DOI: 10.5281/zenodo.8111557
title: Seismic dataset for Ruapehu and Whakaari volcanoes in New Zealand

description: RSAM, MF, HF and DSAR time series for Ruapehu stations FWVZ over the 14 years explored, and for Whakaari stations WIZ over 9 years. Computing datastreams: we harnessed seismic data from a vertical component station for each individual volcano. We applied data processing techniques that resulted in the generation of four distinct time series, with a sampling interval of 10 minutes. Various measures were employed to capture different aspects of the seismic signal. The first measure, known as the Real-time Seismic Amplitude Measurement (RSAM), was obtained by calculating the 10-minute moving average of the velocity recorded by the vertical station This signal was then subjected to bandpass filtering within the frequency range of 2 to 5 Hz, which focuses on tremor signal of frequent volcanic origin while excluding ocean noise at lower frequencies. Similarly, the Median Frequency (MF) and High Frequency (HF) measures were derived using a comparable approach to RSAM, but with specific bandpass filtering applied. MF was obtained by filtering the signal within the frequency range of 4.5 to 8 Hz, while HF was obtained by filtering within the frequency range of 8 to 16 Hz. The 4.5 Hz threshold between RSAM and MF reflects an assumption that tremor mostly radiates energy below 4.5 Hz. To exclude this effect and explore attenuation related to permeability change (such as sealing), this frequency value is used as a threshold. Lastly, the Displacement Seismic Amplitude Ratio (DSAR) was calculated as the ratio of the integrals of the MF and HF signals. High values of DSAR have been inferred to correlate with high gas levels in the edifice, suggesting either reduced fluid motion and/or trapping that has led to a gas-accumulation.
description: Quick recipes The steps below describe calculation of precursors discussed in this study. The first step is to calculate the data stream. There are several sub-steps: (1) After removing the instrument response to the seismic signals, apply a bandpass filters to each 24 hours of data, between 2-4.5, 4-8 and 8-16 Hz (corresponding to the RSAM, MF and HF bands). (2) Compute the absolute values of each signal. (3) Subdivide the signals into 10 minutes intervals. For each interval, compute the average value as the RSAM, MF and HF datapoints assigned to that interval. (4) (optional) Removing outliers associated with regional earthquakes is optional. We procced as follow: from (2), subdivide the signals into 10 minutes intervals. Calculated the mean and standard deviation (mu and sigma) for each interval. Apply z-score normalization in log-space to the interval using mu and sigma. Check if any value in the interval exceeds a threshold of 3.2 standard deviations above the mean. If yes, exclude data points from a 150s mask starting 15s before the outlier located. Calculate the average value in the interval excluding points inside the mask: this the RSAM, MF and HF value for the interval. To calculate the DSAR, procced as follow: (1) Integrate the bandpass filtered MF and HF data with time. (2) Take the absolute value and compute averages on 10-minute intervals. (3) Compute the ratio between integrated MF and HF.