Simulation of water-induced seismic waveforms in glaciers through hydrodynamic modelling

This work aims to contribute to progress in the detection of hidden or transient hydrological events. Passive seismic methods offer high temporal resolution and the ability to monitor seismic sources hidden from direct view, making it an ideal candidate to complement other in-situ and satellite methods in these cases. The dynamics of a glacier can be greatly affected by its hydrological system, whether this be through water mediated ice fracturing, or the influence the water has on friction at the ice-bed interface. Effective detection of moving meltwater is therefore of great interest for anticipating future glacier changes and sensitivities.

To effectively infer any hidden process from the observed seismic waveforms, we require a physically rigorous modelling framework. Our work therefore combines hydrodynamic models depicting meltwater flow with seismic wave propagation methods to produce synthetic seismograms. The hydrodynamic model of choice is smoothed particle hydrodynamics (SPH). This is a full, three-dimensional computational fluid dynamics method, meaning we can make minimal assumptions on the exact seismogenic mechanism prior to simulation. SPH has the capacity to capture a broad range of signal-generating processes that may prove to be of interest for modelling meltwater flow, such as fluid-solid impact events, free-surface behaviour (e.g., wave breaks), and some forms of turbulence. Beyond the modelling of complex flow, SPH also allows a simple implementation of arbitrarily shaped solid boundaries and the computation of force of the water on these boundaries; a necessary output for waveform simulation.

We propose a correspondence between different types of meltwater flow and the attributes of the waveforms they produce, as a step towards better detection and characterisation of hidden and short-lived events. Across a diverse set of model geometries and flow types, we anticipate the collection of synthetically generated signals will be useful for categorising archived and real-time signals according to a mechanistic process using unsupervised machine learning methods in ongoing work.