，根据振幅值样点序列中按预设顺序排列的每个振幅值样点的马氏距离以及反射波振幅值样点的预设马氏距离阈值，计算振幅值样点序列中的样点去除数量；
根据预设马氏距离阈值和样点去除数量，对振幅值样点序列中按预设顺序排列的振幅值样点进行样点去除处理，得到振幅值样点序列中剩余的振幅值样点；
提取剩余的振幅值样点作为振幅值样点序列中符合预设条件的振幅值样点。
结合第一方面的第四种可能的实施方式，本发明实施例提供了第一方面的第五种可能的实施方式，其中，分别对每个单炮数据的绕射波振幅值样点进行成像处理，得到每个单炮数据的绕射波成像结果包括：
将在每个振幅值样点序列中提取的符合预设条件的振幅值样点分别进行求和处理，得到每个振幅值样点序列对应的绕射波成像结果；
将单炮数据对应的所有振幅值样点序列的绕射波成像结果进行叠加处理，得到对应于单炮数据的绕射波成像结果。
结合第一方面的第五种可能的实施方式，本发明实施例提供了第一方面的第六种可能的实施方式，其中，将在每个振幅值样点序列中提取的符合预设条件的振幅值样点分别进行求和处理，得到每个振幅值样点序列对应的绕射波成像结果包括：
通过以下公式将在每个振幅值样点序列中提取的符合预设条件的振幅值样点分别进行求和处理
Figure PCTCN2016113064-appb-000007
得到每个振幅值样点序列对应的绕射波成像结果；其中，V(m)为绕射波成像结果，m＝m(x,y,z)为地下成像空间每个成像点位置，u(r,tS+tR)为预处理后的地震炮集数据，r(x,y,z)为检波点位置，tS,tR分别为由炮点位置到成像点位置和由成像点位置到检波器位置的走时，A为偏移成像孔径，w(m,r)为几何扩散因子，TriM为基于马氏距离和反射波振幅去除的绕射波成像算子。
结合第一方面，本发明实施例提供了第一方面的第七种可能的实施方式，其中，获取目标区域的地震炮集数据和地震炮集数据对应的地震波偏移速度文件包括：
获取目标区域的地震炮集数据；
对地震炮集数据进行地震预处理，得到可用于偏移成像的地震炮集数据；其中，地震预处理至少包括：去除噪声处理和静校正处理；
对地震炮集数据进行偏移速度分析处理，得到地震炮集数据对应的地震波偏移速度文件。
第二方面，本发明实施例还提供了一种煤炭陷落柱识别装置，装置包括：
获取模块，用于获取目标区域的地震炮集数据和地震炮集数据对应的地震波偏移速度文件；其中，地震炮集数据包括多个单炮数据；每个单炮数据均包括地下岩层界面反射或者折射的多个地震波；地震波携带有地质信息；
绕射波走时计算模块，用于根据每个单炮数据对应的检波器空间位置和地震波偏移速度文件，计算由每个单炮数据的地震波炮点位置经地下成像空间任意一个成像点位置到每个单炮数据的地震波检波点位置的每个单炮数据的绕射波走时；
马氏距离计算处理模块，用于对每个单炮数据及单炮数据的绕射波走时进行马氏距离计算处理，获取每个单炮数据的绕射波振幅值样点；其中，绕射波振幅值样点携带有小尺度地质信息；小尺度地质信息至少包括：地层层位信息、断层信息和陷落柱信息；
成像处理模块，用于分别对每个单炮数据的绕射波振幅值样点进行成像处理，得到每个单炮数据的绕射波成像结果；
叠加处理模块，用于将地震炮集数据对应的所有单炮数据的绕射波成像结果进行叠加处理，得到地震炮集数据的绕射波成像结果，以便根据地震炮集数据的绕射波成像结果进行煤炭陷落柱识别。
结合第二方面，本发明实施例提供了第二方面的第一种可能的实施方式，其中，绕射波走时计算模块包括：
第一走时计算单元，用于根据每个单炮数据对应的检波器空间位置和地震波偏移速度文件，计算由每个单炮数据的地震波炮点位置到每一个地下成像空间成像点位置的走时tS；
第二走时计算单元，用于根据每个单炮数据对应的检波器空间位置和地震波偏移速度文件，计算由每一个地下成像空间成像点位置到每个单炮数据的地震波检波点位置的走时tR；其中，每个单炮数据在一个成像点的走时tR与走时tS相对应；
第一求和处理单元，用于分别对每个单炮数据的走时tS以及与走时tS对应的走时tR进行求和处理，得到对应于每个单炮数据的多个绕射波走时。
本发明实施例提供的一种煤炭陷落柱识别方法和装置，包括：获取目标区域的地震炮集数据和地震波偏移速度文件；根据每个单炮数据对应的检波器空间位置和地震波偏移速度文件，计算每个单炮数据对应在不同成像点的绕射波走时；对每个单炮数据及单炮数据的绕射波走时进行马氏距离计算处理，获取每个单炮数据的绕射波；分别对每个单炮数据的绕射波进行成像处理，得到每个单炮数据的绕射波成像结果；将地震炮集数据对应的所有单炮数据的绕射波成像结果进行叠加处理，得到地震炮集数据的绕射波成像结果，并根据地震炮集数据的绕射波成像结果进行煤炭陷落柱识别。
与现有技术中的针对反射波研发的地震波成像技术进行陷落柱识别的效果不佳相比，其首先计算地震炮集数据中每个单炮数据对应的绕射波走时，并通过马氏距离对单炮数据及单炮数据的绕射波走时进行计算处理，获取所有单炮数据对应的绕射波，并对绕射波进行成像处理；由于绕射波携带的是更小尺度的地质信息，故通过成像后的绕射波能够准确的评价陷落柱地质体，即能够准确的进行目标区域中煤炭陷落柱的识别，从而能够降低煤炭开采中由陷落柱而引发的突水、瓦斯泄露等事故发生风险，减少不必要的人员伤亡及经济损失。
为使本发明的上述目的、特征和优点能更明显易懂，下文特举较佳实施例，并配合所附附图，作详细说明如下。
附图说明
为了更清楚地说明本发明实施例的技术方案，下面将对实施例中所需要使用的附图作简单地介绍，应当理解，以下附图仅示出了本发明的某些实施例，因此不应被看作是对范围的限定，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他相关的附图。
图1示出了本发明实施例所提供的一种煤炭陷落柱识别方法的流程图；
图2示出了本发明实施例所提供的另一种煤炭陷落柱识别方法的流程图；
图3示出了本发明实施例所提供的另一种煤炭陷落柱识别方法的流程图；
图4示出了本发明实施例所提供的另一种煤炭陷落柱识别方法的流程图；
图5示出了本发明实施例所提供的另一种煤炭陷落柱识别方法的流程图；
图6示出了本发明实施例所提供的常规反射波成像结果的示意图；
图7示出了应用本发明实施例所提供的一种煤炭陷落柱识别方法得到的绕射波成像结果的示意图；其中，包括了相应位置的剖面和切片图；
图8示出了本发明实施例所提供的一种煤炭陷落柱识别装置的结构示意图；
图9示出了本发明实施例所提供的一种煤炭陷落柱识别装置中绕射波走时计算模块和马氏距离计算处理模块的结构示意图。
具体实施方式
下面将结合本发明实施例中附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。通常在此处附图中描述和示出的本发明实施例的组件可以以各种不同的配置来布置和设计。因此，以下对在附图中提供的本发明的实施例的详细描述并非旨在限制要求保护的本发明的范围，而是仅仅表示本发明的选定实施例。基于本发明的实施例，本领域技术人员在没有做出创造性劳动的前提下所获得的所有其他实施例，都属于本发明保护的范围。
目前采取地震波成像技术识别陷落柱的方法主要依赖于反射波的成像结果，从而不能很好的对煤炭陷落柱等小尺度的地质信息进行识别。实际中，绕射波携带的是小尺度的地质信息，因此可以探测到空间展布小于一个地震波长的地质信息；随着地震波成像技术的完善和计算机处理能力的发展，目前已发展到利用绕射波直接成像，以探测小尺度地质体，如岩溶洞穴、裂缝等。在绕射波成像技术研 究上，主要分为三大类，包括基于信号的绕射波分离及成像方法(Harlan,et al.,1984；Taner et al.,2006；Fomel et al.,2006,2007；Bansal and Inhof,2005)、基于聚焦思想的绕射波成像方法(Berkovitch et al.,2009；Dell and Gajewski,2011；Asgedom et al.,2011)和修改Kirchhoff成像函数的绕射波成像方法(Zhang,2004；Moser and Howard,2008；Figueiredo et al.,2013；Zhao et al.,2015)。
本发明实施例提供的一种煤炭陷落柱识别方法和装置(也可以称为基于地震绕射波成像的煤炭陷落柱识别方法和装置)，属于上述第三类绕射波成像技术，原理上依据绕射波与反射波振幅特征差异，通过统计学马氏距离准则实现绕射波成像，由于绕射波携带的是更小尺度的地质信息，故直接应用绕射波成像评价煤炭陷落柱，能够更加真实反映地下地质情况，从而准确的进行目标区域中煤炭陷落柱的识别，且无需事先去除反射波，具有高效计算能力。
参考图1，本发明实施例提供了一种煤炭陷落柱识别方法，所述方法包括：
S101、获取目标区域的地震炮集数据和所述地震炮集数据对应的地震波偏移速度文件；其中，地震炮集数据包括多个单炮数据；每个单炮数据均包括地下岩层界面反射或者折射的多个地震波；地震波携带有地质信息。
具体的，目标区域为预先选定的待进行煤炭陷落柱识别的区域，该目标区域包括多个震源和多个检波器，一个震源对应多个检波器；其中，多个震源对应多个地震波炮点位置，多个检波器对应多个地震波检波点位置；其中，多个震源可以发射多个单炮数据，多个单炮数据组成地震炮集数据；对应的，多个检波器能够采集多个单炮数据，多个单炮数据组成地震炮集数据。
具体的，上述地震波偏移速度文件是对上述地震炮集数据进行偏移速度分析处理得到的；其中，上述地震波偏移速度文件，对应于地层速度参数，用于计算地震波传播旅行时(即时间)，其是由对地震炮集数据进行偏移速度分析获得；而上述偏移速度分析是一种速度建模技术，该速度建模技术通过分析地震采集数据中的地震波聚焦性确定速度参数，即获得地震波偏移速度文件。
其中，上述地震波中携带的地质信息包括小尺度地质信息，还包括大尺度地质信息；其中，上述小尺度地址信息包括：地层层位信息、断层信息和陷落柱信息。
S102、根据每个所述单炮数据对应的检波器空间位置和所述地震波偏移速度文件，计算由每个单炮数据的地震波炮点位置经地下成像空间任意一个成像点位置到每个单炮数据的地震波检波点位置的每个单炮数据的绕射波走时；其中，每一个所述单炮数据对应的检波器为多个，且每个所述检波器均设置在对应的所述检波点位置上。
具体的，根据每个单炮数据对应的检波器空间位置和该单炮数据对应的地震波偏移速度文件进行绕射波走时计算，即根据射线追踪程函方程进行绕射波走时计算，而射线追踪程函方程是速度(即单炮数据对应的地震波偏移速度文件)，空间位置(即单炮数据对应的检波器空间位置)和地震波走时的微分关系。
由于获取的目标区域的地震炮集数据包括多个单炮数据，那么对于每一个单炮数据，根据该单炮数据和对应的地震波偏移速度文件，首先计算该单炮数据的地震波炮点位置到地下成像空间任意一个成像点位置对应的走时(为了区分下面的走时，此处成为第一走时)，由于地下成像空间中包括多个成 像点，故计算的该单炮数据的地震波炮点位置到地下成像空间任意一个成像点位置对应的第一走时也为多个；
然后再计算地下成像空间任意一个成像点位置到该单炮数据的地震波检波点位置的多个绕射波走时(此处称为第二走时)，其中，每一个成像点处的一个第二走时均与一个第一走时相对应。
最后将上述第一走时和第一走时对应的第二走时相加即可得到一个单炮数据对应的绕射波走时。
对于地震炮集数据中的每个单炮数据均可以按照上述单炮数据的绕射波走时方式进行计算，得到地震炮集数据中的每个单炮数据的绕射波走时。
S103、对每个所述单炮数据及所述单炮数据的绕射波走时进行马氏距离计算处理，获取每个所述单炮数据的绕射波振幅值样点；其中，所述绕射波振幅值样点携带有小尺度地质信息；所述小尺度地质信息至少包括：地层层位信息、断层信息和陷落柱信息。
具体的，对于每一个单炮数据，根据该单炮数据及该单炮数据的绕射波走时可以计算得出该单炮数据对应的地下成像空间每个成像点的多个振幅值样点序列(即多个成像振幅值样点序列)；
对于每一个振幅值样点序列，计算该振幅值样点序列中每个振幅值样点的马氏距离，最后根据计算的马氏距离剔除该振幅值样点序列中的反射波对应振幅值样点，得到的剩余的振幅值样点即为该振幅值样点序列中对应的绕射波振幅值样点。
其中，一个单炮数据包括多个振幅值样点序列，故根据上述一个振幅值样点序列中绕射波振幅值样点的计算方法即可以计算出一个单炮数据对应的多组振幅值样点序列。
按照上述单炮数据对应的多组振幅值样点序列的计算方法，可以计算地震炮集数据中所有单炮数据对应的振幅值样点序列。
S104、分别对每个所述单炮数据的绕射波振幅值样点进行成像处理，得到每个所述单炮数据的绕射波成像结果。
具体的，对于每个单炮数据，每个单炮数据包括多个振幅值样点序列；将每一个振幅值样点序列中的所有的绕射波振幅值样点进行求和，即可得到每一个振幅值样点序列对应的绕射波成像结果；
对于每个单炮数据，将其包括的每一个振幅值样点序列对应的绕射波成像结果进行叠加处理，即可得到该单炮数据对应的绕射波成像结果。
S105、将所述地震炮集数据对应的所有单炮数据的绕射波成像结果进行叠加处理，得到所述地震炮集数据的绕射波成像结果，以便根据所述地震炮集数据的绕射波成像结果进行煤炭陷落柱识别。
由于获取的地震炮集数据才是携带反应目标区域的地质信息的数据，而地震炮集数据中的每个单炮数据只是携带反应目标区域的地质信息的一部分数据；在实际计算过程中是针对每个单炮数据单独进行成像处理，最后将每一个单炮数据的成像处理结果进行叠加处理，即可得到地震炮集数据的绕射波成像结果。
根据最终得到的地震炮集数据的绕射波成像结果进行煤炭陷落柱识别，即可准确的评价陷落柱地质体，从而准确的进行目标区域中煤炭陷落柱的识别。
本发明实施例提供的一种煤炭陷落柱识别方法，与现有技术中的针对反射波研发的地震波成像技术进行陷落柱识别的效果不佳相比，其首先计算地震炮集数据中每个单炮数据对应的绕射波走时，并通过马氏距离对单炮数据及单炮数据的绕射波走时进行计算处理，获取所有单炮数据对应的绕射波，并对绕射波进行成像处理；由于绕射波携带的是更小尺度的地质信息，故通过成像后的绕射波能够准确的评价陷落柱地质体，即能够准确的进行目标区域中煤炭陷落柱的识别，从而能够降低煤炭开采中由陷落柱而引发的突水、瓦斯泄露等事故发生风险，减少不必要的人员伤亡及经济损失。
具体的，由于获取的目标区域的地震炮集数据包括多个单炮数据，本发明实施例中技术每一个单炮数据对应的绕射波走时，其中，每一个单炮数据对应的地下成像空间包括多个成像点，故每一个单炮数据对应的绕射波走时也为多个，参考图2，计算每个单炮数据对应的多个绕射波走时的具体方法包括如下步骤：
S201、根据每个所述单炮数据对应的检波器空间位置和所述地震波偏移速度文件，计算由每个单炮数据的地震波炮点位置到每一个地下成像空间成像点位置的走时tS。
具体的，由于每个单炮数据对应的地下成像空间成像点有多个，故对应的地下成像空间成像点位置也有多个，故计算该单炮数据到每一个地下成像空间成像点位置的走时tS也对应有多个；即不同的地下成像空间成像点位置对应不同的走时tS。
S202、根据每个所述单炮数据对应的检波器空间位置和所述地震波偏移速度文件，计算由每一个地下成像空间成像点位置到每个单炮数据的地震波检波点位置的走时tR；其中，每个所述单炮数据在一个成像点的走时tR与走时tS相对应。
同样的，由于每个单炮数据对应的地下成像空间成像点有多个，故对应的地下成像空间成像点位置也有多个，故每一个地下成像空间成像点位置到每个单炮数据的地震波检波点位置的走时tR有对应有多个，即不同的地下成像空间成像点位置对应不同的走时tR。
需要说明的是，每个所述单炮数据在一个成像点的走时tR与走时tS相对应。
S203、分别对每个单炮数据的走时tS以及与所述走时tS对应的走时tR进行求和处理，得到对应于每个所述单炮数据的多个绕射波走时。
参考图3，本发明实施例中，上述步骤103中对每个所述单炮数据及所述单炮数据的绕射波走时进行马氏距离计算处理，获取每个所述单炮数据的绕射波振幅值样点的具体步骤包括：
S301、对于任意一个单炮数据，根据所述单炮数据和所述单炮数据的多个绕射波走时，计算所述单炮数据对应的地下成像空间中每个成像点的振幅值样点序列，得到对应于所述单炮数据的多个振幅值样点序列。
具体的，每一个单炮数据对应于不同的地下成像空间中的成像点有不同的绕射波走时；对于任意一个单炮数据，根据该单炮数据和该单炮数据在一个成像点处的一个绕射波走时，可以计算处该单炮 数据在该成像点的一个振幅值样点序列，而对于该单炮数据的每一个成像点，均能计算出对应的振幅值样点序列，而一个单炮数据又对应多个成像点，故可以计算得到对应于所述单炮数据的多个振幅值样点序列。
S302、根据振幅值样点的能量大小，分别对得到的每一个振幅值样点序列中的振幅值样点进行排序处理。
其中，对应于每个单炮数据，每个单炮数据包括多个振幅值样点序列，每个振幅值样点序列中包括多个振幅值样点；由于每个振幅值样点对应的能量大小不同，故根据振幅值样点的能量大小，可以分别对得到的每一个振幅值样点序列中的振幅值样点进行排序处理，使得每一个振幅值样点序列中的振幅值样点均按预设顺序进行排列。
S303、分别对排序处理后的每一个振幅值样点序列进行马氏距离计算处理，得到每一个振幅值样点序列中按预设顺序排列的所有振幅值样点的马氏距离。
具体的，每个单炮数据均包括多个振幅值样点序列，对于每一个振幅值样点序列，计算该振幅值样点序列中的按照预设顺序进行排列的每一个振幅值样点的马氏距离。
对应的，将每个单炮数据包括多个振幅值样点序列均按照上述马氏距离计算方法进行计算处理，即可得到每一个振幅值样点序列中按预设顺序排列的每个振幅值样点的马氏距离。
S304、根据每个振幅值样点序列中按预设顺序排列的所有振幅值样点的马氏距离，提取对应的每个振幅值样点序列中符合预设条件的振幅值样点，得到对应于每个所述单炮数据的多组绕射波振幅值样点。
具体的，绕射波的振幅值样点的马氏距离和反射波的振幅值样点的马氏距离均是有预设阈值的，该预设阈值是根据多次实验计算得到，因此，可以根据绕射波的振幅值样点的第一预设马氏距离，提取对应的每个振幅值样点序列中符合该第一预设马氏距离的振幅值样点，将提取的振幅值样点作为绕射波振幅值样点；也可以根据反射波的振幅值样点的第二预设马氏距离，剔除对应的每个振幅值样点序列中符合该第二预设马氏距离的振幅值样点，得到的剩余的振幅值样点即为符合预设条件的振幅值样点，同样将提取的振幅值样点作为绕射波振幅值样点。
由于每个单炮数据对应多个振幅值样点序列，故每个单炮数据也对应多组绕射波振幅值样点。即每个单炮数据的绕射波振幅值样点的组数与该单炮数据的振幅值样点序列的个数相同。
本发明实施例中，上述步骤303中计算振幅值样点的马氏距离的方法具体如下：
通过以下公式计算每一个按预设顺序排列的振幅值样点序列的中值
Figure PCTCN2016113064-appb-000008
其中，μx为振幅值样点序列的中值，median为中值运算，
Figure PCTCN2016113064-appb-000009
为按预设顺序排序后的振幅样点序列，N为振幅值样点序列中样点个数；
通过以下公式计算每一个按预设顺序排列的所述振幅值样点序列的中值绝对值偏差平方
Figure PCTCN2016113064-appb-000010
其中，
Figure PCTCN2016113064-appb-000011
为中值绝对值偏差平方；
根据所述中值和所述中值绝对值偏差平方，计算每一个振幅值样点序列中按预设顺序排列的所有振幅值样点的马氏距离
Figure PCTCN2016113064-appb-000012
其中，
Figure PCTCN2016113064-appb-000013
为马氏距离。
参考图4，本发明实施例中的上述步骤304中，计算每个单炮数据的多组绕射波振幅值样点的具体方法包括：
S401、对于任意一个振幅值样点序列，根据所述振幅值样点序列中按预设顺序排列的每个振幅值样点的马氏距离以及反射波振幅值样点的预设马氏距离阈值，计算所述振幅值样点序列中的样点去除数量。
具体的，对于任意一个振幅值样点序列，将计算的马氏距离与反射波振幅值样点的预设马氏距离阈值(即上述第二预设马氏距离)进行对比，将符合反射波振幅值样点的预设马氏距离阈值的计算结果作为剔除处理对象，按照该种方式即可以计算出振幅值样点序列中的样点去除数量。
S402、根据所述预设马氏距离阈值和所述样点去除数量，对所述振幅值样点序列中按预设顺序排列的振幅值样点进行样点去除处理，得到所述振幅值样点序列中剩余的振幅值样点。
具体的，根据样点去除数量在排序后的振幅值样点序列中提取绕射波振幅值样点，即对排序后的振幅值样点序列，去除序列首段和末端对应的不符合条件的振幅值样点，去除个数则根据上述计算的样点去除数量决定。
S403、提取所述剩余的振幅值样点作为所述振幅值样点序列中符合预设条件的振幅值样点。
具体的，剩余的振幅值样点都是满足绕射波的振幅值样点的第一预设马氏距离，故提取这些剩余的振幅值样点作为符合预设条件的振幅值样点。
本发明实施例中，步骤104中分别对每个所述单炮数据的绕射波振幅值样点进行成像处理，得到每个所述单炮数据的绕射波成像结果的具体方法包括：
将在每个振幅值样点序列中提取的符合预设条件的振幅值样点分别进行求和处理，得到每个振幅值样点序列对应的绕射波成像结果；
将所述单炮数据对应的所有振幅值样点序列的绕射波成像结果进行叠加处理，得到对应于所述单炮数据的绕射波成像结果。
具体的，由于每个单炮数据包括多个振幅值样点序列，针对每个振幅值样点序列中提取出了预设条件的振幅值样点(即绕射波的振幅值样点)，然后将每个振幅值样点序列的绕射波的振幅值样点进行求和处理，即可得到该振幅值样点序列对应的绕射波成像结果；
在求出每个单炮数据中的所有的该振幅值样点序列对应的绕射波成像结果之后，将这些绕射波成像结果进行叠加处理，即可得到对应于该单炮数据的绕射波成像结果。
对于地震炮集数据，则将其包括的每个单炮数据的绕射波成像结果同样进行叠加处理，即可得到地震炮集数据的绕射波成像结果。
具体的，上述计算每个振幅值样点序列对应的绕射波成像结果包括：
通过以下公式将在每个振幅值样点序列中提取的符合预设条件的振幅值样点分别进行求和处理
Figure PCTCN2016113064-appb-000014
得到每个振幅值样点序列对应的绕射波成像结果；其中，V(m)为绕射波成像结果，m＝m(x,y,z)为地下成像空间每个成像点位置，u(r,tS+tR)为预处理后的地震炮集数据，r(x,y,z)为检波点位置，tS,tR分别为由炮点位置到成像点位置和由成像点位置到检波器位置的走时，A为偏移成像孔径，w(m,r)为几何扩散因子，TriM为基于马氏距离和反射波振幅去除的绕射波成像算子。
本发明实施例中，为了保证更好在获取的地震炮集数据中提取绕射波，还需要对获取的地震炮集数据进行处理，参考图5，具体方法包括：
S501、获取目标区域的地震炮集数据；
S502、对所述地震炮集数据进行地震预处理，得到可用于偏移成像的地震炮集数据；其中，所述地震预处理至少包括：去除噪声处理和静校正处理。
S503、对所述地震炮集数据进行偏移速度分析处理，得到所述地震炮集数据对应的地震波偏移速度文件。
结合上述步骤501-步骤503，由检波器接收炮集数据(即地震波)，加载观测系统，将获取的炮集数据发送至观测系统，由观测系统对地震炮集数据进行地震预处理，即观测系统加载接收的地震炮集数据，并对加载的地震波进行噪声去除、速度分析和偏移后获得地震炮集数据和地震炮集数据对应的地震波偏移速度文件。
下面结合具体实施例对本发明实施例提供的一种煤炭陷落柱识别方法进行说明
通过煤炭三维地震资料，说明一种基于地震绕射波成像的煤炭陷落柱评价技术及装置在煤炭陷落柱评价中的应用效果。
(1)读入地震炮集数据和偏移速度文件；
(2)由输入的偏移速度模型，计算地震波走时表；
(3)根据走时表和地震炮集数据，得出绕射波成像结果；
(4)为对比绕射波成像结果和常规反射波成像结果在陷落柱评价上的应用效果，本实施例提供了两者对比图，如图6为常规反射波成像结果，包括了主测线方向和联络线方向剖面以及0.24秒切片图，如图7为绕射波成像结果，包括了相应位置的剖面和切片图。
如图6和图7所示，绕射波成像结果在切片主测线和联络线交叉位置很好的显示了已验证陷落柱的形态，而该陷落柱在反射波成像切片上并没有出现。
本发明实施例提供的一种煤炭陷落柱识别方法和装置，与现有技术中的针对反射波研发的地震波成像技术进行陷落柱识别的效果不佳相比，其首先计算地震炮集数据中每个单炮数据对应的绕射波走时，并通过马氏距离对单炮数据及单炮数据的绕射波走时进行计算处理，获取所有单炮数据对应的绕射波，并对绕射波进行成像处理；由于绕射波携带的是更小尺度的地质信息，故通过成像后的绕射波 能够准确的评价陷落柱地质体，即能够准确的进行目标区域中煤炭陷落柱的识别，从而能够降低煤炭开采中由陷落柱而引发的突水、瓦斯泄露等事故发生风险，减少不必要的人员伤亡及经济损失。
本发明实施例还提供了一种煤炭陷落柱识别装置，参考图8，所述装置包括：
获取模块11，用于获取目标区域的地震炮集数据和所述地震炮集数据对应的地震波偏移速度文件；其中，所述地震炮集数据包括多个单炮数据；每个所述单炮数据均包括地下岩层界面反射或者折射的多个地震波；所述地震波携带有地质信息；
绕射波走时计算模块12，用于根据每个单炮数据对应的检波器空间位置和地震波偏移速度文件，计算由每个单炮数据的地震波炮点位置经地下成像空间任意一个成像点位置到每个单炮数据的地震波检波点位置的每个单炮数据的绕射波走时；
马氏距离计算处理模块13，用于对每个单炮数据及单炮数据的绕射波走时进行马氏距离计算处理，获取每个单炮数据的绕射波振幅值样点；其中，绕射波振幅值样点携带有小尺度地质信息；小尺度地质信息至少包括：地层层位信息、断层信息和陷落柱信息；
成像处理模块14，用于分别对每个单炮数据的绕射波振幅值样点进行成像处理，得到每个单炮数据的绕射波成像结果；
叠加处理模块15，用于将地震炮集数据对应的所有单炮数据的绕射波成像结果进行叠加处理，得到地震炮集数据的绕射波成像结果，以便根据地震炮集数据的绕射波成像结果进行煤炭陷落柱识别。
进一步的，参考图9，所述基于地震绕射波成像的煤炭陷落柱识别装置中，绕射波走时计算模块12包括：
第一走时计算单元121，用于根据每个单炮数据对应的检波器空间位置和地震波偏移速度文件，计算由每个单炮数据的地震波炮点位置到每一个地下成像空间成像点位置的走时tS；
第二走时计算单元122，用于根据每个单炮数据对应的检波器空间位置和地震波偏移速度文件，计算由每一个地下成像空间成像点位置到每个单炮数据的地震波检波点位置的走时tR；其中，每个单炮数据在一个成像点的走时tR与走时tS相对应；
第一求和处理单元123，用于分别对每个单炮数据的走时tS以及与走时tS对应的走时tR进行求和处理，得到对应于每个单炮数据的多个绕射波走时。
进一步的，参考图9，所述基于地震绕射波成像的煤炭陷落柱识别装置中，马氏距离计算处理模块13包括：
振幅值样点序列计算单元131，用于对于任意一个单炮数据，根据单炮数据和单炮数据的多个绕射波走时，计算单炮数据对应的地下成像空间中每个成像点的振幅值样点序列，得到对应于单炮数据的多个振幅值样点序列；
排序处理单元132，用于根据振幅值样点的能量大小，分别对得到的每一个振幅值样点序列中的振幅值样点进行排序处理；
马氏距离计算单元133，用于分别对排序处理后的每一个振幅值样点序列进行马氏距离计算处理，得到每一个振幅值样点序列中按预设顺序排列的所有振幅值样点的马氏距离；
提取单元134，用于根据每个振幅值样点序列中按预设顺序排列的所有振幅值样点的马氏距离，提取对应的每个振幅值样点序列中符合预设条件的振幅值样点，得到对应于每个所述单炮数据的多组绕射波振幅值样点。
进一步的，所述基于地震绕射波成像的煤炭陷落柱识别装置中，马氏距离计算单元133包括：
第一计算子单元，用于通过以下公式计算每一个按预设顺序排列的振幅值样点序列的中值
Figure PCTCN2016113064-appb-000015
其中，μx为振幅值样点序列的中值，median为中值运算，
Figure PCTCN2016113064-appb-000016
为按预设顺序排序后的振幅样点序列，N为振幅值样点序列中样点个数；
第二计算字单元，用于通过以下公式计算每一个按预设顺序排列的振幅值样点序列的中值绝对值偏差平方
Figure PCTCN2016113064-appb-000017
其中，
Figure PCTCN2016113064-appb-000018
为中值绝对值偏差平方；
第三计算子单元，用于根据中值和中值绝对值偏差平方，计算每一个振幅值样点序列中按预设顺序排列的所有振幅值样点的马氏距离
Figure PCTCN2016113064-appb-000019
其中，
Figure PCTCN2016113064-appb-000020
为马氏距离。
进一步的，所述基于地震绕射波成像的煤炭陷落柱识别装置中，提取单元334包括：
第四计算子单元，用于对于任意一个振幅值样点序列，根据振幅值样点序列中按预设顺序排列的每个振幅值样点的马氏距离以及反射波振幅值样点的预设马氏距离阈值，计算振幅值样点序列中的样点去除数量；
样点去除处理子单元，用于根据所述预设马氏距离阈值和所述样点去除数量，对所述振幅值样点序列中按预设顺序排列的振幅值样点进行样点去除处理，得到所述振幅值样点序列中剩余的振幅值样点；
提取子单元，用于提取所述剩余的振幅值样点作为所述振幅值样点序列中符合预设条件的振幅值样点。
进一步的，所述基于地震绕射波成像的煤炭陷落柱识别装置中，成像处理模块14包括：
第二求和处理单元，用于将在每个振幅值样点序列中提取的符合预设条件的振幅值样点分别进行求和处理，得到每个振幅值样点序列对应的绕射波成像结果；
叠加处理单元，用于将所述单炮数据对应的所有振幅值样点序列的绕射波成像结果进行叠加处理，得到对应于所述单炮数据的绕射波成像结果。
进一步的，所述基于地震绕射波成像的煤炭陷落柱识别装置中，所述第二求和处理单元还用于，通过以下公式分别对每个单炮数据的绕射波进行成像处理
Figure PCTCN2016113064-appb-000021
其中，V(m)为绕射波成像结果，m＝m(x,y,z)为地下成像空间每个成像点位置，u(r,tS+tR)为预处理 后的地震炮集数据，r(x,y,z)为检波点位置，tS,tR分别为由炮点位置到成像点位置和由成像点位置到检波器位置的走时，A为偏移成像孔径，w(m,r)为几何扩散因子，TriM为基于马氏距离和反射波振幅去除的绕射波成像算子。
进一步的，所述基于地震绕射波成像的煤炭陷落柱识别装置中，获取模块11包括：
获取单元，用于获取目标区域的地震炮集数据；
地震预处理单元，用于对地震炮集数据进行地震预处理，得到可用于偏移成像的地震炮集数据；其中，地震预处理至少包括：去除噪声处理和静校正处理；
偏移速度分析处理单元，用于对地震炮集数据进行偏移速度分析处理，得到地震炮集数据对应的地震波偏移速度文件。
本发明实施例提供的一种煤炭陷落柱识别装置，与现有技术中的针对反射波研发的地震波成像技术进行陷落柱识别的效果不佳相比，其首先计算地震炮集数据中每个单炮数据对应的绕射波走时，并通过马氏距离对单炮数据及单炮数据的绕射波走时进行计算处理，获取所有单炮数据对应的绕射波，并对绕射波进行成像处理；由于绕射波携带的是更小尺度的地质信息，故通过成像后的绕射波能够准确的评价陷落柱地质体，即能够准确的进行目标区域中煤炭陷落柱的识别，从而能够降低煤炭开采中由陷落柱而引发的突水、瓦斯泄露等事故发生风险，减少不必要的人员伤亡及经济损失。
在本申请所提供的几个实施例中，应该理解到，所揭露的系统、装置和方法，可以通过其它的方式实现。以上所描述的装置实施例仅仅是示意性的，例如，所述单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，又例如，多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些通信接口，装置或单元的间接耦合或通信连接，可以是电性，机械或其它的形式。
所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。
另外，在本发明各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。
所述功能如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本发明的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本发明各个实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质。
以上所述，仅为本发明的具体实施方式，但本发明的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本发明揭露的技术范围内，可轻易想到变化或替换，都应涵盖在本发明的保护范围之内。因此，本发明的保护范围应以所述权利要求的保护范围为准。

Claims (10)

  1. 一种煤炭陷落柱识别方法，其特征在于，包括：
    获取目标区域的地震炮集数据和所述地震炮集数据对应的地震波偏移速度文件；其中，所述地震炮集数据包括多个单炮数据；每个所述单炮数据均包括地下岩层界面反射或者折射的多个地震波；所述地震波携带有地质信息；
    根据每个所述单炮数据对应的检波器空间位置和所述地震波偏移速度文件，计算由每个单炮数据的地震波炮点位置经地下成像空间任意一个成像点位置到每个单炮数据的地震波检波点位置的每个单炮数据的绕射波走时；
    对每个所述单炮数据及所述单炮数据的绕射波走时进行马氏距离计算处理，获取每个所述单炮数据的绕射波振幅值样点；其中，所述绕射波振幅值样点携带有小尺度地质信息；所述小尺度地质信息至少包括：地层层位信息、断层信息和陷落柱信息；
    分别对每个所述单炮数据的绕射波振幅值样点进行成像处理，得到每个所述单炮数据的绕射波成像结果；
    将所述地震炮集数据对应的所有单炮数据的绕射波成像结果进行叠加处理，得到所述地震炮集数据的绕射波成像结果，以便根据所述地震炮集数据的绕射波成像结果进行煤炭陷落柱识别。
  2. 根据权利要求1所述的方法，其特征在于，所述根据每个所述单炮数据对应的检波器空间位置和所述地震波偏移速度文件，计算由每个单炮数据的地震波炮点位置经地下成像空间任意一个成像点位置到每个单炮数据的地震波检波点位置的每个单炮数据的绕射波走时包括：
    根据每个所述单炮数据对应的检波器空间位置和所述地震波偏移速度文件，计算由每个单炮数据的地震波炮点位置到每一个地下成像空间成像点位置的走时tS；
    根据每个所述单炮数据对应的检波器空间位置和所述地震波偏移速度文件，计算由每一个地下成像空间成像点位置到每个单炮数据的地震波检波点位置的走时tR；其中，每个所述单炮数据在一个成像点的走时tR与走时tS相对应；
    分别对每个单炮数据的走时tS以及与所述走时tS对应的走时tR进行求和处理，得到对应于每个所述单炮数据的多个绕射波走时。
  3. 根据权利要求2所述的方法，其特征在于，所述对每个所述单炮数据及所述单炮数据的绕射波走时进行马氏距离计算处理，获取每个所述单炮数据的绕射波振幅值样点包括：
    对于任意一个单炮数据，根据所述单炮数据和所述单炮数据的多个绕射波走时，计算所述单炮数据对应的地下成像空间中每个成像点的振幅值样点序列，得到对应于所述单炮数据的多个振幅值样点序列；
    根据振幅值样点的能量大小，分别对得到的每一个振幅值样点序列中的振幅值样点进行排序处理；
    分别对排序处理后的每一个振幅值样点序列进行马氏距离计算处理，得到每一个振幅值样点序列中按预设顺序排列的所有振幅值样点的马氏距离；
    根据每个振幅值样点序列中按预设顺序排列的所有振幅值样点的马氏距离，提取对应的每个振幅值样点序列中符合预设条件的振幅值样点，得到对应于每个所述单炮数据的多组绕射波振幅值样点。
  4. 根据权利要求3所述的方法，其特征在于，所述分别对排序处理后的每一个振幅值样点序列进行马氏距离计算处理，得到每一个振幅值样点序列中按预设顺序排列的所有振幅值样点的马氏距离包括：
    通过以下公式计算每一个按预设顺序排列的振幅值样点序列的中值
    Figure PCTCN2016113064-appb-100001
    其中，μx为振幅值样点序列的中值，median为中值运算，
    Figure PCTCN2016113064-appb-100002
    i＝1,2,…,N为按预设顺序排序后的振幅样点序列，N为振幅值样点序列中样点个数；
    通过以下公式计算每一个按预设顺序排列的所述振幅值样点序列的中值绝对值偏差平方
    Figure PCTCN2016113064-appb-100003
    其中，
    Figure PCTCN2016113064-appb-100004
    为中值绝对值偏差平方；
    根据所述中值和所述中值绝对值偏差平方，计算每一个振幅值样点序列中按预设顺序排列的所有振幅值样点的马氏距离
    Figure PCTCN2016113064-appb-100005
    其中，
    Figure PCTCN2016113064-appb-100006
    为马氏距离。
  5. 根据权利要求4所述的方法，其特征在于，所述根据每个振幅值样点序列中按预设顺序排列的所有振幅值样点的马氏距离，提取对应的每个振幅值样点序列中符合预设条件的振幅值样点包括：
    对于任意一个振幅值样点序列，根据所述振幅值样点序列中按预设顺序排列的每个振幅值样点的马氏距离以及反射波振幅值样点的预设马氏距离阈值，计算所述振幅值样点序列中的样点去除数量；
    根据所述预设马氏距离阈值和所述样点去除数量，对所述振幅值样点序列中按预设顺序排列的振幅值样点进行样点去除处理，得到所述振幅值样点序列中剩余的振幅值样点；
    提取所述剩余的振幅值样点作为所述振幅值样点序列中符合预设条件的振幅值样点。
  6. 根据权利要求5所述的方法，其特征在于，所述分别对每个所述单炮数据的绕射波振幅值样点进行成像处理，得到每个所述单炮数据的绕射波成像结果包括：
    将在每个振幅值样点序列中提取的符合预设条件的振幅值样点分别进行求和处理，得到每个振幅值样点序列对应的绕射波成像结果；
    将所述单炮数据对应的所有振幅值样点序列的绕射波成像结果进行叠加处理，得到对应于所述单炮数据的绕射波成像结果。
  7. 根据权利要求6所述的方法，其特征在于，所述将在每个振幅值样点序列中提取的符合预设条件的振幅值样点分别进行求和处理，得到每个振幅值样点序列对应的绕射波成像结果包括：
    通过以下公式将在每个振幅值样点序列中提取的符合预设条件的振幅值样点分别进行求和处理
    Figure PCTCN2016113064-appb-100007
    得到每个振幅值样点序列对应的绕射波成像结果；其中，V(m)为绕射波成像结果，m＝m(x,y,z)为地下成像空间每个成像点位置，u(r,tS+tR)为预处理后的地震炮集数据，r(x,y,z)为检波点位置，tS,tR分别为由炮点位置到成像点位置和由成像点位置到检波器位置的走时，A为偏移成像孔径，w(m,r)为几何扩散因子，TriM为基于马氏距离和反射波振幅去除的绕射波成像算子。
  8. 根据权利要求1所述的方法，其特征在于，所述获取目标区域的地震炮集数据和所述地震炮集数据对应的地震波偏移速度文件包括：
    获取目标区域的地震炮集数据；
    对所述地震炮集数据进行地震预处理，得到可用于偏移成像的地震炮集数据；其中，所述地震预处理至少包括：去除噪声处理和静校正处理；
    对所述地震炮集数据进行偏移速度分析处理，得到所述地震炮集数据对应的地震波偏移速度文件。
  9. 一种煤炭陷落柱识别装置，其特征在于，所述装置包括：
    获取模块，用于获取目标区域的地震炮集数据和所述地震炮集数据对应的地震波偏移速度文件；其中，所述地震炮集数据包括多个单炮数据；每个所述单炮数据均包括地下岩层界面反射或者折射的多个地震波；所述地震波携带有地质信息；
    绕射波走时计算模块，用于根据每个所述单炮数据对应的检波器空间位置和所述地震波偏移速度文件，计算由每个单炮数据的地震波炮点位置经地下成像空间任意一个成像点位置到每个单炮数据的地震波检波点位置的每个单炮数据的绕射波走时；
    马氏距离计算处理模块，用于对每个所述单炮数据及所述单炮数据的绕射波走时进行马氏距离计算处理，获取每个所述单炮数据的绕射波振幅值样点；其中，所述绕射波振幅值样点携带有小尺度地质信息；所述小尺度地质信息至少包括：地层层位信息、断层信息和陷落柱信息；
    成像处理模块，用于分别对每个所述单炮数据的绕射波振幅值样点进行成像处理，得到每个所述单炮数据的绕射波成像结果；
    叠加处理模块，用于将所述地震炮集数据对应的所有单炮数据的绕射波成像结果进行叠加处理，得到所述地震炮集数据的绕射波成像结果，以便根据所述地震炮集数据的绕射波成像结果进行煤炭陷落柱识别。
  10. 根据权利要求9所述的装置，其特征在于，所述绕射波走时计算模块包括：
    第一走时计算单元，用于根据每个所述单炮数据对应的检波器空间位置和所述地震波偏移速度文件，计算由每个单炮数据的地震波炮点位置到每一个地下成像空间成像点位置的走时tS；
    第二走时计算单元，用于根据每个所述单炮数据对应的检波器空间位置和所述地震波偏移速度文件，计算由每一个地下成像空间成像点位置到每个单炮数据的地震波检波点位置的走时tR；其中，每个所述单炮数据在一个成像点的走时tR与走时tS相对应；
    第一求和处理单元，用于分别对每个单炮数据的走时tS以及与所述走时tS对应的走时tR进行求和处理，得到对应于每个所述单炮数据的多个绕射波走时。
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