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This paper introduces the Instructed Glacier Model (IGM) – a model that simulates ice dynamics, mass balance and its coupling to predict the evolution of glaciers, icefields or ice sheets. The novelty of IGM is that it models the ice flow by a Convolutional Neural Network, which is trained from data generated with hybrid SIA + SSA or Stokes ice flow models. By doing so, the most computationally demanding model component is substituted by a cheap emulator. Once trained with representative data, we demonstrate that IGM permits to model mountain glaciers up to 1000 × faster than Stokes ones on Central Processing Units (CPU) with fidelity levels above 90% in terms of ice flow solutions leading to nearly identical transient thickness evolution. Switching to the GPU often permits additional significant speed-ups, especially when emulating Stokes dynamics or/and modelling at high spatial resolution. IGM is an open-source Python code which deals with two-dimensional (2-D) gridded input and output data. Together with a companion library of trained ice flow emulators, IGM permits user-friendly, highly efficient and mechanically state-of-the-art glacier and icefields simulations.

Glacier seismology is a valuable tool for investigating ice flow dynamics, but sufficient data acquisition in remote and exposed glaciated terrain remains challenging. For data acquisition on a highly crevassed and remote outlet glacier in Greenland we developed self-sufficient and easily deployable seismic stations, "SG-boxes". The SG-boxes contain their own power supply via solar panel, a three-component omni-directional geophone and a GNSS receiver. The SG-boxes can be deployed and retrieved from a hovering helicopter, allowing for deployment in difficult terrain. To assess their performance we conducted a field test comparing the SG-boxes to established on-ice geophone installations at Gornergletscher in Switzerland. Moreover, data from a first SG-box deployment in Greenland were analyzed. The SG-boxes exhibit consistently higher noise levels relative to colocated conventional geophones and a correlation between noise levels, wind and air temperature is found. Despite their noise susceptibility, the SG-boxes detected a total of 13,114 Gornergletscher icequakes over 10 days, which is 30% of the total number of icequakes detected by conventional geophone stations. Hence, even in sub-optimal weather conditions and without additional noise reduction measures, the SG-boxes can provide unique and valuable data from challenging glaciated terrain where no conventional seismic installations are possible.