Noncontact Measurement and Detection of Instantaneous Seismic Attributes Based on Complementary Ensemble Empirical Mode Decomposition

Yaping Huang,Zhixiong Li,Xuemei Qi,Haibin Di,Reza Malekian

doi:10.3390/en10101655

Abstract

Hilbert–Huang transform (HHT) is a popular method to analyze nonlinear and non-stationary data. It has been widely used in geophysical prospecting. This paper analyzes the mode mixing problems of empirical mode decomposition (EMD) and introduces the noncontact measurement and detection of instantaneous seismic attributes using complementary ensemble empirical mode decomposition (CEEMD). Numerical simulation testing indicates that the CEEMD can effectively solve the mode mixing problems of EMD and can provide stronger anti-noise ability. The decomposed results of the synthetic seismic record show that CEEMD has a better ability to decompose seismic signals. Then, CEEMD is applied to extract instantaneous seismic attributes of 3D seismic data in a real-world coal mine in Inner Mongolia, China. The detection results demonstrate that instantaneous seismic attributes extracted by CEEMD are helpful to effectively identify the undulations of the top interfaces of limestone.

Highlights

Spectral decomposition technology can improve the level of seismic data processing and interpretation, which is helpful to describe reservoir shape and change rules [1]
This paper introduces a new method using complementary ensemble empirical mode decomposition (CEEMD) for noncontact measurement and the This paper introduces a new method using CEEMD for noncontact measurement and the detection of instantaneous seismic attributes
The detection results using real-world seismic data show that the present CEEMD method effectively solves the problem of mode mixing and has better show that the present CEEMD method effectively solves the problem of mode mixing and has better anti-noise ability for instantaneous seismic attributes detection

Summary

Introduction

Spectral decomposition technology can improve the level of seismic data processing and interpretation, which is helpful to describe reservoir shape and change rules [1]. Spectrum decomposition mainly has a short-time Fourier transform, wavelet transform, matching pursuit algorithm and Hilbert–Huang transform (HHT) [2,3]. Short-time Fourier transforms effectively improves the time resolution, and reduces the frequency resolution. Wavelet transform can provide high frequency resolution in low frequency bands and high time resolution in high frequency bands, but there is a problem in the selection of wavelet function. The matching pursuit algorithm provides better temporal resolution and frequency resolution, but the computational complexity is huge. Hilbert–Huang transform (HHT) provides a popular data-driven method to analyze nonlinear

Methods

Results

Conclusion