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 …

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 …

The additive noise in the condition monitoring system using fiber Bragg grating (FBG)
sensors, including white Gaussian noise and multifrequency interference, has a significantly …

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 …

Seismograms are a vital source of information in seismic signal processing. These records
are contaminated by noise, which should be reduced before processing in seismic …

Rockbursts frequently occur in tunneling projects and pose a serious threat to workers and
the environment. Therefore, accurate prediction of rockbursts is of great practical …

Microseismic signals are characterized by a low signal-to-noise ratio and a high degree of
non-stationary noise. Therefore, attenuation of noise in the microseismic signal is a very …

Microseismic signal denoising is of great significance for P wave, S wave first arrival picking,
source localization, and focal mechanism inversion. Therefore, an Empirical Mode …