Fisher river-lake sedimentary facies discrimination method and system based on granularity parameters

Abstract

The invention discloses a Fisher river and lake sedimentary facies distinguishing method and system based on granularity parameters, and belongs to the field of sedimentary facies. The method comprises the following steps: collecting sedimentary granularity data of a river-lacustrine facies stratum; inputting the sedimentary granularity data of the river-lake facies strata into a Fisher discrimination function, and outputting a river-lake sedimentary facies discrimination result; the Fisher discriminant function construction process comprises the steps that sedimentary granularity data of a river-lake facies stratum is obtained, and river-lake sedimentary facies division is conducted through mathematical analysis software; and on the basis of a result obtained by dividing the river-lake sedimentary facies, combining the granularity parameters of the sedimentary granularity data of the river-lake facies stratum, and performing fitting to form a Fisher discriminant function. According to the method, the river-lake sedimentary facies division result is combined with the granularity parameters, the Fisher discrimination function is formed through fitting, the influence of human factors on river-lake facies division is avoided, the river-lake sedimentary facies discrimination result is obtained through calculation by means of the Fisher discrimination function, and analysis of sedimentary environment evolution of a research area can be achieved.

Description

Technical Field

[0001] The present invention belongs to the field of sedimentary facies, and particularly relates to a Fisher discrimination method and system for river-lake sedimentary facies based on grain size parameters. Background Technique

[0002] Sedimentary environment analysis is of great significance for aquifer structure research, hydrogeological parameter identification, and groundwater flow system division. Due to the large number, variety, and complexity of genetic markers indicating sedimentary environments, qualitative classification methods can no longer fully meet the requirements for sedimentary environment discrimination and river-lake sedimentary facies division. Therefore, previous researchers have made many attempts by combining statistical methods with traditional sedimentary environment analysis methods. As a classic analysis method, grain size analysis has been widely used. With the wide application of laser particle size analyzers in sediment analysis, the acquisition of grain size data has better objectivity and repeatability. Therefore, it is possible to obtain a more general discriminant model using discriminant analysis methods. Some scholars have also established discriminant functions for various river-lake sedimentary facies based on new grain size analysis methods. However, no one has established a discriminant method for distinguishing river-lake sedimentary facies in complex basins. Although there are significant differences in grain size characteristics between river facies and lake facies in modern sediments, due to the influence of tectonic movements, climatic environments, etc., the sedimentary conditions of rivers and lakes were more complex and the alternating effects were frequent during historical periods, causing many difficulties in the identification of river-lake facies.

[0003] Therefore, it is necessary to design and develop a Fisher discrimination method and system for river-lake sedimentary facies based on grain size parameters to facilitate the discrimination of river-lake sedimentary facies. Summary of the Invention

[0004] The purpose of the present invention is to address the problems existing in the prior art and provide a Fisher discrimination method and system for river-lake sedimentary facies based on grain size parameters. By combining the results of river-lake sedimentary facies division with grain size parameters and fitting to form a Fisher discriminant function, it is not only possible to avoid the influence of human factors on river-lake facies division, but also greatly improve the universality of river-lake sedimentary facies discrimination results.

[0005] According to one aspect of the present specification, a Fisher discrimination method for river-lake sedimentary facies based on grain size parameters is provided, including:

[0006] Collect sedimentary grain size data of river-lake facies strata;

[0007] Input the sedimentary grain size data of river-lake facies strata into the Fisher discriminant function to output the discrimination result of river-lake sedimentary facies; wherein the construction process of the Fisher discriminant function includes:

[0008] Obtain the sedimentary grain size data of river-lake facies strata and perform river-lake sedimentary facies division using mathematical analysis software;

[0009] Based on the results obtained from the division of river-lake sedimentary facies and combining with the grain size parameters of the sedimentary grain size data of river-lake facies strata, a Fisher discriminant function is fitted and formed.

[0010] Further, the method further includes selecting the average grain size, skewness value, kurtosis value, and the auxiliary variable chromaticity as the grain size parameters of the sedimentary grain size data of river-lake facies strata.

[0011] Further, the results of the division of river-lake sedimentary facies include four types, namely secondary loess, shallow lake, lakeside, and delta.

[0012] Further, the expression of the Fisher discriminant function is: ,

[0013]

[0014] According to one aspect of the specification of the present invention, there is provided a Fisher discriminant river-lake sedimentary facies system based on grain size parameters, including:

[0015] A data acquisition module for acquiring the sedimentary grain size data of river-lake facies strata;

[0016] A river-lake sedimentary facies discrimination module for inputting the sedimentary grain size data of river-lake facies strata into the Fisher discriminant function and outputting the river-lake sedimentary facies discrimination result; wherein the construction process of the Fisher discriminant function includes:

[0017] Obtain the sedimentary grain size data of river-lake facies strata, and use mathematical analysis software to divide the river-lake sedimentary facies;

[0018] Based on the results obtained from the division of river-lake sedimentary facies and combining with the grain size parameters of the sedimentary grain size data of river-lake facies strata, a Fisher discriminant function is fitted and formed.

[0019] According to one aspect of the specification of the present invention, there is provided an electronic device including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned Fisher discriminant river-lake sedimentary facies method based on grain size parameters are implemented.

[0020] According to one aspect of the specification of the present invention, there is provided a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned Fisher discriminant river-lake sedimentary facies method based on grain size parameters are implemented.

[0021] According to one aspect of the specification of the present invention, there is provided a computer program product including instructions, which, when running on a computer, causes the computer to execute the steps of the method for discriminating river-lake sedimentary facies based on grain size parameters as described above.

[0022] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0023] 1. For the method and system for discriminating river-lake sedimentary facies based on grain size parameters proposed by the present invention, by combining the division results of river-lake sedimentary facies with grain size parameters and fitting to form a Fisher discrimination function, not only can the influence of human factors on the division of river-lake facies be avoided, but also with the standardization of grain size analysis, the universality of the discrimination function is greatly improved.

[0024] 2. For the method and system for discriminating river-lake sedimentary facies based on grain size parameters proposed by the present invention, using the Fisher discrimination function to calculate the discrimination results of river-lake sedimentary facies can realize the analysis of the evolution of the sedimentary environment in the study area. In addition, by comparing the discrimination results of porous river-lake sedimentary facies with the qualitative division results, the practicability of the Fisher discrimination function in discriminating river-lake sedimentary facies can be further verified. Description of the Drawings

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0026] Figure 1 It is a flowchart of the method for discriminating river-lake sedimentary facies based on grain size parameters of the present invention;

[0027] Figure 2 It is a flowchart for establishing the Fisher discrimination function in the method for discriminating river-lake sedimentary facies based on grain size parameters of the present invention;

[0028] Figure 3 It is a schematic diagram of skewness value in the method for discriminating river-lake sedimentary facies based on grain size parameters of the present invention;

[0029] Figure 4 It is a schematic diagram of kurtosis value in the method for discriminating river-lake sedimentary facies based on grain size parameters of the present invention;

[0030] Figure 5 It is a location map of the HX hole study area in the method for discriminating river-lake sedimentary facies based on grain size parameters of the present invention;

[0031] Figure 6 This is a schematic diagram for the subdivision of sedimentary facies in the Fisher discrimination method for river and lake sediments based on grain size parameters of the present invention. Specific implementation mode

[0032] It should be noted that:

[0033] The mathematical analysis software refers to SPSS software.

[0034] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention.

[0035] As Figure 1-2 shown, the present invention provides a Fisher discrimination method for river and lake sedimentary facies based on grain size parameters, including: collecting sedimentary grain size data of river and lake facies strata; inputting the sedimentary grain size data of river and lake facies strata into the Fisher discrimination function to output the discrimination result of river and lake sedimentary facies; wherein the construction process of the Fisher discrimination function includes: obtaining the sedimentary grain size sample data of river and lake facies strata, and using mathematical analysis software to divide the river and lake sedimentary facies; based on the result obtained from the division of the river and lake sedimentary facies, combining with the grain size parameters of the sedimentary grain size data of the river and lake facies strata, fitting to form the Fisher discrimination function.

[0036] Specifically, the grain size parameters include the average grain size, skewness value, kurtosis value, and auxiliary variables chromaticity L*, A*, B*, which can represent the clastic particle attributes of the sedimentary facies and can discriminate different sedimentary environments and their changes. Among them, the average grain size (Mz) reflects the average size of the grain size distribution, indicating the average strength of water / wind power and the overall fineness and coarseness of the provenance clastics, which is an embodiment of the average energy of the dynamic medium. From the aeolian facies to the fluvial facies, the dynamic action gradually increases, and the average grain size generally changes from fine to coarse. The skewness value (S k ) reflects the symmetry of the grain size distribution, and the expression is:

[0037]

[0038] where each φ represents the grain size value at the corresponding percentage content on the cumulative curve. Greater than 0 indicates that the grain size is generally finer, and vice versa, less than 0 indicates that the grain size is generally coarser. As Figure 3As shown, when Sk = 0, the distribution is symmetric, indicating good sorting of the sediment; when Sk > 0, the distribution is positively skewed (or right-skewed), the curve shape is asymmetric, the peak is biased towards the coarser grain size side, and there is a low tail on the finer grain side. At this time, there are more particle size data on the right side of the average value than on the left side, and the long tail drags on the right side, indicating that the particle size of the sediment sample is coarser, mainly composed of coarse components, and the sorting becomes worse; when Sk < 0, the distribution is negatively skewed (or left-skewed), the curve shape is also asymmetric, the peak is biased towards the finer grain size side, and there is a low tail on the coarser grain side. There are more data on the left side of the average value than on the right side, and the long tail drags on the left side, indicating that the particle size of the sediment sample is finer, mainly composed of fine components, and the sorting also becomes worse. The kurtosis value ( K G ) reflects the width of the peak shape of the grain size frequency curve, and the expression is:

[0039] where each φ represents the particle size value at the corresponding percentage content on the cumulative curve. When it is less than 1, a wide peak shape indicates complex sedimentary dynamics, and when it is greater than 1, a steep peak shape indicates stable sedimentary dynamics. As Figure 4 shown, because the kurtosis value of the normal distribution is 1, if K G = 1, the peak shape is the same as the steepness of the normal distribution; if K G > 1, the kurtosis of the peak is steeper than that of the normal distribution - a sharp peak, indicating that the particle size distribution is more concentrated; K G < 1, the kurtosis of the peak is flatter than that of the normal distribution - a flat peak, indicating that the hydrodynamic force is more unstable.

[0040] Specifically, as shown in Table 1, the particle size distributions of different sedimentary facies have different characteristics.

[0041] Table 1 Characteristics of different sedimentary facies

[0042]

[0043] Specifically, the embodiments of the present invention provide that based on the Fisher discriminant method, the average particle size, skewness value, and kurtosis value can be used as discriminant factors (grain size parameters that can fully reflect the sedimentary facies attributes) to quantitatively discriminate different sedimentary facies environments. The Fisher discriminant method is an important method for dealing with classification problems with unknown probability distributions. Its basic idea is to project all data onto a certain direction to maximize the differences between different category samples while minimizing the within-category differences.

[0044] Specifically, the embodiments of the present invention provide the use of the Fisher statistical method of SPSS software to divide the 100-meter sedimentary grain size samples of the HX well (Huxian well) in the fluvial-lacustrine strata of the Weihe Basin into fluvial-lacustrine sedimentary facies, Figure 5The location of the HX borehole study area, with the independent variable parameters being the average grain size, skewness, kurtosis data, and the auxiliary variable chromaticity L*, A*, B*. The fitting samples for establishing the Fisher discriminant function are based on the results of the sedimentary facies division of the HX borehole, as shown in Table 2. For the convenience of calculation, the secondary loess, shallow lake, lakeside, and delta (river-lake transitional facies) are defined as 1, 2, 3, and 4 respectively.

[0045] Table 2 Fitting samples of the Fisher discriminant function

[0046]

[0047]

[0048]

[0049] Specifically, import the fitting sample data in Table 2 into the SPSS software. Among them, the average grain size, skewness, kurtosis data, and the auxiliary variable chromaticity L*, A*, B* data are imported into the independent variables, and the sedimentary facies division of the river-lake is imported into the grouping variable. The grouping range is defined as 1-4, and the following Fisher discriminant function is obtained. Table 3 shows the coefficients of the discriminant function:

[0050]

[0051]

[0052]

[0053] Table 3 Coefficients of the canonical discriminant function

[0054]

[0055] Specifically, based on a large amount of data collation and combined with the analysis of index characteristics, the present invention embodiment has carried out a detailed division of the sedimentary system and river-lake sedimentary facies of the 100-meter borehole in Hu County. The river-lake sedimentary facies of the borehole core are subdivided into the top aeolian sedimentary system, the middle-lower shore-shallow lake sedimentary system, and the river-lake delta sedimentary system, as Figure 6 shown. The lithological change characteristics of different river-lake sedimentary facies are described as follows:

[0056] (1) Top aeolian loess - secondary loess. The lithology is interbedded with brownish - red silt and light - yellow clay loam, occurring above 25 m at the top. The upper section is pure aeolian loess - paleosol deposit, with fine grain size, good sorting, and containing calcareous nodules; the lower section gradually shows very few sand layers, which is the secondary loess affected and modified by gullies. The average grain size of the loess / secondary loess is relatively fine, and the skewness value and kurtosis value change smoothly, indicating relatively stable transporting dynamics. The aeolian deposition in the Weihe Basin began to appear in the Youhe Formation of the Pliocene and was most developed in the Quaternary, widely distributed throughout the basin. The beginning of the dust accumulation indicates the demise of the lake basin, the increase in regional aridity, and the strengthening of the northerly airflow (winter monsoon).

[0057] (2) Fluvial - lacustrine delta sedimentary system. The lithology is mainly gray muddy silt intercalated with brownish - yellow to grayish - white gravelly medium - coarse sand, with loose accumulation, general or poor sorting, large quartz content, and relatively complex detrital components. As the sand layers increase, the skewness value and kurtosis value show little difference between the delta facies and the shore - shallow lake facies, both presenting an unstable dynamic environment with large fluctuations. The lake - entering delta facies in the Huxian borehole was formed under the combined action of the lake and the river (meandering river). Its basic characteristics are similar to those of river - sea deltas, but the intensity and scale of the lake water action are much smaller. Without the influence of tidal action, it is modified by lake waves and has the characteristics of a wave - dominated delta, mostly spreading in a bird's - foot or tongue - like shape on the plane. The lake - entering delta deposits are widely distributed in the hinterland and periphery of the basin and developed in each Cenozoic period.

[0058] (3) Dominant shore - shallow lake sedimentary system. It includes two sub - facies deposits: shallow lake and lakeshore. The Quaternary of the basin is not well - developed or lacks deep - lake facies. The lake - facies sediments are darker in color, mainly showing interbeds of dark - gray or dark - brown sandy mudstone, siltstone, and sandstone. The lakeshore deposit is dark - brown and grayish - brown silty sand intercalated with yellow medium - coarse sand and locally intercalated with thin layers of gravel and sand. The lakeshore zone where the lake is located is a high - energy environment with a large supply of sediment sources, and coarser lakeshore gravels are mostly developed. The shallow - lake deposit is mainly interbeds of dark - gray and brownish - yellow muddy - silty, with a slightly finer sediment particle size than the lakeshore sub - facies. Wavy, inclined, etc. bedding can be seen in the silt - fine sandstone, and horizontal bedding can be seen in the mudstone. The average grain size of the shore - shallow lake facies deposit fluctuates greatly and frequently, and the skewness value and kurtosis value also fluctuate significantly, indicating an unstable dynamic environment.

[0059] The implementation basis of each embodiment of the present invention is achieved through programmed processing by a device with a processor function. Therefore, in engineering practice, the technical solutions and their functions of each embodiment of the present invention are encapsulated into various modules. Based on this actual situation, on the basis of the above - mentioned embodiments, an embodiment of the present invention provides a Fisher discrimination system for fluvial - lacustrine sedimentary facies based on grain - size parameters, which is used to execute a method for Fisher discrimination of fluvial - lacustrine sedimentary facies based on grain - size parameters in the above - mentioned method embodiments.

[0060] The system includes: a data acquisition module for acquiring sediment grain size data of fluvial-lacustrine strata; a fluvial-lacustrine sediment facies discrimination module for inputting the sediment grain size data of fluvial-lacustrine strata into a Fisher discrimination function and outputting a fluvial-lacustrine sediment facies discrimination result; wherein the construction process of the Fisher discrimination function includes: obtaining sediment grain size sample data of fluvial-lacustrine strata and using mathematical analysis software to divide fluvial-lacustrine sediment facies; based on the results obtained from the division of fluvial-lacustrine sediment facies and combining with the grain size parameters of the sediment grain size data of fluvial-lacustrine strata, fitting to form a Fisher discrimination function.

[0061] The Fisher discrimination system for fluvial-lacustrine sediment facies based on grain size parameters provided by the embodiments of the present invention, since no one has established a discrimination method for distinguishing fluvial-lacustrine sediment facies in complex basins, uses several modules. By combining the results of the division of fluvial-lacustrine sediment facies with grain size parameters and fitting to form a Fisher discrimination function, it can avoid the influence of human factors on the division of fluvial-lacustrine sediment facies. With the standardization of grain size analysis, the universality of the discrimination model is greatly improved.

[0062] Based on the same inventive concept as the foregoing embodiments, the embodiments of the present invention also provide an electronic device, including 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 to implement the Fisher discrimination method for fluvial-lacustrine sediment facies based on grain size parameters as proposed in the above embodiments.

[0063] The embodiments of the present invention also provide a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, it is used for the discrimination of fluvial-lacustrine sediment facies and realizes the analysis of the evolution of the sedimentary environment in the study area. The storage medium can be any non-volatile storage device such as a hard disk, a solid-state drive, a flash drive, an optical disc, etc., for storing computer program codes and necessary data files. The stored computer program includes: a data acquisition module and a fluvial-lacustrine sediment facies discrimination module.

[0064] The embodiments of the present invention also provide a computer program product containing instructions. When it runs on a computer, it wholly or partially generates the Fisher discrimination method for fluvial-lacustrine sediment facies based on grain size parameters as proposed in the above embodiments. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0065] Finally, it should be noted that the above specific embodiments are only relatively representative examples of the present invention. Obviously, the present invention is not limited to the above specific embodiments and there can be many variations. Any simple modification, equivalent change, and modification made to the above specific embodiments based on the technical essence of the present invention should be considered to fall within the protection scope of the present invention.

Claims

1. A Fisher method for distinguishing river and lake sedimentary facies based on grain size parameters, characterized in that: include: Collect sedimentary grain size data of river-lake strata; The sedimentary grain size data of river-lake facies strata are input into Fisher discriminant function, and the discriminant results of river-lake sedimentary facies are output; The construction process of the Fisher discriminant function includes: Obtain sedimentary grain size data of river-lake strata and use mathematical analysis software to divide river-lake sedimentary facies; Based on the results of river-lake sedimentary facies division and combined with the grain size parameters of sedimentary grain size data of river-lake facies strata, the Fisher discriminant function was fitted.

2. The Fisher method for distinguishing river and lake sedimentary phases based on grain size parameters according to claim 1 is characterized in that: The method further comprises selecting average particle size, skewness value, kurtosis value and auxiliary variable chromaticity as particle size parameters of sedimentary particle size data of fluvial-lacustrine strata.

3. The Fisher method for distinguishing river and lake sedimentary phases based on grain size parameters according to claim 1 is characterized in that: The results of the river-lake sedimentary phase division include four types, namely secondary loess, shallow lake, lakeside and delta.

4. The Fisher method for distinguishing river and lake sedimentary phases based on grain size parameters according to claim 1 is characterized in that: The expression of the Fisher discriminant function is: , , 5. A Fisher system for distinguishing river and lake sedimentary phases based on grain size parameters, characterized in that: include: The data acquisition module is used to collect sedimentary grain size data of river and lake strata; The river-lake sedimentary phase discrimination module is used to input the sedimentary grain size data of river-lake facies strata into the Fisher discriminant function and output the river-lake sedimentary phase discrimination results; The construction process of the Fisher discriminant function includes: Obtain sedimentary grain size data of river-lake strata and use mathematical analysis software to divide river-lake sedimentary facies; Based on the results of river-lake sedimentary facies division and combined with the grain size parameters of sedimentary grain size data of river-lake facies strata, the Fisher discriminant function was fitted.

6. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the Fisher method for distinguishing river and lake sedimentary phases based on grain size parameters described in any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the Fisher method for distinguishing river and lake sedimentary phases based on grain size parameters as described in any one of claims 1 to 4 are implemented.

8. A computer program product comprising instructions, characterized in that When the method is run on a computer, the computer is enabled to execute the steps of the Fisher method for distinguishing river and lake sedimentary phases based on grain size parameters as described in any one of claims 1 to 4.

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