  In this paper, an end-to-end based LSTM scheme is proposed to address the
problem of volcano event localization without any a priori model relating phase
picking with localization estimation. It is worth emphasizing that automatic
phase picking in volcano signals is highly inaccurate because of the short
distances between the event epicenters and the seismograph stations. LSTM was
chosen due to its capability to capture the dynamics of time varying signals,
and to remove or add information within the memory cell state and model
long-term dependencies. A brief insight into LSTM is also discussed here. The
results presented in this paper show that the LSTM based architecture provided
a success rate, i.e., an error smaller than 1.0Km, equal to 48.5%, which in
turn is dramatically superior to the one delivered by automatic phase picking.
Moreover, the proposed end-to-end LSTM based method gave a success rate 18%
higher than CNN.
