We develop a deep learning framework based on deep image prior (DIP) and attention
networks for 3-D seismic data enhancement. First, the 3-D noisy data are divided into …

CO2 geological storage, as a large-scale, low-cost, carbon reduction technology, has
garnered widespread attention due to its safety. Monitoring potential leaks is critical to …

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 …

We propose to use unsupervised deep learning (DL) and attention networks to mute the
unwanted components of the single-channel earthquake data. The proposed algorithm is an …

Cable-stayed bridges are widely used all around the world. Unfortunately, during their
service life, they are exposed to adverse conditions that may cause their deterioration and …

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 …

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 event detection and location are two primary components in microseismic
monitoring, which offers us invaluable insights into the subsurface during reservoir …