End-to-end seismic signals denoising via deep residual convolution and self-attention mechanisms
Denoising of seismic waveform signals is crucial for seismic monitoring and seismological research. To this end, we propose an end-to-end deep learning method for denoising seismic waveforms.
Zhao Botao +9 more
doaj
Automated seismic fault mapping using convolutional neural networks for the Berenice Field, Western Desert, Egypt. [PDF]
Amer M, Mabrouk WM, Eid AM, Metwally A.
europepmc +1 more source
Fault detection in seismic data using a true 3D global attention convolutional network with self-supervised denoising pretext training. [PDF]
Mahzad M, Bagheri M.
europepmc +1 more source
Machine Learning and Deep Learning for Earthquake Monitoring: A Systematic Review of Distributed Acoustic Sensing Applications. [PDF]
Iqbal N +4 more
europepmc +1 more source
Imaging Earth's subsurface with thunderstorm-generated seismic waves. [PDF]
Roth N +5 more
europepmc +1 more source
Curvelet-Based Stochastic Noise Suppression for Downhole DAS Microseismic Data. [PDF]
Zhang Y +6 more
europepmc +1 more source
Kiruna region earthquakes, blasts and mining induced events
Data set of 189 earthquakes, 221 mining induced events from the Kiruna mine and 221 blasts from the Kiruna mine, recorded by seismic stations of the Swedish National Seismic Network (SNSN) and neighbouring countries. The earthquakes are mostly located to
Swedish National Seismic Network
core +1 more source
Efficient seismic data denoising via multi-scale attention network with depthwise separable and residual dilated convolutions. [PDF]
Yao Z, Hao L, Qin L, Chen J, Li W.
europepmc +1 more source
Directional adaptive mode total variation for seismic data denoising. [PDF]
Banjade TP +4 more
europepmc +1 more source
Model-Driven Processing of Passive Seismic While Drilling Data Acquired Using Distributed Acoustic Sensing Without Conventional Drill-Bit Pilot Measurements. [PDF]
Al-Hemyari E, Pevzner R, Tertyshnikov K.
europepmc +1 more source

