CN112802085B - Soil layer thickness estimation method based on landform parameters - Google Patents
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Abstract
The invention discloses a soil layer thickness estimation method based on landform parameters, and belongs to the field of soil geological survey. The method comprises the following steps: and acquiring a first parameter of the research area by using the grid point profile curvature distribution condition of the research area, calculating a second parameter of each sub-area by using the gradient distribution condition of the research area and the critical gradient and internal friction angle of each sub-area, and fitting a third parameter function by using the projection condition of the relative position of each sample point of the research area. Training the first parameter, the second parameter and the third parameter and the thickness value of the sample point, respectively calculating the first parameter, the second parameter and the third parameter through the section curvature, the affiliated subarea and the relative position of the point to be evaluated, inputting a trained model, and predicting the thickness value of the point to be evaluated. The method integrates the regional slope evolution history and the existing surface morphology information, and can break through the problem that the accuracy and the universality are not compatible in the conventional estimation method to a certain extent.
Description
Technical Field
The invention relates to the field of soil geological survey, in particular to a soil layer thickness estimation method based on landform parameters.
Background
Soil layer thickness is an important parameter in many geological environment studies, such as slope stability, slope hydrology, seismic local effects, topography and relief evolution, soil water distribution, heat flux distribution, soil protection and the like. When the range of the research area is smaller, the soil layer thickness of a plurality of sample points can be accurately measured by a direct or indirect measuring method, so that the precision requirement of the research area can be met. When the research area is of regional scale, due to time, labor, equipment cost and other reasons, large-scale precise field measurement cannot be completed, so that a scientific estimation method is needed to obtain a soil layer thickness map of the whole area to be used as an input parameter of a complex environmental model. Currently, the methods for thickness estimation application on the area scale are mainly two types: firstly, the soil layer thickness is directly approximate to the morphological parameters (elevation, gradient or curvature) of the earth surface, and the result of the method is too coarse and can only be applied to areas with lower requirements on the soil layer thickness precision; and secondly, establishing a relation model of soil layer thickness and environmental sign factors, wherein the environmental sign factors are elevation, gradient, curvature, stratum lithology, specific water collection area, topography humidity index, runoff intensity index and the like which are directly extracted in a digital elevation, and the soil layer thickness estimation distribution diagram under the method has the problems of poor spatial continuity and larger error in numerical value.
Disclosure of Invention
The invention aims at: aiming at the problems, the soil layer thickness estimation method based on the geomorphic parameters is provided to improve the accuracy of soil layer thickness estimation of the geomorphic region of the three gorges reservoir region accumulation layer.
The technical scheme adopted by the invention is as follows:
a soil layer thickness estimation method based on landform parameters is applied to the stacked landform of a three gorges reservoir area, and the method comprises the following steps:
respectively calculating a first parameter C, a second parameter S and a third parameter P of each sample point of the research area:
A. acquiring the section curvature c of all grid points in a research area; setting a duty ratio such that the cross-sectional curvature C of points within the duty ratio is between (-x, x), the first parameter C for each grid point: for the points with the section curvature C being greater than x, the first parameter C is set to be 0, for the points with the section curvature being less than-x, the first parameter C is set to be 1, and the first parameters of the rest points are (x-C)/2 x; calculating a first parameter C of each sample point according to the section curvature C of each sample point;
B. dividing a research area into a plurality of subareas, and respectively obtaining the gradient s, the critical gradient theta and the internal friction angle of each subarea
For each subregion, according to its gradient s, critical gradient θ and internal friction angle +.>
Setting a second parameter S:
calculating a second parameter S of each sample point according to the gradient S of each sample point;
C. taking the thickness value of a sample point as an ordinate, and taking the relative position p of the sample point as an abscissa to project on a coordinate system, wherein the value of the relative position p of the point is the ratio of the horizontal distance from the point to the vertex to the horizontal length of the slope; fitting a function which can just cover all sample points in the coordinates according to the projected sample points, wherein the abscissa of the function is a relative position P, and the ordinate is a third parameter P; calculating a third parameter P of each sample point through the relative position P of each sample point;
and inputting the thickness value T, the first parameter C, the second parameter S and the third parameter P of each sample point in the research area into a pre-constructed machine learning model for training to obtain a prediction model.
Further, in the method C for calculating the third parameter, the thickness value of the sample point projected onto the coordinate system is the normalized thickness value.
Further, in the method C for calculating the third parameter, the function that just covers all sample points in the coordinates is a broken line function.
Further, the ratio is 90%.
Further, the machine learning model is a random forest machine learning model.
Further, the soil layer thickness estimation method based on the landform parameters is realized through a soil layer thickness estimation device, and the soil layer thickness estimation device comprises:
the first calculation unit is configured with a geographic information system and a first configuration module, and obtains the section curvature c of all grid points in the research area through the geographic information system; the first configuration module is used for configuring the duty ratio; the first calculating unit calculates x and-x according to the duty ratio configured by the first configuration unit, and constructs a calculation formula of a first parameter C:
a second calculation unit configured with a region dividing module for dividing the study region into a plurality of sub-regions, and a second configuration module for configuring the gradient s, critical gradient θ, and internal friction angle of each sub-region
The second calculation unit configures a second parameter S for each sub-region:
a third calculation unit configured with a coordinate projection module for projecting the thickness value of the sample point as an ordinate and the relative position p of the sample point as an abscissa onto a coordinate system, and a function fitting module; the function fitting module is used for fitting out a function which can just cover all sample points in the coordinates, wherein the abscissa of the function is a relative position P, and the ordinate is a third parameter P;
the machine learning model is a pre-constructed learning model and is used for learning and modeling the thickness value T, the first parameter C, the second parameter S and the third parameter P of each sample point;
inputting the section curvature C of the point to be estimated into a first calculation unit to obtain a first parameter C, inputting the subregion of the point to be estimated into a second calculation unit to obtain a second parameter S, inputting the relative position P of the point to be estimated into a third calculation unit to obtain a third parameter P, and inputting the first parameter C, the second parameter S and the third parameter P into a trained model to predict the corresponding thickness T.
In summary, due to the adoption of the technical scheme, the beneficial effects of the invention are as follows:
1. according to the invention, the natural law of geological landforms is utilized, the characteristics of the geological landforms are subjected to parameter characterization, and the accurate assessment of the thickness of the region is realized by combining the relation between the geological parameters and the thickness and by means of machine learning. In addition, the method and the device are used for estimating the geological feature of the research area completely according to the parameter characteristics of the geological feature of the research area, so that the method and the device have strong universality.
2. The method combines the regional slope evolution history and the existing surface morphology information, so that the estimation of regional thickness estimation can break through the accuracy of the conventional estimation method.
Drawings
The invention will now be described by way of example and with reference to the accompanying drawings in which:
FIG. 1 is a schematic view of sample point projection and function fitting.
Detailed Description
All of the features disclosed in this specification, or all of the steps in a method or process disclosed, may be combined in any combination, except for mutually exclusive features and/or steps.
Any feature disclosed in this specification (including any accompanying claims, abstract) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise. That is, each feature is one example only of a generic series of equivalent or similar features, unless expressly stated otherwise.
Example 1
The embodiment discloses a soil layer thickness estimation method based on landform parameters, which is applied to the stacked layer landform of a three gorges reservoir area, and comprises the following steps:
A. a step of calculating a first parameter C: the parameter C represents the contribution of the profile curvature C to the soil layer thickness.
Acquiring section curvature c of all grid points in a research area through a GIS (geographic information system); setting a duty ratio such that a cross-sectional curvature c of a point within the duty ratio is between (-x, x); setting a first parameter C for each grid point:
for the point with the section curvature C larger than x, setting the first parameter C as 0; for the point with the section curvature smaller than-x, setting the first parameter C as 1; the first parameter of the remaining points is (x-c)/2 x.
Obviously, the value of x varies from one investigation region to another, which also results in a variation of the first parameter C for each grid point in the different investigation regions.
B. A step of calculating a second parameter S:
dividing a research area into a plurality of subareas, and respectively obtaining the gradient s, the critical gradient theta and the internal friction angle of each subarea
Critical gradient θ and internal friction angle +.>
Depending on the characteristics of the rock and soil mass parameters in the investigation region. For each subregion, according to its gradient s, critical gradient θ and internal friction angle +.>
Setting a second parameter S:
when the gradient is larger than the critical gradient theta, the corrosion and the sliding of the surface substances are considered to be more thorough, and only the bedrock surface is left; when the gradient is smaller than the internal friction angle
When the effect of the gradient on the soil layer thickness is considered negligible.
C. A step of calculating a third parameter P: the parameter P is used to represent the relation between the relative position P of the point on the slope section and the soil layer thickness.
The relative position p of the point location is the ratio of the horizontal distance from the point location to the vertex to the horizontal length of the ramp. Taking the thickness value of the sample point as an ordinate, and taking the relative position p of the sample point as an abscissa to project on a coordinate system; and fitting a function which can just cover all sample points in the coordinates according to the projected sample points, wherein the abscissa of the function is the relative position P, and the ordinate of the function is the third parameter P, and the third parameter P of any point in the research area can be obtained through the function.
And inputting the thickness value T, the first parameter C, the second parameter S and the third parameter P of each sample point in the research area into a pre-constructed machine learning model for training, wherein the trained model can be used for predicting the thickness value of the whole research area. Three parameters C, S, P of the point to be estimated are input into a trained model, and the corresponding thickness T can be predicted. The calculation method of the three parameters C, S, P comprises the following steps: the section curvature C of the point to be evaluated is calculated by a first parameter C calculation method, the subarea to which the point to be evaluated belongs is calculated by a second parameter S calculation method, and the relative position P of the point to be evaluated is calculated by a third parameter P calculation method.
The scheme of the embodiment of the invention can be further expanded into other geological landforms for use, and parameters are only required to be adaptively adjusted according to the characteristics of the landforms of the researched area.
Example two
The embodiment discloses a soil layer thickness estimation method based on landform parameters, which is applied to the stacked landform of a three gorges reservoir area, and relates to three parameters, namely a first parameter C, a second parameter S and a third parameter P. The thickness value T of the sample point in the study area has a nonlinear function relation (t=f (C, S, P)) with three parameters C, S, P, and in this embodiment, the sample point is subjected to training modeling through a random forest machine learning model, and the thickness value of the whole study area is predicted through the trained model. The three parameters were calculated as follows:
1. calculation method of first parameter C
The first parameter C represents the contribution of the profile curvature C to the soil layer thickness. First, through field investigation and geological awareness, the relationship (positive correlation, negative correlation or other simple multi-segment linear relationship) of the section curvature and the soil layer thickness in the investigation region is determined. Taking the stacked layer landform of the three gorges reservoir area as an example, the slope section curvature c and the soil layer thickness T of the three gorges reservoir area are in negative correlation, the section curvature of all grid points in the research area is extracted through GIS, and 90% of section curvature c is between (-x, x), then: a point with a section curvature greater than x, the parameter C of which is 0; a point with a profile curvature less than-x, the first parameter C of which is 1; the first parameter C value of the remaining points is (x-C)/2 x.
2. Calculation method of second parameter S
The second parameter S represents the contribution of the slope S to the soil layer thickness. The method can be approximately judged according to the natural law: when the gradient is larger than the critical gradient theta, the corrosion and the sliding of the surface substances are considered to be more thorough, and only the bedrock surface is left; when the gradient is smaller than the internal friction angle
When the effect of the gradient on the soil layer thickness is considered negligible. Critical gradient θ and internal friction angle +.>
Depending on the characteristics of the rock and soil mass parameters in the investigation region. In this embodiment, the study area is divided into several sub-areas, and the second parameter S in the same sub-area is a constant value. The second parameter S is taken as the value and gradient S, the critical gradient θ and the internal friction angle +.>
The three are related, and the formula is as follows:
3. third parameter P calculation method
The third parameter P is used to represent the relation between the relative position P of the point on the slope section and the soil layer thickness. On a slope, the relative position p of the slope top is 0, the relative position p of the slope bottom is 1, and the value of the relative position p at any position is the ratio of the horizontal distance from the position to the vertex to the horizontal length of the slope. And (3) establishing a two-dimensional coordinate graph, wherein the abscissa is the relative position p, the ordinate is the thickness value of the sample point after thickness normalization, and all the sample points are cast into the graph. A broken line or a simple functional curve (i.e. represented by a function) is used to cover exactly all sample points in the graph, as shown in fig. 1, with the broken line/curve having an abscissa as the relative position P and an ordinate as the third parameter P (e.g. as shown in the following graph). By means of this broken line/curve function (p=f (P)), the value of the third parameter P at any position of the investigation region can be derived.
Example III
The embodiment discloses a soil layer thickness estimation system based on a geomorphic parameter, which comprises a first calculation unit, a second calculation unit, a third calculation unit and a machine learning model.
The first calculation unit is configured with a geographic information system and a first configuration module, and obtains the section curvature c of all grid points in the research area through the geographic information system; the first configuration module is used for configuring the duty ratio; the first calculating unit calculates x and-x according to the duty ratio configured by the first configuration unit, and constructs a calculation formula of a first parameter C:
inputting the section curvature C of the point to be evaluated into a first calculation unit to obtain a corresponding first parameter C.
A second calculation unit configured with a region dividing module for dividing the study region into a plurality of sub-regions, and a second configuration module for configuring the gradient s, critical gradient θ, and internal friction angle of each sub-region
The second calculation unit configures a second parameter S for each sub-region:
and the second parameters S of the points to be evaluated can be matched through the sub-regions to which the points to be evaluated belong and the second parameters S of the sub-regions calculated by the second calculation unit.
A third calculation unit configured with a coordinate projection module for projecting the thickness value of the sample point as an ordinate and the relative position p of the sample point as an abscissa onto a coordinate system, and a function fitting module; the function fitting module is used for fitting out a function which can just cover all sample points in the coordinates, wherein the abscissa of the function is the relative position P, and the ordinate is the third parameter P. For the function fitting module, generally, as long as the fitted function can be expressed by the function and meets the requirement of covering sample points, the third parameter P calculated by the fitting result is not too different, the influence on the final training result is not great, the conditions of jump and the like are avoided, and the method meets the natural law.
And (3) corresponding the relative position P of the point to be evaluated to a function fitted by a third calculation unit, so as to obtain a corresponding third parameter P.
The machine learning model is a pre-built learning model (such as a random forest machine learning model or other machine learning models, the invention does not limit the learning model), and the machine learning model is used for learning the thickness value T, the first parameter C, the second parameter S and the third parameter P of each sample point to construct a prediction model. It will be appreciated by those skilled in the art that for the trained model, the first parameter C, the second parameter S and the third parameter P are taken as inputs and the thickness value T is taken as an output.
The embodiment also discloses a soil layer thickness estimation method based on the landform parameters by using the soil layer thickness estimation device, which comprises the following steps:
inputting the section curvature C of the point to be estimated into a first calculation unit to obtain a first parameter C, inputting the subregion of the point to be estimated into a second calculation unit to obtain a second parameter S, inputting the relative position P of the point to be estimated into a third calculation unit to obtain a third parameter P, and inputting the first parameter C, the second parameter S and the third parameter P into a trained model to predict the corresponding thickness T.
The invention is not limited to the specific embodiments described above. The invention extends to any novel one, or any novel combination, of the features disclosed in this specification, as well as to any novel one, or any novel combination, of the steps of the method or process disclosed.
Claims (6)
1. The soil layer thickness estimation method based on the landform parameters is applied to the stacked landform of the three gorges reservoir area, and is characterized by comprising the following steps:
respectively calculating a first parameter C, a second parameter S and a third parameter P of each sample point of the research area:
A. acquiring the section curvature c of all grid points in a research area; setting a duty ratio such that the cross-sectional curvature C of points within the duty ratio is between (-x, x), the first parameter C for each grid point: for the points with the section curvature C being greater than x, the first parameter C is set to be 0, for the points with the section curvature being less than-x, the first parameter C is set to be 1, and the first parameters of the rest points are (x-C)/2 x; calculating a first parameter C of each sample point according to the section curvature C of each sample point;
B. dividing a research area into a plurality of subareas, and respectively obtaining the gradient s, the critical gradient theta and the internal friction angle of each subarea
For each subregion, according to its gradient s, critical gradient θ and internal friction angle +.>
Setting a second parameter S:
second parameter
Calculating a second parameter S of each sample point according to the gradient S of each sample point;
C. taking the thickness value of a sample point as an ordinate, and taking the relative position p of the sample point as an abscissa to project on a coordinate system, wherein the value of the relative position p of the point is the ratio of the horizontal distance from the point to the vertex to the horizontal length of the slope; fitting a function which can just cover all sample points in the coordinates according to the projected sample points, wherein the abscissa of the function is a relative position P, and the ordinate is a third parameter P; calculating a third parameter P of each sample point through the relative position P of each sample point;
and inputting the thickness value T, the first parameter C, the second parameter S and the third parameter P of each sample point in the research area into a pre-constructed machine learning model for training to obtain a prediction model.
2. The soil layer thickness estimation method based on the geomorphic parameters according to claim 1, wherein in the method C of calculating the third parameter, the sample point thickness value projected onto the coordinate system is a normalized thickness value.
3. The soil layer thickness estimation method based on the geomorphic parameters according to claim 1, wherein in the method C for calculating the third parameter, the function that just covers all sample points in the coordinates is a broken line function.
4. The method for estimating a soil layer thickness based on a geomorphic parameter according to claim 1, wherein the ratio is 90%.
5. The soil layer thickness estimation method based on the landform parameters according to any one of claims 1 to 4, wherein the machine learning model is a random forest machine learning model.
6. The soil layer thickness estimation method based on the relief parameters according to claim 1, wherein the soil layer thickness estimation method based on the relief parameters is implemented by a soil layer thickness estimation device comprising:
the first calculation unit is configured with a geographic information system and a first configuration module, and obtains the section curvature c of all grid points in the research area through the geographic information system; the first configuration module is used for configuring the duty ratio; the first calculating unit calculates x and-x according to the duty ratio configured by the first configuration unit, and constructs a calculation formula of a first parameter C:
first parameter
A second calculation unit configured with a region dividing module for dividing the study region into a plurality of sub-regions, and a second configuration module for configuring the gradient s, critical gradient θ, and internal friction angle of each sub-region
The second calculation unit configures a second parameter S for each sub-region:
second parameter
A third calculation unit configured with a coordinate projection module for projecting the thickness value of the sample point as an ordinate and the relative position p of the sample point as an abscissa onto a coordinate system, and a function fitting module; the function fitting module is used for fitting out a function which can just cover all sample points in the coordinates, wherein the abscissa of the function is a relative position P, and the ordinate is a third parameter P;
the machine learning model is a pre-constructed learning model and is used for learning and modeling the thickness value T, the first parameter C, the second parameter S and the third parameter P of each sample point;
inputting the section curvature C of the point to be estimated into a first calculation unit to obtain a first parameter C, inputting the subregion of the point to be estimated into a second calculation unit to obtain a second parameter S, inputting the relative position P of the point to be estimated into a third calculation unit to obtain a third parameter P, and inputting the first parameter C, the second parameter S and the third parameter P into a trained model to predict the corresponding thickness T.
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