Microseismic Time Delay Estimation Method Based on Continuous Wavelet

Abstract

The microseismic signal is easily affected by observation noise and the inaccurate estimation of traditional methods will seriously reduce the location accuracy of the microseismic event. Therefore, based on the continuous wavelet spectrum and the similarity coefficient, a fast and efficient microseismic time delay estimation method is proposed. Firstly, the original signals are denoised by continuous wavelet transform. Subsequently, the time-frequency transform of the original signal by continuous wavelet transform, time-frequency signal extraction is the process of band-pass filtering, which can further reduce the influence of noise interference on the time delay estimation. Finally, we calculated the similarity between the time-frequency signals via the time domain and frequency domain integration. The similarity function is based on correlation and proposed according to the time-frequency transformation provided by the phase spectrum to evaluate the similarity between two noisy signals. The time delay estimation is determined by searching for the similarity function peak. The experimental results show the precision and accuracy of the method over the cross-correlation method and generalized cross-correlation phase transformation method, especially when the signal-to-noise ratio is low. Therefore, a new time delay estimation method for non-stationary random signals is presented in this paper.

Reliable Denoising Strategy to Enhance the Accuracy of Arrival Time Picking of Noisy Microseismic Recordings

“…Similarly, wavelet transforms also need to set different parameters according to the different characteristics of microseismic data, such as wavelet basis, wavelet decomposition level, etc. [28,29]. Traditional machine learning methods depend strongly on some hand-engineered features of the sample dataset, requiring training predictive models through analyzing these crucial features [30,31].…”

Optimization and Quality Assessment of Arrival Time Picking for Downhole Microseismic Events

“…Estimating time delays among received signals is fundamental for multi-level algorithms. Several techniques are used to estimate the time delay, such as the cross-correlation method [23][24][25][26] and phase-only correlation method [27].…”