2-2-2-2-2-2-2-2-2-2-2-2-2 From: Sam Poppe <sampoppe@xxxxxxxxxx> Dear colleagues, we would like to invite you to submit an abstract to our new session "GMPV8.2: Building the next generation of realistic models of magma propagation and volcano deformation" to be held at the EGU 2023 Assembly in April 2023 in Vienna. Please submit before 10th of January 2023 here: https://urldefense.com/v3/__https://egu23.eu/programme/how_to_submit.html__;!!IKRxdwAv5BmarQ!cvEcA_61ViIuO_mhbRNS8EGY_FFkuNSuZAgsu3xsMSX-ajpuXXo6R1tOCoTF9liVOwjZkRSd2md72knI$ <https://urldefense.com/v3/__https://egu23.eu/programme/how_to_submit.html__;!!IKRxdwAv5BmarQ!bu6my5SJ8fmog6jwB_nrhh7jdTebGolXTVqUrqvUPSWHFB7LEvURtK3BFB8nhjfBWZNEUffXkGHub18bq7_e$> We aim to organize a focused discussion on exciting paths forward in modelling of volcano deformation induced by magma propagation, among other processes, and how we can further introduce complexities that may have little understood effects on the accuracy of the models that serve as input for studying the long-term behavior of volcanic plumbing systems and short-term eruption forecasts. This session aligns with session "V12: Toward realistic modelling of volcano deformation" at the IUGG 23 assembly in Berlin, Germany in July 2023, for which abstract submission is open until 14 February 2023: https://urldefense.com/v3/__https://www.iugg2023berlin.org/abstract-submission/__;!!IKRxdwAv5BmarQ!cvEcA_61ViIuO_mhbRNS8EGY_FFkuNSuZAgsu3xsMSX-ajpuXXo6R1tOCoTF9liVOwjZkRSd2s9C0GLS$ <https://urldefense.com/v3/__https://www.iugg2023berlin.org/abstract-submission/__;!!IKRxdwAv5BmarQ!bu6my5SJ8fmog6jwB_nrhh7jdTebGolXTVqUrqvUPSWHFB7LEvURtK3BFB8nhjfBWZNEUffXkGHub5hHVvDZ$> We describe the session's motivation further in an EGU GMPV blog post: https://urldefense.com/v3/__https://blogs.egu.eu/divisions/gmpv/2022/11/30/gmpv82/__;!!IKRxdwAv5BmarQ!cvEcA_61ViIuO_mhbRNS8EGY_FFkuNSuZAgsu3xsMSX-ajpuXXo6R1tOCoTF9liVOwjZkRSd2jtRj9-q$ <https://urldefense.com/v3/__https://blogs.egu.eu/divisions/gmpv/2022/11/30/gmpv82/__;!!IKRxdwAv5BmarQ!bu6my5SJ8fmog6jwB_nrhh7jdTebGolXTVqUrqvUPSWHFB7LEvURtK3BFB8nhjfBWZNEUffXkGHubzInvaxB$> Please do spread this message further in your networks so we can secure an inspiring session slot in Vienna! Best regards, Sam Poppe, Claire Harnett and Tim Davis ***session description*** In this session we will review and discuss the latest magma propagation and ground deformation models. The data collected at active volcanoes is rapidly increasing in quality; there has been an explosion in high-resolution geodetic and seismic data that captures magma movement and storage conditions in the subsurface. It is becoming routine to fit ground deformation and seismic signals of such events using static models, typically with constant opening or piece-wise static deformation sources in homogeneous elastic half-spaces. Simple fitting of such models lacks predictive power about what will happen to the system next and provides little insight into the physics of the system. Mechanical modelling can answer how such intrusions develop through time, can help investigate the processes controlling where and when magma erupts and can quantify the influence of mechanical complexities and when these should be considered. Such models are typically theoretical, but due to rapid increases in the data quality of magmatic events we can begin to test the predictive power of these models. We welcome contributions across numerical and laboratory modelling, physical volcanology, planetary geology, geodesy and geophysics that focus on building and informing cutting art mechanical models of magma-induced deformation by: - simulating more realistic rheologies and mechanical heterogeneities from rock testing, geophysical measurements or geological field observation - exploring limitations of typical model assumptions by comparing and integrating field/experimental/numerical methods - developing new modelling applications that simulate previously unconstrained mechanics and dynamic propagation - developing dedicated 3D modelling approaches - using AI or machine learning to analyse model sensitivities in large data sets https://urldefense.com/v3/__https://meetingorganizer.copernicus.org/EGU23/session/45195__;!!IKRxdwAv5BmarQ!cvEcA_61ViIuO_mhbRNS8EGY_FFkuNSuZAgsu3xsMSX-ajpuXXo6R1tOCoTF9liVOwjZkRSd2rRxojyO$ <https://urldefense.com/v3/__https://meetingorganizer.copernicus.org/egu23/sessionprogramme*__;Iw!!IKRxdwAv5BmarQ!bu6my5SJ8fmog6jwB_nrhh7jdTebGolXTVqUrqvUPSWHFB7LEvURtK3BFB8nhjfBWZNEUffXkGHub0PciaAl$> 2-2-2-2-2-2-2-2-2-2-2-2-2 ------------------------------
