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

Aiming at the situation that complementary ensemble empirical mode decomposition
(CEEMD) noise suppression method may produce redundant noise and wavelet transform …

The automatic P-wave onset time (P-onset) picking of microseismic (MS) waveforms
generated during rock failure is the basis of and key to locating the source and exploring the …

Current development of unconventional resources (such as shale gas, shale oil, and tight
sands) requires hydraulic fracturing, which involves injecting fluid at high pressure into the …

Microseismic monitoring is one of the most critical technologies for hydraulic fracturing in oil
and gas production. To detect events in an accurate and efficient way, there are two major …

Previous deep learning-based event denoising methods mostly suffer from poor
interpretability and difficulty in real-time processing due to their complex architecture …

During the mining activities, coal–rock dynamic disasters have caused grievous casualties
and massive property losses. It is the severe problem for regional monitoring of abnormal …

Weak signal preservation is critical in the application of seismic data denoising, especially in
deep seismic exploration. It is hard to separate those weak signals in seismic data from …

Waveform stacking is frequently employed in the analysis of microseismic data due to its
adaptability, effectiveness, and noise immunity. However, the low signal-to-noise ratio and …

Accurate picking of first‐arrival times is important in many seismic studies, particularly in
seismic tomography and reservoirs or aquifers monitoring. Many techniques have been …