Titolo della tesi: Artificial Intelligence in Geoscience Applications
Earthquakes remain challenging to analyze in real time because waveforms are noisy, source pa-
rameters are uncertain, and shaking patterns evolve rapidly. This thesis advances the seismic
monitoring pipeline with deep learning (DL) methods that improve data quality, reduce latency,
and raise predictive accuracy on operational datasets and networks. First, we introduce CDiffSD,
a cold diffusion model for seismic denoising that learns non-Gaussian degradation processes directly
from station noise. On the STEAD-based test, CDiffSD increases median signal-to-noise ratio
and cross-correlation over a strong deep-denoiser baseline, while markedly increasing P-pick recall
within ±50 samples (from ∼ 0.60 to ∼ 0.90 at T =300). Second, we propose a masked Graph
Convolutional Network for rapid shaking prediction in Italy. Using only the first 10 s of three-
component waveforms from national-scale networks (INSTANCE), the model yields up to 6% and
5.5% mean-squared-error reductions for PGA and PGV, respectively, near-zero median residuals
across intensity measures, and mitigates biases relative to GMM (an Italy-specific ground-motion
model). Third, we develop LLM4Geo, a domain-adapted large language model that estimates epi-
center, hypocenter, origin time, and magnitude within five seconds of first detection. On 3,094 test
events, average errors are ∼4 km (epicenter), ∼6.8 km (hypocenter), ∼0.6 s (origin time), and ∼0.2
magnitude units, outperforming early-stage automated INGV solutions while using fewer picks. Fi-
nally, we present SeismicAE, a waveform autoencoder that transfers audio-domain representation
learning to seismology. The learned embeddings improve few-shot classification and reduce error
in ground-motion regression relative to models trained on raw traces. In the end, we build a pro-
totype AI agent that combines different AI models for specific tasks (expert) in a single workflow
(orchestration), giving compatible results for the evaluation of operational seismic risk.