Coal mine three-dimensional seismic exploration full-process automatic quality control method and system

Abstract

The invention discloses a coal mine three-dimensional seismic exploration full-process automatic quality control method and system, and the method comprises the steps: forming a complete quality control closed loop through covering three core stages: field data collection, indoor processing and geological interpretation, and building a correlation model of quality indexes of each stage in each stage, according to the method, the whole-process tracking of the quality problem from parameter verification to final result evaluation is realized, meanwhile, a multi-level and progressive quality control system is finally formed through a whole-process quality comprehensive evaluation scheme in each stage, and the whole process is based on a signal processing theory and geostatistics, has the characteristics of being scientific, comprehensive and rigorous in implementability, and has a good application prospect. In the whole process, data traceability is achieved by quantifying data models, effective and flexible application can be achieved by adjusting basic parameters of all the models for different environments, meanwhile, the whole process is controlled in a full-automatic mode, and the error possibility caused by manual operation is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of coal mine exploration, in particular to a coal mine three-dimensional seismic exploration full-process automatic quality control method and system. BACKGROUND

[0002] In the process of coal mine exploration, the characteristic data of coal mine three-dimensional seismic exploration needs to be converted into visual output image data, so that relevant personnel can intuitively understand the current coal mine geological structure and possible problems, and high-precision three-dimensional image models can help technical personnel quickly judge the coal mine hierarchical structure, which is conducive to guiding coal mining operations and ensuring operation safety.

[0003] However, the entire process includes field data acquisition, indoor data processing and data-model conversion, which gradually realizes the conversion of data to model from the source, processing method and conversion technology level, but each process is affected by the sensitivity of the data, the processing method and the reliability of the processing method, resulting in a deviation between the finally obtained model and the actual structure. Therefore, a coal mine three-dimensional seismic exploration full-process automatic quality control method and system is provided for data quality control in the entire process. SUMMARY

[0004] In view of the deficiencies of the prior art, the present application provides a coal mine three-dimensional seismic exploration full-process automatic quality control method and system, which solves the problems raised in the background art.

[0005] To achieve the above purpose, the present application realizes the following technical scheme: a coal mine three-dimensional seismic exploration full-process automatic quality control method, comprising the following steps: S1, field data acquisition quality control, including intelligent verification of observation system parameters, real-time seismic signal quality evaluation and abnormal elimination, and intelligent verification of data space integrity, which controls the quality of the obtained original geological signal from three aspects of device tuning, signal acquisition and signal coverage; S2, indoor data processing quality control, including intelligent evaluation of preprocessing quality and stacking and migration processing quality control, which evaluates the preprocessing quality of the original qualified data collected in the field, then evaluates the effect of stacking and migration, and provides high signal-to-noise ratio and high resolution seismic data for geological interpretation, which is the key step of data purification in the entire process; S3, geological interpretation quality control, which ensures that the interpretation results meet the actual geological conditions through horizon calibration, structure verification and coal seam precision evaluation, so as to realize the conversion of data to geological information; S4, comprehensive quality evaluation and feedback optimization of the whole process, through the calculation of the comprehensive index of the whole process quality, the quality index of the three stages of field collection, data processing and geological interpretation is comprehensively evaluated, and different optimization suggestions are output according to the evaluation score, so as to realize the data correlation of "quality problem-influencing factor", so as to realize the quality problem traceability closed loop.

[0006] Preferably, the step S1 comprises: S11, intelligent verification of observation system parameters This step ensures the accuracy of the basic setting of field collection through multi-dimensional parameter verification, which lays a foundation for subsequent data quality, including: S111, parameter deviation coefficient calculation This process uses the weighted average method to calculate the overall parameter deviation, and the formula is:

[0007] Where C represents the parameter deviation coefficient, the qualified threshold ; The weight of the th parameter, wherein the coverage times 0.3, the offset distance 0.2, the offset distance 0.2, the point distance 0.15, and the line distance 0.15; The actual measured parameter value is represented by The designed parameter standard value is represented by The total number of parameters participating in the evaluation is represented by

[0008] S112, key parameter verification process, including the following items: S1121, first, check the coverage times to ensure the effective coverage of the underground reflection point; S1122, check whether the offset distance distribution meets the design requirements to ensure the reflection signal strength; S1123, check whether the point distance and line distance are uniform to ensure the consistency of data space sampling; S1124, automatically mark and alarm if the parameter deviation of all parameters exceeds 10%.

[0009] Preferably, the step S1 further comprises: S12, real-time seismic signal quality evaluation and abnormal elimination This step realizes the automatic identification and elimination of abnormal data by monitoring the quality of seismic signal in real time through multi-feature analysis, including the following specific steps: S121, signal-to-noise ratio calculation The energy ratio of effective signal and sound is calculated by using the frequency domain separation method, and the formula can be represented as:

[0010] Wherein SNR represents signal-to-noise ratio, the qualified threshold is SNR≥3dB; S represents the effective signal frequency interval; N represents the noise frequency interval; S(n) represents the sampling point in the effective signal frequency band; N(n) represents the sampling point in the noise frequency band.

[0011] S122, amplitude stability analysis This step evaluates the stability of the amplitude by the coefficient of variation, which can be expressed as:

[0012] Wherein: The amplitude coefficient of variation represents the amplitude coefficient of variation, and the qualified threshold is ; The standard deviation of the amplitude sequence represents the standard deviation of the amplitude sequence. The average value of the amplitude sequence represents the average value of the amplitude sequence.

[0013] S123, frequency characteristic consistency test The purpose is to evaluate the deviation of the actual signal main frequency from the design main frequency, and the formula can be expressed as:

[0014] Wherein The frequency deviation rate represents the frequency deviation rate, and the qualified threshold is ; The actual signal main frequency obtained by Fourier transform represents the actual signal main frequency obtained by Fourier transform. The main frequency value designed according to the geological target represents the main frequency value designed according to the geological target.

[0015] S124, abnormal processing mechanism By setting the abnormal processing mechanism for automatically executing corresponding data processing actions on the data with abnormality according to the source, type and abnormal range of the data, which includes: S1241, marking the trace with SNR lower than the threshold in single-shot data; S1242, positioning the region with unstable amplitude in 3 continuous shots; S1243, automatically generating a supplementary survey suggestion, including a supplementary survey position and a parameter adjustment scheme.

[0016] Preferably, the step S1 further comprises: S13, intelligent verification of data space integrity The purpose of this step is to ensure the continuity and integrity of the collected data in space, and to avoid the occurrence of data blank area, which includes the following specific processes: S131, data integrity rate calculation, which can be expressed as:

[0017] Wherein Data integrity rate, when I < 95%, need to be measured again; Effective data channel number; Total data channel number according to the design of the observation system.

[0018] S132, spatial coverage uniformity evaluation The grid analysis method is used to evaluate the uniformity of data coverage, and the formula can be expressed as:

[0019] Among them Spatial uniformity index, and , The greater the value of the more uniform; The number of coverages of the first grid unit; The average number of coverages; The total number of grid units.

[0020] Preferably, the step S2 comprises: S21, pre-processing quality intelligent evaluation This step focuses on evaluating the effect of static correction noise suppression, which includes: S211, static correction accuracy evaluation Combined with the measured data of the borehole, the static correction error is quantified, including the travel time deviation caused by the surface topography and low-velocity zone, and the formula can be expressed as:

[0021] Among them Static correction error (unit: ms), threshold value ; The travel time of the first control point after correction; The true travel time of the first control point, provided by borehole logging data; H represents the number of control points participating in the evaluation, and N> 10, covering the entire exploration area.

[0022] S212 noise suppression effect evaluation By comparing the noise energy before and after denoising, the effect of denoising is evaluated, and the formula can be expressed as:

[0023] Among them Noise suppression rate, qualified threshold ; The noise energy before denoising is calculated by integrating the noise frequency band; The noise energy after denoising is represented by the same frequency band integral calculation.

[0024] S213, spectrum contrast verification The formula can be represented as:

[0025] Among them The spectrum difference integral value is represented by The smaller the value is, the more complete the effective signal is retained. The power spectral density before denoising is represented by The power spectral density after denoising is represented by And The analysis frequency range is represented by

[0026] Preferably, the step S2 further comprises: S22, superposition and migration processing quality control This step focuses on evaluating the effect of superposition migration, which includes: S221, superposition energy gain calculation This step evaluates the enhancement effect of superposition processing on effective signal according to the matching degree of superposition times and signal gain, and the formula can be represented as:

[0027] Among them The superposition gain is represented by dB, ; The data energy after superposition is represented by The single-channel energy of the th channel is represented by; M represents the number of channels participating in superposition.

[0028] S222, migration imaging accuracy evaluation This step verifies the accuracy of the structure position after migration based on known geological control points (such as faults and coal seam boundaries revealed by drilling), and the formula can be represented as:

[0029] Among them The imaging accuracy is represented by , and the qualified threshold ; The position of the th structure point after migration is represented by The true position of the th structure point is determined by drilling data; K represents the number of structure points participating in evaluation.

[0030] Preferably, the step S3 comprises: S31, horizon calibration and structure interpretation quality control By calculating the calibration accuracy and reliability of horizons and faults, the authenticity of geological information is ensured, and the process includes; S311, horizon calibration accuracy control By establishing the "corresponding relationship" between seismic waves and geological horizons, the matching degree of seismic reflection and well logging horizon is quantified, and the formula can be expressed as;

[0031] Among them; Calibration error, unit ms, qualified threshold; ; The seismic reflection time of the first calibration point is represented; The well logging interpretation time of the first calibration point is represented; Z represents the number of calibration points, and Z>3 for each main coal seam.

[0032] S312, fault interpretation reliability calculation The process evaluates the reliability of fault interpretation by comprehensively considering the continuity of the same phase axis, amplitude change, and cross-section reflection, and the formula can be expressed as;

[0033] Among them The fault reliability is represented; and , the qualified threshold ; The continuity index of the same phase axis is represented, and the more continuous the same phase axis on both sides of the fault, the closer the value is to 1; The amplitude change index is represented, and the more the amplitude change at the fault conforms to the geological law, the closer the value is to 1; The cross-section reflection definition is represented, and the more obvious the fault plane reflection, the closer the value is to 1; The weight coefficient is represented, and satisfies .

[0034] Preferably, the step S3 further comprises: S32, coal seam interpretation and reserve calculation accuracy control By evaluating and analyzing the geological information data interpretation structure, the state and reserves of the coal seam are determined, which includes; S321, coal seam interpretation accuracy evaluation This step quantifies the interpretation error of coal seam thickness and burial depth by comparing the interpretation results with the measured data of boreholes, and the formula can be expressed as;

[0035] wherein represents the relative error of coal seam interpretation, and the qualified threshold value ; represents the interpreted coal seam depth; represents the measured coal seam depth of the borehole.

[0036] S322, reserve calculation error evaluation Comparing the seismic interpretation and the borehole controlled reserve results to ensure the accuracy of resource estimation, and the formula is:

[0037] wherein represents the relative error of reserve calculation, and ; represents the reserve calculation result based on seismic interpretation; represents the reserve calculation result based on borehole data; The step S3 further comprises: S33, intelligent geological mapping generation and optimization Based on the geological information data, the text is converted into images to generate a geological model map, and the model is optimized to output a high-quality image representation, and the process includes: S331, contour smoothness control This process is to avoid the "sawtooth fluctuation" of the contour, and to ensure that the geological map meets the structural form rule, and the formula can be expressed as:

[0038] wherein represents the smoothness index, and , The larger the value, the smoother it is; represents the adjustment coefficient, , the higher the geological complexity The smaller the value is; represents the second derivative of the contour function at , reflecting the bending degree of the contour; represents the planar coordinate point of the point.

[0039] S332, map consistency test This step is to ensure the consistency of information of different types of maps such as profile maps and plane maps, and to avoid contradictions, and the formula is:

[0040] wherein represents the map consistency index , The larger the value, the higher the consistency. represent the value of the first verification point in the profile view; represent the value of the first verification point in the plan view; P represents the number of verification points, and P>20.

[0041] Preferably, the formula for calculating the overall process quality comprehensive index in step S4 can be represented as: wherein represents the overall process quality comprehensive index; represents the field data acquisition quality index, represents the indoor data processing quality index; . geological interpretation quality index, wherein , and , and are respectively calculated according to the weighted function of each index in steps S1 to S3. , and , represent the weight of each quality index in S1 to S3, and then the corresponding quality grade is output according to the index value, and the corresponding optimization suggestion is given according to different quality grades.

[0042] The coal mine three-dimensional seismic exploration full-process automatic quality control system comprises the following functional modules: A data acquisition module comprising a plurality of sensors and data entry devices for field data acquisition; A data processing module for executing the indoor data processing process in step S2; A data storage module for providing a data storage encapsulation container for the full process of steps S1 to S4; A virtual engine for providing data heterogeneity and processing computing power for the full process of steps S1 to S4; A communication module for ensuring the circulation of the full-process data and providing a stable data transmission channel.

[0043] The coal mine three-dimensional seismic exploration full-process automatic quality control method and system have the following beneficial effects: ​​The method forms a complete quality control closed loop through three core stages of field data acquisition, indoor processing and geological interpretation, and establishes a correlation model of quality indicators at each stage to realize full-process tracking of quality problems from parameter verification to final result evaluation. Meanwhile, each stage finally forms a multi-level and progressive quality control system through a full-process quality comprehensive evaluation scheme. The whole process is based on signal processing theory and geostatistics, and has the characteristics of scientific, comprehensive and rigorous feasibility. Through the quantitative data model in the whole process, data traceability is realized, and different environments can be effectively and flexibly applied by adjusting the basic parameters of each model. At the same time, the whole process is automatically controlled, reducing the error possibility caused by manual operation. BRIEF DESCRIPTION OF DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0045] Figure 1 Flow chart of the coal mine three-dimensional seismic exploration full-process automatic quality control method of the present application; Figure 2 Functional architecture diagram of the coal mine three-dimensional seismic exploration full-process automatic quality control system of the present application. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical scheme and advantages of the embodiments of the present application more clear, the technical scheme in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0047] In order to better understand the above technical solutions, the above technical solutions will be described in detail in combination with the drawings in the specification and specific embodiments.

[0048] Embodiment one, the coal mine three-dimensional seismic exploration full-process automatic quality control method and system provided by the embodiments of the present application are shown in Figure 1 The method includes the following steps: S1, field data acquisition quality control; This step controls the quality of the acquired original geological signal from three aspects of device tuning, signal acquisition and signal coverage, including: S11, intelligent verification of observation system parameters This step ensures the accuracy of the field collection basic settings through multi-dimensional parameter verification, laying the foundation for subsequent data quality, including; S111, parameter deviation coefficient calculation This process uses the weighted average method to calculate the overall parameter deviation, and its formula is;

[0049] Where C represents the parameter deviation coefficient, the qualified threshold ; represents the weight of the th parameter, where the fold is 0.3, the offset distance is 0.2, the offset distance is 0.2, the point distance is 0.15, and the line distance is 0.15; represents the actual measured parameter value; represents the designed parameter standard value; represents the total number of parameters participating in the evaluation.

[0050] S112, key parameter verification process, including the following; S1121, first, check the fold to ensure effective coverage of the underground reflection point; S1122, check whether the offset distance distribution meets the design requirements to ensure the reflection signal strength; S1123, check whether the point distance and line distance are uniform to ensure the consistency of data space sampling; S1124, automatically mark and alarm if the parameter deviation of all parameters exceeds 10%.

[0051] S12, real-time seismic signal quality evaluation and abnormality rejection This step monitors the quality of seismic signals in real time through multi-feature analysis, realizes automatic identification and rejection of abnormal data, including the following specific steps; S121, signal-to-noise ratio calculation The effective signal-to-noise energy ratio is calculated using the frequency domain separation method, and its formula can be expressed as;

[0052] Where SNR represents the signal-to-noise ratio, the qualified threshold SNR≥3dB; S represents the effective signal frequency interval; N represents the noise frequency interval; S(n) represents the sampling point in the effective signal frequency band; N(n) represents the sampling point in the noise frequency band.

[0053] S122, amplitude stability analysis This step evaluates the stability of the amplitude through the coefficient of variation, which can be expressed as;

[0054] Where; denotes the amplitude variation coefficient, the qualified threshold ; denotes the standard deviation of the amplitude sequence; denotes the mean value of the amplitude sequence.

[0055] S123, frequency characteristic consistency check The purpose is to evaluate the deviation of the actual signal dominant frequency from the design dominant frequency, and the formula can be expressed as:

[0056] wherein denotes the frequency deviation rate, the qualified threshold ; denotes the actual signal dominant frequency obtained by Fourier transform; denotes the dominant frequency value designed according to the geological target.

[0057] S124, abnormal processing mechanism By setting the abnormal processing mechanism for automatically performing corresponding data processing actions on the data with abnormalities according to the source, type and abnormal range of the data, which includes: S1241, marking the traces with SNR lower than the threshold in single-shot data; S1242, positioning the region with unstable amplitude appearing in 3 continuous shots; S1243, automatically generating a supplementary survey suggestion, including a supplementary survey position and a parameter adjustment scheme.

[0058] S13, intelligent verification of data space integrity The purpose of this step is to ensure the continuity and integrity of the acquired data in space, and to avoid the occurrence of data blank area, which includes the following specific processes: S131, data integrity rate calculation, which can be expressed by the formula:

[0059] wherein denotes the data integrity rate, which needs to be supplemented when I < 95%; denotes the number of effective data traces; denotes the total number of data traces designed according to the observation system.

[0060] S132, spatial coverage uniformity evaluation The grid analysis method is used to evaluate the uniformity of data coverage, and the formula can be expressed as:

[0061] wherein denotes the spatial uniformity index, and , A larger value indicates greater uniformity; Indicates the first The number of times each grid cell is covered; Indicates the average number of times coverage is achieved; This indicates the total number of grid cells.

[0062] S2, Indoor Data Processing Quality Control This process involves preprocessing and quality assessment of the raw, qualified data collected in the field. It is the core step in the entire data purification process, providing high signal-to-noise ratio and high resolution seismic data for geological interpretation. The specific process includes the following steps. S21, Intelligent Assessment of Preprocessing Quality This step focuses on evaluating the effectiveness of static correction and noise suppression, including: S211, Static Calibration Accuracy Assessment Based on borehole measurement data, the static correction error is quantified, including travel time deviation caused by surface topography and low-velocity zones. The formula can be expressed as follows:

[0063] in Represents the static correction error (in milliseconds, combined with the threshold value). ; Indicates the first Travel time after control point correction; Indicates the first The actual travel time of each control point is provided by borehole logging data; H represents the number of control points involved in the assessment, and N>10, covering the entire exploration area.

[0064] S212. Noise Suppression Effectiveness Evaluation The effect is quantified by the change in noise energy before and after denoising, and its formula can be expressed as follows:

[0065] in Table noise suppression rate, acceptable threshold ; This represents the noise energy before denoising, calculated by integrating across the noise frequency band. This represents the noise energy after denoising, calculated by integration within the same frequency band.

[0066] S213, Spectrum Comparison Verification The spectral difference integral method is used to avoid "over-suppression of effective signals" during the removal process. Its formula can be expressed as follows:

[0067] in Spectrum difference integral value The smaller, the more complete the effective signal preservation. Power spectrum density before denoising Power spectrum density after denoising And Analysis frequency range

[0068] S22, superposition and migration processing quality control This step focuses on evaluating the effect of superposition migration, which includes: S221, superposition energy gain calculation This step evaluates the enhancement effect of superposition processing on effective signal according to the matching degree of superposition times and signal gain, and its formula can be expressed as:

[0069] Wherein Superposition gain, unit: dB, ; Superposed data energy Single-channel energy of the th channel; M represents the number of channels participating in superposition.

[0070] S222, migration imaging accuracy evaluation This step verifies the accuracy of the structure position after migration based on known geological control points (such as faults and coal seam boundaries revealed by drilling), and its formula can be expressed as:

[0071] Wherein Imaging accuracy, and , qualified threshold ; Position of the th structure point after migration True position of the th structure point, determined by drilling data; K represents the number of structure points participating in evaluation.

[0072] S3, geological interpretation quality control The geological interpretation process ensures that the interpretation results meet the actual geological conditions through horizon calibration, structure verification, and coal seam accuracy evaluation, so as to realize the conversion of data to geological information. This process includes: S31, horizon calibration and structure interpretation quality control Through calibration accuracy and reliability calculation of horizon and fault, the authenticity of geological information is ensured, and the process includes: S311, horizon calibration accuracy control By establishing the "corresponding relationship" between seismic waves and geological horizons, the matching degree of seismic reflection and well logging horizon is quantified, and its formula can be expressed as:

[0073] wherein, the calibration error, unit ms, qualified threshold; ; the seismic reflection time of the first calibration point is represented; the well logging interpretation time of the first calibration point is represented; Z represents the number of calibration points, and Z>3 for each main coal seam.

[0074] S312, fault interpretation reliability calculation The process evaluates the reliability of fault interpretation by comprehensively considering the three characteristics of event continuity, amplitude variation and cross-section reflection, and its formula can be expressed as:

[0075] wherein the fault reliability is represented; and , qualified threshold; ; the event continuity index is represented, the more continuous the events on both sides of the fault, the closer the value to 1; the amplitude variation index is represented, the more consistent the amplitude variation at the fault with the geological rules, the closer the value to 1; the cross-section reflection definition is represented, the more obvious the fault plane reflection, the closer the value to 1; the weight coefficient is represented, and it satisfies .

[0076] S32, coal seam interpretation and reserve calculation precision control By evaluating and analyzing the geological information data interpretation structure, the state and reserves of coal seams are determined, which includes: S321, coal seam interpretation precision evaluation This step quantifies the interpretation error of coal seam thickness and burial depth by comparing the interpretation results with the measured data of boreholes, and its formula can be expressed as:

[0077] wherein the relative error of coal seam interpretation is represented, and the qualified threshold ; the interpreted coal seam burial depth is represented; the coal seam burial depth measured by boreholes is represented.

[0078] S322, reserve calculation error evaluation Comparing the seismic interpretation and drilling controlled reserve results to ensure resource estimation accuracy, the formula is;

[0079] Wherein represents the relative error of reserve calculation, and ; represents the reserve calculation result based on seismic interpretation; represents the reserve calculation result based on drilling data.

[0080] S33, intelligent geological mapping generation and optimization Based on geological information data, the text and image are converted to generate a geological model map, and the model is optimized to output high-quality image representation, which includes; S331, structure contour smoothness control This process is to avoid the "sawtooth fluctuation" of the contour line, and to ensure that the geological map meets the structural shape rule, which can be expressed as;

[0081] Wherein represents the smoothness index, and , The larger the value, the smoother it is; represents the adjustment coefficient, , the higher the geological complexity The smaller the value is; represents the second derivative of the contour function at , reflecting the degree of contour bending; represents the plane coordinate point of the point.

[0082] S332, map consistency test This step is to ensure the consistency of information of different types of maps such as cross section and plan, and to avoid contradictions, the formula is;

[0083] Wherein represents the map consistency index , The larger the value, the higher the consistency; represents the value of the th verification point in the cross section; represents the value of the th verification point in the plan; P represents the number of verification points, and P>20.

[0084] S4, comprehensive evaluation and feedback optimization of the whole process quality This step is to calculate the whole process quality comprehensive index, which comprehensively reflects the quality index of the three stages of field collection, data processing and geological interpretation, and forms a whole evaluation, and its formula can be expressed as: Among them The whole process quality comprehensive index is represented by Q; The field data acquisition quality index is represented by Q1, The indoor data processing quality index is represented by Q2; The geological interpretation quality index is represented by Q3, wherein , and , and are respectively calculated by using a weighted function according to each index in steps S1 to S3. , and , which represent the weights of each quality index in S1 to S3.

[0085] Then, according to the index value, the corresponding quality level is output, and according to different quality levels, the corresponding optimization suggestions are given.

[0086] In this way, the data correlation of "quality problem-impact factor" is realized, and the quality problem traceability closed loop is realized.

[0087] This method covers the three core stages of field data collection, indoor processing and geological interpretation, forms a complete quality control closed loop, and establishes a correlation model of each stage quality index in each stage, realizes the whole process tracking of quality problems from parameter verification to final result evaluation, and at the same time, each stage finally forms a multi-level and progressive quality control system through the whole process quality comprehensive evaluation scheme, and the whole process is based on signal processing theory and geological statistics, which has the characteristics of scientific, comprehensive and rigorous implementability, and through the quantitative data model in the whole process, the data traceability is realized, and through adjusting the basic parameters of each model, the effective and flexible application in different environments can be realized. At the same time, the whole process is automatically controlled, which reduces the error possibility caused by manual operation.

[0088] Embodiment two, the embodiment of the present application provides a coal mine three-dimensional seismic exploration whole process automatic quality control system, which comprises the following functional modules: The data acquisition module comprises a plurality of sensors and data entry devices for field data acquisition; The data processing module is used for executing the indoor data processing process in step S2; The data storage module provides a data storage packaging container for the whole process of steps S1 to S4; A virtual engine provides data heterogeneity and processing power for the whole process from step S1 to step S4; A communication module is used to ensure data flow in the whole process and provide a stable data transmission channel.

[0089] The basic principles and main features of the present application and the advantages of the present application are shown and described above. Those skilled in the art should understand that the present application is not limited to the above-mentioned embodiments, and the above-mentioned embodiments and descriptions in the specification are only to illustrate the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A method for full-process automation quality control of three-dimensional seismic exploration in coal mines, characterized in that, The method comprises the following steps: S1, field data acquisition quality control, including intelligent verification of observation system parameters, real-time seismic signal quality evaluation and abnormality elimination, and intelligent verification of data space integrity, controlling the quality of the obtained original geological signals from three aspects of device tuning, signal acquisition, and signal coverage; S2, indoor data processing quality control, including intelligent evaluation of preprocessing quality and quality control of stacking and migration processing, evaluating the preprocessing quality of the original qualified data collected in the field, then evaluating the effect of stacking and migration, and providing high signal-to-noise ratio and high resolution seismic data for geological interpretation, which is the key step of data purification in the whole process; S3, geological interpretation quality control, ensuring that the interpretation results meet the actual geological conditions through horizon calibration, structure verification, and coal seam precision evaluation, so as to realize the conversion of data into geological information; S4, comprehensive evaluation and feedback optimization of the whole process, calculating the comprehensive index of the whole process, comprehensively evaluating the quality index of the three stages of field acquisition, data processing, and geological interpretation, forming an overall evaluation, and outputting different optimization suggestions according to the evaluation score, so as to realize the data correlation of "quality problem-impact factor", and realize the quality problem tracing closed loop.

2. The method according to claim 1, characterized in that, The step S1 comprises: S11, intelligent verification of observation system parameters This step ensures the accuracy of the basic settings of field acquisition through multi-dimensional parameter verification, which lays the foundation for subsequent data quality, including: S111, parameter deviation coefficient calculation This process calculates the overall parameter deviation by using the weighted average method, and the formula is: where C denotes a parameter deviation coefficient, a pass threshold ; denotes the weight of the th parameter, where the number of coverages is 0.3, the offset distance is 0.2, the offset distance is 0.2, the point distance is 0.15, and the line distance is 0.15; denotes the actual measured parameter value; denotes the designed parameter standard value; denotes the total number of parameters participating in the evaluation. S112, key parameter verification process, including the following items: S1121, first, check the number of coverages to ensure the effective coverage of underground reflection points; S1122, check whether the distribution of shot-receiver distance meets the design requirements to ensure the intensity of reflection signals; S1123, check whether the point distance and line distance are uniform to ensure the consistency of data space sampling; S1124, automatically mark and alarm if the deviation of all parameters exceeds 10%.

3. The method according to claim 2, characterized in that, The step S1 further comprises: S12, real-time seismic signal quality evaluation and abnormality elimination This step realizes the automatic identification and elimination of abnormal data by monitoring the quality of seismic signals in real time through multi-feature analysis, including the following specific steps: S121, signal-to-noise ratio calculation The effective signal-to-noise energy ratio is calculated by using the frequency domain separation method, and the formula can be expressed as: Where SNR represents the signal-to-noise ratio, the qualified threshold is SNR≥3dB; S represents the effective signal frequency interval; N represents the noise frequency interval; S(n) represents the sampling point in the effective signal frequency band; N(n) represents the sampling point in the noise frequency band. S122, amplitude stability analysis This step evaluates the stability of the amplitude by using the coefficient of variation, which can be expressed as: wherein; denotes the amplitude coefficient of variation, the pass threshold ; denotes the standard deviation of the amplitude sequence; denotes the mean value of the amplitude sequence. S123, frequency characteristic consistency test The purpose is to evaluate the deviation of the actual signal main frequency and the design main frequency, and the formula can be expressed as: wherein represents a frequency deviation rate, a qualified threshold ; represents an actual signal main frequency obtained by Fourier transform; represents a main frequency value designed according to a geological target. S124, abnormality processing mechanism The abnormality processing mechanism is used to automatically perform corresponding data processing actions on the abnormal data according to the source, type, and abnormal range of the data, including: S1241, mark the traces in single shot data whose SNR is lower than the threshold value; S1242, locate the area where the amplitude is unstable in 3 continuous shots; S1243, automatically generate a suggestion for supplementary survey, including the position of supplementary survey and the adjustment scheme of parameters.

4. The method according to claim 3, characterized in that, The step S1 further includes: S13, intelligent checking of data space integrity The purpose of this step is to ensure the continuity and integrity of the acquired data in space, and to avoid the occurrence of data blank area, which includes the following specific processes; S131, calculation of data completeness rate, which can be expressed by the formula: wherein represents the data integrity rate, and when I < 95%, the measurement needs to be supplemented; represents the number of effective data channels; represents the total number of data channels according to the design of the observation system. S132, evaluation of spatial coverage uniformity The grid analysis method is used to evaluate the uniformity of data coverage, which can be expressed by the formula: wherein represents the spatial uniformity index, and , the greater the value of the more uniform; represents the number of covers for the grid cell; represents the average number of covers; represents the total number of grid cells.

5. The method according to claim 1, characterized in that, The step S2 includes: S21, intelligent evaluation of pre-processing quality This step focuses on the evaluation of the effects of static correction and noise suppression, which includes: S211, evaluation of static correction accuracy Combined with the measured data from the borehole, the static correction error is quantified, including the travel time deviation caused by surface topography and low-velocity zone, which can be expressed by the formula: wherein represents the static correction error, a threshold value ; represents the travel time of the th control point after correction; represents the true travel time of the th control point, provided by borehole logging data; H represents the number of control points involved in the evaluation, and N > 10, covering the entire exploration area. S212, evaluation of noise suppression effect The effect is quantified by the change of noise energy before and after denoising, which can be expressed by the formula: wherein Table noise sound suppression rate, qualified threshold ; Denotes noise energy before denoising, calculated by noise frequency band integration; Denotes noise energy after denoising, calculated by the same frequency band integration. S213, frequency spectrum comparison verification The frequency spectrum difference integral is used to avoid "over-suppression of effective signals" in the process, which can be expressed by the formula: wherein represents the spectral difference integral value, The smaller, the more complete the effective signal retention; represents the power spectral density before denoising; represents the power spectral density after denoising and represents the analysis frequency range.

6. The full-process automatic quality control method for coal mine 3D seismic exploration according to claim 5, characterized in that, The step S2 further includes: S22, quality control of stacking and migration processing This step focuses on the evaluation of the effects of stacking and migration, which includes: S221, calculation of stacking energy gain This step evaluates the enhancement effect of effective signals by stacking processing according to the matching degree of stacking times and signal gain, which can be expressed by the formula: wherein represents the superposition gain in dB, ; represents the superposed data energy; represents the single-channel energy of the th trace; M represents the number of traces participating in the superposition. S222, evaluation of migration imaging accuracy This step verifies the accuracy of the structure position after migration based on the known geological control points, which can be expressed by the formula: wherein represents the imaging accuracy, and , the pass threshold ; represents the position of the th construction point after the offset; represents the true position of the th construction point, determined from the drilling data; and K represents the number of construction points participating in the evaluation.

7. The method according to claim 1, characterized in that, The step S3 includes: S31, quality control of horizon calibration and structure interpretation The authenticity of geological information is ensured by calculating the calibration accuracy and reliability of horizons and faults, which includes: S311, control of horizon calibration accuracy The matching degree of seismic reflection and well logging horizon is quantified by establishing the "corresponding relationship" between seismic wave and geological horizon, which can be expressed by the formula: wherein; Calibration error, unit ms, qualified threshold value; ; indicates the seismic reflection time of the th calibration point; indicates the well logging interpretation time of the th calibration point; Z represents the number of calibration points, Z>3 for each main coal seam. S312, calculation of fault interpretation reliability This process evaluates the reliability of fault interpretation by comprehensively considering the continuity of event, amplitude variation, and cross-section reflection, which can be expressed by the formula: wherein represents fault confidence; and , a pass threshold; ; represents a coherence continuity indicator, the more continuous the events on both sides of the fault, the closer the value is to 1; represents an amplitude variation indicator, the more the amplitude variation at the fault conforms to geological rules, the closer the value is to 1; represents the cross-sectional reflectivity, the more distinct the fault plane reflection, the closer the value is to 1; denote the weight coefficients and satisfy .

8. The full-process automatic quality control method for coal mine 3D seismic exploration according to claim 7, characterized in that, The step S3 further includes: S32, control of coal seam interpretation and reserve calculation accuracy The state of coal seam and reserves are determined by evaluating and analyzing the interpretation structure of geological information data, which includes: S321, evaluation of coal seam interpretation accuracy This step quantifies the interpretation error of coal seam thickness and burial depth by comparing the interpretation results with the measured data from the borehole, which can be expressed by the formula: wherein represents the relative error of coal seam interpretation, the qualified threshold ; Represents interpreted coal seam depth; Represents drilled coal seam depth. S322, evaluation of reserve calculation error The accuracy of resource estimation is ensured by comparing the reserve results of seismic interpretation and borehole control, which is expressed by the formula: wherein represents the reserve calculation relative error, and ; represents the reserve calculation result based on seismic interpretation; represents the reserve calculation result based on drilling data; The step S3 further includes: S33, intelligent geological mapping generation and optimization The geological model map is generated by converting the geological information data into graphics, and the model is optimized to output high-quality images, which includes: S331, control of structure contour smoothness This process is to avoid the isograms "sawtooth fluctuation", ensure that the geological map in line with the structural pattern of the law, its formula can be expressed as; wherein represents a smoothness index, and , The greater the value, the smoother it is; represents a regulation coefficient, The higher the geological complexity The smaller the value; represents the second derivative of the contour function at , reflecting the degree of contour bending; represents the planar coordinate point of the point position. S332, map consistency test This step is to ensure that the profile, plan and other different types of information consistent, avoid contradictions, its formula is; wherein consistency index , The greater the value, the higher the consistency. represents the value of the first verification point in the profile view; represents the value of the first verification point in the plan view; P represents the number of verification points, and P >

20.

9. The method according to claim 8, characterized in that, In step S4, the formula for calculating the overall process quality index can be expressed as: wherein represents the overall quality index of the entire workflow; represents the field data acquisition quality index, represents the laboratory data processing quality index; . the geological interpretation quality index, wherein , and , and are respectively calculated according to the indicators in S1 to S3 steps using a weighted function; , and , the weight of each quality index of S1 to S3, then according to the index value, the corresponding quality grade output is carried out, and the corresponding optimization suggestion is given according to different quality grades.

10. A coal mine 3D seismic exploration full-process automatic quality control system for performing the coal mine 3D seismic exploration full-process automatic quality control method according to any one of claims 1-9, characterized in that, The following functional modules are included: Data acquisition module, including a plurality of sensors for field data acquisition and data entry equipment; Data processing module, for executing step S2 indoor data processing procedure; Data storage module, for steps S1 to S4 whole process provides data storage encapsulation container; Virtual engine, for steps S1 to S4 whole process provides data heterogeneity and processing of computing power; Communication module, for ensuring the whole process of data flow, provides stable data transmission channel.

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