Green's function-spwvd time-frequency analysis method for enpemf data

Abstract

The present invention provides a Green's function-SPWVD time-frequency analysis method of ENPEMF data. First, smooth compression preprocessing is performed on the original ENPEMF data, and then the Green's function based on the Poisson equation is used for processing to obtain Green's functionalized ENPEMF data. The smooth pseudo-WVD algorithm is used for frequency sweep processing, and finally time-frequency analysis and summary are performed to obtain the time-frequency joint distribution diagram of the original data. This invention makes full use of the superposition idea of ​​Green's function, has better noise reduction and cross-terms generated during WVD frequency sweep, and has a novel structure and simple steps. It can improve the processing efficiency when analyzing a large number of complex earth's natural pulsed electromagnetic field data. It has good application prospects.

Description

technical field [0001] The invention relates to a Green's function-SPWVD time-frequency analysis method of ENPEMF data, which belongs to the field of time-frequency analysis and processing of non-stationary data. Background technique [0002] Earthquakes have brought huge disasters to human life. According to statistics, among the natural disasters in the world, the death toll caused by earthquakes accounts for 54% of all natural disasters, which is the highest among natural disasters. How to predict earthquakes has always been a hot and sensitive topic. However, due to the three major difficulties in earthquake prediction, the "impenetrability" of the earth's interior, the "infrequent occurrence" of major earthquakes, and the "complexity of the physical process of earthquakes", earthquake prediction has become a recognized worldwide problem. Prediction is of great significance to the safety of human life and social property. [0003] In the existing research process of se...

AI Technical Summary

Problems solved by technology

The method shown in formula (a) has poor flexibility, and the input signal amplitude or energy directly affects the selection of c value. At present, no one can propose an effective adaptive threshold selection method, and the useful component amplitude in the actual signal The value and energy are often different or even quite different, so it is easy to eliminate the information item by setting the threshold to eliminate the cross item; the method shown in formula (b) is slightly improved, but there is no theoretical basis for the determination of the power adjustment coefficient. How to determine the power adjustment coefficient according to the characteristics of the signal to be analyzed needs to be further studied, which will cause inconvenience to use, and at the same time, there will be unavoidable cross-item interference. It is often necessary to further process the obtained signal by filtering and other methods in order to obtain a time that is easy to interpret. Spectrograms and spectrograms

Images