Chinese Academy of Sciences

Zhiguo An

Ruo Wang

Qingyun Di

Zhongxing Wang

Changmin Fu

Da Lei

Guoqiang Xue

Xiaotian Wang

Lili Kang

Xiong Yin

Miaoyue Wang

Jieyu Tan

Xiaolei Tu

Xianjun Zhuo

Disclosed is a magnetotelluric inversion method based on a fully convolutional neural network. The magnetotelluric inversion method includes: constructing a multi-dimensional geoelectric model; constructing a fully convolutional neural network structure model to obtain initialized fully convolutional neural network model parameters; training and testing the fully convolutional neural network structure model based on the training sets and the test sets to obtain optimized fully convolutional neural network structure model parameters; determining whether training of the fully convolutional neural network structure model is completed according to fitting error changes corresponding to the training sets and the test sets; and finally, inputting measured apparent resistivity into a trained fully convolutional neural network structure model for inversion, and further optimizing the fully convolutional neural network structure model by analyzing precision of an inversion result until an inversion fitting error satisfies a set error requirement.

Magnetotelluric inversion method based on fully convolutional neural network

An electromagnetic exploration method using a full-coverage anti-interference artificial source, comprising the steps of: (1) determining the scope and location of the measuring area; (2) field-exploring to determine the location of the transmitting source and the angle of the transmitting antenna; (3) calculating the maximum polarization direction angle of the electric field generated by the antenna at each measuring point; (4) arranging electric field sensors according to the polarization directions; (5) calculating the apparent resistivity of each measuring point. The method of the present disclosure obtains the earth resistivity using the reliable data with high signal-to-noise ratio. The field construction is flexible and convenient, the construction efficiency is high and the cost is low. The present disclosure provides a new development direction for the electromagnetic exploration.

Electromagnetic exploration method using full-coverage anti-interference artificial source

A WEM-based method for deep resource detection using sky waves refers to the technical field of deep resource detection. The proposed method of deep detection using sky waves improves the traditional 'atmospheric-lithosphere' half-space propagation theory into a full-space 'sky wave' theory of &#x201c;ionosphere-atmosphere-rock layer&#x201d;, that is, the influence of the ionosphere and the displacement current in the air are taken into consideration to obtain a new precise expression of &#x201c;sky wave&#x201d; response, which is suitable for full space, slow attenuation and long distance propagation. A receiving device for sky wave signal has been developed. Through theoretical model calculation and actual data measurement, it is known that it is possible to use the sky wave for detection within the scope of China's national territory to realize the high-precision electrical structural exploration within a depth of 10 kilometers and open a new era of artificial source electromagnetic detection.

WEM-based method for deep resource detection using sky waves

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Beijing, China

Zhiguo An