Electromagnetic signal extraction and processing method based on deep convolutional neural network

Abstract

The invention discloses an electromagnetic signal extraction and processing method based on a deep convolutional neural network. The electromagnetic signal extraction and processing method based on the deep convolutional neural network comprises the steps of performing primary denoising on original data through wavelet filtering, performing measurement point data leveling before channel extraction, performing measurement point superposition and channel extraction processing, designing a structure of a deep convolutional neural network (DCNN) model, performing calculation and identification by using the model and the like. According to the method, a noise and signal feature extraction mechanism is established, a signal-noise classification and recognition model is established through continuous iterative learning of a large amount of measured data, the signal and noise separation degree of underground large-depth, full-coverage and blind-area-free detection data is effectively improved, and data information used for inversion calculation and interpretation analysis is obtained to the maximum extent.

Description

technical field [0001] The invention relates to the field of earth exploration and information technology, in particular to an electromagnetic signal extraction and processing method based on a deep convolutional neural network. Background technique [0002] Aeronautical electromagnetic detection technology transmits a pulsed electromagnetic field underground through the launch loop mounted on the flight platform. Under the excitation of the electromagnetic field, an eddy current is generated inside the earth. Under the action of the Ohm effect, the eddy current inside the earth is attenuated, thereby exciting The new electromagnetic field, by observing the new electromagnetic field, extracting and analyzing the geoelectric information contained in it, can achieve the purpose of detecting underground geological structures. It is fast, efficient, economical, and adaptable, and can enter forests, The advantages of exploration in deserts, swamps, lakes, plateaus and other areas...

AI Technical Summary

Problems solved by technology

Traditional signal processing methods, such as wavelet filtering, median filtering, etc., can effectively distinguish the sudden change and noise in the signal, so as to achieve the purpose of denoising the signal, but the processed data still has some whiteness in the late measurement track. Noise interference, as the noise level increases, the denoising effect gradually becomes worse

The signal extraction technology based on statistical analysis and machine learning methods can identify the characteristics of the data itself without considering the source of the specific noise type, so as to better remove the noise data. not deep enough

[0008] At present, the continuous development of various inversion calculation and interpretation technologies for airborne detection instruments has made the theory of time-domain airborne electromagnetic method more mature, but the current research on airborne electromagnetic data preprocessing is relatively weak, which cannot meet the needs of exploration work , which will directly affect the development of airborne electromagnetic detection technology

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