CN112782755A - Method and device for constructing near-surface structure model - Google Patents
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Abstract
The present specification provides a method and apparatus for constructing a near-surface structure model, the method comprising: acquiring near-surface structure data of a plurality of near-surface structure models to be fused; the multiple near-surface structure models to be fused comprise at least two of a micro-logging near-surface structure model, a small-track-distance chromatography ground surface structure model and a cannon chromatography ground surface structure model, a shallow reflection ground surface structure model and a geological radar ground surface structure model; acquiring the weighting parameters of each near-surface structure model to be fused in a coordinate interval; and performing weighted calculation on the near-surface structure data of the corresponding coordinate interval according to the weighted parameters of each near-surface structure model to be fused in each coordinate interval to obtain a fused near-surface structure model. The near-surface structure data obtained through weighted summation calculation integrates the advantages of different near-surface structure models, so that the near-surface description of the fused near-surface structure model is closer to the real situation.
Description
Technical Field
The specification relates to the technical field of geophysical exploration, in particular to a method and a device for constructing a near-surface structure model.
Background
The near-surface medium is directly influenced by the atmospheric environment or the water environment in lithologic characteristics, bedding characteristics, cementation degree, speed parameters, density parameters and the like, and has the characteristic of uneven distribution. From the seismic geology analysis, the uneven change of the near-surface medium can cause uneven delay of seismic wave propagation, and then the distortion of the phase of a seismic section is caused, so that the accuracy of a stratum model constructed by using seismic data is influenced. Therefore, constructing a near-surface structure model that simulates reality as much as possible has an important influence on processing seismic data and constructing a deep stratum structure more accurately.
At present, methods for constructing a near-surface structure model mainly include a micro-logging modeling method, a seismic shot data first-arrival chromatography modeling method, a shallow reflection surface structure model, a geological radar surface structure model and the like. The earth model accuracy of each method has advantages and disadvantages, such as: the single-point result formed by the micro-logging modeling method is high in precision, but the transverse density is sparse, and the structure close to the earth surface cannot be continuously described; the seismic shot data first-arrival chromatography modeling method can continuously depict the earth surface and reflect the change trend of the earth surface structure, but the accuracy of the earth surface shallow layer is low.
Disclosure of Invention
The specification provides a method for constructing a near-surface structure model, which is used for constructing a model for describing the near-surface structure characteristics more accurately in a mode of fusing different near-surface structure models.
In one aspect, the present description provides a method of constructing a near-surface structure model, comprising:
acquiring near-surface structure data of a plurality of near-surface structure models to be fused; the multiple near-surface structure models to be fused comprise at least two of a micro-logging near-surface structure model, a small-track-distance chromatography ground surface structure model, a cannon chromatography ground surface structure model, a shallow reflection ground surface structure model and a geological radar ground surface structure model;
acquiring the weighting parameters of each near-surface structure model to be fused in a coordinate interval;
and performing weighted calculation on the near-surface structure data of the corresponding coordinate interval according to the weighted parameters of each near-surface structure model to be fused in each coordinate interval to obtain a fused near-surface structure model.
Optionally, obtaining a weighting parameter of each to-be-fused near-surface structure model in the coordinate interval includes:
and acquiring the transverse weighting parameters and the longitudinal weighting parameters of the near-surface structure model to be fused in the coordinate interval.
Optionally, according to the weighting parameter of each to-be-fused near-surface structure model in each coordinate interval, performing weighted calculation on the near-surface structure data in the corresponding coordinate interval to obtain a fused near-surface structure model, including:
according to the transverse weighting parameters of each near-surface structure model to be fused in each coordinate interval, carrying out weighting calculation on the near-surface structure data of the corresponding coordinate interval to obtain transverse weighting data; and the number of the first and second groups,
according to the longitudinal weighting parameters of each near-surface structure model to be fused in each coordinate interval, carrying out weighting calculation on the near-surface structure data of the corresponding coordinate interval to obtain longitudinal weighting data;
and carrying out equal-weight weighted average on the transverse weighted data and the longitudinal weighted data to obtain the fused near-surface structure model.
Optionally, the obtaining of the weighting parameter of each to-be-fused near-surface structure model in the coordinate interval includes: and inquiring a pre-stored weight-coordinate interval mapping table to obtain the weighting parameters of each near-surface structure model to be fused in the coordinate interval.
Optionally, the obtaining of near-surface structure data of multiple near-surface structure models to be fused includes: and carrying out discrete sampling on the near-surface structure model to be fused to acquire corresponding near-surface structure data.
In another aspect, the present specification provides an apparatus for constructing a model of a near-surface structure, comprising:
the model data acquisition module to be fused acquires near-surface structure data of various near-surface structure models to be fused; the multiple near-surface structure models to be fused comprise at least two of a micro-logging near-surface structure model, a small-track-distance surface structure model and a cannon chromatography surface structure model;
the weighting parameter acquisition module is used for acquiring the weighting parameters of the near-surface structure models to be fused in the coordinate interval;
and the fusion model calculation module is used for performing weighted calculation on the near-surface structure data of the corresponding coordinate interval according to the weighted parameters of each near-surface structure model to be fused in each coordinate interval to obtain a fusion near-surface structure model.
Optionally, the weighting parameter obtaining module obtains a horizontal weighting parameter and a vertical weighting parameter of each near-surface structure model to be fused in the coordinate interval.
Optionally, the fusion model calculation module includes:
the transverse weighting calculation unit is used for carrying out weighting calculation on the near-surface structure data of the corresponding coordinate interval according to the transverse weighting parameters of each near-surface structure model to be fused in each coordinate interval to obtain transverse weighting data;
the longitudinal weighting calculation unit is used for carrying out weighting calculation on the near-surface structure data of the corresponding coordinate interval according to the longitudinal weighting parameters of each near-surface structure model to be fused in each coordinate interval to obtain longitudinal weighting data;
and the equal weight calculation unit is used for carrying out equal weight weighted average on the transverse weighted data and the longitudinal weighted data to obtain the fused near-surface structure model.
Optionally, the weighting parameter obtaining module obtains the weighting parameters corresponding to all the coordinate intervals by querying a pre-stored weight-coordinate interval mapping table.
Optionally, the model data to be fused obtaining module obtains the corresponding near-surface structure data by performing discrete sampling on the near-surface structure model to be fused.
The present specification also provides a storage medium having a plurality of instructions stored thereon; the instructions are adapted to be loaded by a processor and to perform a method of constructing a model of a near-surface structure as described above.
The present specification also provides an electronic device comprising a memory and a processor; the memory stores a plurality of instructions; the instructions are adapted to be loaded by the processor and to perform the method of constructing a model of a near-surface structure as described above.
Because the precision of each near-surface structure model to be fused in different coordinate intervals is different, the weighting parameters corresponding to different coordinate intervals are different. In addition, in a coordinate interval with good precision of the near-surface structure model to be fused, the corresponding weighting parameter value is large; therefore, the near-surface structure data obtained through weighted summation calculation fully integrates the advantages of different near-surface structure models, so that the near-surface description of the fused near-surface structure model is closer to the real situation.
Drawings
FIG. 1 is a flow chart of a method for constructing a near-surface structure model provided by an embodiment;
FIG. 2 is a flow diagram of a method for constructing a near-surface structure model provided by an embodiment;
FIG. 3 is a schematic structural diagram of an apparatus for constructing a near-surface structure model according to an embodiment;
FIG. 4 is a schematic structural diagram of a fusion model calculation module according to an embodiment
FIG. 5 is a schematic view of an electronic device provided by an embodiment;
wherein: 11-a model data acquisition module to be fused, 12-a weighting parameter acquisition module, 13-a fusion model calculation module, 131-a transverse weighting calculation unit, 132-a longitudinal weighting calculation unit, 133-an equal weight calculation unit; 21-processor, 22-memory, 23-input component, 24-output component, 25-power supply, 26-communication module.
Detailed Description
The embodiment of the specification provides a method and a device for constructing a near-surface structure model, so that various known near-surface structure models are utilized to perform fusion processing, and the characteristics of each near-surface structure model are integrated to obtain a new near-surface structure model.
The present specification will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.
Fig. 1 is a flowchart of a method for constructing a near-surface structure model according to an embodiment. As shown in fig. 1, the method provided by the present embodiment includes steps S101-S103.
S101: and acquiring the near-surface structure data of a plurality of near-surface structure models to be fused.
In a specific application, the multiple near-surface structure models to be fused can be at least two of a micro-logging near-surface structure model, a small-track-distance near-chromatography ground-surface structure model, a cannon chromatography near-surface structure model, a shallow reflection ground-surface structure model and a geological radar ground-surface structure model; that is, two or three known near-surface structure models are employed as the near-surface structure models to be fused, and corresponding near-surface structure data are acquired, respectively. The aforementioned near-surface structure data includes position coordinate data in addition to characteristic near-surface structure feature data such as amplitude.
In this embodiment, the first three near-surface structure models are all used as the near-surface structure model to be fused.
In practical applications, because the near-surface structure model may be characterized by using continuous data, the process of obtaining the near-surface structure data of the near-surface structure model to be fused may be a process of performing discrete datamation on the near-surface structure model to be fused. In specific application, the data volume of the discrete datamation data is determined according to the precision of a fusion near-surface structure model which is constructed according to needs, and the processing speed and the processing capacity of processing equipment.
S102: and acquiring the weighting parameters of the near-surface structure models to be fused in the coordinate interval.
Because different near-surface structure models have different accuracies in different coordinate intervals, different coordinate intervals are assigned with different weighting parameters for each near-surface structure model to be fused.
For example, in lateral accuracy, the accuracy of the micro-log near-surface structure model increases with distance from the logging point; in the aspect of longitudinal precision, the precision of the micro-logging near-surface structure model is reduced along with the increase of the well depth, so that the weighting parameter of the micro-logging near-surface structure model can be reduced along with the increase of the distance and the increase of the depth by taking the position of the logging point as the center.
Correspondingly, the precision difference of the small track distance near-surface structure model and the cannon chromatography near-surface structure model is not large; in order to keep the sum of the weighting coefficients of the data to be 1 all the time, the weighting parameters of the short-path near-surface structure model and the cannon chromatography near-surface structure model can be increased along with the increase of the distance and the increase of the depth by taking the position of the logging point as the center.
S103: and performing weighted calculation on the near-surface structure data of the corresponding coordinate interval according to the weighted parameters of each near-surface structure model to be fused in each coordinate interval to obtain a fused near-surface structure model.
After the near-surface structure data of each near-surface structure model to be fused and the weighting parameters of each near-surface structure model to be fused in different coordinate intervals are determined, the weighting parameters of the corresponding coordinate intervals and the near-surface structure data can be weighted and averaged to obtain fused near-surface structure data, and the fused near-surface structure model is constructed by adopting the fused near-surface structure model data.
According to the analysis of the steps, as the precision of each near-surface structure model to be fused in different coordinate intervals is different, the weighting parameters corresponding to the different coordinate intervals are different; and in the coordinate interval with better precision of the near-surface structure model to be fused, the corresponding weighting parameter value is larger. Therefore, the near-surface structure data obtained through weighted summation calculation fully integrates the advantages of different near-surface structure models, so that the near-surface description of the fused near-surface structure model is closer to the real situation.
Fig. 2 is a flow chart of a method of constructing a near-surface structure model provided herein. As shown in FIG. 2, in one embodiment, the foregoing method includes steps S201-S204.
S201: and acquiring the near-surface structure data of a plurality of near-surface structure models to be fused.
The multiple near-surface structure models to be fused can be at least two of a micro-logging near-surface structure model, a small-track-distance near-surface structure model and a cannon chromatography near-surface structure model.
In practical applications, because the near-surface structure model may be characterized by using continuous data, the process of obtaining the near-surface structure data of the near-surface structure model to be fused may be a process of performing discrete datamation on the near-surface structure model to be fused. In specific application, the data volume of the discrete datamation data is determined according to the precision of a fusion near-surface structure model which is constructed according to needs, and the processing speed and the processing capacity of processing equipment.
S202: and acquiring the transverse weighting parameters and the longitudinal weighting parameters of each near-surface structure model to be fused in the coordinate interval.
The horizontal weighting parameter is a weighting parameter in the horizontal plane direction, and the vertical weighting parameter is a weighting parameter in the depth direction.
Because the precision of the micro-logging near-surface structure model gradually decreases with increasing distance from the logging location, the lateral weighting parameters of the micro-logging near-surface structure model can be made to decrease with increasing distance from the logging location. For example, in one application, the lateral weighting parameter of the micro-logging near-surface structure model is 0.9 within 100m from the logging position, and the lateral weighting parameter of the corresponding interval is reduced by 0.1 with every 100m increase of the distance; conversely, the transverse weighting parameters of the small track distance near-surface structure model and the cannon chromatography near-surface structure model within a range of 100m from the logging position can be 0.05, and the transverse weighting parameters of the corresponding coordinate intervals are respectively increased by 0.05 with every 100m increase of the distance.
Because the precision of the micro-logging near-surface structure model is gradually reduced along with the increase of the logging depth, the longitudinal weighting parameter of the micro-logging near-surface structure model can be reduced along with the increase of the distance from the surface position. For example, in one application, the longitudinal weighting parameter for the micro-log near-surface structure model is set to 0.8 at a depth of 0-15m near the surface, 0.3 at a depth of 15-40m near the surface, and 0.05 at a depth exceeding 40m near the surface. Correspondingly, at the depth of 0-15m near the earth's surface, the longitudinal weighting parameter of the small track distance near-earth surface structure model is 0.15, and the longitudinal weighting parameter of the cannon chromatography near-earth surface structure model is 0.05; at the depth of 15-40m near the earth's surface, the longitudinal weighting parameter of the small track distance near-earth surface structure model is 0.6, and the longitudinal weighting parameter of the cannon chromatography near-earth surface structure model is 0.1; and when the depth from the earth surface exceeds 40m, the longitudinal weighting parameter of the small track distance near-earth surface structure model is 0.3, and the longitudinal weighting parameter of the cannon chromatography near-earth surface structure model is 0.65.
It should be noted that the weighting parameters are data obtained by comprehensively analyzing the characteristics of various near-surface structure models, and are not arbitrarily assigned. In areas with different near-surface features, the weight coefficients of different near-surface structure models to be fused are changed, and the specific weight coefficients are determined according to experience and field data.
S203: according to the transverse weighting parameters of each near-surface structure model to be fused in each coordinate interval, carrying out weighting calculation on the near-surface structure data of the corresponding coordinate interval to obtain transverse weighting data, and according to the longitudinal weighting parameters of each near-surface structure model to be fused in each coordinate interval, carrying out weighting calculation on the near-surface structure data of the corresponding coordinate interval to obtain longitudinal weighting data.
Step S203 is a process of performing horizontal weighting calculation on the near-surface structure feature data to be fused in the corresponding coordinate interval by using the determined horizontal weighting parameter, and performing vertical weighting calculation on the near-surface structure data to be fused in the corresponding coordinate interval by using the determined vertical weighting parameter.
For example, for a 100m range of logging positions, the transverse weighted data X is 0.8 × a +0.1 × B +0.1 × C, and the longitudinal weighted data Y is 0.8 × a +0.15 × B +0.05 × C, where a is the micro-logging near-surface structure data, B is the short-track-distance near-surface structure data, and C is the cannon-shot-tomography near-surface structure data.
S204: and carrying out equal-weight weighted average on the transverse weighted data and the longitudinal weighted data to obtain the fused near-surface structure model.
Step S204 is a process of re-fusing the horizontal weighting data and the vertical weighting data, and the specific fused near-surface structure data Z is 0.5 × (X + Y); and then, configuring all the fused near-surface structure data according to the coordinate interval, so as to form a fused near-surface structure model.
According to the process description, the fused near-surface structure model integrates the characteristics of various near-surface structure models to be fused, so that the near-surface structure characteristics can be better described.
In the foregoing embodiment, the horizontal weighting parameter and the vertical weighting parameter are respectively set, the data in different near-surface structure models are respectively weighted and averaged by using the horizontal weighting parameter and the vertical weighting parameter, and then the horizontal weighting data and the vertical weighting data obtained by the two weighting methods are averaged. In other embodiments, weighting parameters reflecting three-dimensional features may be set for each near-surface structure model to be fused, and the near-surface structure feature data may be obtained by directly using the three-dimensional feature weighting parameters to form a fused near-surface structure model.
In practical applications, the weighting parameters of each near-surface structure model to be fused in the coordinate interval may be input by an engineer or obtained by calling a stored weight-coordinate interval mapping table, and the embodiment of this specification is not particularly limited.
Based on the same inventive concept, the embodiments of the present disclosure further provide an apparatus for constructing a near-surface structure model, which can be used to implement the method described in the above embodiments, such as the following embodiments. Because the principle of solving the problem of the device for constructing the near-surface structure model is similar to that of the method, the specific method of the device for processing the seismic data can be referred to the content of the method, and repeated parts are not described again. As used hereinafter, the term "unit" or "module" may be a combination of software and/or hardware that implements a predetermined function. While the system described in the embodiments below is preferably implemented in software, implementations in hardware, or a combination of software and hardware are also possible and contemplated.
Fig. 3 is a schematic structural diagram of an apparatus for constructing a near-surface structure model according to an embodiment. As shown in fig. 3, the apparatus for constructing a near-surface structure model includes a model data obtaining module 11 to be fused, a weighting parameter obtaining module 12 and a fusion model calculating module 13.
The model data acquisition module to be fused 11 is used for acquiring near-surface structure data of various near-surface structure models to be fused; the multiple near-surface structure models to be fused comprise at least two of a micro-logging near-surface structure model, a small-track-distance chromatography ground surface structure model, a cannon chromatography ground surface structure model, a shallow reflection ground surface structure model and a geological radar ground surface structure model; the weighting parameter obtaining module 12 is configured to obtain weighting parameters of the to-be-fused near-surface structure models in the coordinate interval; and the fusion model calculation module 13 is configured to perform weighted calculation on the near-surface structure data in the corresponding coordinate interval according to the weighted parameters of each to-be-fused near-surface structure model in each coordinate interval, so as to obtain a fusion near-surface structure model.
Because the precision of each near-surface structure model to be fused in different coordinate intervals is different, the weighting parameters corresponding to different coordinate intervals are different. In addition, in a coordinate interval with good precision of the near-surface structure model to be fused, the corresponding weighting parameter value is large, so that the advantages of different near-surface structure models are fully fused by the near-surface structure data obtained through weighting summation calculation, and the near-surface depiction of the fused near-surface structure model to the near-surface is closer to the real condition.
In a specific application, the weighting parameter obtaining module 12 obtains a horizontal weighting parameter and a vertical weighting parameter of each to-be-fused near-surface structure model in a coordinate interval. Correspondingly, the functional units of the fusion model calculation module 13 can be subdivided. Fig. 4 is a schematic structural diagram of a fusion model calculation module according to an embodiment. As shown in fig. 4, the fusion model settlement module 13 may include a horizontal weight calculation unit 131, a vertical weight calculation unit 132, and an equal weight calculation unit 133.
The transverse weighting calculation unit 131 performs weighting calculation on the near-surface structure data of the corresponding coordinate interval according to the transverse weighting parameter of each near-surface structure model to be fused in each coordinate interval to obtain transverse weighting data; the longitudinal weighting calculation unit 132 performs weighting calculation on the near-surface structure data of the corresponding coordinate interval according to the longitudinal weighting parameters of each near-surface structure model to be fused in each coordinate interval to obtain longitudinal weighting data; the equal weight calculation unit 133 performs equal weight weighted average on the horizontal weighted data and the vertical weighted data to obtain the fused near-surface structure model.
In a specific application, the weighting parameter obtaining module 12 obtains weighting parameters corresponding to all coordinate intervals by querying a pre-stored weight-coordinate interval mapping table; the data acquisition module to be fused acquires the corresponding near-surface structure data by performing discrete sampling on the near-surface structure model to be fused.
In addition to providing the foregoing method and apparatus, the present implementation also provides an electronic device implementing the foregoing method, and a storage medium storing a program implementing the foregoing method.
Fig. 5 is a schematic diagram of an electronic device provided by an embodiment. As shown in fig. 5, the electronic device includes a processor 21 and a memory 22, and the memory 22 and the processor 21 are electrically connected.
In practice, the memory 22 may be a solid state memory such as a Read Only Memory (ROM), a Random Access Memory (RAM), a SIM card, or the like. There may also be a memory that holds information even when power is off, can be selectively erased, and is provided with more data, an example of which is sometimes called an EPROM or the like. The memory may also be other memory known in the art of computer devices.
In one application, the processor 21 may load a program stored in the memory 22 or other device connected to the electronic device to implement the aforementioned method for constructing the near-surface structure model.
Referring to fig. 5, the electronic device provided in this embodiment further includes an input unit 23 and an output unit 24 in addition to the processor 21 and the memory 22.
The input component 23 is used to obtain the near-surface structure model to be fused, and various weighting parameter parameters.
The output component 24 is used for outputting the fused near-surface structure data.
Furthermore, the electronic device should also comprise a power supply 25; a communication module 26 may also be included to enable contact with other electronic devices, as may be the case.
Embodiments of the present application also provide a computer-readable storage medium having stored thereon a computer program, which when executed by a processor, implements all the steps of the method of constructing a near-surface structure model in the above embodiments, and can achieve the aforementioned effects when executing the above method.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor 21 of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor 21 of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory 22 that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory 22 produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present description and is illustrative of the principles of the technology employed. It will be appreciated by a person skilled in the art that the scope of the invention as referred to in the present description is not limited to the specific combination of features described above, but also covers other embodiments where any combination of the features described above or their equivalents is made without departing from the inventive concept described above. For example, the above features and the technical features disclosed in the present specification but not limited to having similar functions are mutually replaced to form the technical solution.
Claims (12)
1. A method of constructing a near-surface structure model, comprising:
acquiring near-surface structure data of a plurality of near-surface structure models to be fused; the multiple near-surface structure models to be fused comprise at least two of a micro-logging near-surface structure model, a small-track-distance chromatography ground surface structure model, a cannon chromatography ground surface structure model, a shallow layer reflection ground surface structure model and a geological radar ground surface structure model;
acquiring the weighting parameters of each near-surface structure model to be fused in a coordinate interval;
and performing weighted calculation on the near-surface structure data of the corresponding coordinate interval according to the weighted parameters of each near-surface structure model to be fused in each coordinate interval to obtain a fused near-surface structure model.
2. The method of constructing a near-surface structure model of claim 1,
obtaining the weighting parameters of each near-surface structure model to be fused in the coordinate interval, wherein the weighting parameters comprise:
and acquiring the transverse weighting parameters and the longitudinal weighting parameters of the near-surface structure model to be fused in the coordinate interval.
3. The method for constructing a near-surface structure model according to claim 2, wherein the obtaining of the fused near-surface structure model by performing weighted calculation on the near-surface structure data of the corresponding coordinate interval according to the weighted parameters of each near-surface structure model to be fused in each coordinate interval comprises:
according to the transverse weighting parameters of each near-surface structure model to be fused in each coordinate interval, carrying out weighting calculation on the near-surface structure data of the corresponding coordinate interval to obtain transverse weighting data; and the number of the first and second groups,
according to the longitudinal weighting parameters of each near-surface structure model to be fused in each coordinate interval, carrying out weighting calculation on the near-surface structure data of the corresponding coordinate interval to obtain longitudinal weighting data;
and carrying out equal-weight weighted average on the transverse weighted data and the longitudinal weighted data to obtain the fused near-surface structure model.
4. The method of constructing a near-surface structure model of claim 1,
the obtaining of the weighting parameters of each near-surface structure model to be fused in the coordinate interval comprises: and inquiring a pre-stored weight-coordinate interval mapping table to obtain the weighting parameters of each near-surface structure model to be fused in the coordinate interval.
5. The method of constructing a near-surface structure model of claim 1,
obtaining near-surface structure data of a plurality of near-surface structure models to be fused, including: and carrying out discrete sampling on the near-surface structure model to be fused to acquire corresponding near-surface structure data.
6. An apparatus for constructing a model of a near-surface structure, comprising:
the model data acquisition module to be fused acquires near-surface structure data of various near-surface structure models to be fused; the multiple near-surface structure models to be fused comprise at least two of a micro-logging near-surface structure model, a small-track-distance chromatography ground surface structure model, a cannon chromatography ground surface structure model, a shallow reflection ground surface structure model and a geological radar ground surface structure model;
the weighting parameter acquisition module is used for acquiring the weighting parameters of the near-surface structure models to be fused in the coordinate interval;
and the fusion model calculation module is used for performing weighted calculation on the near-surface structure data of the corresponding coordinate interval according to the weighted parameters of each near-surface structure model to be fused in each coordinate interval to obtain a fusion near-surface structure model.
7. The apparatus for constructing a model of a near-surface structure of claim 6,
the weighting parameter acquisition module acquires a transverse weighting parameter and a longitudinal weighting parameter of each near-surface structure model to be fused in a coordinate interval.
8. The apparatus for constructing a model of a near-surface structure of claim 7,
the fusion model calculation module includes:
the transverse weighting calculation unit is used for carrying out weighting calculation on the near-surface structure data of the corresponding coordinate interval according to the transverse weighting parameters of each near-surface structure model to be fused in each coordinate interval to obtain transverse weighting data;
the longitudinal weighting calculation unit is used for carrying out weighting calculation on the near-surface structure data of the corresponding coordinate interval according to the longitudinal weighting parameters of each near-surface structure model to be fused in each coordinate interval to obtain longitudinal weighting data;
and the equal weight calculation unit is used for carrying out equal weight weighted average on the transverse weighted data and the longitudinal weighted data to obtain the fused near-surface structure model.
9. The apparatus for constructing a model of a near-surface structure of claim 6,
the weighting parameter acquisition module acquires weighting parameters corresponding to all coordinate intervals by inquiring a pre-stored weight-coordinate interval mapping table.
10. The apparatus for constructing a model of a near-surface structure of claim 6,
the model data acquisition module to be fused acquires corresponding near-surface structure data by performing discrete sampling on the near-surface structure model to be fused.
11. A storage medium, characterized by: the storage medium stores a plurality of instructions; the instructions are adapted to be loaded by a processor and to perform the method of constructing a model of a near-surface structure according to any one of claims 1 to 5.
12. An electronic device, characterized in that: comprising a memory and a processor;
the memory stores a plurality of instructions; the instructions are adapted to be loaded by the processor and to perform the method of constructing a near-surface structure model according to any one of claims 1-5.
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