3:30 PM - 3:45 PM
[SVC38-24] A machine-learning-based estimation of large amplitude regions for monitoring volcano seismicity
Keywords:Volcano seismology, Machine learning, Mt. Ontake
In this study, we developed a method to investigate a spatiotemporal distribution of large amplitude regions to easily monitor the activities of earthquakes and tremors. The amplitudes could be a measure to monitor the seismicity including those without clear initial motions, but could be affected by local structures beneath the stations. To reduce this effect, we used information of whether the amplitudes are significantly larger than the normal level. We determined a threshold to distinguish significantly large and normal amplitudes from every 5-min-long window of each data trace, based on an idea that Gaussian and non-Gaussian portions of the frequency distribution of the amplitudes are normal and significantly large parts, respectively. This idea is implemented by creating a cumulative frequency distribution of absolute amplitudes in the 5-min-long window using only the smallest N data samples; calculating the fitting error between this distribution and an error function; and searching for an optimal N which minimizes the fitting error. The amplitude corresponding to this optimal N was regarded as the threshold. We then computed the ratio of large amplitude samples in every 1-sec-long window of each trace. We used the spatial distribution of this ratio as the input to a machine learning of a neural network model to investigate the large amplitude region for the 1-sec window. In this way, the spatial distribution of the large amplitude region is investigated for every 1-sec window, which could be used to distinguish true volcano-seismic signals from spurious ones.
We applied this method to the continuous records in November and December 2017 at Mt. Ontake. For most earthquakes occurred in the analysis domain, the large amplitude region distributed within a narrow area around the epicenter or spread with time from the epicentral area to a wider region. For distant earthquakes and local noise, the large amplitude region extended over the entire analysis domain with a small probability. These different patterns of the large amplitude regions were useful to distinguish the true volcano-seismic signals from spurious ones, although there were some missing detections of small earthquakes and tremors. Using the large amplitude region, only one figure is needed for each second, enabling a seismicity monitoring easier than detailed evaluations of continuous waveform records. We are now trying the 2nd stage of the machine learning to automatically detect earthquakes and tremors, which uses the spatiotemporal distribution of the large amplitude as the input, giving 93% success as a preliminary result.