output
20142026
most citedDeep learning-based shot-domain seismic deblending

15 citations

6 papers

astro-ph.GA2026★ 2 cited

A Quality Framework for Testing Gravity with Wide Binaries: No Evidence for MOND

Stephen A. Cookson, Indranil Banik, Kareem El-Badry +4

Wide binaries (WBs) offer a unique opportunity to test gravity in the low-acceleration regime, where modifications such as Milgromian dynamics (MOND) predict measurable deviations…

physics.geo-ph2024★ 15 cited

Deep learning-based shot-domain seismic deblending

Jing Sun, Song Hou, Vetle Vinje +2

To streamline fast-track processing of large data volumes, we have developed a deep learning approach to deblend seismic data in the shot domain based on a practical strategy for g…

physics.geo-ph2024★ 1 cited

DNN-based workflow for attenuating seismic interference noise and its application to marine towed streamer data from the Northern Viking Graben

Jing Sun, Song Hou, Alaa Triki

To separate seismic interference (SI) noise while ensuring high signal fidelity, we propose a deep neural network (DNN)-based workflow applied to common shot gathers (CSGs). In our…

astro-ph.GA2024★ 4 cited

Influence of selection criteria on the interpretation of rotational behaviour of wide-binary star systems

Stephen Cookson

Binary star systems are expected to follow Newtonian dynamics similarly to planetary systems. However, reports have been made of wide binary systems with separations around 0.01 pc…

math.DS2014

Spectral estimates for Ruelle transfer operators with two parameters and applications

Vesselin Petkov, Luchezar Stoyanov

For weak mixing Axiom A flow on a Riemannian manifold and a basic set for we consider the Ruelle transfer operator …

math.GR2014

Elements in finite classical groups whose powers have large 1-Eigenspaces

Alice C. Niemeyer, Cheryl E. Praeger

We estimate the proportion of several classes of elements in finite classical groups which are readily recognised algorithmically, and for which some power has a large fixed point…