Coal mine fault seismic exploration method, system and equipment based on active and passive source cooperation

Abstract

The present application belongs to the technical field of geophysical survey, and relates to a coal mine fault seismic exploration method, system and equipment based on active and passive source cooperation, aiming to solve the problems of limited application of the existing active source exploration method in the filling area and insufficient information of the passive source exploration method. The present application comprises: obtaining passive source data of a target area, passive source data of an edge area and active source data of the edge area; obtaining the corresponding relationship between the active source data and the passive source data based on the active source data and the passive source data of the edge area; predicting the active source data of the target area based on the passive source data of the target area and the corresponding relationship; and obtaining the exploration result of the target area by interpretation according to the passive source data of the target area and the active source data of the target area. The present application realizes high-precision and high-reliability detection of the coal mine area fault through the cooperative fusion of active and passive source data.

Description

Technical Field

[0001] This invention belongs to the field of geophysical measurement technology, and specifically relates to a method, system and electronic equipment for coal mine fault seismic exploration based on active and passive source coordination. Background Technology

[0002] In the process of coal mine resource development, accurate detection of geological structures such as faults is the core prerequisite for ensuring safe production in coal mines, and is directly related to the formulation of mining plans, prevention of mine disasters, and improvement of resource recovery rate.

[0003] Active source measurement involves artificially applying a seismic source and using a geophone array to receive reflected signals from the strata. Subsurface structure modeling is completed through short-term concentrated observations. This method can acquire high signal-to-noise ratio reflected signals and has excellent identification accuracy for deep strata interfaces and structural morphologies. Especially under non-complex surface conditions, it can achieve detailed characterization of faults deeper than a kilometer, providing reliable geological data for deep coal resource development. Passive source measurement relies on natural energy sources such as natural earthquakes and crustal micro-vibrations, without artificial intervention in the source excitation process. It obtains subsurface strata structure information through the analysis and processing of long-term observation signals. Its core advantage lies in addressing the problem of weak effective signals in fill areas. It can improve the effective signal energy through long-term observation and multiple superposition. Furthermore, because it uses single-channel acquisition and processing, it eliminates the need for multi-channel processing and static correction issues, demonstrating unique advantages in fill area exploration.

[0004] However, in actual coal mine exploration scenarios, both technologies have significant limitations: in the fill areas formed by open-pit coal mine spoil heaps, the application of active source technology faces multiple technical bottlenecks. On the one hand, the loose slag layer covering the surface prevents the artificial source from effectively coupling with the surrounding rock, causing the excitation energy to be severely depleted in the initial propagation stage, making it difficult to form an effective seismic wave field that penetrates the strata; on the other hand, the fill areas are formed by the disordered accumulation of mining waste, and the strata are composed of deposits of different particle sizes and lithologies, exhibiting extremely strong lateral heterogeneity, which triggers a severe wave field interference effect, causing the effective reflected signal to be submerged by strong interference and the signal-to-noise ratio to be significantly reduced; in addition, the surface elevation of the fill areas fluctuates dramatically and the lateral velocity of the underground medium varies significantly, which easily leads to blurred seismic profile imaging and the inability to clearly identify fault boundaries. While passive source technology can adapt to the complex surface conditions of fill areas, its data has inherent limitations: the excitation time, energy intensity, and propagation path of natural seismic signals are random, making it difficult to achieve directional acquisition of specific detection targets; moreover, natural source signals are mainly low-frequency components, and due to frequency resolution limitations, their ability to identify fine fault structures is insufficient, and the amount of underground stratigraphic information they contain is insufficient, making it difficult to meet the needs of fine fault detection in coal mines. When used alone, it cannot provide comprehensive and reliable geological data for coalfield mining.

[0005] Therefore, how to break through the application bottleneck of single-source seismic exploration technology and achieve integrated and accurate detection of faults in filled and unfilled areas through technological collaboration has become a key issue that urgently needs to be addressed in the field of coal mine geophysical exploration. Summary of the Invention

[0006] To address the aforementioned problems in existing technologies, namely the limited application of existing active-source seismic exploration methods in coal mine fill areas and the insufficient information content of passive-source seismic exploration methods, the first aspect of this invention proposes a coal mine fault seismic exploration method based on active and passive source synergy, achieving high-precision and high-reliability detection of faults in coal mine areas (including fill areas). This method includes the following steps:

[0007] Acquire passive source data of the target area, passive source data of the edge area, and active source data of the edge area. The passive source data is continuously acquired seismic data, the active source data is seismic data acquired when a seismic source is actively applied, and the edge area is the area within a set distance outside the target area.

[0008] Based on the active source data and passive source data of the edge region, obtain the correspondence between the active source data and the passive source data;

[0009] Based on the passive source data of the target region and the correspondence between the active source data and the passive source data, predict the active source data of the target region;

[0010] Based on the passive source data of the target area and the predicted active source data of the target area, the exploration results of the target area are interpreted.

[0011] In some preferred embodiments, the method for obtaining the correspondence between active source data and passive source data based on the active source data and passive source data of the edge region is as follows:

[0012] Determine the time-depth conversion relationship between the target area and the peripheral area based on well logging data;

[0013] Based on the aforementioned time-depth conversion relationship, the depth domain position corresponding to each point in the active source data and passive source data of the edge region is determined;

[0014] Based on the active source data and passive source data whose depth domain locations have been determined, the correspondence between the active source data and the passive source data is determined.

[0015] In some preferred embodiments, the method for determining the time-depth conversion relationship between the target area and the edge area based on well logging data is as follows:

[0016] Based on the well logging data, the layer velocity of the seismic wave in the formation is calculated. Combined with the calibration results of the well logging depth and the reflection phase axis of the seismic trace near the well, the time-depth conversion relationship between seismic reflection time and geological depth is constructed.

[0017] In some preferred embodiments, determining the correspondence between active source data and passive source data based on the determined depth domain location includes at least one of the following methods:

[0018] Identify the geological interfaces and reflection feature points corresponding to active source data and passive source data, and calculate the similarity of the geological interfaces and reflection feature points between active source data and passive source data; based on the similarity, establish the correspondence between the reflected wave waveforms of active source data and passive source data;

[0019] The characteristic frequencies of active source data and passive source data at different depths are identified by spectral analysis, and the characteristic frequency variation patterns between adjacent strata are extracted. Based on the frequency characteristics and their variation patterns, the frequency correlation coefficient or proportional relationship between active source data and passive source data is calculated, thereby determining the corresponding relationship of their frequency variations at different depths.

[0020] The amplitude characteristics of active source data and passive source data are identified and amplitude anomaly characteristics are detected. The correlation coefficient of amplitude anomaly characteristics between active source data and passive source data is determined. Based on the correlation coefficient, the correspondence between amplitude characteristics between active source data and passive source data is established.

[0021] In some preferred embodiments, after obtaining the correspondence between active source data and passive source data, the method further includes:

[0022] The correspondence between active source data and passive source data is used as the initial correspondence. A mapping model is constructed based on the initial correspondence. The passive source data of the edge region is input into the mapping model to obtain the predicted active source data.

[0023] By iteratively adjusting the parameters of the mapping model, the difference between the predicted active source data and the actual measured active source data of the edge region is made less than a preset threshold, thereby obtaining an optimized mapping relationship.

[0024] In some preferred embodiments, based on the passive source data of the target region and the correspondence between the active source data and the passive source data, the active source data of the target region is predicted, including:

[0025] Based on the optimized mapping relationship, the passive source data of the target area is input into the mapping relationship model segment by segment to obtain the predicted active source data within the corresponding depth range, and the active source data of the target area is predicted layer by layer inward.

[0026] In some preferred embodiments, the step of interpreting the exploration results of the target area based on the passive source data of the target area and the predicted active source data of the target area includes:

[0027] Based on the characteristic frequency changes of passive source data and predicted active source data in the target area, the stratigraphic depth of the target area is delineated, and at least two stratigraphic segments are obtained to determine the boundary depth of each stratigraphic segment.

[0028] For each stratigraphic segment, differentiated weights are set for the passive source data of the target area and the predicted active source data of the target area. The passive source data of the target area and the predicted active source data of the target area of ​​each stratigraphic segment are weighted according to the corresponding weight coefficients to obtain the local exploration data of each stratigraphic segment.

[0029] The exploration results are obtained by weighting and overlaying local exploration data from different strata within the target area according to depth continuity or frequency correlation.

[0030] In some preferred embodiments, after obtaining the exploration results, the method further includes:

[0031] Establish a regional geological model, and use the reflection characteristics and geological structure information of passive source data and active source data of the marginal areas as prior knowledge to optimize the exploration results, including at least one of the following methods:

[0032] Based on the reflection characteristic parameters and geological structure information of the edge region, combined with the geological structure law, the geological structure change type and evolution trend of the target region are determined;

[0033] Using the reflection characteristic parameters and geological structure information of the edge region as a benchmark reference system, the similar reflection wave characteristics of the exploration results are compared and differentiated to optimize the detailed geological structure features of the exploration results in the target area.

[0034] A second aspect of this invention proposes a coal mine fault seismic exploration system based on active and passive source coordination, the system comprising:

[0035] The data acquisition module is configured to acquire passive source data of the target area, passive source data of the edge area, and active source data of the edge area. The passive source data is continuously acquired seismic data, the active source data is seismic data acquired when a seismic source is actively applied, and the edge area is the area within a set distance outside the target area.

[0036] The relationship building module is configured to obtain the correspondence between active source data and passive source data based on the active source data and passive source data of the edge region;

[0037] The inference module is configured to predict the active source data of the target area based on the passive source data of the target area and the correspondence between the active source data and the passive source data;

[0038] The joint interpretation module is configured to interpret the exploration results of the target area based on the passive source data of the target area and the predicted active source data of the target area.

[0039] A third aspect of this invention proposes an electronic device for coal mine fault seismic exploration based on active and passive source coordination, comprising:

[0040] At least one processor; and

[0041] A memory communicatively connected to at least one of the processors; wherein,

[0042] The memory stores instructions that can be executed by the processor to implement the aforementioned method for coal mine fault seismic exploration based on active and passive source coordination.

[0043] The beneficial effects of this invention are:

[0044] The combined exploration method of "active source reflection seismic + natural source seismic frequency imaging" is adopted. In view of the complex geological conditions of the fill area, the advantages of passive source long-term observation and no static correction are used to achieve effective signal acquisition in the central area. Then, the combined exploration characteristics of the edge area are used to constrain and optimize the central area. This method effectively solves the problem that traditional methods are difficult to image in this area, and significantly improves the identification accuracy of geological structure changes and the accuracy of parameter estimation.

[0045] Based on the survey results of the edge area, the correspondence between the active and passive source data is determined. The active source data of the target area is predicted layer by layer inward. The high resolution and rich information of the active source data are used to optimize the inversion of the passive source data. The depth domain transformation and dynamic weight adjustment strategy are introduced. Combined with the automatic analysis of the continuity, frequency change and amplitude characteristics of the reflected wave, the efficient fusion and joint interpretation of the active and passive source data are realized.

[0046] By using the survey results of the peripheral area to constrain and optimize the data of the central area, the accuracy of identifying geological structural changes and the accuracy of parameter estimation have been significantly improved, the risk of misjudgment has been reduced, more reliable geological basis has been provided for coalfield mining, and the application and development of seismic exploration technology under complex geological conditions has been promoted. Attached Figure Description

[0047] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0048] Figure 1 This is a flowchart of a coal mine fault seismic exploration method based on active and passive source coordination, according to the present invention. Detailed Implementation

[0049] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0050] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0051] This invention finds the correlation between active and passive source measurements based on passive and active source data in the edge region. Then, for the target region, the active source measurement results are estimated by using the passive source measurement results and the correlation. Joint interpretation is then performed to obtain the analysis results of the target region, achieving high-precision and high-reliability detection of faults in coal mining areas (including fill areas).

[0052] The present invention provides a method for coal mine fault seismic exploration based on active and passive source coordination, comprising the following steps:

[0053] Acquire passive source data of the target area, passive source data of the edge area, and active source data of the edge area. The passive source data is continuously acquired seismic data, the active source data is seismic data acquired when a seismic source is actively applied, and the edge area is the area within a set distance outside the target area.

[0054] Based on the active source data and passive source data of the edge region, obtain the correspondence between the active source data and the passive source data;

[0055] Based on the passive source data of the target region and the correspondence between the active source data and the passive source data, predict the active source data of the target region;

[0056] Based on the passive source data of the target area and the predicted active source data of the target area, the exploration results of the target area are interpreted.

[0057] To more clearly illustrate the coal mine fault seismic exploration method based on active and passive source coordination of the present invention, the following is combined with... Figure 1 The steps in the embodiments of the present invention will be described in detail below.

[0058] The first embodiment of the coal mine fault seismic exploration method based on active and passive source coordination according to the present invention includes the following steps S1 to S4, each step is described in detail below:

[0059] S1. Acquire passive source data of the target area, passive source data of the edge area, and active source data of the edge area, wherein the passive source data is continuously acquired seismic data, the active source data is seismic data acquired when a seismic source is actively applied, and the edge area is the area within a set distance outside the target area.

[0060] In some embodiments, based on coal mine geological data, surface topography, and mining plans, the detection area is divided into a target area and an outer edge area surrounding the target area. The target area is the key detection area with concentrated coverage of open-pit coal mine spoil heaps, while the outer edge area is the normal stratum area around the target area without significant fill cover.

[0061] Active source reflection seismic construction acquires active source data and is suitable for geological conditions with no fill or fill thickness not exceeding 20 meters. In some embodiments, active source reflection seismic construction employs a small-scale heavy-duty seismic source vehicle and a single-component seismic acquisition node; the small-scale heavy-duty seismic source is used to provide stable excitation energy; the single-component seismic acquisition node is used to receive seismic wave components reflected from different strata, i.e., reflected wave signals.

[0062] Because fill areas are often composed of loose deposits with uneven particle size, significant lithological differences, and large surface elevation variations, seismic waves are prone to strong scattering and energy attenuation during propagation, making it difficult to obtain high-quality reflected signals using conventional active source seismic imaging. In contrast, acquiring passive source data through natural source seismic frequency imaging has strong terrain adaptability and is applicable to almost all geological types, including the aforementioned complex fill areas; its hardware mainly consists of three-component seismic acquisition nodes for observing natural source seismic data.

[0063] S2. Based on the active source data and passive source data of the edge region, obtain the correspondence between the active source data and the passive source data; the method is as follows:

[0064] Determine the time-depth conversion relationship between the target area and the peripheral area based on well logging data;

[0065] Based on the aforementioned time-depth conversion relationship, the depth domain position corresponding to each point in the active source data and passive source data of the edge region is determined;

[0066] Based on the active source data and passive source data whose depth domain locations have been determined, the correspondence between the active source data and the passive source data is determined.

[0067] Preferably, the method for determining the time-depth conversion relationship between the target area and the edge area based on well logging data is as follows:

[0068] Based on the well logging data, the layer velocity of the seismic wave in the formation is calculated. Combined with the calibration results of the well logging depth and the reflection phase axis of the seismic trace near the well, the time-depth conversion relationship between seismic reflection time and geological depth is constructed.

[0069] Preferably, the method for determining the depth domain position of each point in the active source data and passive source data of the edge region is as follows:

[0070] Based on the time-depth conversion relationship of the edge region, and combined with the emission and reception times of the active source in the edge region, the depth domain position corresponding to each data point in the active source data of the edge region is determined.

[0071] Based on the time-depth transformation relationship of the edge region, and combined with the velocity model (layer velocity), the depth domain position corresponding to each data point in the passive source data of the edge region is calculated and determined;

[0072] The active source data and passive source data with determined depth domain positions are matched and corrected to obtain active and passive source data for edge regions with unified depth domain.

[0073] Preferably, determining the correspondence between active source data and passive source data based on the determined depth domain location includes at least one of the following methods:

[0074] The continuity analysis of reflected waves specifically involves: identifying the geological interfaces and reflection feature points corresponding to the active source data and the passive source data; calculating the similarity between the geological interfaces and reflection feature points between the active source data and the passive source data; and establishing a correspondence between the reflected wave waveforms of the active source data and the passive source data based on the similarity.

[0075] Frequency variation analysis specifically involves: using spectral analysis to identify the characteristic frequencies of active source data and passive source data at different depths, and extracting the characteristic frequency variation patterns between adjacent strata; based on the frequency characteristics and their variation patterns, calculating the frequency correlation coefficient or proportional relationship between the active source data and passive source data, and then determining the corresponding relationship of their frequency variations at different depths.

[0076] Amplitude feature analysis specifically involves: identifying the amplitude features of active source data and passive source data respectively and detecting amplitude anomalies; determining the correlation coefficient of amplitude anomalies between active source data and passive source data; and establishing a correspondence between the amplitude features of active source data and passive source data based on the correlation coefficient.

[0077] Preferably, the similarity verification method is the correlation coefficient method, which calculates the correlation coefficient of the reflection waveforms of the active source data and the passive source data at the same geological interface, and uses the value of the correlation coefficient to characterize the degree of similarity between the two. The closer the correlation coefficient is to 1, the higher the similarity of the reflection feature points and waveforms between the active source data and the passive source data.

[0078] Preferably, a frequency variation mapping model can be further constructed to map the characteristic frequency distribution of passive source data to the characteristic frequency distribution of active source data, so as to achieve frequency domain correspondence between different source data.

[0079] Preferably, after obtaining the correspondence between active source data and passive source data, the method further includes:

[0080] The passive source data and active source data of the edge region are divided into training set and validation set. The correspondence between active source data and passive source data in the training set is used as the initial correspondence. A mapping model is constructed based on the initial correspondence. The passive source data of the edge region in the training set is input into the mapping model to obtain the predicted active source data.

[0081] By iteratively adjusting the parameters of the mapping model, the difference between the predicted active source data and the actual measured active source data of the edge region in the training set is less than a preset threshold, thereby obtaining an optimized mapping relationship.

[0082] The accuracy of the optimized mapping relationship is evaluated using the validation set.

[0083] S3. Based on the passive source data of the target region and the correspondence between the amplitude characteristics of the active source data and the passive source data, predict the active source data of the target region, including:

[0084] Based on the optimized mapping relationship, the passive source data of the target area is input into the mapping relationship model segment by segment to obtain the predicted active source data within the corresponding depth range. The active source data of the target area is then predicted layer by layer inward. Here, the "layer" of the segmented prediction refers to the geological unit determined by stratigraphic boundaries or characteristic frequency boundaries within the target area. After completing the prediction of the outer layer and correcting the error, the prediction is then pushed inward layer by layer to obtain the active source data of the target area.

[0085] S4. Based on the passive source data of the target area and the predicted active source data of the target area, interpret the exploration results of the target area, including:

[0086] Based on the characteristic frequency changes of passive source data and predicted active source data in the target area, the stratigraphic depth of the target area is delineated, and the target area is divided into multiple stratigraphic segments. Each segment contains at least one complete stratigraphic unit, resulting in no less than two segments. The boundary depth of each stratigraphic segment is then determined.

[0087] For each stratigraphic segment, based on the geological significance, data signal-to-noise ratio, and resolution of the segment, differentiated weights are set for the passive source data of the target area and the predicted active source data of the target area within the segment. The passive source data of the target area and the predicted active source data of the target area of ​​each stratigraphic segment are weighted according to the corresponding weight coefficients to obtain the local exploration data of each stratigraphic segment.

[0088] The exploration results are obtained by weighting and overlaying local exploration data from different strata within the target area according to depth continuity or frequency correlation.

[0089] Preferably, the geological significance of the segments is determined by combining well logging and drilling data with the regional geological background.

[0090] Preferably, the differentiated weights are specifically:

[0091] In shallow layers, active source data is given a higher weight to highlight its high resolution advantage, while in deep layers, passive source data is given a higher weight to reflect its strong penetration. At the same time, the weight is dynamically adjusted by taking into account both geological importance and data quality.

[0092] Preferably, the active source data is advanced layer by layer from the edge region inward. During the advancement process, the interpretation parameters, such as the reflected wave velocity, amplitude, and frequency, are adjusted according to the changes in the reflected waveform and geological patterns. A recursive algorithm or iterative method can be used to gradually expand the joint interpretation area, while ensuring the consistency and geological rationality of the interpretation results.

[0093] In this embodiment, a recursive algorithm based on the least squares method is employed to predict the geological features of the next layer based on existing joint interpretation results, and to continuously revise and improve the interpretation model. Assuming the reflection wave characteristics of the current layer are known, the reflection wave characteristics of the next layer are estimated using the least squares method, as shown in the formula:

[0094] ;

[0095] in, For the estimated characteristics of the reflected wave, A is the observation matrix. T Let A be the transpose of A, and b be the observed data.

[0096] Preferably, after obtaining the exploration results, the method further includes:

[0097] Establish a regional geological model, and use the reflection characteristics and geological structure information of passive source data and active source data of the marginal areas as prior knowledge to optimize the exploration results, including at least one of the following methods:

[0098] Based on the reflection characteristic parameters and geological structure information of the edge region, combined with the geological structure law, the geological structure change type and evolution trend of the target region are determined;

[0099] Using the reflection characteristic parameters and geological structure information of the edge region as a benchmark reference system, the similar reflection wave characteristics of the exploration results are compared and differentiated to optimize the detailed geological structure features of the exploration results in the target area.

[0100] The correction methods include adjusting parameters such as fault location, stratigraphic boundary depth, structural strike or dip angle in the target area, or re-matching and phase-correcting reflected wave waveforms that have differences.

[0101] More preferably, after determining the covariance relationship in the edge region, the preferred direction for interpretation extension is determined by calculating the similarity coefficient of the phase axis of the reflected wave, tracking continuity index, and combining the statistical results of geological strike and dip angle.

[0102] Interpreters can use three-dimensional visualization technology of seismic data volumes to intuitively observe the continuity of the reflection wave phase axis and the geological structure trend, and jointly interpret the direction of advancement from the peripheral area to the target area.

[0103] More preferably, the joint interpretation results of the central region are optimized by utilizing the reflection characteristics and geological structure information of the edge region:

[0104] By establishing a regional geological model, surrounding information is used as prior knowledge to guide the interpretation of the central area; geostatistical methods, such as Kriging interpolation, are used to integrate geological information from the peripheral areas into the interpretation of the central area, thereby improving the accuracy and reliability of the interpretation.

[0105] The basic formula for Kriging interpolation is:

[0106] ;

[0107] in, This is the estimated value at the point x0 to be estimated. These are the weighting coefficients. Given point x i The observed value at that location.

[0108] Using surrounding information as prior knowledge to guide the interpretation of the central area, the method is as follows:

[0109] By establishing a regional geological model, the reflection characteristic parameters (including amplitude, frequency, phase characteristics, etc.) and geological structural information (including fault location, layer depth, strike and dip angle, etc.) of the marginal area are used as training samples and boundary conditions to fit and correct the model, thereby providing constraints when interpreting the central area.

[0110] In some embodiments, the joint interpretation results of the central region are constrained by the reflection characteristics and geological structure information of the edge region, including at least one of the following methods:

[0111] 1) Enhance the identification of geological structural changes:

[0112] Multi-directional constraints on reflection characteristics in peripheral regions can enhance the ability to identify geological structural changes in target areas. By analyzing the reflection wave characteristics and geological structural morphology of peripheral regions, a correspondence model between peripheral region characteristics and structural types can be established. Then, by applying this model to the central region, possible geological structural changes in the target area can be inferred.

[0113] In this embodiment, a pattern recognition-based method for detecting geological structural changes is employed. Specifically, a support vector machine (SVM) classifier is trained using the reflected wave characteristics and geological structural information of the edge region as training samples. After the model training is complete, the joint interpretation results of the central region are input into the classifier, and the type of structural change in the target region is determined based on its classification output, thereby achieving the identification and accuracy improvement of geological structural changes. For example, the classification formula of the SVM is used:

[0114] ;

[0115] in, For the classification results, For Lagrange multipliers, The labels for the training samples. is the kernel function, and b is the bias term.

[0116] 2) Improve the recognizability of structural details:

[0117] By conducting joint analysis layer by layer from the periphery inward, and using the reflection characteristics and geological structure information of the periphery as a continuity reference during the interpretation process, the cumulative error caused by geological heterogeneity can be reduced as the analysis progresses, thereby preserving the true details of the reflection characteristics as much as possible. On this basis, similar reflection wave characteristics of the target area are distinguished and compared to improve the identification of geological structure details and reveal the geological structure characteristics of the target area more clearly.

[0118] In this embodiment, a multi-scale analysis method based on wavelet transform is used to decompose and reconstruct the reflected wave data of the surrounding and target areas at multiple scales, extracting structural detail features at different scales and improving the discernibility of structural details. The basic formula of wavelet transform is:

[0119] ;

[0120] Where W(a,b) are wavelet coefficients, a is the scaling factor, b is the translation factor, and x(t) is the input signal. It is the complex conjugate of the wavelet function.

[0121] 3) Improve the accuracy of parameter estimation:

[0122] In the parameter estimation process, the accurate acquisition of parameters such as coal seam thickness and fault displacement in the central region depends on reasonable constraints of the tectonic background and reflection details. Therefore, this embodiment, without introducing an additional support vector machine model, jointly optimizes the parameter estimation based on the first step of tectonic identification results and the second step of detail identification analysis.

[0123] The specific method is as follows: The classification results obtained in the first two steps are... and multi-scale characteristic response By incorporating the parameter estimation function, the optimization model is constructed as follows:

[0124] ;

[0125] in, These are the optimized parameter estimates (such as thickness, drop, etc.). This is an initial estimate obtained based on traditional methods (such as Kriging interpolation, physical property relations, etc.); To construct classification labels for support vector machines; For the detailed feature indicators extracted from multi-scale analysis of reflection features; For structural correction functions and detail response functions; To control the coefficients of the weights of each optimization term.

[0126] This method uses construction type and detailed features as weighting factors to adjust and compensate for parameter estimation results, thereby enhancing the semantics and improving the accuracy of the parameter field and effectively reducing error propagation in boundary regions or complex construction regions.

[0127] 4) Reduce the risk of misjudgment:

[0128] To effectively reduce the risk of misjudgment in the geological interpretation of the central area, in this embodiment, a consistency probability model is established by combining Bayesian theory with the results of the aforementioned support vector machine model, and the confidence level of the interpretation results is assessed and risk is controlled.

[0129] Specifically, let H represent the hypothesis that "the geological interpretation result is correct," and E represent the observational evidence that "the current point is highly consistent with the marginal zone in terms of structural classification, detailed features, and parameter residuals." Then, according to Bayes' theorem, we have:

[0130] ;

[0131] in: To explain the correctness of the results (which can be set to a global empirical value or based on the accuracy of SVM in the edge region); To approximate the probability of observing consistency of the current features, assuming correct interpretation, the following formula can be used:

[0132] ;

[0133] in, These are respectively: construction recognition difference, detail matching deviation, and parameter residual. α, β, γ As weight; The total probability of occurrence of current feature consistency can be provided by fitting the edge region distribution or by empirical priors;

[0134] The credibility of the final interpretation results for the central region ;

[0135] when If the value is below the set threshold (e.g., 0.6), it indicates a potential misjudgment at that location, and correction or review is recommended.

[0136] This embodiment preferably uses a coal mine fault seismic exploration method based on active and passive source synergy for simulation analysis in a certain mining area. The northern part of the mining area is a fill zone with a thick overburden and significant surface disturbance, resulting in severe attenuation of active source excitation signals and making it difficult to obtain effective active source reflection wave data. In contrast, the southern part of the mining area has a thinner overburden and suitable surface conditions for active source excitation and reception; therefore, the active source reflection seismic method is primarily used for exploration. For the northern fill zone, the passive source frequency imaging method based on natural seismic signals is mainly relied upon for detection. In some areas, a synergistic approach combining active and passive sources is also used to obtain more comprehensive geological information. This method successfully identified the geological structural characteristics and coal seam occurrence conditions of the mining area.

[0137] A small, controllable seismic source vehicle equipped with a hammer is operated wirelessly to manipulate the hammering and hovering of the source system, thereby controlling the generation of seismic waves. This small, controllable seismic source has the following characteristics:

[0138] (1) Wireless remote control operation

[0139] Modified using a model aircraft remote control module, the all-terrain seismic source vehicle can be operated by a model aircraft remote control to move forward, backward, turn, stop, and perform other movement operations. It can also operate the hammering and hovering of the seismic source system.

[0140] (2) Safety

[0141] Using wireless remote control keeps the operator away from the risky areas of the hammer's running track and the lifting wire rope, as well as away from the all-terrain vehicle, avoiding safety hazards caused by rollover or wire rope breakage.

[0142] (3) Surface adaptability

[0143] The vehicle uses a 4-wheel all-terrain body to improve the passability of all-terrain sources, and the climbing angle is greater than 30 degrees when fully loaded.

[0144] (4) Low power consumption and high torque system

[0145] The main hammering drive system uses a brushless motor and an electromagnetic clutch, achieving the capability to pull a hammer weighing over 200kg with only 400W of power. Its low power consumption and high energy efficiency significantly increase the hammering duration of the vibratory source.

[0146] (5) Low-cost consumables and rapid maintenance

[0147] Using 10mm universal steel wire rope as the lifting wear part is convenient and inexpensive to procure. When the hammer is 200kg, the wire rope needs to be replaced approximately every 500 times. The specially designed wire rope drum facilitates quick and easy wire rope replacement. This saves construction costs and improves construction efficiency.

[0148] (6) Two-section detachable lifting track

[0149] The use of a two-section detachable lifting track serves two purposes: firstly, it facilitates transportation by enclosed trucks, as the track can be detached into two ends, thereby reducing the transportation height; secondly, it allows for adjustment of the maximum hammering height according to construction requirements.

[0150] (7) Variable weight hammer design

[0151] The hammerhead mainly consists of two parts: the lower part is the fixed-weight hammerhead, and the upper part is the area for installing adjusting weights. Each adjusting weight weighs approximately 5 kg, and the overall weight of the hammerhead can be changed by increasing or decreasing the number of adjusting weights.

[0152] During data acquisition, the acquisition equipment includes single-component seismic acquisition nodes and three-component seismic acquisition nodes, as detailed below:

[0153] Receiver parameters for a single-component seismic acquisition node:

[0154] Detector configuration: 5Hz seismic detector; Digital solution: 24-bit high-precision Δ-Σ analog-to-digital converter, instantaneous dynamic range better than 144dB; Solid-state storage capacity: 32G; Power supply: internal 160Wh rechargeable lithium battery pack, continuous working time greater than 600 hours; Satellite timing accuracy: + / -1μs; Satellite timekeeping accuracy: + / -1ms (within 6 hours after satellite signal loss); Working mode: autonomous acquisition + industrial-grade tablet computer on-site wireless quality control; Data retrieval method: data retrieval cable + wireless data backhaul; Operating temperature range: -40℃~+70℃.

[0155] Acquisition parameters for single-component seismic acquisition nodes:

[0156] ADC resolution: 24-bit; Sampling interval: 1ms; Preamplifier programmable amplifier: x1, x2, x4, x8, x16 (0dB, 6dB, 12dB, 18dB, 24dB); Gain accuracy: 0.1%; Analog signal input: ±2.5Vp-p@x1 gain, ±625mVp-p@x4 gain, ±156mVp-p@x16 gain; Real-time dynamic range @4ms: 144dB @ x1 gain, 140dB @ x4 gain, 133dB @ x16 gain; Equivalent input noise: 0.1μV @ x1 gain, 0.04μV @ x4 gain, 0.025μV @ x16 gain; Interchannel crosstalk: <110dB; Common-mode rejection ratio: >110dB.

[0157] The three-component seismic acquisition node has the following characteristics:

[0158] Internally integrated orthogonal three-component low-frequency seismic detector (0.2Hz); features a 24-bit Δ-Σ analog-to-digital converter with an instantaneous dynamic range of up to 125dB; built-in high-sensitivity satellite positioning and timing module, combined with an internal high-stability clock source, to achieve high-precision wide-area system-level synchronous acquisition; optional external high-gain satellite positioning and timing antenna, supporting deep burial applications or underwater acquisition applications in shallow water areas;

[0159] Equipped with a built-in BLE wireless module, the communication distance is >20m, supporting on-site construction management, equipment self-testing, wireless status query, data readback, and real-time transmission. Combined with a satellite positioning unit, it supports precise positioning and locating of buried equipment in the field. The internal 130Wh rechargeable lithium battery pack provides over 800 hours of continuous or intermittent recording time. An optional external high-capacity lithium battery unit or mains power supply is available, automatically switched by the seismometer's built-in intelligent battery management unit, enabling ultra-long-term continuous data acquisition and recording. The P&R (place and collect) working mode maximizes field construction efficiency. It features a waterproof, corrosion-resistant, high-strength engineering plastic casing. Its compact size (14D x 17H cm) and lightweight (2.9Kg) facilitate field transportation and construction.

[0160] The functions of the three-component seismic acquisition node are summarized as follows:

[0161] (1) Detector configuration: Orthogonal three-component high-sensitivity short-period seismic detector (0.2Hz);

[0162] (2) Digitalization solution: 3x24-bit high precision Analog-to-digital converter;

[0163] (3) Solid-state storage capacity: 32G (2ms sampling, four channels can continuously record for 45 days);

[0164] (4) Power supply: Internal 130WH rechargeable lithium battery pack, supporting >800 hours of continuous or interval recording, and supporting external large-capacity lithium battery unit or solar panel power supply.

[0165] (5) Satellite timing and timekeeping accuracy: timing accuracy: + / - 10μs, timekeeping accuracy: + / - 1ms (within 1 hour after satellite signal loss), can be connected to an external high-gain satellite positioning and timing antenna;

[0166] (6) Working mode: autonomous data acquisition + handheld near-field wireless quality control;

[0167] (7) LED indicators: data acquisition station status, satellite clock synchronization status, and wireless data transmission status;

[0168] (8) Data recovery method: indoor centralized data recovery + outdoor wireless data backhaul;

[0169] (9) Weight: 2.9 kg;

[0170] (10) External dimensions: Diameter 14cm, height 17cm;

[0171] (11) Operating temperature range: -30℃ ~ +70℃;

[0172] (12) Waterproof rating: No leakage after 48 hours in water depth of 3 meters.

[0173] The acquisition parameters of the three-component seismic acquisition node are as follows:

[0174] ADC resolution: 24-bit; Sampling interval: configurable 0.5ms, 1ms, 2ms, 4ms, 10ms; Gain accuracy: 0.1%; Analog signal input: ±2.5Vp-p; Real-time dynamic range: 125dB @ 2ms (typical); Equivalent input noise: 1μV @ 2ms (typical); Anti-aliasing filter: Linear filter, -3dB bandwidth 86.6% Nyquist frequency; Common-mode rejection ratio: >95dB; Stopband attenuation: >105dB @ Nyquist frequency.

[0175] Passive source data of the target area, passive source data of the edge area, and active source data of the edge area are acquired based on single-component seismic acquisition nodes and three-component seismic acquisition nodes. Based on the active source data and passive source data of the edge area, depth domain unified imaging of the active source data and passive source data of the edge area is performed by calibrating the target layer, and the correspondence between the active source data and passive source data is obtained.

[0176] Preferably, the depth domain position corresponding to each point in the active source data and passive source data of the edge region is determined by any of the following methods:

[0177] 1) Synthetic record calibration: Convert well logging data (sonic waves, density) into synthetic seismic records, compare them with actual seismic traces, and determine the geological strata corresponding to the seismic reflections;

[0178] 2) VSP calibration: Convert well logging data (sonic waves, density) into synthetic seismic records, compare them with actual seismic traces, and determine the geological strata corresponding to the seismic reflections;

[0179] 3) Time-depth relationship calibration: The seismic time domain data is converted into the depth domain by logging time-depth curves (obtained by sonic logging integration) and compared with the drilling geological stratification.

[0180] Specifically, the synthetic record calibration includes the following steps:

[0181] A1. Data acquisition: Acquire acoustic logging and density logging data; extract seismic wavelets (zero phase / mixed phase);

[0182] A2. Calculate the reflection coefficient:

[0183] ;

[0184] in, The current sound wave velocity in the strata. The velocity of sound waves in the next stratum; This represents the density of the current stratum. The density of the next stratum;

[0185] A3. Generate a synthetic record, convolving the reflection coefficient with the seismic wavelet:

[0186] ;

[0187] A4. Calibrate and compare, adjust the wavelet phase or time-depth relationship to match the synthetic record with the actual seismic trace.

[0188] Specifically, VSP calibration includes the following steps:

[0189] B1. Data acquisition, deployment of geophones in the well, and surface excitation (controllable seismic source or explosives).

[0190] B2. First arrival time extraction: Pick the first arrival time of the direct wave and establish a time-depth curve;

[0191] B3. Corridor overlay: The VSP uplink wave field is overlaid to form a well-side seismic trace and compared with the ground seismic data.

[0192] Specifically, time-depth relationship calibration includes the following steps:

[0193] Seismic time-domain data was converted to depth-domain data using well logging time-depth curves (obtained by sonic logging integration) and compared with well geological stratification. The time-depth curves were calculated as follows:

[0194] ;

[0195] in, The speed of sound waves.

[0196] Furthermore, the P-wave velocity is used to convert the time profile of an active source reflection seismic event to a depth profile, while the S-wave velocity is used to convert the depth profile of a natural source frequency imaging event. The S-wave velocity and P-wave velocity can be converted using empirical formulas:

[0197] The conversion is performed based on the empirical formula: 1.7 ≤ Vp / Vs ≤ 2.2; in this embodiment:

[0198] If the longitudinal wave velocity is 1000 m / s, the transverse wave velocity is approximately 588 m / s (the ratio is approximately 1.7).

[0199] If the longitudinal wave velocity is 3000 m / s, the transverse wave velocity is approximately 1364 m / s (the ratio is approximately 2.2).

[0200] Based on the aforementioned time-depth transformation relationship, the depth domain position corresponding to each point in the active source data and passive source data of the edge region is determined, and any of the following methods can be selected:

[0201] Ray tracing method: Utilizing the propagation speed of seismic waves in different media, the relationship between the travel time and depth of seismic waves is calculated using a ray tracing algorithm. The formula is:

[0202] ;

[0203] in, t For travel time, d For depth, v(z) For depth z Seismic wave velocity at the location;

[0204] Wave Equation Method: A depth domain transformation method based on the wave equation, considering the wave characteristics of seismic waves, which can calculate depth information more accurately; its basic formula is:

[0205] ;

[0206] in, p Here, x represents the pressure field of the seismic wave, and x is the horizontal direction. z In the depth direction, t For time, v(z) For depth z The velocity of seismic waves at that location.

[0207] After converting the data to the depth domain, the passive source data and the active source data were matched to obtain the correspondence, including at least one of the following methods:

[0208] 1) Continuity analysis of reflected waves:

[0209] Waveform similarity algorithms, such as dynamic time warping (DTW) or correlation coefficient methods, are used to automatically track in-phase axes and analyze the similarity and continuity of reflected wave waveforms from passive and active source data to identify corresponding geological interfaces and reflection feature points in different source data.

[0210] The correlation coefficient of the reflected wave waveforms is calculated to measure their similarity. The formula for the correlation coefficient is:

[0211] ;

[0212] in, and These are the amplitude values ​​at the i-th sampling point of the two reflected wave waveforms, respectively. and These are the average amplitude values ​​of the two waveforms, respectively.

[0213] 2) Frequency variation analysis:

[0214] Using spectral analysis techniques, such as Fourier transform, wavelet transform, or continuous waveform analysis, the frequency characteristics of reflected waves from passive and active source data are extracted to identify the characteristic frequencies of strata at different depths. By comparing the frequency spectra of passive and active source data, the correlation between frequency variation patterns and stratigraphic structure is established, and the corresponding relationship is determined.

[0215] 3) Amplitude characteristic analysis:

[0216] In the context of geological background, the relationship between reflected wave amplitude variations and factors such as stratigraphic lithology and fluids is analyzed. Amplitude anomaly detection algorithms, such as neural networks or machine learning methods, are used to identify amplitude anomaly characteristics in passive and active source data, verify the rationality of the correspondence, and map them into a joint interpretation framework.

[0217] Based on the above analysis results, a mapping relationship between passive source data and active source data is established. Support vector machines are then used for processing to achieve a non-linear mapping between input and output data. For example, the mapping formula of a neural network can be used: , where y is the mapped output, W is the network weight, x is the input data, b is the bias term, and f is the activation function.

[0218] Preferably, in this embodiment, the correlation coefficient of the frequency spectrum can be calculated or the frequency feature vector can be used for pattern recognition. The active source data has a significant peak at frequency f1, and the passive source data has a corresponding peak at frequency f2. Furthermore, there is a certain proportional relationship between f1 and f2, thus establishing a correspondence between frequency changes.

[0219] Preferably, in this embodiment, an amplitude anomaly detection method based on Bayesian theory is used to calculate the probability that the reflected wave amplitude is anomaly, as shown in the formula: ,in, Given that feature B is observed, the probability that the amplitude of the reflected wave belongs to anomaly A is given. Let P(A) be the probability that anomaly A occurs with feature B, and P(B) be the prior probability that anomaly A occurs with feature B.

[0220] Continuity analysis of reflected waves is achieved by tracing in-phase axes on seismic profiles, ensuring good matching of interpretation results from different sources in overlapping areas. Frequency variation analysis uses spectral analysis techniques to identify characteristic frequencies of strata at different depths, thereby determining correspondences. Amplitude characteristic analysis utilizes amplitude variations of reflected waves, combined with geological background, to further verify the rationality of the correspondences. Through these techniques, efficient integration of passive and active source data is achieved, significantly improving the interpretation accuracy of geological structures and coal seam occurrence conditions.

[0221] In the target area, the correspondence between reflection characteristics and stratigraphic structure is determined by analysis, followed by segmented interpretation. The weights of different sources are dynamically adjusted based on the target depth and geological characteristics to optimize the effect of joint interpretation.

[0222] Based on the characteristic frequency changes of passive source data and predicted active source data in the target area, the stratigraphic depth of the target area is delineated, and at least two stratigraphic segments are divided to determine the boundary depth of each stratigraphic segment.

[0223] For each stratigraphic segment, differentiated weights are set for the passive source data of the target area and the predicted active source data of the target area. The weight setting needs to take into account the geological target depth, data quality and characteristics, and can also be dynamically adjusted according to the data signal-to-noise ratio and resolution. The passive source data of the target area and the predicted active source data of the target area of ​​each stratigraphic segment are weighted and superimposed or fused according to the corresponding weight coefficients to obtain the local exploration data of each stratigraphic segment.

[0224] ;

[0225] Among them, D 融合 For the merged data, D 主动源 and D 被动源 Data from active and passive sources, respectively. and For the corresponding weights, and + =1;

[0226] The exploration results are obtained by weighting and overlaying local exploration data from different strata within the target area according to depth continuity or frequency correlation.

[0227] Preferably, an analysis method based on the amplitude and frequency of reflected waves is used to determine the boundary depth between shallow and deep layers; characteristic frequencies of strata at different depths are determined through spectral analysis, and the changes in characteristic frequencies are used as the basis for segmentation. Assuming that at depth z0, the resolution advantage of active source data is significantly higher than that of passive source data, the boundary depth is set at z0. Shallow active source data has high resolution and concentrated excitation energy, clearly reflecting the shallow stratigraphic structure; passive source data, on the other hand, is more advantageous at deeper layers, capturing more profound geological structural information.

[0228] Ultimately, based on the exploration results, it can be concluded that:

[0229] The work area covers approximately 1.1 square kilometers, with the surface mainly consisting of plains, river wetlands, and spoil heaps. Through meticulous data collection, processing, and interpretation, the undulations and extent of the coal seam floor were revealed, and the geological structure was analyzed in detail. The results show that the coal seam floor exhibits a monocline structure dipping southeast, with the highest elevation at approximately -340 meters and the lowest at approximately -610 meters, resulting in a maximum elevation difference of approximately 270 meters. Furthermore, faults have a relatively small impact on the distribution of the coal seam, suggesting that the faults developed slightly later than the coal seam depositional age.

[0230] Although the steps in the above embodiments are described in the above order, those skilled in the art will understand that in order to achieve the effect of this embodiment, different steps do not need to be executed in such an order. They can be executed simultaneously (in parallel) or in a reverse order. These simple variations are all within the protection scope of this invention.

[0231] The second embodiment of the present invention is a coal mine fault seismic exploration system based on active and passive source coordination, the system comprising:

[0232] The data acquisition module is configured to acquire passive source data of the target area, passive source data of the edge area, and active source data of the edge area. The passive source data is continuously acquired seismic data, the active source data is seismic data acquired when a seismic source is actively applied, and the edge area is the area within a set distance outside the target area.

[0233] The relationship building module is configured to obtain the correspondence between active source data and passive source data based on the active source data and passive source data of the edge region;

[0234] The inference module is configured to predict the active source data of the target region based on the passive source data of the target region and the corresponding relationship;

[0235] The joint interpretation module is configured to interpret the exploration results of the target area based on the passive source data of the target area and the predicted active source data of the target area.

[0236] It should be noted that the coal mine fault seismic exploration system based on active and passive source coordination provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the modules or steps in the embodiments of the present invention can be further decomposed or combined. For example, the modules in the above embodiments can be merged into one module, or further divided into multiple sub-modules to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of the present invention are only for distinguishing the various modules or steps and are not considered as an improper limitation of the present invention.

[0237] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process and related descriptions of the system described above can be found in the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0238] The third embodiment of the coal mine fault seismic exploration equipment based on active and passive source coordination of the present invention includes:

[0239] At least one processor; and

[0240] A memory communicatively connected to at least one of the processors; wherein,

[0241] The memory stores instructions that can be executed by the processor to implement the above-described coal mine fault seismic exploration method based on active and passive source coordination.

[0242] The fourth embodiment of the present invention provides a computer-readable storage medium storing computer instructions, which are executed by a computer to implement the above-described coal mine fault seismic exploration method based on active and passive source coordination.

[0243] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process and related descriptions of the electronic device and computer-readable storage medium described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0244] Those skilled in the art will recognize that the modules and method steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. The programs corresponding to the software modules and method steps can be placed in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium known in the art. To clearly illustrate the interchangeability of electronic hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in electronic hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the invention.

[0245] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0246] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0247] The terms “first”, “second”, etc., are used to distinguish similar objects, not to describe or indicate a specific order or sequence.

[0248] The term "comprising" or any other similar term is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus / device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent in such process, method, article, or apparatus / device.

[0249] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A method for coal mine fault seismic exploration based on active and passive source synergy, characterized in that, Includes the following steps: Acquire passive source data of the target area, passive source data of the edge area, and active source data of the edge area. The passive source data is continuously acquired seismic data, the active source data is seismic data acquired when a seismic source is actively applied, and the edge area is the area within a set distance outside the target area. Based on the active source data and passive source data of the edge region, obtain the correspondence between the active source data and the passive source data; Based on the passive source data of the target region and the correspondence between the active source data and the passive source data, predict the active source data of the target region; Based on the passive source data of the target area and the predicted active source data of the target area, the exploration results of the target area are interpreted. The method for obtaining the correspondence between active source data and passive source data based on the active source data and passive source data of the edge region is as follows: Determine the time-depth conversion relationship between the target area and the peripheral area based on well logging data; Based on the aforementioned time-depth conversion relationship, the depth domain position corresponding to each point in the active source data and passive source data of the edge region is determined; Based on the active source data and passive source data whose depth domain locations have been determined, the correspondence between the active source data and the passive source data is determined.

2. The method for coal mine fault seismic exploration based on active and passive source synergy as described in claim 1, characterized in that, The method for determining the time-depth conversion relationship between the target area and the edge area based on well logging data is as follows: Based on the well logging data, the layer velocity of the seismic wave in the formation is calculated. Combined with the calibration results of the well logging depth and the reflection phase axis of the seismic trace near the well, the time-depth conversion relationship between seismic reflection time and geological depth is constructed.

3. The method for coal mine fault seismic exploration based on active and passive source synergy as described in claim 1, characterized in that, The determination of the correspondence between active source data and passive source data based on the determined depth domain location includes at least one of the following methods: Identify the geological interfaces and reflection feature points corresponding to active source data and passive source data, and calculate the similarity of geological interfaces and reflection feature points between active source data and passive source data; Based on the aforementioned similarity, a correspondence between the reflected wave waveforms of active source data and passive source data is established. The characteristic frequencies of active source data and passive source data at different depths are identified by spectral analysis, and the characteristic frequency variation patterns between adjacent strata are extracted. Based on the characteristic frequencies and their variation patterns, the frequency correlation coefficient or proportional relationship between active source data and passive source data is calculated, thereby determining the corresponding relationship of their frequency variations at different depths. The amplitude characteristics of active source data and passive source data are identified and amplitude anomalies are detected. The correlation coefficient of amplitude anomalies between active source data and passive source data is determined. Based on the correlation coefficient, a correspondence between the amplitude characteristics of active source data and passive source data is established.

4. The method for coal mine fault seismic exploration based on active and passive source synergy as described in claim 3, characterized in that, After obtaining the correspondence between active source data and passive source data, the method further includes: The correspondence between active source data and passive source data is used as the initial correspondence. A mapping model is constructed based on the initial correspondence. The passive source data of the edge region is input into the mapping model to obtain the predicted active source data. By iteratively adjusting the parameters of the mapping model, the difference between the predicted active source data and the actual measured active source data of the edge region is made less than a preset threshold, thereby obtaining an optimized mapping relationship.

5. The method for coal mine fault seismic exploration based on active and passive source synergy as described in claim 4, characterized in that, Based on the passive source data of the target region and the correspondence between the active source data and the passive source data, the active source data of the target region is predicted, including: Based on the optimized mapping relationship, the passive source data of the target area is input into the mapping model segment by segment to obtain the predicted active source data within the corresponding depth range, and the active source data of the target area is predicted layer by layer inward.

6. The method for coal mine fault seismic exploration based on active and passive source synergy as described in claim 1, characterized in that, The process of interpreting the exploration results of the target area based on the passive source data and the predicted active source data of the target area includes: Based on the characteristic frequency changes of passive source data and predicted active source data in the target area, the stratigraphic depth of the target area is delineated, and at least two stratigraphic segments are obtained to determine the boundary depth of each stratigraphic segment. For each stratigraphic segment, differentiated weights are set for the passive source data of the target area and the predicted active source data of the target area. The passive source data of the target area and the predicted active source data of the target area of ​​each stratigraphic segment are weighted according to the corresponding weight coefficients to obtain the local exploration data of each stratigraphic segment. The exploration results are obtained by weighting and overlaying local exploration data from different strata within the target area according to depth continuity or frequency correlation.

7. The method for coal mine fault seismic exploration based on active and passive source synergy as described in claim 6, characterized in that, After obtaining the exploration results, the method further includes: Establish a regional geological model, and use the reflection characteristics and geological structure information of passive source data and active source data of the marginal areas as prior knowledge to optimize the exploration results, including at least one of the following methods: Based on the reflection characteristic parameters and geological structure information of the edge region, combined with the geological structure law, the geological structure change type and evolution trend of the target region are determined; Using the reflection characteristic parameters and geological structure information of the edge region as a benchmark reference system, the similar reflection wave characteristics of the exploration results are compared and differentiated to optimize the detailed geological structure features of the exploration results in the target area.

8. A coal mine fault seismic exploration system based on active and passive source coordination, characterized in that, The system includes: The data acquisition module is configured to acquire passive source data of the target area, passive source data of the edge area, and active source data of the edge area. The passive source data is continuously acquired seismic data, the active source data is seismic data acquired when a seismic source is actively applied, and the edge area is the area within a set distance outside the target area. The relationship building module is configured to obtain the correspondence between active source data and passive source data based on the active source data and passive source data of the edge region; The inference module is configured to predict the active source data of the target area based on the passive source data of the target area and the correspondence between the active source data and the passive source data; The joint interpretation module is configured to interpret the exploration results of the target area based on the passive source data of the target area and the predicted active source data of the target area. The method for obtaining the correspondence between active source data and passive source data based on the active source data and passive source data of the edge region is as follows: Determine the time-depth conversion relationship between the target area and the peripheral area based on well logging data; Based on the aforementioned time-depth conversion relationship, the depth domain position corresponding to each point in the active source data and passive source data of the edge region is determined; Based on the active source data and passive source data whose depth domain locations have been determined, the correspondence between the active source data and the passive source data is determined.

9. An electronic device for coal mine fault seismic exploration based on active and passive source coordination, characterized in that, include: At least one processor; as well as A memory communicatively connected to at least one of the processors; wherein, The memory stores instructions that can be executed by the processor to implement the coal mine fault seismic exploration method based on active and passive source coordination as described in any one of claims 1-7.

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