1. Introduction (Liverpool)

a. State of the art of infrasound (what, where, when, importance)
Seismic and acoustic signals are key in monitoring and characterising volcanic unrest. Automatic algorithms for detection and characterization of seismic signals and related processes have been developed and enhanced during the last years. The large amount of geophysical data collected in the observatories makes it hardly impossible to detect and characterize manually seismic signals. Therefore, implementation of automatic tools is essential.
At the present, there are several examples of these automatic algorithms used for detection of volcanic and seismic related processes, such as: earthquakes (\citep{Bhatti_2016,Di_Stefano_2006,Alvarez_2013}; Low-frequency events  \cite{Frank_2014} Garcia et al., 2017) avalanches (\cite{Marchetti_2015}); debris flows (\cite{Schimmel_2015})
a. Santiaguito Database

2. The algorithm (Granada)

a. Description

3. Tests (Granada & Liverpool)

a. Comparison between Detector vs Manual vs STA/LTA

4. Results (Granada & Liverpool)

a. Behaviour on different swarms and explosions

5. Conclusions (Liverpool- Granada)

a. Applicability