English version of this page

Disputas: Volodya Valeriev Hlebnikov

Ph.d.-kandidat Volodya Valeriev Hlebnikov ved Institutt for geofag, Det matematisk-naturvitenskapelige fakultet, vil forsvare avhandlingen Deep learning as a tool for seismic data interpolation for graden Philosophiae Doctor.

Volodya Valeriev Hlebnikov. Foto: Privat

Volodya Valeriev Hlebnikov. Foto: Privat

Disputas og prøveforelesning vil bli holdt i Auditorium 1, Geologibygningen. I noen tilfeller vil det være mulig å delta på prøveforelesningen og disputas digitalt, i så fall blir det lagt ut en lenke til Zoom.

Prøveforelesning

Onsdag 25 mai, 10:15, Aud 1, Geologibygningen:

Recording elastic wavefields via multicomponent measurements, and their use in seismic processing

Kreeringssammendrag

Seismiske data brukes til å avbilde undergrunnen, men kan ikke brukes direkte. Disse dataene er aldri støyfrie og registreres ikke uniformt. Denne doktorgradsavhandlingen består av to hovedkomponenter. De første komponentene tar for seg støyproblemet i seismiske data fra marin tauing. Den andre komponenten prøver å løse problemet med manglende og uregelmessig samplede seismiske data ved hjelp av dyp læring.

Hovedfunn

Populærvitenskapelig artikkel om Hlebnikovs avhandling:

Deep learning as a tool for seismic data interpolation

During seismic acquisition, reflected waves from the subsurface are recorded by sensors in seismic cables in the water column. The measured data are used to create images of the subsurface to explore natural resources. Achieving good-quality images requires separating unwanted energy and interpolating missing data, which are essential in seismic data processing. Historically, a tailored physics-based workflow consisting of multiple procedures would be implemented. This is usually costly in terms of manpower and use of computing resources.

The doctoral thesis comprises two main components. The first component addresses the problem of noise in marine towed seismic data, where a detailed description of a wide range of marine noise types in towed streamer data is given. The work also includes some widely adopted techniques for attenuating noise and specific tricks applied in industrial de-noising. The second component tries to solve the problem of missing and irregularly sampled seismic data using deep learning. This work led to the development of deep learning approaches for interpolation of regularly and/or irregularly sampled data. The latest iteration of our deep learning algorithms now delivers better results compared to the state-of-the-art available conventional physics-based algorithms. Also, new insights to further develop the processing quality of the data-driven deep neural networks have been proposed.

 

Foto og annen informasjon:

Pressefoto: Volodya Valeriev Hlebnikov, portrett; 800px. Foto: Privat

Publisert 13. mai 2022 10:18 - Sist endret 24. mai 2022 14:59