A coal mine three-dimensional seismic detection data analysis and processing system

Abstract

The present invention relates to the technical field of coal mine detection data analysis, and specifically to a three-dimensional coal mine seismic detection data analysis and processing system, comprising: a data acquisition module, for setting multiple acquisition points and acquiring reflected wave signals corresponding to the acquisition points; a velocity identification module, for identifying the travel time of the reflected wave signals and establishing a velocity distribution model of each underground layer according to the positions of the multiple acquisition points; a reflection calculation module, for identifying the reflection points corresponding to the reflected wave signals based on the velocity distribution model, calculating the reflected wave signals at the reflection points, and acquiring the reflection coefficients corresponding to the reflection points; and an offset shaping module, for mapping the reflected wave signals from the observed time domain to the spatial domain according to the identified reflection points, to obtain offset images corresponding to the reflected wave signals; and matching the obtained offset images with the multiple acquisition points, thereby improving the accuracy and efficiency of three-dimensional seismic detection.

Description

Technical Field

[0001] The present 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. Background Art

[0002] Geological exploration is an essential part of coal mining. Traditional geological exploration methods primarily include geological mapping, drilling, and geophysical prospecting. However, these methods often present challenges in practical application. For example, while geological mapping can intuitively reflect surface geological conditions, it struggles to accurately assess conditions deep underground. Drilling, while capable of obtaining direct information about underground rock formations, is expensive and time-consuming. Geophysical prospecting, while highly efficient, often suffers from insufficient accuracy in complex geological conditions.

[0003] For example, Chinese Patent Publication No. CN117492091A discloses a method and system for coal mine fold detection, wherein the method includes: determining the fold core region based on a first fold profile in the depth domain corresponding to the strata in the detection area, and obtaining a first oscillation signal received by a first oscillation wave receiver when a first seismic source is excited, wherein the first seismic source and the first oscillation receiver are spaced apart in a plurality of surface boreholes arranged at a first spacing distance within the fold core region, and multiple first seismic sources or multiple first oscillation wave receivers are arranged at a second spacing distance in each surface borehole, and then constructing a second fold profile based on the first oscillation signal. Thus, the second fold profile is constructed based on the first fold profile obtained from large-scale geological structure geophysical detection 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 for setting multiple acquisition points and obtaining reflected 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 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.

[0008] The migration shaping module is used to map the reflected wave signal from the observed time domain to the spatial domain based on the identified reflection points to obtain the migration image corresponding to the reflected wave signal; match the obtained migration image with multiple acquisition points, and adjust the migration image according to the differences between the migration images corresponding to the multiple acquisition points, and output the adjusted migration 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 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 recognition module to identify the travel time of the reflected wave signals, thereby establishing a velocity distribution model for each underground layer. This can more accurately reflect the velocity distribution of different underground layers and provide a reliable basis for subsequent geological analysis.

[0011] Second, the present invention uses a reflection calculation module to identify the reflection point corresponding to the reflected wave signal based on the velocity distribution model and calculate the reflection coefficient. The migration shaping module maps the reflected wave signal from the time domain to the spatial domain to obtain a migration image, and improves the image quality through adjustment processing. This process ensures the accurate extraction and imaging of reflection information, which helps to discover subtle underground geological structures.

[0012] Third, the present invention uses a geological assessment module to analyze the offset image, extract coordinate increments, calculate conditional probabilities and occurrence probabilities, and generate geological analysis results. This module comprehensively considers multiple geological factors, improving the accuracy and reliability of geological assessments. Furthermore, the system's output is clear and intuitive, facilitating decision-making during coal mining, reducing the cost and time of manual intervention, and improving the efficiency and accuracy of data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The present invention will be further described below with reference to the accompanying drawings and examples.

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

[0016] Figure 3 The present 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 present 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 chart 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 present invention is a flow chart of an offset shaping module of a coal mine three-dimensional seismic detection data analysis and processing system. DETAILED DESCRIPTION

[0020] The following embodiments of the present invention are described in detail. The embodiments described below are exemplary and are only used to explain the present invention, and are not to be construed as limiting the present invention. Where specific techniques or conditions are not specified in the embodiments, the techniques or conditions described in the literature in the art or in the product specifications shall be followed.

[0021] See 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 shaping module and a geological assessment 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 based on 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 migration shaping module is used to map the reflected wave signal from the observed time domain to the spatial domain based on the identified reflection points to obtain the migration image corresponding to the reflected wave signal; match the obtained migration image with multiple acquisition points, and adjust the migration image according to the differences between the migration images corresponding to the multiple acquisition points, and output the adjusted migration 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 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 module uses reflected wave signals 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. These data are analyzed to realize 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 positions, 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 show the thickness and distribution of the coal seams on multiple levels underground, and these distributions will represent 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: obtaining the reflected wave signal and travel time received by each acquisition point according to the distribution of the current acquisition points. The travel time represents the time interval from 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, and 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 possible layer distribution 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 is essentially to compare the similarity between the current signal and the preset velocity model, and combine the similar contents to obtain a velocity distribution model that is highly correlated with the current reflected wave signal.

[0033] The time-amplitude curve is compared with a preset speed model, and a method for 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 corresponding parameters in the preset speed model in sequence, 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 for the current preset speed model according to the amplitude, travel time and the speed value on the path corresponding to the travel time, and obtaining the corresponding speed distribution model; when selecting the time-amplitude curve that is optimal for the current preset speed model, the most similar part is essentially found based on the speed and travel time, thereby facilitating the formation of the speed distribution model.

[0034] The preset velocity model is a model simulated according to the number of underground layers in the coal mine. It is used to express the velocity values ​​of the reflected wave signals when propagating in each layer of the coal mine, and divide these velocity values ​​according to the number of layers and the location of the seismic wave emitting equipment to form preset velocity models related to multiple underground layers. At this time, according to the current time amplitude curve, the identified data 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 based on the speed value on the path is essentially comparing the corresponding speed value with the preset speed model based on the time-amplitude curve. For example, using the Pearson correlation coefficient, the amplitude and travel time in the current time-amplitude curve are compared with the preset speed model to calculate the Pearson correlation coefficient, and the amplitude and travel time are calculated using the corresponding data in the preset speed model. When the Pearson correlation coefficient of the amplitude and travel time with the preset speed model reaches 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 layer of the coal mine. When outputting these paths, these paths will include the layer where the corresponding path is located, as well as 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 ultimately 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 so on. The layer velocity can be the ratio of the thickness of each underground layer of the 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 common reflection points and reflection points appearing in the reflected wave signal to obtain the reflection coefficient at that 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 the specific location where seismic waves are reflected when traveling from one medium to another. This location is typically the interface between two different geological materials, such as a coal seam and surrounding rock. Determining the reflection point relies on travel time and amplitude information from seismic data. By analyzing the time delay of the signal received by the receiver, the location and morphology of the subsurface structure can be inferred.

[0039] The reflection coefficient depends on the value of the 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 processing 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 acoustic impedance profile that best conforms to the actual situation 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. It is necessary to deduce the reflected signal at this time to obtain the speed and density, and further calculate the corresponding acoustic impedance, so that the reflection coefficient can be obtained; the acoustic impedance at this time 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 scenario.

[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 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] The reflection point of the current reflected wave signal is selected according to the amplitude analysis coefficient, the phase analysis coefficient, and the correlation analysis coefficient, 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 underground layer is determined, and the reflection coefficient is calculated.

[0044] The amplitude analysis coefficient for the reflected wave signal involves obtaining the peak amplitude and average absolute amplitude of the reflected wave signal at adjacent acquisition points, and combining the average absolute amplitude with the peak amplitude to obtain the amplitude analysis coefficient. The peak amplitude represents the maximum absolute amplitude value among all acquisition points, while the average absolute amplitude represents the average of the sum of the absolute amplitude values ​​of all acquisition points.

[0045] Amplitude analysis coefficient A coeff 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 coefficient is expressed as: in, represents the phase analysis coefficient, τ represents the time delay, which is an integer greater than 0 at this time. It is used to determine whether the phase values ​​corresponding to two adjacent acquisition points are correlated 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 at the corresponding time point of the speed distribution model, combining the speed value and travel time according to the location 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 velocity in the velocity 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 is established between the position point of the reflected wave signal and the amplitude analysis coefficient, phase analysis coefficient and correlation analysis coefficient. 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 conditions of these position points can be observed using the amplitude analysis coefficient, phase analysis coefficient, and correlation analysis coefficient. For example, 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 sets 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 in 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 sets 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 the reflected wave signals corresponding to the common position points, that is, the ratio of data with exactly the same reflected wave signals to the total data corresponding to the common position points; the deviation value indicates the sum of the differences between 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 based on 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 point, it is necessary to restrict 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. This image 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 collected data in the form of fusion and superposition. For example, the corresponding key points and status 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, and then the corresponding offset image is output.

[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 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 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 calculation at this time determines 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, the superposition value of each reflection point relative to the initial standard value is verified, and the final superposition value is input 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, the adjustment of 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 representation of these points that clearly represent the coal mine layer, so as to obtain the final offset image.

[0064] When obtaining the coordinate increments of a reflection point on the horizontal and vertical coordinates, the horizontal coordinate represents the coordinate value in the east-west direction, and the vertical coordinate represents the coordinate value in the north-south direction. The coordinate increment is calculated by adding the values ​​of the coordinates of the reflection point in all the offset images when the offset images are superimposed.

[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 multiple reflection points are targeted, 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 when constructing the fitting curve. If only the coordinate increment generated by multiple calculations of a single reflection point is targeted, 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 to calculate the preset adjustment coefficient for the preset adjustment method, 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, the preset adjustment method will set multiple sets of data corresponding to the coordinate increment and increment direction; 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 increment direction and the preset adjustment method, the tie pairs include the tie pairs on the horizontal coordinate and the tie pairs on the vertical coordinate, and based on the number values ​​of the consistent pairs, inconsistent pairs and tie pairs, the preset adjustment coefficient is calculated.

[0069] The coordinate increment and increment direction are consistent with the preset adjustment method. When there is a set of coordinate increment index values ​​in the horizontal coordinate, vertical coordinate, and incremental direction that are all smaller than the corresponding data values ​​in the preset adjustment method, this set is considered a consistent pair. When there is a set of coordinate increment index values ​​in the horizontal coordinate, vertical coordinate, and incremental direction, if any one or two of these three values ​​are inconsistent with the size relationship of the preset adjustment method, this data is considered an inconsistent pair. When the coordinate increment index values ​​in the horizontal coordinate, vertical coordinate, and incremental direction are equal to the corresponding data values ​​in the preset adjustment method, the corresponding data is considered a tie pair. For tie pairs on the horizontal coordinate, there is no need to compare the coordinate increment's vertical coordinate value. For tie pairs on the vertical coordinate, the coordinate increment's horizontal coordinate value is not considered. The index value in the incremental direction is the average of the fitting slopes corresponding to the coordinate increments.

[0070] The preset adjustment coefficient 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 of the geological assessment module can be used to identify the conditional probability and occurrence probability of the coordinate increment to verify under what conditions the corresponding probability of the coordinate increment appears, and how the occurrence probability 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 as 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 the specific values.

[0076] Therefore, the implementation method of the geological analysis results 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 output the geological evaluation coefficient as the geological evaluation result.

[0077] Where 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 travel time and reflection coefficient, and P4 represents the probability of the coordinate increment occurring. e represents the exponential constant, and λ and ε represent adjustment coefficients, which are set to 5 and 0.15, respectively. The geological assessment coefficients obtained here identify the coordinate increments of the current reflection point and quantify these values. The comprehensive analysis of multiple probability values ​​can better handle uncertainty and noise in the data, providing more stable assessment results. Cross-validation of probability values ​​under different conditions can enhance the understanding and reliability of the interpretation of 3D seismic detection. Finally, the corresponding situation in the current 3D seismic detection is interpreted and content related to the current coefficient is generated to complete the geological assessment results. External personnel can observe this data and make timely adjustments to the 3D seismic data to obtain more accurate measurements.

[0078] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative 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, which are still covered by the scope of protection of the present invention.

Claims

1. A coal mine three-dimensional seismic detection data analysis and processing system, characterized in that: include: The data acquisition module is used to set multiple acquisition points and obtain the 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; 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; The migration shaping module is used to map the reflected wave signal from the observed time domain to the spatial domain based on the identified reflection points, and obtain the migration image corresponding to the reflected wave signal; match the obtained migration image with multiple acquisition points, and adjust the migration image according to the differences between the migration images corresponding to the multiple acquisition points, and output the adjusted migration image; A geological assessment module is used to analyze the offset image, extract the coordinate increments in the adjusted offset image, calculate the conditional probability and the occurrence probability of the coordinate increments, and obtain geological analysis results based on the conditional probability and the occurrence probability of the coordinate increments; The method for realizing 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 calculating the geological evaluation coefficient in combination with the occurrence probability of the coordinate increment, and outputting the geological evaluation coefficient as the geological evaluation result; ； in, represents the geological assessment coefficient, represents the conditional probability of the coordinate increment with respect to the reflection coefficient, represents the conditional probability of the coordinate increment with respect to the travel time, represents the conditional probability of the co-occurrence of coordinate increments with respect to travel time and reflection coefficient, represents the probability of occurrence of coordinate increments, represents the exponential constant, 、 Indicates the adjustment factor.

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 at each collection point according to the distribution of the current collection points; 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. The velocity distribution model corresponding to the current reflected wave signal is determined based on the obtained similarity.

3. The 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: Obtain 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, phase analysis coefficient, and 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 underground layer 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: Methods 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 include: Converting the reflected wave signal data 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, phase analysis coefficient and correlation analysis coefficient; Determine a common location point among the location points according to a mapping relationship corresponding to the location 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. The coal mine three-dimensional seismic detection data analysis and processing system according to claim 1, characterized in that: Methods for implementing offset image adjustment 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 of the reflection points, the coordinate increments of the reflection points on the horizontal and vertical coordinates are determined in sequence, and the positions of the reflection points are regressed according to the coordinate increments, and the fitting slope of the coordinate increment after the regression processing is determined. The coordinate increment is processed according to the fitting slope to determine the incremental 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 using the coordinate increments of the reflection points. If multiple reflection points are used, the relative position distance of the reflection points is used as the abscissa, and the distance value of the coordinate increment is used as the ordinate for analysis. If the coordinate increments generated by multiple calculations for a single reflection point are used, the value on the abscissa of the coordinate increment is used as the abscissa, and the value on the ordinate of the coordinate increment is used as the ordinate 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 slope is output as the increment direction.

7. The coal mine three-dimensional seismic detection data analysis and processing system according to claim 5, characterized in that: Obtaining an implementation representation of an adjustment method for the offset image based on the coordinate increment and the increment direction, comparing the coordinate increment and the increment direction with a preset adjustment method for the offset image, calculating a preset adjustment coefficient for the preset adjustment method using the coordinate increment and the increment direction as input and the preset adjustment method as a comparison object, and selecting an adjustment method for the offset image based on the value of the preset adjustment coefficient; The calculation method of the preset adjustment coefficient includes: obtaining the consistent pairs, inconsistent pairs and tie pairs of the coordinate increment and increment direction and the preset adjustment method, the tie pairs include the tie pairs on the horizontal coordinate and the tie pairs on the vertical coordinate, and based on the number values ​​of the consistent pairs, inconsistent pairs and tie pairs, the preset adjustment coefficient is calculated.

8. The coal mine three-dimensional seismic detection data analysis and processing system according to claim 7, characterized in that: The preset adjustment coefficient is expressed as: ； in, Indicates the preset adjustment coefficient, represents the number of identical pairs, represents the number of discordant pairs, Represents the number of tie pairs on the horizontal axis, Indicates the number of tie pairs on the vertical axis.

9. The 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 of the upper medium corresponding to the current reflection point and the acoustic impedance of the underlying medium , the reflection coefficient is calculated : .

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