Magnetotelluric inversion method based on full convolutional neural network

A convolutional neural network and convolutional neural technology, applied in the field of magnetotelluric inversion based on full convolutional neural network, can solve the problems of loss of position information, overfitting, slow network convergence, etc., and achieve high fitting accuracy , fast convergence speed, and the effect of reducing the loss of computing memory and time

Active Publication Date: 2021-07-23
INST OF GEOLOGY & GEOPHYSICS CHINESE ACAD OF SCI
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  • Abstract
  • Description
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  • Application Information

AI Technical Summary

Problems solved by technology

Although this kind of nonlinear global optimization inversion method can overcome the local extremum problem and obtain the global optimal solution, it requires a large amount of computing memory and a long computing time
In addition, the network convergence speed used by the artificial neural network m

Method used

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  • Magnetotelluric inversion method based on full convolutional neural network
  • Magnetotelluric inversion method based on full convolutional neural network
  • Magnetotelluric inversion method based on full convolutional neural network

Examples

Experimental program
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Example Embodiment

[0076] Example 1: Two-dimensional abtenometry

[0077] Establish high, low-resistance constant model, and use a rectangular mesh to pass the cross-line interpolation finite unit method to calculate the input data of the simulation test model.

[0078] image 3 It is a test model sample and a grid score for the embodiment of the present invention. image 3 Distance image 3 (A) represents low-resistance abnormalities, (b) represents high resistance; image 3 Medium abandoned area ( image 3 The peripheral area of ​​(a), image 3 The middle rectangular area of ​​(b)) represents a high resistance, and the resistivity range is between 1000-1500 Ω · m, the light region ( image 3 The middle rectangular area of ​​(a), image 3 The peripheral area of ​​(b) represents a low-resistance, and the resistivity range is from 100 to 600 Ω · m. image 3 The middle rectangular area of ​​(a) and (b) indicates an abnormally, and other areas are uniform surrounding rocks. The lateral and longitudinal dimension...

Example Embodiment

[0086] Example 2: Measured data inversion

[0087] This example selects a line in the electromagnetic field detection application test for inversion testing, which is 12km long, measurement point distance of 500m, and removes partial interference measures, a total of 20 test points. The MT measurement in the work area uses the self-developing equipment IEM-I electromagnetic method, the receiver is a DRU-1C type, and the magnetic sensor is an IMC-03 type. The system operating frequency range is from 0.0001 to 10 kHz, and the acquisition time is designed according to the minimum frequency of the work area, which is more than 8 hours. Intercepting the observation data of the 320-0.088Hz band, the frequency is distributed, with a total of 48 frequency points.

[0088] Figure 7 It is a schematic view of the measured inversion model of the second embodiment of the present invention. Figure 7 As shown, according to known geological materials, Figure 7 The sample set shown, using a recta...

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Abstract

The invention discloses a magnetotelluric inversion method based on a full convolutional neural network, and the method comprises the steps: constructing a multi-dimensional geoelectric model, carrying out the forward calculation of the apparent resistivity of a corresponding dimension, forming a sample set, and dividing the sample set into a plurality of training sets and test sets; constructing a full convolutional neural network structure model to obtain an initialized full convolutional neural network model parameter; training and testing the model based on the training set and the test set to obtain optimized full convolutional neural network structure model parameters; determining whether the training of the full convolutional neural network structure model is completed or not according to the fitting error change corresponding to the training set and the test set; finally, inputting the actually measured apparent resistivity into the trained model for inversion, and further optimizing the model by analyzing the precision of an inversion result until an inversion fitting error meets a set error requirement. By using the nonlinear features of the full convolutional neural network, the problem of local extremum in conventional linear inversion is solved, the operation memory and time loss are effectively reduced, and the fitting precision is improved.

Description

technical field [0001] The invention relates to the technical field of magnetotelluric sounding, in particular to a magnetotelluric inversion method based on a fully convolutional neural network. Background technique [0002] The magnetotelluric method (Magnetotelluric Method, MT) is a geophysical exploration method that uses natural alternating electromagnetic fields to study the earth's electrical structure. -4 -10 4 Hz), using the skin effect principle of electromagnetic wave propagation, that is, the high-frequency electromagnetic field penetrates shallowly, and the low-frequency electromagnetic field penetrates deeply. Under the condition that the distance between the field source and the receiving point remains unchanged, the frequency of the electromagnetic field is changed to achieve the purpose of sounding, namely The mutually orthogonal electromagnetic field components are collected on the surface, and the vertical electrical structure information of the undergrou...

Claims

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Application Information

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IPC IPC(8): G06F30/27G06F30/23G06N3/04
CPCG06F30/27G06F30/23G06N3/045
Inventor 王中兴康利利安志国王若尹雄
Owner INST OF GEOLOGY & GEOPHYSICS CHINESE ACAD OF SCI
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