For more than half a century, geoscientists have sought new ways to solve inverse problems, which occur when observations only indirectly constrain some property of interest. In the case of geophysics this usually means using surface observations to quantify properties of the Earth hidden from us within its interior, or processes which occurred in the past. Both the target is not directly accessible, and measurements which constrain it are not completely under our control. This is a challenging situation, where the search for new efficient and practical methods of solution to inversion problems has received regular attention.

A convenient way to view inverse problems as a way of asking questions of data. A common class of question might be to ask `Which set of model parameters, within a chosent class, fits a subset of the data best?’  How one measures `best fit’ constitutes a fundamental component of the question being asked. Another example might be `Which probability distribution best describes a `state of knowledge’ about a set of representative parameters?’. As the question changes, naturally so does the solution, even if the data does not. This talk will examine this approach to inversion and explore some new forms of question that can be asked of data. In particular, cases will be examined where the same answer can arise from different style of questions, some of which are much easier to solve than others. A focus will be on optimal model generation in nonlinear cases using data questions based on mathematical ideas from the field of Optimal Transport.

How to cite: Sambridge, M.: Asking questions of data in nonlinear geophysical inversion, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13755, https://doi.org/10.5194/egusphere-egu21-13755, 2021.

Volcanic eruptions can affect the climate system, the environment and society. On ice covered volcanoes this threat intensifies due to the increasing explosivity in contact with water. Monitoring and early-warning of such eruptions is closely linked to real-time, multidisciplinary data analysis. This builds on a good understanding and location of the recorded signals.

I will summarize my work on understanding and modelling volcanic tremor, a long-lasting seismic signal with emergent onset. This tremor accompanies various volcano- and glacier-related processes and has to be reliably detected and distinguished from other sources. My examples range from modelling pre-eruptive subglacial tremor and silent magma flow, to monitoring eruptive tremor, to early warning of subglacial flooding, to hydrothermal explosions and boiling and other sources such as helicopters. These results are based on array analysis, amplitude location techniques and single-station arrays but I will also risk a look into the future embracing the emerging field of rotational seismology which might solve some challenges we face in volcanic and glacial environments and advance our understanding and modelling of volcanic signals at remote sites.

How to cite: Eibl, E. P. S.: Volcanic Tremor and its Relation to Volcano- and Glacier-related Processes, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5143, https://doi.org/10.5194/egusphere-egu21-5143, 2021.