Thomas Anthony Catanach

Higher-frequency waveform simulation and processing could improve seismic monitoring of low-magnitude events at local to regional distances, but it is unclear when the investment is worthwhile. Earth model uncertainty (EU) and background noise create information-theoretic limits on how informative a waveform can be. We introduce a Bayesian experimental design framework to rigorously predict the benefit of incorporating waveform data or extracted waveform features into seismic monitoring for source-parameter inference. Using a synthetic, model-based study, we leverage this framework to answer questions about the value of high frequencies for constraining event location and source parameters. We identify the minimum frequency requirements for inference and a maximum frequency at which there are diminishing returns under different EU and background noise assumptions. Ultimately, this informs how we should invest research and development efforts across Earth model refinement, higher frequency computational simulation, and reducing background noise.

Climate models play a pivotal role in enhancing our understanding and prediction of climatological phenomena. Parameter calibration is crucial to obtaining realistic model simulations and reducing model uncertainty. We compare traditional and non-traditional methods that are particularly promising for calibrating climate models, and we compare their effectiveness for parameter calibration for a simple climate model (the Lorenz ’96 multiscale system).