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Mineral content spectrum inversion method based on deep neural network

A deep neural network and neural network technology, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve problems such as optimal solutions, difficult data acquisition, and overfitting

Active Publication Date: 2019-07-05
BEIJING RES INST OF URANIUM GEOLOGY
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0003] As a nonlinear analysis method, neural network has been widely used in mineral spectral analysis, but the current application still has the following problems: shallow neural network requires a large number of spectral sample data with known components, and it is difficult to obtain data; it is easy to fall into local optimal solution and the phenomenon of overfitting

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  • Mineral content spectrum inversion method based on deep neural network
  • Mineral content spectrum inversion method based on deep neural network
  • Mineral content spectrum inversion method based on deep neural network

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Experimental program
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Embodiment 1

[0060]A kind of mineral content spectral inversion method based on deep neural network of the present embodiment, comprises the following steps:

[0061] Step S1: Collect the mixed spectra of various minerals as samples of the deep neural network, and identify the mineral name and content corresponding to each spectrum.

[0062] Step S2: Set the first layer of deep neural network architecture, and calculate the input layer, hidden layer (ie, spectral feature layer) and output layer forward, such as figure 1 shown.

[0063] For the input layer: convert each sample spectrum into a column vector X as the input layer, the number of neurons in the input layer is the number of bands of the spectrum, +1 is the offset, and the neurons in the input layer are the reflection of each band Rate.

[0064] For the hidden layer: the number of neurons in the hidden layer is the number of sample mineral types, and the activation value of each neuron in the hidden layer is calculated by using ...

Embodiment 2

[0102] Four minerals including muscovite, calcite, dolomite and feldspar are selected for content inversion, and other minerals can refer to the steps of this embodiment.

[0103] (1) Calculate the single-scattering albedo of the laboratory spectrum of muscovite, calcite, dolomite, and feldspar, randomly set the contents of the four minerals, calculate the mixed single-scattering albedo, and calculate the mixed single-scattering albedo according to the mixed single-scattering albedo rate, and simulated 15,000 spectra of the mixture of four minerals. Among them, 10,000 samples are used as samples of the deep neural network, and the mineral name and content corresponding to each spectrum are identified, and 5,000 samples are used as test data.

[0104] (2) According to the number of samples and mineral types, a three-layer neural network is initialized, the input neurons are 420 bands, the output neurons (mineral types) are 4, the first hidden layer neurons are 100, and the seco...

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Abstract

The invention belongs to the technical field of geological exploration, and particularly relates to a mineral content spectral inversion method based on a deep neural network. According to the mechanism and inversion characteristics of the rock mineral spectrum, a neural network model capable of rapidly and accurately inverting the mineral content of the surface rock soil is established, and the mineral content of the surface rock soil can be determined, analyzed and evaluated by using hyperspectral data.

Description

technical field [0001] The invention belongs to the technical field of geological exploration, and in particular relates to a mineral content spectrum inversion method based on a deep neural network. Background technique [0002] The rapid development of hyperspectral technology has made it possible to use spectral information of tens to hundreds of "narrow" bands in succession for mineral type identification, content inversion, and chemical composition identification. The spectral resolution of currently available data sources (mainly aviation and ground hyperspectral equipment) can support the analysis and extraction of mineral information to a certain extent, and on this basis, hyperspectral thematic geoscience data products can be produced (including mineral distribution and abundance maps, hydrothermal activity fault structures, strata, and rock mass interpretation maps), these information, which were difficult to obtain in the past with multi-spectral remote sensing te...

Claims

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Application Information

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IPC IPC(8): G06N3/08G06N3/06G06N3/04G01N21/31
CPCG06N3/084G06N3/061G01N21/31G06N3/047
Inventor 秦凯赵英俊赵宁博杨越超
Owner BEIJING RES INST OF URANIUM GEOLOGY
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