Abstract Machine learning algorithms have been applied widely in the fields of natural
science, social science and engineering. It can be expected that machine learning …

Regardless of successful applications of the convolutional neural networks (CNNs) in
different fields, its application to seismic waveform classification and first-break (FB) picking …

We proposed a novel wavelet threshold denoising method based on the discrete wavelet
transform for noisy microseismic recordings. This algorithm can simultaneously suppress …

Noise attenuation is a crucial phase in seismic signal processing. Enhancing the signal-to-
noise ratio (SNR) of registered seismic signals improves subsequent processing and …

There has been a recent rise in the uses and applications of passive seismic data, such as
tomographic imaging, volcanic monitoring, and hydrocarbon exploration. Consequently, the …

There are high-and low-frequency noise signals in a microseismic signal that can lead to the
distortion and submersion of an effective waveform. At present, effectively removing high …

To extract partial discharge (PD) signals from white noise efficiently, this paper proposes a
denoising method for PD signals, named adaptive short-time singular value decomposition …

随着常规油气藏资源不断枯竭, 非常规油气藏的勘探开发已逐渐成为一种必然趋势,
从而使得微地震监测技术快速发展. 微地震事件的发生持续时间短, 声波频率高 …

Reliable analysis of low-energy earthquakes (microseismic) depends on how accurately
one can detect and pick the arrival times, which are strongly influenced by the noise content …

Reliable detection and recovery of a microseismic event in large volume of passive
monitoring data is usually a challenging task due to the low signal-to-noise ratio …