  Thanks to recent advancements in numerical methods, computer power, and
monitoring technology, seismic ambient noise provides precious information
about the structural behavior of old buildings. The measurement of the
vibrations produced by anthropic and environmental sources and their use for
dynamic identification and structural health monitoring of buildings initiated
an emerging, cross-disciplinary field engaging seismologists, engineers,
mathematicians, and computer scientists. In this work, we employ recent deep
learning techniques for time-series forecasting to inspect and detect anomalies
in the large dataset recorded during a long-term monitoring campaign conducted
on the San Frediano bell tower in Lucca. We frame the problem as an
unsupervised anomaly detection task and train a Temporal Fusion Transformer to
learn the normal dynamics of the structure. We then detect the anomalies by
looking at the differences between the predicted and observed frequencies.
