Maximum entropy spectral analysis of volcanic tremor using data from Etna (Sicily) and Merapi (central Java)

Abstract

The maximum entropy spectral analysis (MESA) method is applied to synthetic and observed tremor time series using autoregressive processes and recordings from the volcanoes Etna (Sicily) and Merapi (central Java). The MESA analysis can be used to estimate power spectra with sharp peaks from short data records. If the tremor source process can be modelled by an autoregressive process, the MESA method is well-suited for determining the coefficients of the underlying difference equations. As in the standard periodogram method of power spectrum estimation, a mesagram estimate using record segmentation and MESA spectrum averaging reduces the variance of the spectral estimator. In combination with periodogram estimates, mesagram estimates confirm that the tremor source may be modelled as an ensemble of randomly excited resonators. Used together, these estimates provide a valuable method for short-term monitoring of volcanic activity. In addition, they can be applied to the determination of new source parameters such as resonator frequencies, damping coefficients, excitation probabilities, correlation of exciting forces, and resonator coupling and in the pattern recognition of source types.