CN118568446B - Comprehensive geological exploration information management system - Google Patents
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Abstract
本发明涉及地质数据管理技术领域，具体为一种综合地质勘探信息管理系统，系统包括：非线性动态数据建模模块将地层振动与压力变化的时间序列数据转换为多维相空间，通过延迟坐标映射地质内部动态结构，分析重构的多维相空间中的关键转折点，生成地质动态吸引子图。本发明中，通过非线性动态数据建模，能有效捕捉地质内部的动态变化，分析和预测地质结构的关键转折点，构建基于物理和化学特性的岩层连续性网络，使得对岩层间的连续性和潜在断层的监测更为直观和实时，进一步强化了对地质结构稳定性的评估，应用高级降维技术对地质数据进行处理，有效减少了数据处理的复杂度同时保留了关键信息，从而使得地质特征的提取更加高效和准确。
The present invention relates to the field of geological data management technology, specifically to a comprehensive geological exploration information management system, the system comprising: a nonlinear dynamic data modeling module converts the time series data of stratum vibration and pressure change into a multidimensional phase space, maps the internal dynamic structure of the geology through delayed coordinates, analyzes the key turning points in the reconstructed multidimensional phase space, and generates a geological dynamic attractor diagram. In the present invention, through nonlinear dynamic data modeling, the dynamic changes inside the geology can be effectively captured, the key turning points of the geological structure can be analyzed and predicted, and a rock layer continuity network based on physical and chemical properties can be constructed, so that the monitoring of the continuity between rock layers and potential faults is more intuitive and real-time, and the assessment of the stability of the geological structure is further strengthened. The geological data is processed using advanced dimensionality reduction technology, which effectively reduces the complexity of data processing while retaining key information, thereby making the extraction of geological features more efficient and accurate.
Description
Technical Field
The invention relates to the technical field of geological data management, in particular to a comprehensive geological exploration information management system.
Background
The field of geological data management relates to the collection, storage, processing, analysis and interpretation of data related to earth structure, composition and history. Such data typically originate from geological exploration, such as seismic surveying, drilling and surface sampling. The key task in the field is to systemize and digitize a large amount of geological information to support various applications such as mineral exploration, oil and gas field development, geological disaster prevention and the like. Geological data management systems can help scientists and engineers better understand subsurface structures and material distribution through maps, three-dimensional models, and other analytical tools. In addition, the technical field also comprises visualization, sharing and updating of data, and accuracy and instantaneity of the data in scientific research and business decision are ensured.
The comprehensive geological exploration information management system refers to an integrated technical solution, and is used for managing and analyzing various data generated in a geological exploration process. The main purpose of such systems is to provide data support for geological exploration, including seismic data, formation data, sample analysis results, etc., so that geologists and exploration engineers can effectively assess the distribution and development potential of subsurface resources. The system typically includes data acquisition, storage, processing, and visualization functions that enable a user to create comprehensive geologic models and conduct complex data analysis to guide actual exploration and development activities.
The prior art often faces the problems of slow processing speed and insufficient accuracy when processing large-scale geological data. Particularly when analyzing seismic data and formation information, conventional methods may not be able to effectively identify minor variations and complex patterns in the data, thereby affecting the accuracy of geologic structure and resource assessment. In addition, the lack of effective dynamic monitoring tools results in inadequate formation continuity and identification of potential faults, which in turn affects the decision-making efficiency of mining strategies and disaster prevention. This technical limitation is particularly evident in emergency situations, such as when a geological disaster is imminent, the response time and processing power of existing systems may be insufficient to provide the necessary support, increasing the difficulty of risk management.
Disclosure of Invention
The invention aims to solve the defects in the prior art and provides a comprehensive geological exploration information management system.
In order to achieve the above purpose, the present invention adopts the following technical scheme: a comprehensive geological survey information management system comprising:
The nonlinear dynamic data modeling module converts time series data of stratum vibration and pressure change into a multidimensional phase space, maps a geological internal dynamic structure through a delay coordinate, analyzes key turning points in the reconstructed multidimensional phase space and generates a geological dynamic attractor graph;
The stratum continuity dynamic evaluation module utilizes the geological dynamic attractor graph and combines physical and chemical characteristics of rocks to construct and update nodes and edges of an undirected graph or a directed graph in real time, monitors stratum continuity and potential faults, and evaluates stability indexes of a network by calculating shortest paths among the nodes in real time and identifying strong communication components to generate a stratum continuity network graph;
The high-dimensional dimension reduction analysis module utilizes the structural features in the stratum continuity network diagram, applies local linear embedding and equidistant mapping to reduce dimension of the high-dimensional geological data, extracts key geological features and establishes a dimension reduction geological feature set;
And the geological change mode prediction module analyzes the key geological structure and the dynamic change mode based on the dimensionality reduction geological feature set, identifies the change trend of the geological structure, predicts the development trend of a geological event and generates a geological event prediction analysis result.
As a further scheme of the invention, the acquisition steps of the geology dynamic attractor graph specifically comprise:
Converting the time series data of formation vibration and pressure change into a multi-dimensional data point set, and using the formula:
Mapping the time sequence data into a high-dimensional space to generate a multi-dimensional data point set;
wherein, Representative time of dayIs a function of the data of (a),In order to provide for the time interval of time,Is the dimension number;
using the multi-dimensional data point set, adopting the formula:
Calculating Euclidean distance between data points to generate a distance matrix;
wherein, Representing data pointsAndThe distance between the two plates is set to be equal,As the number of dimensions to be used,Is a time interval;
and screening key turning points from the distance matrix, wherein the formula is adopted:
Acquiring a key turning point set;
wherein, Representing the set of data points that are selected,Representation pointsPoint of attachmentThe distance between the two electrodes is equal to the distance between the two electrodes,For each pair of pointsAndThe weight between the two is set according to the relative importance of the data points or the connection strength;
based on the set of critical turning points, the formula is used:
constructing a geological dynamic attractor graph;
wherein, A quantitative description of the geostatistical dynamic attractor graph is presented,As a function of the data points,Is a scaling parameter for adjusting the influence of the distance, making the attraction force more sensitive or more suppressed.
As a further aspect of the present invention, the step of obtaining the stratum continuity network map specifically includes:
According to the physical and chemical characteristics of the geology dynamic attractor graph and the rock, fusing the attractor graph and the characteristic data, and adopting the formula:
generating a preliminary stratum network map;
wherein, Representing a preliminary stratigraphic network map,On behalf of the node(s),The representative edge of the sheet is represented by,Representing a dynamic attractor graph of the geology,Representing the data of the physical characteristics,Representing chemical property data;
calculating the shortest path between nodes by using the preliminary stratum network diagram, and adopting the formula:
Calculating shortest paths among nodes, and generating a shortest path analysis result among the nodes;
wherein, Representative nodeAndThe shortest path between the two paths is defined,The weight of the representative edge is calculated,Representing the adjustment parameters;
and identifying strong connected components in the network by using the shortest path analysis result between the nodes, and adopting the formula:
Wherein the method comprises the steps of Generating a strong connected component analysis result as a threshold value;
wherein, Representing a strong connected component of the signal,On behalf of the node(s),AndRepresenting the nodes of the differentiation and,Representing a threshold value;
integrating the shortest path analysis result and the strong communication component analysis result among the nodes, and adopting the formula:
generating a stratum continuity network diagram;
wherein, A network map representing the continuity of the formation,Representing a strong connected component of the signal,Representing the shortest path between the nodes,Representing a network stability indicator.
As a further scheme of the invention, the step of acquiring the dimension-reducing geological feature set specifically comprises the following steps:
by utilizing the structural characteristics in the stratum continuity network diagram, a local linear embedding method is applied, and the formula is adopted:
Calculating local neighborhood embedding to generate a local embedding mapping result;
wherein, Representing the weight of the object to be weighed,Representing data points;
and combining the local embedded mapping result, using an equidistant mapping method, and adopting the following formula:
Optimizing the distance mapping of the global geological data to generate an equidistant mapping result;
wherein, Representing a core distance adjustment factor;
Integrating the local embedded mapping result and the equidistant mapping result, and using the formula:
Optimizing the dimension reduction process to generate a dimension reduction result;
wherein, Represents an adjustment coefficient;
extracting key geological features from the dimension reduction result, and using the formula:
generating a dimension-reducing geological feature set;
wherein, Representing a geologic feature.
As a further scheme of the invention, the geological event prediction analysis result obtaining step specifically comprises the following steps:
Based on the dimensionality reduction geological feature set, analyzing a key geological structure, and adopting the formula:
Calculating the significance of the key geological structure, and generating a key geological structure analysis result;
wherein, Representing the result of the analysis of the key geological structure,The correlation coefficient is represented by a correlation coefficient,The characteristic item is represented by a characteristic term,Representing the number of features;
According to the analysis result of the key geological structure, analyzing a dynamic change mode, and adopting the formula:
quantifying the geologic structure change trend to generate a dynamic change mode analysis result;
wherein, Representing the result of the analysis of the dynamic change pattern,The rate of change is indicated as being indicative of,Representing the result of the analysis of the key geological structure,Representing the number of features;
And using the dynamic change mode analysis result to adopt the formula:
Determining the most obvious change trend, and generating a geological structure change trend identification result;
wherein, Representing the result of identifying the change trend of the geological structure,Representing trend intensity;
based on the geological structure change trend identification result, the development trend of geological events is predicted, and the formula is adopted:
integrating all the variation trends to generate a geological event prediction analysis result;
wherein, Representing the result of the predictive analysis of the geological event,The prediction parameters are represented by a set of parameters,Representing the result of identifying the change trend of the geological structure,Representing the number of features.
Compared with the prior art, the invention has the advantages and positive effects that:
According to the invention, through nonlinear dynamic data modeling, dynamic changes in the geology can be effectively captured, and key turning points of the geological structure can be analyzed and predicted. And a stratum continuity network based on physical and chemical characteristics is constructed, so that the continuity between strata and the monitoring of potential faults are more visual and real-time, and the evaluation of the stability of a geological structure is further enhanced. The advanced dimension reduction technology is applied to process the geological data, so that the complexity of data processing is effectively reduced, and key information is reserved, thereby enabling the extraction of geological features to be more efficient and accurate. The processing means not only optimizes the data analysis flow, but also enhances the prediction capability of the geological event development trend, and provides more scientific data support for geological exploration and related decisions.
Drawings
FIG. 1 is a system flow diagram of the present invention;
FIG. 2 is a flowchart of the steps for obtaining a geostatistical dynamic attractor graph according to the present invention;
FIG. 3 is a flowchart of the steps for obtaining a map of formation continuity according to the present invention;
FIG. 4 is a flowchart of the steps for acquiring a reduced-dimension set of geologic features of the present invention;
FIG. 5 is a flowchart illustrating the steps for obtaining the result of the analysis of a prediction of a geological event according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
In the description of the present invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientation or positional relationships shown in the drawings, merely to facilitate describing the present invention and simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present invention. Furthermore, in the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
Examples
Referring to fig. 1, a comprehensive geological exploration information management system includes:
The nonlinear dynamic data modeling module converts time series data of stratum vibration and pressure change into a multidimensional phase space, maps a geological internal dynamic structure through a delay coordinate, analyzes key turning points in the reconstructed multidimensional phase space and generates a geological dynamic attractor graph;
The stratum continuity dynamic evaluation module utilizes a geological dynamic attractor graph and combines physical and chemical characteristics of rocks to construct and update nodes and edges of an undirected graph or a directed graph in real time, monitors stratum continuity and potential faults, calculates the shortest path between the nodes in real time and identifies strong communication components, evaluates stability indexes of a network, and generates a stratum continuity network graph;
The high-dimensional geological data dimension reduction analysis module utilizes structural features in the stratum continuity network diagram, applies local linear embedding and equidistant mapping to reduce dimension of the high-dimensional geological data, extracts key geological features and establishes a dimension reduction geological feature set;
The geological change mode prediction module analyzes the key geological structure and the dynamic change mode based on the dimensionality reduction geological feature set, recognizes the change trend of the geological structure, predicts the development trend of a geological event, and generates a geological event prediction analysis result.
The geological dynamic attractor graph specifically comprises key turning points, multidimensional phase space and delay coordinate mapping, the stratum continuity network graph specifically comprises nodes, edges and strata continuity of an undirected graph or a directed graph and potential faults, the dimension-reducing geological feature set specifically comprises key geological features, high-dimensional geological data and structural features, and the geological event prediction analysis result specifically comprises key geological structures, dynamic change modes and geological event development trends.
Referring to fig. 2, the steps for obtaining the geosteering subgraph specifically include:
Converting the time series data of formation vibration and pressure change into a multi-dimensional data point set, and using the formula:
Mapping the time sequence data into a high-dimensional space to generate a multi-dimensional data point set;
wherein, Representative time of dayIs a function of the data of (a),In order to provide for the time interval of time,Is the dimension number;
using a multidimensional data point set, adopting the formula:
Calculating Euclidean distance between data points to generate a distance matrix;
wherein, Representing data pointsAndThe distance between the two plates is set to be equal,As the number of dimensions to be used,Is a time interval;
and (3) selecting key turning points from the distance matrix, and adopting the formula:
Acquiring a key turning point set;
wherein, Representing the set of data points that are selected,Representation pointsPoint of attachmentThe distance between the two electrodes is equal to the distance between the two electrodes,For each pair of pointsAndThe weight between the two is set according to the relative importance of the data points or the connection strength;
based on the set of key turning points, the formula is used:
constructing a geological dynamic attractor graph;
wherein, A quantitative description of the geostatistical dynamic attractor graph is presented,As a function of the data points,Is a scaling parameter for adjusting the influence of the distance, making the attraction force more sensitive or more suppressed.
The formula:
Detailed description of parameters
: At a single momentIs assumed to be the intensity of the formation vibrations.
: Time intervals are used to define sampling intervals of data points in a time series.
: The embedding dimension, i.e. the number of delay time sequences included in the vector.
Calculation example
Assume thatIntensity value, time for seismic monitoringIs 5.0, time intervalSet to 1 hour, embed dimensionSet to 3.
The calculation flow is as follows:
In the time-course of which the first and second contact surfaces,
When it is assumed that
When it is assumed that
Thus, the first and second substrates are bonded together,
This vector reflects the slave timeSeismic intensity values for the first three consecutive time points.
The formula:
Detailed description of parameters
The starting point of the time series is differentiated.
The dimensions are embedded.
Calculation example
Continued use of dataAnd。
The calculation flow is as follows:
Distance value represents a vector AndThe euclidean distance between the two time series points reflects the degree of fitting between the two time series points.
The formula:
detailed description of parameters:
: the selected set of data points.
: Data pointsAndDistance between them.
: Each pair of pointsAndThe weight between the two is set according to the relative importance or the connection strength.
Calculation example
Suppose three data points are selectedAnd assuming weights for these points1 (I.e., the relationship between all points is equally important).
From already calculatedAssume that。
The calculation flow is as follows:
this value represents the set of data points Weighted distance sum between all points in the model. Selecting a set of critical turning points that minimizes the valueIn this example, because the weights are the same, the sum of all points is the same, and the key turning point set is directly obtained。
The formula:
Detailed description of parameters
: Quantitative description of the geosynamic attractor graph.
: Data points.
: And the scaling parameter is used for adjusting the influence of the distance.
: Data pointsAndDistance between them.
Calculation example
Assume thatSet to 1, known asAnd assume their distance as follows:
The calculation flow is as follows:
For each point The attractor value is calculated.
Calculation ofAttractor value of (2):
Attractor value:
Calculation of Attractor value of (2):
Attractor value:
Calculation of Attractor value of (2):
Attractor value:
Calculating a geostationary subgraph :
This value isThe total attractive force of the geology dynamic attractor graph is represented and reflects the comprehensive attractive force of all key turning points.
Referring to fig. 3, the formation continuity network map is obtained by the steps of:
According to the physical and chemical characteristics of the geological dynamic attractor graph and the rock, fusing the attractor graph and the characteristic data, and adopting the formula:
generating a preliminary stratum network map;
wherein, Representing a preliminary stratigraphic network map,On behalf of the node(s),The representative edge of the sheet is represented by,Representing a dynamic attractor graph of the geology,Representing the data of the physical characteristics,Representing chemical property data;
calculating the shortest path between nodes by using the preliminary stratum network diagram, and adopting the formula:
Calculating shortest paths among nodes, and generating a shortest path analysis result among the nodes;
wherein, Representative nodeAndThe shortest path between the two paths is defined,The weight of the representative edge is calculated,Representing the adjustment parameters;
and identifying strong connected components in the network by utilizing the shortest path analysis result among the nodes, and adopting the formula:
Wherein the method comprises the steps of Generating a strong connected component analysis result as a threshold value;
wherein, Representing a strong connected component of the signal,On behalf of the node(s),AndRepresenting the nodes of the differentiation and,Representing a threshold value;
integrating the shortest path analysis result and the strong connected component analysis result among the nodes, and adopting the formula:
generating a stratum continuity network diagram;
wherein, A network map representing the continuity of the formation,Representing a strong connected component of the signal,Representing the shortest path between the nodes,Representing a network stability indicator.
The formula:
Wherein:
: representing nodes.
: Edge set, by attractor graphAnd chemical properties of rockAnd (5) deriving.
: The geosynamic attractor graph, assuming calculated from previous steps.
: A function combined with physicsAnd chemistryCharacteristics to define the weights of the edges.
The calculation process comprises the following steps:
assumed geology dynamic attractor graph Comprising 3 nodes, each representing a rock sample of a site. Connectivity between nodes is defined by physical distanceDegree of chemical fittingAnd (5) determining. The weights of the edges are calculated from the feature synthesis. For example:
physical distance between node 1 and node 2 Unit (B)Chemical similarity。
Assume thatThis means that edge weights between the nearer and chemically similar nodes are larger.
Edge weights between node 1 and node 2 are calculated:
Thus, preliminary stratigraphic network map Is constructed to include nodes and edges having the weights described above.
The formula:
Wherein:
: weighting of edges.
: Adjusting coefficients of path sensitivity, assuming。
The calculation process comprises the following steps:
It is assumed that in addition to the connection between node 1 and node 2 described above, there is a connection between node 2 and node 3, the weight of which 0.005. The shortest path from node 1 to node 3 needs to be captured.
Direct path:
With node 2, the weights are calculated as follows:
thus, the weight of the shortest path between node 1 and node 3 is 0.000495.
The formula:
Wherein:
: threshold value of 。
The calculation process comprises the following steps:
for each node, calculate the sum of the inverse of the path weights of all other nodes to this node and check if it is greater than a threshold 。
For node 1:
Because of Node 1 does not belong to any strongly connected component.
By similar calculations it can be determined whether other nodes belong to a strongly connected component.
The formula:
Wherein:
: network stability index, assuming that it was derived from previous analysis 。
The calculation process comprises the following steps:
combining information of shortest path and strong connectivity components among all nodes and network stability index And constructing a final stratum continuity network diagram. This figure can show the connection strength between nodes and the overall stability of the network.
Referring to fig. 4, the steps for acquiring the dimension-reduced geological feature set specifically include:
by utilizing the structural characteristics in the stratum continuity network diagram, a local linear embedding method is applied, and the formula is adopted:
Calculating local neighborhood embedding to generate a local embedding mapping result;
wherein, Representing the weight of the object to be weighed,Representing data points;
in combination with the local embedding mapping result, using an equidistant mapping method, the following formula is used:
Optimizing the distance mapping of the global geological data to generate an equidistant mapping result;
wherein, Representing a core distance adjustment factor;
integrating the local embedded mapping result and the equidistant mapping result, and using the formula:
Optimizing the dimension reduction process to generate a dimension reduction result;
wherein, Represents an adjustment coefficient;
extracting key geological features from the dimension reduction result, and using the formula:
generating a dimension-reducing geological feature set;
wherein, Representing a geologic feature.
The formula:
detailed description of parameters:
: weight coefficient representing data point AndIs the relative importance or the strength of the connection.
Data points in a high-dimensional space.
Computational flow and example:
assume three data points And weight coefficient. Calculation ofThe following are provided:
Substituting the value into the formula:
Results 7.6 represent the locally linear embedded total distance based on the given weights, which can be used for further data analysis and feature extraction.
The formula:
detailed description of parameters:
: a core distance adjustment factor for adjusting the point AndThe distance between them.
: And E, calculating Euclidean distance.
Computational flow and example:
using the same data points And (2) and. Calculation ofThe following are provided:
Substituting the value into the formula:
result 9 represents the global distance sum based on the given distance adjustment factor for globally optimizing the data mapping.
The formula:
Detailed description of parameters
AndIs an adjustment factor for balancing the effects of local embedding and global mapping in the dimension reduction result.
AndThe results of the above-mentioned calculated local embedding and global mapping, respectively.
Computing flow and examples
The assumption is that the results are respectivelyAndIf set upAndThen:
Substituting the value into the formula:
Results 8.44 represent a dimension-reduced dataset that combines local and global information for further analysis and feature extraction.
The formula:
Detailed description of parameters
Representing key geologic features extracted from the comprehensive reduced-dimension dataset.
Computing flow and examples
Hypothesis resultsIs used to identify key features in the dataset. The model is simplified here, assuming three key features are identified. The specific extraction method depends on the feature extraction technique employed, such as Principal Component Analysis (PCA).
It is assumed that these features represent the main direction of change or cluster center, respectively, in the dataset. For example, ifRepresenting a geological dataset after the dimension reduction process,And respectively represent key indexes such as stratum density, mineral content, geologic age and the like.
Referring to fig. 5, the steps for obtaining the result of geological event prediction analysis specifically include:
based on the dimensionality reduction geological feature set, analyzing a key geological structure, and adopting the formula:
Calculating the significance of the key geological structure, and generating a key geological structure analysis result;
wherein, Representing the result of the analysis of the key geological structure,The correlation coefficient is represented by a correlation coefficient,The characteristic item is represented by a characteristic term,Representing the number of features;
according to the analysis result of the key geological structure, the dynamic change mode is analyzed, and the formula is adopted:
quantifying the geologic structure change trend to generate a dynamic change mode analysis result;
wherein, Representing the result of the analysis of the dynamic change pattern,The rate of change is indicated as being indicative of,Representing the result of the analysis of the key geological structure,Representing the number of features;
using the dynamic change mode analysis result, adopting the formula:
Determining the most obvious change trend, and generating a geological structure change trend identification result;
wherein, Representing the result of identifying the change trend of the geological structure,Representing trend intensity;
Based on the geological structure change trend identification result, the development trend prediction of geological events is carried out, and the formula is adopted:
integrating all the variation trends to generate a geological event prediction analysis result;
wherein, Representing the result of the predictive analysis of the geological event,The prediction parameters are represented by a set of parameters,Representing the result of identifying the change trend of the geological structure,Representing the number of features.
The formula:
Parameter interpretation and derivation process:
: analysis of key geologic structures results, quantifying the significance of each structure.
: The correlation coefficients of the key geologic structures represent the importance of each feature in the geologic analysis.
: And extracting the geological features from the dimension-reducing geological feature set.
: Total number of features.
Calculating:
Assume three features Correlation coefficientCharacteristic value。
The calculation process comprises the following steps:
Analysis of results:
Obtained by Representing a significance score for the key geologic structure calculated based on the weight correlation coefficients and the eigenvalues.
The formula:
Parameter interpretation and derivation process:
: and the analysis result of the dynamic change mode represents the trend quantification of the change of the geological structure.
: And (5) quantifying the change intensity of each geological structure along with time according to the change rate obtained by time sequence analysis.
: Significance scores for key geologic structures are known.
Calculating:
Using the preceding steps Value and assumed rate of change。
The calculation process comprises the following steps:
Analysis of results:
representing the trend score of the key geologic structure over time.
The formula:
Parameter interpretation and derivation process:
: and the geologic structure change trend identification result shows the most obvious one of all change trends.
Representing the intensity of a single trend.
Calculating:
Assume three trend strengths 。
The calculation process comprises the following steps:
Analysis of results:
the most pronounced trend intensity is 5 out of the three trends.
The formula:
Parameter interpretation and derivation process:
: and (5) predicting analysis results of the geological event, and synthesizing the estimated development of all the trend predicted geological events.
: Prediction parameters, representing the prediction weights of the multiple trends.
: The most pronounced trend intensity.
Calculating:
assuming prediction parameters And before。
The calculation process comprises the following steps:
Analysis of results:
total predictive score representing the geological time resulting from the integrated multi-trend.
The present invention is not limited to the above embodiments, and any equivalent embodiments which can be changed or modified by the technical disclosure described above can be applied to other fields, but any simple modification, equivalent changes and modification made to the above embodiments according to the technical matter of the present invention will still fall within the scope of the technical disclosure.
Claims (5)
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