Chile is among the world’s most seismically active countries, with an annual average of over 1,000 seismic events exceeding moment magnitude () 4.0. In the past 20 years, the country has experienced two major events surpassing 8.0. While deep neural network models have been widely employed to detect patterns in seismic data, the classification of seismic events into foreshocks, mainshocks, and aftershocks remains a challenging task. This study proposes a hybrid approach for the classification of earthquakes in Chile. The methodology comprises three main steps: first, a spatio-temporal density-based clustering algorithm is applied to group seismic events based on their spatial and temporal similarities; second, the seismic events within each cluster are labeled as foreshocks, mainshocks, or aftershocks; and finally, deep neural networks, including Long Short-Term Memory (LSTM) and Transformer models, are employed to classify earthquakes. Features such as longitude, latitude, magnitude, depth, and distances between events are used as inputs. For aftershock classification, the LSTM model achieves the highest accuracy at 0.8. Meanwhile, for precursor event classification, the Transformer network outperforms the LSTM, achieving a recall of 0.6. Future work will focus on a more detailed exploration of the precursor class and the incorporation of additional seismic data from other countries to enhance the model’s generalization.

Publisher: Environmental and Ecological Statistics  Link>

ABSTRACT

Publisher: 2023 IEEE World Conference on Applied Intelligence and Computing (AIC) Link>

ABSTRACT

Marketing is essential for the success of any company today, both to project itself abroad and to achieve its business objectives. The personalized approach to marketing plays a crucial role, enabling more effective interaction with potential customers and reducing errors in promotional campaigns. One way to improve personalized marketing is by using artificial intelligence to predict the effectiveness of campaigns in converting users to customers. In this study, the use of Siamese neural networks for elevation modelling in a Chilean online retail company is proposed. The results obtained using this model are presented, as well as the classical elevation modelling techniques. These results show that the Siamese neural network model achieves an increase of more than 30% compared to the area under the Qini curve, while also being competitive in other metrics. As future work, it is planned to obtain more real data to carry out a better validation of this proposal as well as to compare the model with other proposed neural networks.