no code implementations • NeurIPS Workshop AI4Scien 2021 • Artemii Novoselov, Krisztina Sinkovics, Goetz Bokelmann
This study applies Conditional Generative Adversarial Networks (cGAN) to the field of seismology.
no code implementations • 24 Nov 2020 • Artemii Novoselov, Peter Balazs, Götz Bokelmann
We show that separation is possible also for seismic recordings, using techniques from machine learning (and even those recorded with a single sensor).<br />This may have an impact on seismic applications such as <br />ambient noise tomography, induced seismicity, earthquake analysis, aftershock analysis, nuclear verification, and seismoacoustics/infrasound.<br />The machine learning technique that we use for seismic signal separation is based on a dual-path recurrent neural network which is applied directly to the time domain data.