In this letter, we proposed a graph convolution network for seismic event classification using raw waveform data from multiple stations. In our proposed model, we use convolution layers to extract …

Feb 6, 2021 · The mapping from the weighted feature map to the final feature map consists of three major operations, which are transposed convolution, unpooling, and interpolation. EUG-CAM finally …

Feb 22, 2021 · In this paper, we propose a novel conditional convolution network, named location-variable convolution, to model the dependencies of the waveform sequence. Different from the use of …

Jan 22, 2019 · Convolution is one of the more sophisticated processes regularly used in audio production. Its ability to accurately impart the characteristic timbres of spaces and objects on other …

Apr 30, 2022 · We propose an unknown radar waveform recognition system for identifying unknown radar waveforms and classifying known radar waveforms simultaneously, which can be summarized …

In practice, a relatively simple application of convolution is where we have the "impulse response" of a space. This is obtained by recording a short burst of a broad-band signal and recording the …

Before starting a difficult continuous convolution problem, there is another approach that you should consider. Ask yourself the question: Is a mathematical expression really needed for the output signal, …

The stronger the truncation, the heavier the ringing. Finally, we make the time-domain representation periodic, so that we get a discrete and periodic time-domain waveform as well as a periodic …

Sep 20, 2024 · As a result, multiscale seismic waveform inversion is a natural manner for retrieving the subsurface model from long- to short-scale components. In this paper, we introduce an algorithm that …

May 5, 2021 · Multiscale convolution is used to replace the core convolution in DRSN. By stacking MSRU, we present a multiscale residual deep neural network (MSRDN) for underwater acoustic …