Block micro-motion inversion method and device based on geological information

Abstract

The invention discloses a block micro-motion inversion method and device based on geological information, and the method comprises the steps: obtaining various types of geological information from a target region, carrying out the data preprocessing of the geological information, and constructing a feature vector based on the various types of geological information after data preprocessing; performing block division based on a density clustering algorithm and the feature vectors to obtain a plurality of independent blocks, and optimizing block boundaries of the plurality of independent blocks based on the feature vectors to obtain a plurality of optimized independent blocks; constructing an independent inversion model, and performing inversion processing based on the independent inversion model to obtain an inversion result in each optimized independent block; according to the method, the inversion result is subjected to integrated processing to obtain an inversion integrated result, the target area is subjected to block division, and adaptive inversion is performed through the inversion model in combination with the geological characteristics in each block, so that the inversion model can adapt to different geological characteristics, block inversion of different geological characteristics is realized, and the inversion precision and reliability are improved.

Description

Technical Field

[0001] The present application relates to the field of geological exploration technology, and more specifically, to a method and device for sub-block micro-seismic inversion based on geological information. Background Art

[0002] Microtremor detection technology is widely used in geological exploration and geological disaster monitoring due to its non-destructive, efficient, and economical nature. In particular, in the detection of shallow geological structures, microtremor detection has become an important method for stratum identification and lithologic analysis. However, in complex geological conditions, such as mountainous terrain, areas with dense faults, and regions with diverse stratigraphic structures, traditional microtremor inversion methods face significant challenges.

[0003] Complex geological environments are often accompanied by numerous complex features, including significant variations in stratum thickness, sudden changes in lithology, differential distribution of velocity layers, faults, weathering layers, and underground cavities. These geological characteristics lead to diverse and heterogeneous stratigraphic structures, increasing the difficulty of acquiring microseismic data and the complexity of interpreting them. Under these conditions, traditional single inversion models often struggle to effectively adapt to all geological blocks within a region, resulting in inversion results that fail to accurately reflect the actual stratigraphic characteristics of each block. This situation directly impacts the accuracy of stratigraphic analysis, leading to significant errors in the identification of stratigraphic interfaces and lithologic distribution, and thus compromising the scientific nature and reliability of geological interpretation.

[0004] The accuracy and adaptability of microtremor inversion are often limited under these diverse stratigraphic conditions. Research has shown that the applicability of traditional inversion models is primarily based on homogeneous or relatively uniform stratigraphic structures. Under highly heterogeneous geological conditions, inversion models are prone to "cross-block errors"—accumulation of errors between different geological blocks. This phenomenon leads to a significant decrease in data consistency and accuracy within the overall model, especially in fault zones or rapidly changing stratigraphic environments. These problems are particularly prominent in exploration projects in areas prone to geological hazards or with strong stratigraphic structural uncertainties.

[0005] In addition, the development of global microseismic detection technology has also revealed the limitations of a single inversion model. For example, a microseismic detection study conducted by Japan in an earthquake zone with dense faults showed that traditional inversion methods are difficult to accurately describe the complex stratigraphic structure near the fault zone, resulting in low resolution of the inversion results and a lack of detailed geological information. When the United States conducted microseismic detection in areas with lithologic mutations and frequent weathering layers, it was also discovered that a single inversion model could not adapt to the characteristics of different blocks, thereby limiting the accurate interpretation of the data. European researchers have found in mineral resource exploration that by refining the inversion model and applying it to different geological units in blocks, the adaptability and accuracy of the inversion can be significantly improved.

[0006] Therefore, a better solution is urgently needed. Summary of the Invention

[0007] The present invention provides a method and apparatus for block-based microtremor inversion based on geological information, which is used to solve the technical problem in the prior art that a single microtremor inversion model has poor adaptability under highly heterogeneous geological conditions. The method comprises: Acquiring a plurality of geological information from a target area, performing data preprocessing on the plurality of geological information, and constructing a feature vector based on the plurality of geological information after the data preprocessing; Performing block division based on a density clustering algorithm and the feature vector to obtain a plurality of independent blocks, and optimizing block boundaries of the plurality of independent blocks based on the feature vector to obtain a plurality of optimized independent blocks; Constructing an independent inversion model in each optimized independent block, and performing inversion processing based on the independent inversion model to obtain an inversion result in each optimized independent block; The inversion results in each optimized independent block are integrated to obtain the inversion integration results.

[0008] In some specific embodiments, a variety of geological information is obtained from the target area, data preprocessing is performed on the various geological information, and a feature vector is constructed based on the various geological information after data preprocessing, specifically: Acquiring a variety of geological information from the target area, the various geological information including stratum thickness information, lithology information, seismic wave velocity information, weathering layer thickness information, and fault information; Performing outlier removal, missing value filling, and standardization operations on the plurality of geological information to obtain the plurality of geological information after data preprocessing; For each micro-motion measurement point, a feature vector is constructed based on the various geological information after data preprocessing. The feature vector includes multiple geological features of each micro-motion measurement point, and the multiple geological features include formation thickness, lithology parameters, velocity layer values, weathering layer thickness and fault effects.

[0009] In some specific embodiments, block division is performed based on a density clustering algorithm and the feature vector to obtain multiple independent blocks, specifically: Set the neighborhood radius and minimum number of core points for the density clustering algorithm; Randomly selecting one of the micro-motion measuring points by the density clustering algorithm, and determining whether a neighborhood of the randomly selected micro-motion measuring point meets a quantity condition; If the neighborhood of the randomly selected micro-motion measurement point meets the quantity condition, the randomly selected micro-motion measurement point is used as a core point, and the core point and the neighboring points in the neighborhood of the core point are used as target clusters; Acquire other core points in the neighborhood of the target cluster, and form other clusters based on the other core points and neighborhood points in the neighborhood of the other core points; aggregating the target cluster and the other clusters into the independent block, wherein the similarity of geological features of the micro-motion measurement points in the independent block is higher than a predetermined similarity threshold, and the geological features are determined by the feature vector; taking the micro-motion measurement points that do not meet the quantity condition as boundary points, and forming a block boundary based on the boundary points; The quantity condition is that there are at least a minimum number of core points of micro-motion measurement points within the neighborhood radius.

[0010] In some specific embodiments, the block boundaries of the plurality of independent blocks are optimized based on the feature vector to obtain the optimized plurality of independent blocks, specifically: Comparing the geological characteristics of the boundary point with the geological characteristics of each adjacent independent block to obtain a first comparison result; If an independent block with the same geological characteristics as the boundary point exists in the first comparison result, the boundary point is classified into the independent block with the same geological characteristics; Comparing geological characteristics between adjacent independent blocks to obtain a second comparison result, and optimizing the block boundaries based on the second comparison result; When there are fault-intensive areas or significant lithologic changes at the block boundaries, the block boundaries are optimized manually.

[0011] In some specific embodiments, an independent inversion model is constructed in each optimized independent block, and an inversion process is performed based on the independent inversion model to obtain an inversion result in each optimized independent block, specifically: Construct an initial inversion model in each optimized independent block based on regional geological information and calibrated borehole information; performing inversion processing based on the initial inversion model to obtain an initial inversion result; Comparing the initial inversion result with the calibration borehole information to obtain geological parameter difference information; Based on the geological parameter difference information, the initial inversion model in each optimized independent block is optimized to obtain an independent inversion model in each optimized independent block, and inversion processing is performed based on the independent inversion model to obtain an inversion result in each optimized independent block.

[0012] In some specific embodiments, the inversion results in each optimized independent block are integrated to obtain an inversion integration result, specifically: The inversion results in each optimized independent block are integrated through feature matching technology and boundary transition technology to obtain the inversion integration result; Based on the inversion integration results, geological characteristics matching and verification are performed to obtain inversion integration verification results, and based on the inversion integration verification results, the independent inversion models in each optimized independent block are optimized.

[0013] In some specific embodiments, the method further comprises: The inversion results in each optimized independent block are checked for consistency of geological characteristics to obtain consistency check results.

[0014] Accordingly, the present invention also proposes a block-wise micro-seismic inversion device based on geological information, the device comprising: A preprocessing module is used to obtain a variety of geological information from the target area, perform data preprocessing on the multiple geological information, and construct a feature vector based on the multiple geological information after data preprocessing; a block division module, configured to perform block division based on a density clustering algorithm and the feature vector to obtain a plurality of independent blocks, and optimize block boundaries of the plurality of independent blocks based on the feature vector to obtain a plurality of optimized independent blocks; An inversion processing module is used to construct an independent inversion model in each optimized independent block, and perform inversion processing based on the independent inversion model to obtain an inversion result in each optimized independent block; The inversion integration module is used to integrate the inversion results in each optimized independent block to obtain the inversion integration results.

[0015] One embodiment of the present invention further provides a computing device comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the block micro-seismic inversion method based on geological information as described above.

[0016] One embodiment of the present invention further provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the above-described method for micro-seismic inversion based on geological information.

[0017] By applying the above technical solution, a block-based micro-seismic inversion method based on geological information is proposed, the method comprising: acquiring a variety of geological information from a target area, performing data preprocessing on the various geological information, and constructing a feature vector based on the various geological information after data preprocessing; performing block division based on a density clustering algorithm and the feature vector to obtain a plurality of independent blocks, and optimizing the block boundaries of the plurality of independent blocks based on the feature vector to obtain a plurality of optimized independent blocks; constructing an independent inversion model in each optimized independent block, and performing inversion processing based on the independent inversion model to obtain an inversion result within each optimized independent block; integrating the inversion results within each optimized independent block to obtain an inversion integration result. By dividing the target area into blocks and performing adaptive inversion through an inversion model in combination with the geological characteristics within each block, the inversion model can adapt to different geological characteristics, realize block-based inversion of different geological characteristics, and improve inversion accuracy and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 This is a flow chart of a method for micro-seismic inversion based on geological information provided by an embodiment of the present application; Figure 2 This is a schematic structural diagram of a block micro-vibration inversion device based on geological information provided in an embodiment of the present application; Figure 3 This is a structural block diagram of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0020] The following description sets forth many specific details to facilitate a thorough understanding of this specification. However, this specification can be implemented in many other ways than those described herein, and those skilled in the art can make similar generalizations without violating the scope of this specification. Therefore, this specification is not limited to the specific implementations disclosed below.

[0021] The terms used in one or more embodiments of this specification are for the purpose of describing specific embodiments only and are not intended to limit one or more embodiments of this specification. The singular forms "a," "an," and "the" used in one or more embodiments of this specification and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more associated listed items.

[0022] It should be understood that although the terms first, second, etc. may be used to describe various information in one or more embodiments of this specification, such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of one or more embodiments of this specification, the first may also be referred to as the second, and similarly, the second may also be referred to as the first. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0023] like Figure 1 As shown, this application proposes a block micro-seismic inversion method based on geological information, which includes the following steps: Step S101 : acquiring a variety of geological information from a target area, performing data preprocessing on the various geological information, and constructing a feature vector based on the various geological information after the data preprocessing.

[0024] In one possible implementation, a variety of geological information is obtained from the target area, data preprocessing is performed on the various geological information, and a feature vector is constructed based on the various geological information after data preprocessing, specifically: Acquiring a variety of geological information from the target area, the various geological information including stratum thickness information, lithology information, seismic wave velocity information, weathering layer thickness information, and fault information; Performing outlier removal, missing value filling, and standardization operations on the plurality of geological information to obtain the plurality of geological information after data preprocessing; For each micro-motion measurement point, a feature vector is constructed based on the various geological information after data preprocessing. The feature vector includes multiple geological features of each micro-motion measurement point, and the multiple geological features include formation thickness, lithology parameters, velocity layer values, weathering layer thickness and fault effects.

[0025] In this embodiment, the target area refers to the area where micromotion inversion is to be performed. This area is confirmed by personnel, and geological information of the target area is obtained. The geological information includes but is not limited to stratum thickness information, lithology information, seismic wave velocity information, weathering layer thickness information, and fault information. This geological information provides basic data for subsequent cluster analysis and inversion. Therefore, it is understood that those skilled in the art can flexibly select the type of geological information based on actual needs, and the difference in the type or amount of geological information does not affect the scope of protection of this application.

[0026] In this embodiment, after collecting geological information, the geological information is preprocessed, including removing outliers, filling missing values, and normalizing. Normalization can eliminate differences between data from different units and ensure that each dimension of the feature vector has the same weight in cluster analysis.

[0027] In this embodiment, a feature vector is constructed for each micro-seismic measurement point based on standardized geological information. This feature vector contains multiple geological characteristic dimensions, such as formation thickness, lithologic parameters, velocity layer values, weathering layer thickness, and fault effects. Each feature vector represents a "geological fingerprint" for a measurement point, laying the data foundation for cluster analysis.

[0028] Step S102 , performing block division based on a density clustering algorithm and the feature vector to obtain a plurality of independent blocks, and optimizing block boundaries of the plurality of independent blocks based on the feature vector to obtain a plurality of optimized independent blocks.

[0029] In this embodiment, after constructing the characteristic vector of geological information, a density clustering algorithm (DBSCAN) is used to automatically cluster the micro-seismic measurement points to achieve block division. The DBSCAN algorithm is suitable for areas with significantly different stratigraphic characteristics. Based on the density differences of micro-seismic measurement points, it can group measurement points with similar geological characteristics together to form independent blocks with consistent characteristics.

[0030] In a possible implementation, block division is performed based on a density clustering algorithm and the feature vector to obtain multiple independent blocks, specifically: Set the neighborhood radius and minimum number of core points for the density clustering algorithm; Randomly selecting one of the micro-motion measuring points by the density clustering algorithm, and determining whether a neighborhood of the randomly selected micro-motion measuring point meets a quantity condition; If the neighborhood of the randomly selected micro-motion measurement point meets the quantity condition, the randomly selected micro-motion measurement point is used as a core point, and the core point and the neighboring points in the neighborhood of the core point are used as target clusters; Acquire other core points in the neighborhood of the target cluster, and form other clusters based on the other core points and neighborhood points in the neighborhood of the other core points; aggregating the target cluster and the other clusters into the independent block, wherein the similarity of geological features of the micro-motion measurement points in the independent block is higher than a predetermined similarity threshold, and the geological features are determined by the feature vector; taking the micro-motion measurement points that do not meet the quantity condition as boundary points, and forming a block boundary based on the boundary points; The quantity condition is that there are at least a minimum number of core points of micro-motion measurement points within the neighborhood radius.

[0031] In this embodiment, clustering parameters are set first. Specifically, the DBSCAN algorithm needs to set two key parameters: Eps (neighborhood radius) and MinPts (minimum number of core points). In areas with dense geological characteristics, the neighborhood range of each micro-motion measurement point is defined by setting Eps, so that points with a spatial distance less than Eps can be regarded as the same cluster; MinPts is the minimum number of neighborhood points required for a specified micro-motion measurement point to become a "core point", ensuring that only micro-motion measurement points in high-density areas are aggregated into a cluster. The settings of Eps and MinPts need to be adjusted according to the density of geological data in the area to ensure that the geological characteristics in different blocks are consistent and that the geological characteristics between blocks are significantly different.

[0032] In this embodiment, after completing the clustering parameter setting, the clustering analysis process is performed. Specifically, the DBSCAN algorithm randomly selects a micro-motion measurement point and first checks whether there are a sufficient number of micro-motion measurement points in its neighborhood (that is, at least MinPts points are within the Eps radius). If the number of neighborhood points meets the quantity condition, the point is defined as a "core point" and the neighborhood points and the core point form a new cluster. The search continues to expand within the neighborhood of this cluster. If more core points and their neighborhoods are found, the neighborhood points are further aggregated to gradually generate a block with consistent density. For boundary points that fail to meet the minimum density requirement, the DBSCAN algorithm will mark them as "noise points" to ensure that the geological characteristics between blocks are significantly different and to avoid cross-block errors affecting the inversion accuracy.

[0033] In this example, preliminary block division and boundary determination are finally performed. Specifically, the DBSCAN algorithm generates preliminary block division results, where each block has consistent geological characteristics and clear boundaries. This is particularly true in areas with complex geological conditions, such as fault zones and areas of lithologic variation. The DBSCAN algorithm can accurately identify different blocks. These preliminary block division results provide a partitioning basis for the subsequent adaptive construction of the inversion model.

[0034] In a possible implementation, the block boundaries of the plurality of independent blocks are optimized based on the feature vector to obtain the optimized plurality of independent blocks, specifically: Comparing the geological characteristics of the boundary point with the geological characteristics of each adjacent independent block to obtain a first comparison result; If an independent block with the same geological characteristics as the boundary point exists in the first comparison result, the boundary point is classified into the independent block with the same geological characteristics; Comparing geological characteristics between adjacent independent blocks to obtain a second comparison result, and optimizing the block boundaries based on the second comparison result; When there are fault-intensive areas or significant lithologic changes at the block boundaries, the block boundaries are optimized manually.

[0035] In this embodiment, although the DBSCAN algorithm can accurately divide blocks of different geological densities, in actual projects, areas with complex geological boundaries require further optimization of block boundaries to ensure that the geological characteristics of each block at the boundary have good continuity and consistency.

[0036] In this example, the block boundaries generated by DBSCAN clustering are reviewed to determine whether the characteristic vector of each boundary point matches the geological characteristics of the adjacent blocks. Boundary points with similar geological characteristics are reclassified into more appropriate blocks to ensure consistency of geological characteristics within the blocks.

[0037] In this embodiment, during the boundary optimization process, the geological characteristics of the boundary area are compared to further ensure that the differences in geological characteristics inside and outside the block are clear. For example, in a fault zone, the boundary line position is adjusted so that measurement points with large differences in fault activity intensity are classified into different blocks to ensure the accuracy of the inversion results.

[0038] In this example, for areas with dense faults or significant lithologic changes, the boundaries were manually adjusted with the involvement of geological experts. This approach effectively overcomes the limitations of the automatic algorithm, ensuring that each block division conforms to the actual geological conditions and providing a more accurate geological basis for the inversion model.

[0039] Step S103 : constructing an independent inversion model in each optimized independent block, and performing inversion processing based on the independent inversion model to obtain an inversion result in each optimized independent block.

[0040] In one possible implementation, an independent inversion model is constructed in each optimized independent block, and inversion processing is performed based on the independent inversion model to obtain the inversion results in each optimized independent block, specifically: Construct an initial inversion model in each optimized independent block based on regional geological information and calibrated borehole information; performing inversion processing based on the initial inversion model to obtain an initial inversion result; Comparing the initial inversion result with the calibration borehole information to obtain geological parameter difference information; Based on the geological parameter difference information, the initial inversion model in each optimized independent block is optimized to obtain an independent inversion model in each optimized independent block, and inversion processing is performed based on the independent inversion model to obtain an inversion result in each optimized independent block.

[0041] In this embodiment, the initial parameters of the initial inversion model are set within each block based on the specific geological characteristics of the block. For example, for blocks with thick strata and significant velocity layer changes, the model will appropriately increase the number of layers and velocity parameters to improve the resolution depth and resolution.

[0042] In this embodiment, during the inversion process, the model dynamically adjusts inversion parameters based on real-time data feedback, ensuring that the model adapts to changes in geological characteristics within the block. For example, in blocks with thick weathering layers, the inversion model automatically adjusts the sensitivity of the inversion depth and velocity layer to improve the accuracy of the analysis.

[0043] Furthermore, the construction and optimization of the initial inversion model are explained.

[0044] In the block-by-block micro-tremor inversion method, the dynamic parameter adjustment of the inversion model is optimized based on the geological characteristics of each independent block and real-time data feedback, maximizing the accuracy of the inversion results and their match with the actual geological conditions. The specific process is as follows: 1. Initial model settings For each independent block, the initial inversion model is set based on two core information sources: (1) Regional geological information: including the stratigraphic structure, lithologic distribution, and geological structural characteristics of the block. This information mainly comes from geological exploration data or existing geological research results.

[0045] (2) Calibrated drilling information: including detailed data such as actual formation thickness, wave velocity, lithology and elastic modulus of the boreholes in the block.

[0046] Based on this information, an initial inversion model is constructed. Since the accuracy of the initial model is usually limited, the number of layers, layer thickness, and elastic parameters may deviate from the actual situation and therefore require further optimization.

[0047] 2. Difference analysis between inversion results and calibration borehole data (1) Obtaining inversion results: Perform preliminary inversion based on the initial inversion model to obtain initial inversion results, including the wave velocity and layer thickness of each layer.

[0048] (2) Difference assessment: Compare the inversion results with the actual geological parameters provided by the calibration borehole and calculate the deviation between the parameters. The deviation is quantified by the average error (average Misfit) statistical indicator:

[0049] in is the i-th layer parameter of the inversion model (such as wave velocity, layer thickness, etc.); in is the i-th layer parameter of the inversion model (such as wave velocity, layer thickness, etc.); n is the total number of layers 3. Dynamically adjust model parameters According to the above difference analysis results, the parameters of the inversion model are dynamically adjusted. The adjustment process includes the following aspects: (1) Adjustment of the number of layers If the inversion results indicate an insufficient or excessive number of layers, adjust the number of layers in the model. For example, in more complex geological areas, increase the number of layers to refine the structure; in homogeneous areas, reduce the number of layers to simplify the model.

[0050] (2) Layer thickness adjustment Adjust the thickness of each layer based on the calibration drilling data. For layers with large differences, increase or decrease their thickness to make the model more realistic.

[0051] (3) Wave speed adjustment Adjust the wave velocity of each layer based on the distribution characteristics of Misfit. For areas with large errors, locally optimize the wave velocity based on geological characteristics.

[0052] (4) Elastic modulus optimization If the wave impedance of the inversion result does not match the actual situation, the elastic modulus should be adjusted to better reflect the actual geological conditions.

[0053] (5) Parameter optimization strategy During the above adjustment process, gradient descent, genetic algorithms or artificial intelligence technologies (such as deep learning) can be used to intelligently optimize the parameter adjustment process to improve the efficiency and accuracy of the adjustment.

[0054] 4. Iterative process of dynamic adjustment (1) Differential feedback mechanism After each inversion, the current Misfit index is calculated through differential analysis and its changing trend is recorded. If the Misfit value remains above the preset threshold after multiple iterations, the model parameters are adjusted until it converges to the target range.

[0055] (2) Cycle optimization mechanism The process of inversion->difference analysis->parameter adjustment->re-inversion is an optimization cycle, which is continuously iterated until the difference between the inversion result and the calibration data is reduced to an allowable range.

[0056] 5. Applicable range of adjustable parameters During the dynamic adjustment process, the adjustment of parameters needs to be combined with the actual geological characteristics of the block and the constraints of the acquired data. The specific range of adjustment parameters includes: (1) Stratigraphic stratification: The number of strata can be adjusted within the range of 3 to 10 layers, depending on the geological complexity and depth range.

[0057] (2) Layer thickness: The thickness of each layer can be dynamically set according to the calibration drilling results, and the error adjustment range is ±10% (3) Wave velocity: The velocity range is preset according to the lithologic characteristics, and the adjustment range is ±5%. (4) Elastic modulus: Adjust the elastic modulus according to the calibrated lithologic properties to make it more consistent with the actual formation.

[0058] Dynamic parameter adjustment of the inversion model is achieved through repeated iterative optimization. The core process is: build the initial model based on the initial geological information -> obtain the inversion results -> calculate the deviation from the calibration borehole data -> dynamically adjust the model parameters -> re-invert -> optimization iteration.

[0059] Step S104: integrating the inversion results in each optimized independent block to obtain an integrated inversion result.

[0060] In one possible implementation, the inversion results in each optimized independent block are integrated to obtain the inversion integration result, specifically: The inversion results in each optimized independent block are integrated through feature matching technology and boundary transition technology to obtain the inversion integration result; Based on the inversion integration results, geological characteristics matching and verification are performed to obtain inversion integration verification results, and based on the inversion integration verification results, the independent inversion models in each optimized independent block are optimized.

[0061] In this embodiment, after the adaptive inversion is completed in each block, the inversion results of each block need to be integrated to ensure the continuity and consistency of the overall model at the block boundaries.

[0062] In this embodiment, the inversion results of each block are integrated through feature matching and boundary transition technology to ensure a natural transition between the models in different blocks. Especially in areas with large faults or lithologic changes, the integrated model can provide consistent stratigraphic structural information.

[0063] In this embodiment, the integrated results are matched and verified with geological characteristics to ensure that the model reflects the actual geological structure. For areas with inconsistent boundaries, further parameter fine-tuning is performed to improve the applicability of the model.

[0064] In a possible implementation, the method further includes: The inversion results in each optimized independent block are checked for consistency of geological characteristics to obtain consistency check results.

[0065] In this embodiment, the consistency of the geological characteristics of the inversion results within each block is verified through statistical analysis to ensure that the differences in geological characteristics within the block are small.

[0066] In this embodiment, a preliminary test is performed on the independent inversion model to ensure that its adaptability and accuracy meet the requirements of high-precision analysis under complex geological conditions.

[0067] In summary, the present invention aims to address the problems of insufficient adaptability and limited analytical accuracy of existing micro-seismic inversion techniques under complex geological conditions. By introducing a block-based inversion technique based on geological information, the inversion process is refined into different geological blocks, and highly adaptable inversion models are constructed for each block to ensure that each block can accurately reflect its own geological characteristics. Specifically, the advantages of the present invention are: Precise adaptability: Through block division based on geological characteristics, the inversion model of the present invention can accurately adapt to the unique characteristics of different geological blocks, eliminating the problem of insufficient adaptability of a single model under complex geological conditions.

[0068] High-resolution interpretation: Customized inversion models are used within each block to significantly improve the resolution and accuracy of stratigraphic interpretation, accurately capturing subtle geological features and stratigraphic boundaries.

[0069] Continuity and consistency: The inversion results of the present invention are processed for continuity at the block boundaries. By integrating models among multiple blocks, the overall continuity of the stratigraphic structure is ensured, and the overall consistency of the analytical results is improved.

[0070] Strong flexibility: The inversion model has an adaptive parameter adjustment function, which dynamically adjusts the inversion parameters according to the characteristics of different blocks and is suitable for diverse stratigraphic characteristics, thereby significantly improving its applicability under complex geological conditions.

[0071] In summary, by applying the above technical solutions, a block micro-seismic inversion method based on geological information is proposed, the method comprising: obtaining a variety of geological information from a target area, performing data preprocessing on the various geological information, and constructing a feature vector based on the various geological information after data preprocessing; performing block division based on a density clustering algorithm and the feature vector to obtain a plurality of independent blocks, and optimizing the block boundaries of the plurality of independent blocks based on the feature vector to obtain a plurality of optimized independent blocks; constructing an independent inversion model in each optimized independent block, and performing inversion processing based on the independent inversion model to obtain an inversion result within each optimized independent block; integrating the inversion results within each optimized independent block to obtain an inversion integration result, by dividing the target area into blocks and performing adaptive inversion through the inversion model in combination with the geological characteristics within each block, so that the inversion model can adapt to different geological characteristics, realize block inversion of different geological characteristics, and improve the inversion accuracy and reliability.

[0072] The present application also proposes a block micro-vibration inversion device based on geological information, such as Figure 2 As shown, the device includes: A preprocessing module 10 is configured to obtain a variety of geological information from a target area, perform data preprocessing on the various geological information, and construct a feature vector based on the various geological information after the data preprocessing; a block division module 20 for performing block division based on a density clustering algorithm and the feature vector to obtain a plurality of independent blocks, and optimizing block boundaries of the plurality of independent blocks based on the feature vector to obtain a plurality of optimized independent blocks; An inversion processing module 30 is used to construct an independent inversion model in each optimized independent block, and perform inversion processing based on the independent inversion model to obtain an inversion result in each optimized independent block; The inversion integration module 40 is used to integrate the inversion results in each optimized independent block to obtain an inversion integration result.

[0073] Figure 3 The block diagram of a computing device 400 according to one embodiment of the present disclosure is shown. Components of the computing device 400 include, but are not limited to, a memory 410 and a processor 420. The processor 420 is connected to the memory 410 via a bus 430, and a database 450 is used to store data.

[0074] Computing device 400 also includes an access device 440 that enables computing device 400 to communicate via one or more networks 460. Examples of such networks include a public switched telephone network (PSTN), a local area network (LAN), a wide area network (WAN), a personal area network (PAN), or a combination of communication networks such as the Internet. Access device 440 may include one or more of any type of network interface (e.g., a network interface card (NIC)) whether wired or wireless, such as an IEEE 802.11 wireless local area network (WLAN) wireless interface, a Worldwide Interoperability for Microwave Access (Wi-MAX) interface, an Ethernet interface, a universal serial bus (USB) interface, a cellular network interface, a Bluetooth interface, or a near field communication (NFC) interface.

[0075] In one embodiment of the present specification, the above components of the computing device 400 and Figure 3 Other components not shown in the figure may also be connected to each other, for example, via a bus. Figure 3 The computing device structure block diagram shown is for illustrative purposes only and is not intended to limit the scope of this specification. Those skilled in the art may add or replace other components as needed.

[0076] Computing device 400 can be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., a tablet computer, personal digital assistant, laptop computer, notebook computer, netbook computer, etc.), a mobile phone (e.g., a smartphone), a wearable computing device (e.g., a smartwatch, smart glasses, etc.), or other types of mobile devices, or a stationary computing device such as a desktop computer or personal computer (PC). Computing device 400 can also be a mobile or stationary server.

[0077] Processor 420 is configured to execute the following computer-executable instructions, which, when executed by the processor, implement the steps of the aforementioned method for performing block-based micromotion inversion based on geological information. The above is a schematic diagram of a computing device according to this embodiment. It should be noted that the technical solution of this computing device shares the same concept as the technical solution of the aforementioned method for performing block-based micromotion inversion based on geological information. For details not described in detail in the technical solution of the computing device, please refer to the description of the technical solution of the aforementioned method for performing block-based micromotion inversion based on geological information.

[0078] An embodiment of the present specification further provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the above-mentioned geological information-based micro-seismic inversion method.

[0079] The above is a schematic diagram of a computer-readable storage medium according to this embodiment. It should be noted that the technical solution of this storage medium is based on the same concept as the technical solution of the aforementioned method for inversion of micro-seismic data in a block-by-block manner based on geological information. For details not described in detail in the technical solution of the storage medium, please refer to the description of the technical solution of the aforementioned method for inversion of micro-seismic data in a block-by-block manner based on geological information.

[0080] An embodiment of the present specification further provides a computer program, wherein when the computer program is executed in a computer, the computer is instructed to execute the steps of the above-mentioned method for micro-seismic inversion based on geological information.

[0081] The above is an illustrative embodiment of a computer program. It should be noted that the technical solution of this computer program is based on the same concept as the technical solution of the aforementioned method for inversion of micro-seismic data using geological information. For details not described in detail in the technical solution of the computer program, please refer to the description of the technical solution of the aforementioned method for inversion of micro-seismic data using geological information.

[0082] The foregoing description of this specification describes specific embodiments. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0083] The computer instructions include computer program code, which may be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium may include any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal, and software distribution medium. It should be noted that the content of the computer-readable medium may be appropriately increased or decreased based on the requirements of legislation and patent practice within a jurisdiction. For example, in some jurisdictions, based on legislation and patent practice, computer-readable media does not include electric carrier signals and telecommunication signals.

[0084] It should be noted that for the aforementioned method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the embodiments of this specification are not limited by the order of the actions described, because according to the embodiments of this specification, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the embodiments of this specification.

[0085] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0086] The preferred embodiments disclosed above are intended only to help illustrate this specification. The optional embodiments do not exhaustively describe all details, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and variations can be made based on the content of the embodiments of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the embodiments of this specification, so that those skilled in the art can better understand and utilize this specification. This specification is limited only by the claims and their full scope and equivalents.

Claims

1. A block-wise micro-seismic inversion method based on geological information, characterized in that: The method comprises: Acquiring a plurality of geological information from a target area, performing data preprocessing on the plurality of geological information, and constructing a feature vector based on the plurality of geological information after the data preprocessing; Performing block division based on a density clustering algorithm and the feature vector to obtain a plurality of independent blocks, and optimizing block boundaries of the plurality of independent blocks based on the feature vector to obtain a plurality of optimized independent blocks; Constructing an independent inversion model in each optimized independent block, and performing inversion processing based on the independent inversion model to obtain an inversion result in each optimized independent block; The inversion results in each optimized independent block are integrated to obtain the inversion integration results.

2. The method according to claim 1, characterized in that Acquire a variety of geological information from the target area, perform data preprocessing on the various geological information, and construct a feature vector based on the various geological information after data preprocessing, specifically: Acquiring a variety of geological information from the target area, the various geological information including stratum thickness information, lithology information, seismic wave velocity information, weathering layer thickness information, and fault information; Performing outlier removal, missing value filling, and standardization operations on the plurality of geological information to obtain the plurality of geological information after data preprocessing; For each micro-motion measurement point, a feature vector is constructed based on the various geological information after data preprocessing. The feature vector includes multiple geological features of each micro-motion measurement point, and the multiple geological features include formation thickness, lithology parameters, velocity layer values, weathering layer thickness and fault effects.

3. The method according to claim 2, characterized in that Based on the density clustering algorithm and the feature vector, block division is performed to obtain multiple independent blocks, specifically: Set the neighborhood radius and minimum number of core points for the density clustering algorithm; Randomly selecting one of the micro-motion measuring points by the density clustering algorithm, and determining whether a neighborhood of the randomly selected micro-motion measuring point meets a quantity condition; If the neighborhood of the randomly selected micro-motion measurement point meets the quantity condition, the randomly selected micro-motion measurement point is used as a core point, and the core point and the neighboring points in the neighborhood of the core point are used as target clusters; Acquire other core points in the neighborhood of the target cluster, and form other clusters based on the other core points and neighborhood points in the neighborhood of the other core points; aggregating the target cluster and the other clusters into the independent block, wherein the similarity of geological features of the micro-motion measurement points in the independent block is higher than a predetermined similarity threshold, and the geological features are determined by the feature vector; taking the micro-motion measurement points that do not meet the quantity condition as boundary points, and forming a block boundary based on the boundary points; The quantity condition is that there are at least a minimum number of core points of micro-motion measurement points within the neighborhood radius.

4. The method according to claim 3, characterized in that The block boundaries of the plurality of independent blocks are optimized based on the feature vectors to obtain the optimized plurality of independent blocks, specifically: Comparing the geological characteristics of the boundary point with the geological characteristics of each adjacent independent block to obtain a first comparison result; If an independent block with the same geological characteristics as the boundary point exists in the first comparison result, the boundary point is classified into the independent block with the same geological characteristics; Comparing geological characteristics between adjacent independent blocks to obtain a second comparison result, and optimizing the block boundaries based on the second comparison result; When there are fault-intensive areas or significant lithologic changes at the block boundaries, the block boundaries are optimized manually.

5. The method according to claim 4, characterized in that An independent inversion model is constructed in each optimized independent block, and inversion processing is performed based on the independent inversion model to obtain the inversion results in each optimized independent block, specifically: An initial inversion model is constructed in each optimized independent block based on regional geological information and calibrated borehole information; performing inversion processing based on the initial inversion model to obtain an initial inversion result; Comparing the initial inversion result with the calibration borehole information to obtain geological parameter difference information; Based on the geological parameter difference information, the initial inversion model in each optimized independent block is optimized to obtain an independent inversion model in each optimized independent block, and inversion processing is performed based on the independent inversion model to obtain an inversion result in each optimized independent block.

6. The method according to claim 5, characterized in that The inversion results in each optimized independent block are integrated to obtain the inversion integration results, which are as follows: The inversion results in each optimized independent block are integrated through feature matching technology and boundary transition technology to obtain the inversion integration result; Based on the inversion integration results, geological characteristics matching and verification are performed to obtain inversion integration verification results, and based on the inversion integration verification results, the independent inversion models in each optimized independent block are optimized.

7. The method according to claim 6, characterized in that The method further comprises: The inversion results in each optimized independent block are checked for consistency of geological characteristics to obtain consistency check results.

8. A block-based micro-vibration inversion device based on geological information, characterized in that: The device comprises: A preprocessing module is used to obtain a variety of geological information from the target area, perform data preprocessing on the multiple geological information, and construct a feature vector based on the multiple geological information after data preprocessing; a block division module, configured to perform block division based on a density clustering algorithm and the feature vector to obtain a plurality of independent blocks, and optimize block boundaries of the plurality of independent blocks based on the feature vector to obtain a plurality of optimized independent blocks; An inversion processing module is used to construct an independent inversion model in each optimized independent block, and perform inversion processing based on the independent inversion model to obtain an inversion result in each optimized independent block; The inversion integration module is used to integrate the inversion results in each optimized independent block to obtain the inversion integration results.

9. A computing device, characterized in that include: memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the block micro-seismic inversion method based on geological information according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions, when executed by a processor, implement the steps of the block micro-seismic inversion method based on geological information as claimed in any one of claims 1 to 7.

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