The Italian EAGE-SEG Sectioninterviewed Massimo Fervari about the current applications and future perspectives onSeismic Inversion techniques, a technical topic with enormous implicationson the E&P activities of Energy Companies.Seismic Inversion has several applications and Massimo’s brain is certainly among the most passionate ones to pick.
La Sezione Italiana EAGE-SEG ha offerto anche quest’anno la registrazione gratuita ed un rimborso spese forfettario pari a 300 euro a studenti di Università Italiane, per poter partecipare alla EAGE Conference & Exhibition, che si svolgerà a Amsterdam dall’ 8 all’ 11 dicembre 2020.
Dopo l’esame delle domande ricevute, è stata selezionata la studentessa Alessandra Lanzoni, dell’ Università di Trieste.
I’m Luigi Bianco, student at the University of Naples and member of the EAGE-SEG Italian Section. I’m very proud to announce to our community that my team finished at the 2nd place at the European Region Imperial Barrel Award. The Imperial Barrel Award Program (IBA)...
EAGE E-Lecture: Virtual Seismology: monitoring the subsurface with virtual sources and recivers by Kees Wapenaar
This E-Lecture introduces virtual seismology: monitoring the subsurface with virtual sources and receivers, which is a new methodology to create virtual sources and virtual receivers in the subsurface from reflection measurements at the earth’s surface. Unlike in seismic interferometry, no physical instrument (receiver or source) is needed at the position of the virtual source or receiver. Moreover, no detailed knowledge of the subsurface parameters and structures is required: a smooth velocity model suffices. Yet, the responses to the virtual sources, observed by the virtual receivers, fully account for multiple scattering. This method is not only useful for reflection imaging but is has also large potential for monitoring induced seismicity, characterizing the source properties and forecasting the response to potential future induced earthquakes. This will be demonstrated with numerical models and preliminary real-data results.
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