Coal mine three-dimensional seismic detection data analysis and processing system

Abstract

The invention relates to the technical field of coal mine detection data analysis, in particular to a coal mine three-dimensional seismic detection data analysis and processing system, which comprises a data acquisition module used for setting a plurality of acquisition points and acquiring reflected wave signals corresponding to the acquisition points; the speed identification module is used for identifying the travel time of the reflected wave signal and establishing a speed distribution model of each underground layer according to the positions of a plurality of acquisition points; the reflection calculation module is used for identifying a reflection point corresponding to the reflection wave signal according to the speed distribution model, calculating the reflection wave signal on the reflection point, and obtaining a reflection coefficient corresponding to the reflection point; the offset forming module is used for mapping the reflected wave signal from an observed time domain to a spatial domain according to the identified reflection point to obtain an offset image corresponding to the reflected wave signal; matching the obtained offset image with a plurality of acquisition points; and the accuracy and efficiency of three-dimensional seismic detection are improved.

Description

Technical Field

[0001] The invention relates to the technical field of coal mine detection data analysis, and in particular to a coal mine three-dimensional seismic detection data analysis and processing system. Background Art

[0002] In the process of coal mining, geological exploration is an indispensable part. Traditional geological exploration methods mainly include geological mapping, drilling and geophysical exploration. However, these methods often have some problems in practical application. For example, although the geological mapping method can intuitively reflect the surface geological conditions, it is difficult to accurately judge the conditions deep underground; although the drilling method can obtain direct information about underground rock formations, it is costly and time-consuming; although the geophysical exploration method has a high detection efficiency, it often has the problem of insufficient accuracy under complex geological conditions.

[0003] For example, Chinese patent publication number CN117492091A discloses a method and system for detecting folds in coal mines, wherein the method includes: determining the fold core area according to the first fold profile in the depth domain corresponding to the stratum in the detection area, and obtaining the first oscillation signal received by the first oscillation wave receiver when the first seismic source is excited, wherein the first seismic source and the first oscillation receiver are deployed at intervals in a plurality of surface boreholes arranged at a first spacing distance in the fold core area, and a plurality of first seismic sources or a plurality of first oscillation wave receivers are deployed at a second spacing distance in each surface borehole, and then, according to the first oscillation signal, a second fold profile is constructed. Thus, based on the first fold profile of large-scale geological structure geophysical detection, the second fold profile is constructed in combination with small-scale inter-hole seismic detection.

[0004] However, under complex geological conditions, such as when there are faults, folds and other structures, it is necessary to conduct a combined analysis of whether these data are related and the corresponding situations of each data to improve the accuracy of identifying geological factors. Summary of the invention

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: a coal mine three-dimensional seismic detection data analysis and processing system, including: a data acquisition module, used to set multiple acquisition points and obtain reflection wave signals corresponding to the acquisition points.

[0006] The velocity identification module is used to identify the travel time of the reflected wave signal and establish the velocity distribution model of each underground layer according to the positions of multiple acquisition points.

[0007] The reflection calculation module is used to identify the reflection point corresponding to the reflection wave signal according to the velocity distribution model, calculate the reflection wave signal at the reflection point, and obtain the reflection coefficient corresponding to the reflection point.

[0008] The offset shaping module is used to map the reflected wave signal from the observed time domain to the spatial domain according to the identified reflection point, and obtain the offset image corresponding to the reflected wave signal; match the obtained offset image with multiple acquisition points, and adjust the offset image according to the differences between the offset images corresponding to the multiple acquisition points, and output the adjusted offset image.

[0009] The geological assessment module is used to analyze the offset image, extract the coordinate increments in the adjusted offset image, calculate the conditional probability and occurrence probability of the coordinate increments, and obtain the geological analysis results based on the conditional probability and occurrence probability of the coordinate increments.

[0010] The beneficial effects of the present invention are as follows: 1. The present invention obtains reflected wave signals by setting multiple collection points, and uses a velocity identification module to identify the travel time of the reflected wave signals, thereby establishing a velocity distribution model of each underground layer, which can more accurately reflect the velocity distribution of different underground layers and provide a reliable basis for subsequent geological analysis.

[0011] 2. The present invention uses a reflection calculation module to identify the reflection point corresponding to the reflected wave signal and calculate the reflection coefficient according to the velocity distribution model; the offset molding module maps the reflected wave signal from the time domain to the space domain to obtain an offset image, and improves the image quality through adjustment processing; this process ensures the accurate extraction and imaging of the reflection information, which helps to discover subtle underground geological structures.

[0012] 3. The present invention analyzes the offset image through the geological assessment module, extracts the coordinate increment, calculates the conditional probability and the probability of occurrence, and obtains the geological analysis result; it can comprehensively consider various geological factors and improve the accuracy and reliability of geological assessment. At the same time, the output results of the system are clear and intuitive, which is convenient for decision-making in the coal mining process, reduces the cost and time of manual intervention, and improves the efficiency and accuracy of data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0014] Figure 1 The system framework diagram of a coal mine three-dimensional seismic detection data analysis and processing system.

[0015] Figure 2 The present invention is a system schematic diagram of a coal mine three-dimensional seismic detection data analysis and processing system.

[0016] Figure 3 The invention is a flow chart of a velocity identification module of a coal mine three-dimensional seismic detection data analysis and processing system.

[0017] Figure 4The invention is a flow chart of a reflection calculation module of a coal mine three-dimensional seismic detection data analysis and processing system.

[0018] Figure 5 The invention is a flow diagram of reflection points of a reflection calculation module of a coal mine three-dimensional seismic detection data analysis and processing system.

[0019] Figure 6 The invention is a flow chart of an offset forming module of a coal mine three-dimensional seismic detection data analysis and processing system. DETAILED DESCRIPTION

[0020] The embodiments of the present invention are described in detail below. The embodiments described below are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention. If no specific techniques or conditions are specified in the embodiments, the techniques or conditions described in the literature in the art or the product specifications are used.

[0021] See also Figure 1 , Figure 2 A coal mine three-dimensional seismic detection data analysis and processing system includes: a data acquisition module, a velocity recognition module, a reflection calculation module, an offset molding module and a geological evaluation module.

[0022] The output end of the data acquisition module is connected to the velocity identification module, the output end of the velocity identification module is connected to the reflection calculation module, the output end of the reflection calculation module is connected to the offset molding module, and the output end of the offset molding module is connected to the geological assessment module.

[0023] The data acquisition module is used to set multiple acquisition points and obtain the reflected wave signals corresponding to the acquisition points.

[0024] The velocity identification module is used to identify the travel time of the reflected wave signal and establish the velocity distribution model of each underground layer according to the positions of multiple acquisition points.

[0025] The reflection calculation module is used to identify the reflection point corresponding to the reflected wave signal according to the velocity distribution model, calculate the reflected wave signal at the reflection point, and obtain the reflection coefficient corresponding to the reflection point; at this time, the actual overlap and segregation of the common reflection point and the reflection point appearing in the reflected wave signal are calculated to obtain the reflection coefficient at this time; it is also necessary to identify the reflected wave signal and verify the phase change between the reflected wave signals.

[0026] The offset shaping module is used to map the reflected wave signal from the observed time domain to the spatial domain according to the identified reflection point, and obtain the offset image corresponding to the reflected wave signal; match the obtained offset image with multiple acquisition points, and adjust the offset image according to the differences between the offset images corresponding to the multiple acquisition points, and output the adjusted offset image.

[0027] The geological assessment module is used to analyze the offset image, extract the coordinate increments in the adjusted offset image, calculate the conditional probability and occurrence probability of the coordinate increments, and obtain the geological analysis results based on the conditional probability and occurrence probability of the coordinate increments.

[0028] The data acquisition module emits seismic waves by setting up detonators or other equipment such as vibrating vehicles, and uses detectors to collect these waveforms to identify the reflection and refraction of seismic waves when they pass through underground coal seams and encounter interfaces of different densities or elasticities. The reflected wave signal is used to describe this process. At the same time, these reflected wave signals need to be identified one by one to find the common points in these signals, as well as the location and properties of the underground layers that these waveforms contact, and these data are analyzed to achieve three-dimensional seismic detection.

[0029] In one embodiment of the present invention, the velocity identification module is mainly used to identify the travel time of the reflected wave signal to establish a velocity distribution model of each underground layer. Based on this velocity distribution model, it can assist in the subsequent identification of reflection points in these reflected wave signals, thereby obtaining the distribution positions of multiple underground layers during seismic detection.

[0030] In this module, the main task is to identify the velocity distribution in the current scene, to identify the relative distribution of these velocity distributions to the underground levels, and based on these distributions, to adjust the coordinates that need to be identified at these underground locations, to obtain one or more coefficients adjusted according to the current velocity distribution, to complete the identification of the distribution positions; these adjusted velocity distributions will display the thickness and distribution of the coal seams on multiple levels underground, and these distributions will indicate the relative coordinates of these coal seams, thereby indicating the approximate situation identified at this time.

[0031] like Figure 3 As shown, the implementation method of the velocity distribution model in this module includes: according to the distribution of the current acquisition points, the reflected wave signal and travel time received by each acquisition point are obtained, and the travel time represents the time interval from the emission of the earthquake source to the reception of the reflected wave signal.

[0032] According to the amplitude and travel time of the reflected wave signal, a time amplitude curve is formed, the time amplitude curve is compared with the preset velocity model, the similarity between the time amplitude curve and the preset velocity model at different travel times and underground detection depths is calculated, and the velocity distribution model corresponding to the current reflected wave signal is determined according to the obtained similarity; the preset velocity model is a model set in advance according to the layer distribution that may appear underground in the coal mine. This model will represent the speed of the reflected wave signal at different layers, and finally the expression form of the time interval for receiving the reflected wave signal at a specific position can be known, that is, the speed of the layer passed at this time is identified according to the change of the corresponding amplitude in the reflected wave signal, thereby completing the setting of the velocity distribution model. The velocity distribution model essentially compares the similarity between the current signal and the preset velocity model, and combines similar contents to obtain a velocity distribution model that is highly correlated with the current reflected wave signal.

[0033] The method of comparing the time amplitude curve with the preset speed model and calculating the similarity between the time amplitude curve and the preset speed model at different travel times and underground detection depths includes: taking the amplitude and travel time as inputs and comparing them with the corresponding parameters in the preset speed model in turn, the comparison method includes comparing the amplitude, travel time and the speed value on the path corresponding to the travel time, selecting the time amplitude curve that is optimal to the current preset speed model according to the speed value on the path corresponding to the amplitude, travel time and travel time, and obtaining the corresponding speed distribution model; when selecting the time amplitude curve that is optimal to the current preset speed model, it is essentially to find the most similar part based on the speed and travel time, so as to facilitate the formation of the speed distribution model.

[0034] The preset velocity model is a model simulated according to the number of underground layers in a coal mine. It is used to express the velocity values ​​of reflected wave signals when propagating through the layers of a coal mine, and to divide these velocity values ​​according to the number of layers and the location of the equipment emitting seismic waves to form preset velocity models related to multiple underground layers. At this time, according to the current time amplitude curve, the data identified therein is compared with the preset velocity model to find the same part in the preset velocity model under the current time amplitude curve as that in the time amplitude curve. For example, the travel time obtained according to the preset velocity model corresponds to the travel time in the current time amplitude curve. At the same time, the amplitude passing through a specific layer under the corresponding travel time in the preset velocity model is the same as the amplitude of the current time amplitude curve. These identical parts are extracted to form the current velocity distribution model.

[0035] Selecting the time amplitude curve that is optimal for the current preset speed model according to the speed value on the path is essentially to compare the corresponding speed value with the preset speed model according to the time amplitude curve, for example, using the Pearson correlation coefficient, the amplitude and travel time in the current time amplitude curve and the preset speed model to calculate the Pearson correlation coefficient, and using the amplitude and travel time and the corresponding data in the preset speed model for calculation; when the Pearson correlation coefficient of the amplitude and travel time and the preset speed model takes the maximum value, since the speed on the path corresponding to the travel time is also required for comparison, the average value of the speed on the path corresponding to the travel time is output to obtain the average speed of the speed distribution model in each underground level of the coal mine. When outputting these paths, these paths will include the level where the corresponding path is located, and the underground detection depth corresponding to the point on this path, thereby completing the output of the current speed distribution model.

[0036] The velocity distribution model will eventually output multiple velocity values ​​related to the underground distribution, such as the average propagation velocity from the emitted seismic signal to a certain depth underground, the layer velocity of each layer in the underground layers of the coal mine, and the layer velocity can be the ratio of the thickness of each layer of the underground coal mine identified at this time to the travel time; based on these velocity values, the velocity distribution most relevant to the current reflection wave signal is described to complete the description of the corresponding data under the current detection.

[0037] In one embodiment of the present invention, the reflection calculation module calculates the actual overlap and segregation of the common reflection point and the reflection point appearing in the reflected wave signal to obtain the reflection coefficient at this time; it is also necessary to identify the reflected wave signal and verify the phase change between the reflected wave signals.

[0038] A reflection point is a specific location where seismic waves are reflected when they travel from one medium to another. This location is usually the interface between two different geological materials, such as between a coal seam and the surrounding rock. The determination of the reflection point relies on the travel time and amplitude information in the seismic data. By analyzing the time delay of the signal received by the receiver, the location and shape of the underground structure can be inferred.

[0039] The reflection coefficient depends on the value of acoustic impedance. In the detection scenario, it can be obtained based on the speed of sound wave propagation and the density of the medium. If the current implementation scenario is field detection, in actual applications, in order to more accurately determine the distribution of acoustic impedance, an inversion algorithm is often used. Inversion is a mathematical process that aims to solve the properties of underground media based on observed seismic data. By adjusting the velocity and density models to best match the observed data, the most realistic acoustic impedance profile is obtained; the underground medium properties are usually expressed as the speed of medium propagation, the density of the medium, and the acoustic impedance of the corresponding medium. The reflected signal at this time needs to be deduced to obtain the speed and density, and the corresponding acoustic impedance is further calculated to obtain the reflection coefficient; the acoustic impedance is expressed as the product of the speed of medium propagation and the density of the medium.

[0040] At this time, when matching, it is necessary to obtain the amplitude, travel time, and phase of the reflected wave signal to complete the signal analysis. Continuity analysis and multi-attribute analysis can be used to analyze the reflected wave signal and test whether the current reflected signal corresponds to the current reflection point. After completing these processes, the relevant reflection coefficient can be output to clarify the specific situation measured in the current scene.

[0041] like Figure 4 As shown, the implementation method of the reflection calculation module includes: obtaining the amplitude and phase of the reflected wave signal; using the velocity distribution model and the travel time, analyzing the reflected wave signal according to the time point corresponding to the reflected wave signal, including calculating the amplitude analysis coefficient, phase analysis coefficient, and correlation analysis coefficient of the reflected wave signal.

[0042] According to the amplitude analysis coefficient, phase analysis coefficient and correlation analysis coefficient, the reflection point of the current reflected wave signal is selected, and the position of each reflection point is calculated.

[0043] According to the position corresponding to the reflection point, the acoustic impedance distribution in each layer of the current underground is determined, and the reflection coefficient is calculated.

[0044] The amplitude analysis coefficient of the reflected wave signal includes: obtaining the peak amplitude and average absolute amplitude of the amplitude of the reflected wave signal at adjacent acquisition points, combining the average absolute amplitude with the peak amplitude to obtain the amplitude analysis coefficient. At this time, the peak amplitude represents the maximum absolute amplitude value among all acquisition points; the average absolute amplitude represents the average value of the sum of the absolute amplitude values ​​of all acquisition points.

[0045] Amplitude analysis coefficient A coeff It is expressed as: Among them, |a i | represents the amplitude value of the i-th sampling point, n represents the number of sampling points, i ranges from 1 to n, |a1|, |a2|, |a n|represent the amplitude values ​​of the 1st, 2nd, and nth sampling points respectively.

[0046] The phase analysis coefficient of the reflected wave signal includes: obtaining the time series x and y related to the phase in adjacent acquisition points. The time series x and y represent the phase values ​​corresponding to two acquisition points on adjacent acquisition points to measure whether the phases between adjacent acquisition points are consistent. The consistency of these phases is used to identify whether there are faults, folds and other problems in the underground layer where the coal mine is located. The phases between adjacent acquisition points are calculated according to the time series to obtain the phase analysis coefficient.

[0047] The phase analysis coefficients are expressed as: in, represents the phase analysis coefficient, τ represents the time delay, and the time delay is represented as an integer greater than 0 at this time, which is used to determine whether the phase values ​​corresponding to two adjacent acquisition points are related after a certain delay at the time point; represents the mean value of the time series x, represents the average value of the time series y, x(t) represents the phase value of the time series x at time t, y(t) represents the phase value of the time series y at time t, and y(t+τ) represents the phase value of the time series y at time t+τ.

[0048] After the phase analysis coefficient is calculated for each acquisition point, the current maximum phase analysis coefficient will be obtained and output as the phase analysis coefficient of the current reflected wave signal; this maximum phase analysis coefficient will show the phase correlation between adjacent acquisition points at the corresponding time point, so that a group of acquisition points with the highest correlation can be found, as well as the relevant data on these acquisition points. These data can be used as the basis for analyzing the relationship between underground levels of coal mines detected by seismic waves, thereby selecting the specific situation corresponding to the coal mine and completing the overall detection.

[0049] The correlation analysis coefficient for the reflected wave signal includes: obtaining the speed value and travel time of the speed distribution model at the corresponding time point, combining the speed value and travel time according to the position of the acquisition point, and obtaining the correlation analysis coefficient of the reflected wave signal; at this time, the speed value and travel time are used as input, and the Pearson correlation coefficient is used to compare the current speed value and travel time with the average speed value and travel time in the historical data to obtain the correlation analysis coefficient at this time.

[0050] like Figure 5As shown, the method for selecting the reflection point of the current reflected wave signal according to the amplitude analysis coefficient, the phase analysis coefficient and the correlation analysis coefficient includes: converting the data of the reflected wave signal into depth domain data, and the conversion method is to use the average speed in the speed distribution model multiplied by the travel time and divided by two to obtain the depth that the current reflected wave signal can reach, and extracting multiple position points corresponding to the reflected wave signal from the depth domain data.

[0051] A mapping relationship between the position point of the reflected wave signal and the amplitude analysis coefficient, phase analysis coefficient and correlation analysis coefficient is established. The mapping relationship is based on whether the data corresponding to the current position point and the amplitude analysis coefficient, phase analysis coefficient and correlation analysis coefficient are the same set of data, to obtain the amplitude analysis coefficient, phase analysis coefficient and correlation analysis coefficient corresponding to the current position point, and construct the amplitude analysis coefficient, phase analysis coefficient and correlation analysis coefficient mapped by the current position point.

[0052] The corresponding situations of these position points can be observed using the amplitude analysis coefficient, phase analysis coefficient and correlation analysis coefficient. For example, those reflection points with high consistency between adjacent acquisition points are selected. Places showing lower phase difference in the phase analysis coefficient or higher correlation in the correlation analysis coefficient usually mean stronger reflection interfaces.

[0053] Pay attention to the strong amplitude positions in the amplitude analysis coefficients. Strong amplitudes often indicate obvious acoustic impedance differences between media and may be important geological boundaries. The values ​​of the amplitude analysis coefficients, phase analysis coefficients, and correlation analysis coefficients at these locations can also be used to determine whether the spatial distribution of reflection points conforms to the continuity and rationality of the geological structure. For example, the event axis should be kept as smooth and continuous as possible. When these three coefficients show large fluctuations or differences, these geological indicators indicate the presence of fractures or other discontinuities.

[0054] According to the mapping relationship corresponding to the position points, the common position points among the position points are determined. The common position points indicate the same positions through which the reflected wave signals pass when multiple detectors detect reflected wave signals. At this time, the common position points will have a mapping relationship with multiple groups of amplitude analysis coefficients, phase analysis coefficients and correlation analysis coefficients; the overlap value and offset of the common position points are obtained, and the common position points with the largest overlap value and offset value are output as reflection points. The reflection points obtained in this module indicate the points where the overlap and offset are most obvious when reflection identification is performed. This point can clearly indicate the main features of each underground layer of the coal mine to analyze the points that need to be paid attention to at this time; the essence of the common reflection point at this time is to use multiple groups of data to cover a position point to improve the accuracy of resolution and identification at this time.

[0055] The overlap value indicates the ratio of data overlap between reflected wave signals corresponding to common position points, that is, the ratio of data with completely identical reflected wave signals to the total data corresponding to the common position points; the deviation value indicates the sum of the differences among multiple groups of amplitude analysis coefficients, phase analysis coefficients and correlation analysis coefficients.

[0056] After obtaining the reflection point, the acoustic impedance of the current reflection point in each underground layer of the coal mine can be identified from historical data according to the position corresponding to the current reflection point and related calculations to complete the calculation of the reflection coefficient.

[0057] For example, the reflection coefficient can be expressed as follows: Get the acoustic impedance Z1 of the upper medium and the acoustic impedance Z2 of the lower medium corresponding to the current reflection point, and calculate the reflection coefficient R: At this time, it is necessary to limit and verify the acquisition of acoustic impedance to complete the content of the current module to obtain acoustic impedance.

[0058] In one embodiment of the present invention, the offset shaping module is mainly used to integrate the obtained data during processing to obtain a relevant offset image, which is used to correct travel time anomalies, reflection point coordinates, reflection coefficients, and the distribution of phase information. The generated offset image will convert the travel time into a depth value to represent the detection depth and amplitude, the detection depth domain travel time, and the distribution of related data at each depth.

[0059] Since multiple acquisition points are set at this time, it is necessary to comprehensively process the acquired data in the form of fusion and superposition. For example, the corresponding key points and state points are obtained from each offset image, and these points are connected and combined to obtain the current output offset image, or the corresponding points in the offset image are divided to verify whether the values ​​of the points in the image are correct at this time, so as to output the corresponding offset image.

[0060] like Figure 6 As shown, the implementation method of adjusting the offset image includes: obtaining the depth value of the reflection point and the distance value between the reflection points, and determining the relative position between the reflection points.

[0061] According to the relative positions of the reflection points, the coordinate increments of the reflection points on the horizontal and vertical coordinates are determined in turn, and the positions of the reflection points are regressed according to the coordinate increments. The fitting slope of the coordinate increment after regression processing is determined, and the coordinate increment is processed according to the fitting slope to determine the incremental direction corresponding to the current reflection point.

[0062] The coordinate increment is calculated at this time to determine the dynamic superposition value of multiple sets of data on the coordinates of the current reflection point when the offset image is generated, that is, to verify the superposition value of each reflection point relative to the initial standard value, and input the final superimposed value into the offset image to obtain a more accurate offset image.

[0063] According to the coordinate increment and the increment direction, the adjustment method of the offset image is obtained, and the offset image is adjusted according to the adjustment method, and the adjusted offset image is output. At this time, adjusting the offset image is essentially to comprehensively process multiple sets of data, and adjust the coordinates of the reflection points identified in the multiple sets of data to obtain the points that clearly represent the coal mine layer, so as to obtain the final offset image.

[0064] When obtaining the coordinate increment of the reflection point on the horizontal and vertical coordinates, the horizontal coordinate represents the coordinate value in the east-west horizontal direction, and the vertical coordinate represents the coordinate value in the north-south horizontal direction. The coordinate increment at this time is whether the coordinates of the reflection point in all offset images change when multiple offset images are superimposed, and these changed values ​​are added to obtain the coordinate increment.

[0065] At the same time, these reflection points are regressed according to the coordinate increments. The regression processing method includes: constructing a fitting curve with the coordinate increments of the reflection points. If there are multiple reflection points, the relative position distance of the reflection points is used as the horizontal coordinate when constructing the fitting curve, and the distance value of the coordinate increment is used as the vertical coordinate for analysis. If only the coordinate increment generated by multiple calculations of a single reflection point is used, the value on the horizontal coordinate of the coordinate increment is used as the horizontal coordinate, and the value on the vertical coordinate of the coordinate increment is used as the vertical coordinate to form a fitting curve corresponding to the reflection point to determine the increase in the position of the reflection point during multiple identifications; the value of each coordinate increment on the fitting curve is calculated to determine the fitting slope corresponding to the coordinate increment; the fitting slopes are superimposed and the superimposed fitting slopes are output as the incremental direction; the incremental direction represents the process of averaging the current fitting slope to obtain the relative average of the current fitting slope to represent the incremental direction of the current coordinate increment.

[0066] According to the coordinate increment and the increment direction, an implementation method of obtaining the adjustment method of the offset image is expressed, and the coordinate increment and the increment direction are compared with the preset adjustment method of the offset image. For example, the offset image can be processed according to the average value of the coordinate increment when the reflected image is generated multiple times, or it can be processed according to the value range of the coordinate increment; in the present invention, the coordinate increment and the increment direction are used as input, and the preset adjustment method is used as a comparison object, and a preset adjustment coefficient related to the preset adjustment method is calculated, and the adjustment method of the offset image is selected according to the value of the preset adjustment coefficient.

[0067] When comparing with the preset adjustment method, multiple sets of data corresponding to the coordinate increment and increment direction will be set in the preset adjustment method; and the comparison of the preset adjustment method will be completed according to the values ​​of these data.

[0068] Therefore, the calculation method of the preset adjustment coefficient includes: obtaining the consistent pairs, inconsistent pairs and tie pairs of the coordinate increment and the increment direction with the preset adjustment method, the tie pairs include the tie pairs on the horizontal axis and the tie pairs on the vertical axis, and based on the number values ​​of the consistent pairs, inconsistent pairs and tie pairs, the preset adjustment coefficient is calculated.

[0069] The consistent pairs of coordinate increment and increment direction with the preset adjustment method are expressed as follows: when there is a set of coordinate increments whose index values ​​in the horizontal coordinate, vertical coordinate, and increment direction are all smaller than the values ​​of the corresponding data in the preset adjustment method, this set is regarded as a consistent pair; when there is a set of coordinate increments in the horizontal coordinate, vertical coordinate, and increment direction, if any one or two of these three values ​​are inconsistent with the size relationship of the preset adjustment method, the number of these data is regarded as an inconsistent pair; when the index values ​​of the coordinate increments in the horizontal coordinate, vertical coordinate, and increment direction are equal to the values ​​of the corresponding data in the preset adjustment method, the corresponding data is regarded as a tie pair. For a tie pair on the horizontal coordinate, it is not necessary to compare the value of the coordinate increment on the vertical coordinate. For a tie pair on the vertical coordinate, the value of the coordinate increment on the horizontal coordinate is not considered. The index value in the increment direction is the average value of the fitting slope corresponding to the coordinate increment.

[0070] The preset adjustment factor is expressed as: Wherein, χ represents a preset adjustment coefficient, c represents the number of consistent pairs, b represents the number of inconsistent pairs, d1 represents the number of tied pairs on the horizontal axis, and d2 represents the number of tied pairs on the vertical axis.

[0071] At this time, the preset adjustment coefficient is used to compare the coordinate increment and the increment direction with the preset adjustment method. At this time, the focus is on whether the coordinate increment can produce the same sequence as the preset adjustment method during processing, and whether the coordinate increment has corresponding parts in the horizontal and vertical coordinates with the data in the preset adjustment method to identify whether the currently identified offset image is the same as the predicted one, thereby reflecting the current accurate situation for three-dimensional seismic detection. Finally, according to the value of the preset adjustment coefficient, the corresponding adjustment method is extracted from the historical data to complete the processing of the offset image.

[0072] In one embodiment of the present invention, the geological assessment module mainly analyzes the data at this time to identify whether there are cracks or interlayers in the coal seam, etc., to assist in understanding the subtle structural changes inside the coal seam, and to facilitate subsequent mining and analysis of the coal mine.

[0073] The analysis method for the geological assessment module can be used to identify the conditional probability and occurrence probability of the coordinate increment to verify the conditions under which the corresponding probability of the coordinate increment occurs, and how the probability of occurrence changes when the condition is opposite, to verify the impact of the numerical value of the relevant data on the current superimposed offset image after the offset image is generated.

[0074] In the present invention, the conditional probability of the coordinate increment can be set to the conditional probability of the current coordinate increment with respect to the reflection coefficient, the conditional probability of the coordinate increment with respect to the travel time, and the conditional probability of the coordinate increment with respect to the co-occurrence of the travel time and the reflection coefficient; the probability of occurrence of the coordinate increment represents the ratio of the value of the coordinate increment to the total data.

[0075] At this time, the conditional probability and occurrence probability are considered together, which is based on the quantitative analysis of the conditions for the occurrence of coordinate increments to identify the situations that cause coordinate increases and changes during the current seismic detection, as well as the comprehensive impact of these situations on the reflection coefficient and travel time measurements under specific values.

[0076] Therefore, the implementation method of geological analysis results includes: obtaining the conditional probability of coordinate increment with respect to reflection coefficient, the conditional probability of coordinate increment with respect to travel time and the conditional probability of the common occurrence of coordinate increment with respect to travel time and reflection coefficient, and combining the probability of occurrence of coordinate increment to calculate the geological evaluation coefficient, and output the geological evaluation coefficient as the geological evaluation result.

[0077] Among them, GA represents the geological assessment coefficient, P1 represents the conditional probability of the coordinate increment with respect to the reflection coefficient, P2 represents the conditional probability of the coordinate increment with respect to the travel time, P3 represents the conditional probability of the coordinate increment with respect to the co-occurrence of the travel time and the reflection coefficient, P4 represents the probability of the coordinate increment, e represents the exponential constant, λ and ε represent the adjustment coefficients, and the adjustment coefficients are set to 5 and 0.15 respectively. The geological assessment coefficient obtained at this time is to identify the coordinate increment of the current reflection point and quantify these values. The comprehensive analysis of multiple probability values ​​can better handle the uncertainty and noise in the data and provide more stable assessment results; cross-validation of probability values ​​under different conditions can enhance the understanding and reliability of the interpretation of three-dimensional seismic detection; finally, the corresponding situation in the current three-dimensional seismic detection is explained, and a content related to the current coefficient is generated to complete the geological assessment result. External staff can observe these data and adjust the data of three-dimensional seismic detection in time to obtain a more accurate measurement result.

[0078] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations on the present invention. A person skilled in the art may make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention and they are still covered by the protection scope of the present invention.

Claims

1. A coal mine three-dimensional seismic detection data analysis and processing system, characterized in that: include: A data acquisition module is used to set multiple acquisition points and obtain reflected wave signals corresponding to the acquisition points; The velocity identification module is used to identify the travel time of the reflected wave signal and establish the velocity distribution model of each underground layer according to the locations of multiple acquisition points; A reflection calculation module is used to identify the reflection point corresponding to the reflection wave signal according to the velocity distribution model, calculate the reflection wave signal at the reflection point, and obtain the reflection coefficient corresponding to the reflection point; The offset shaping module is used to map the reflected wave signal from the observed time domain to the spatial domain according to the identified reflection point, and obtain the offset image corresponding to the reflected wave signal; match the obtained offset image with multiple acquisition points, and adjust the offset image according to the difference between the offset images corresponding to the multiple acquisition points, and output the adjusted offset image; The geological assessment module is used to analyze the offset image, extract the coordinate increments in the adjusted offset image, calculate the conditional probability and occurrence probability of the coordinate increments, and obtain the geological analysis results based on the conditional probability and occurrence probability of the coordinate increments.

2. A coal mine three-dimensional seismic detection data analysis and processing system according to claim 1, characterized in that: The implementation method of the velocity distribution model includes: obtaining the reflected wave signal and travel time received by each collection point according to the distribution of the current collection points; A time amplitude curve is formed according to the amplitude and travel time of the reflected wave signal, and the time amplitude curve is compared with a preset velocity model. The similarity between the time amplitude curve and the preset velocity model at different travel times and underground detection depths is calculated, and the velocity distribution model corresponding to the current reflected wave signal is determined according to the obtained similarity.

3. A coal mine three-dimensional seismic detection data analysis and processing system according to claim 1, characterized in that: The implementation of the reflection calculation module includes: Acquire the amplitude and phase of the reflected wave signal; use the velocity distribution model and travel time to analyze the reflected wave signal according to the time point corresponding to the reflected wave signal, including calculating the amplitude analysis coefficient, phase analysis coefficient, and correlation analysis coefficient of the reflected wave signal; Select the reflection point of the current reflected wave signal according to the amplitude analysis coefficient, the phase analysis coefficient and the correlation analysis coefficient, and calculate the position of each reflection point; According to the position corresponding to the reflection point, the acoustic impedance distribution in each layer of the current underground is determined, and the reflection coefficient is calculated.

4. A coal mine three-dimensional seismic detection data analysis and processing system according to claim 3, characterized in that: The implementation method of selecting the reflection point of the current reflected wave signal according to the amplitude analysis coefficient, the phase analysis coefficient, and the correlation analysis coefficient includes: Converting the data of the reflected wave signal into depth domain data, and extracting a plurality of position points corresponding to the reflected wave signal from the depth domain data; Establishing a mapping relationship between the position point of the reflected wave signal and the amplitude analysis coefficient, the phase analysis coefficient and the correlation analysis coefficient; Determine a common position point among the position points according to a mapping relationship corresponding to the position points; The overlap value and offset of the common position point are obtained, and the common position point with the largest overlap value and offset value is output as the reflection point.

5. A coal mine three-dimensional seismic detection data analysis and processing system according to claim 1, characterized in that: The implementation methods of adjusting the offset image include: Obtain the depth value of the reflection point and the distance value between the reflection points to determine the relative position between the reflection points; According to the relative positions between the reflection points, the coordinate increments of the reflection points on the horizontal and vertical coordinates are determined in turn, and the positions of the reflection points are regressed according to the coordinate increments, the fitting slope of the coordinate increments after the regression processing is determined, and the coordinate increments are processed according to the fitting slope to determine the increment direction corresponding to the current reflection point; According to the coordinate increment and the increment direction, an adjustment method of the offset image is obtained, and the offset image is adjusted according to the adjustment method, and the adjusted offset image is output.

6. A coal mine three-dimensional seismic detection data analysis and processing system according to claim 5, characterized in that: The regression processing method includes: constructing a fitting curve with the coordinate increment of the reflection point, if there are multiple reflection points, the relative position distance of the reflection points is used as the horizontal coordinate, and the distance value of the coordinate increment is used as the vertical coordinate for analysis; if the coordinate increment is generated during multiple calculations for a single reflection point, the value on the horizontal coordinate of the coordinate increment is used as the horizontal coordinate, and the value on the vertical coordinate of the coordinate increment is used as the vertical coordinate to form a fitting curve corresponding to the reflection point; The value of each coordinate increment on the fitting curve is calculated to determine the fitting slope corresponding to the coordinate increment; the fitting slopes are superimposed and the superimposed fitting slopes are output as the increment direction.

7. A coal mine three-dimensional seismic detection data analysis and processing system according to claim 5, characterized in that: According to the coordinate increment and the increment direction, an implementation method representation of the adjustment method of the offset image is obtained, the coordinate increment and the increment direction are compared with a preset adjustment method of the offset image, the coordinate increment and the increment direction are used as input, the preset adjustment method is used as a comparison object, a preset adjustment coefficient related to the preset adjustment method is calculated, and the adjustment method of the offset image is selected according to the value of the preset adjustment coefficient; The calculation method of the preset adjustment coefficient includes: obtaining consistent pairs, inconsistent pairs and tie pairs of coordinate increments and increment directions with the preset adjustment method, tie pairs including tie pairs on the horizontal axis and tie pairs on the vertical axis, and calculating the preset adjustment coefficient based on the number values ​​of consistent pairs, inconsistent pairs and tie pairs.

8. A coal mine three-dimensional seismic detection data analysis and processing system according to claim 7, characterized in that: The preset adjustment factor is expressed as: Wherein, χ represents a preset adjustment coefficient, c represents the number of consistent pairs, b represents the number of inconsistent pairs, d1 represents the number of tied pairs on the horizontal axis, and d2 represents the number of tied pairs on the vertical axis.

9. A coal mine three-dimensional seismic detection data analysis and processing system according to claim 1, characterized in that: The reflection coefficient is expressed as: Get the acoustic impedance Z1 of the upper medium and the acoustic impedance Z2 of the lower medium corresponding to the current reflection point, and calculate the reflection coefficient R:

10. A coal mine three-dimensional seismic detection data analysis and processing system according to claim 1, characterized in that: The implementation method of the geological analysis result includes: obtaining the conditional probability of the coordinate increment with respect to the reflection coefficient, the conditional probability of the coordinate increment with respect to the travel time, and the conditional probability of the coordinate increment with respect to the co-occurrence of the travel time and the reflection coefficient, and combining the occurrence probability of the coordinate increment to calculate the geological evaluation coefficient, and outputting the geological evaluation coefficient as the geological evaluation result; Among them, GA represents the geological assessment coefficient, P1 represents the conditional probability of the coordinate increment with respect to the reflection coefficient, P2 represents the conditional probability of the coordinate increment with respect to the travel time, P3 represents the conditional probability of the coordinate increment with respect to the co-occurrence of the travel time and the reflection coefficient, P4 represents the probability of the occurrence of the coordinate increment, e represents the exponential constant, and λ and ε represent the adjustment coefficients.

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