# Machine Learning Applications in Seismology
## 3 Conclusions  
The adoption of artificial intelligence in scientific research has gained traction across various disciplines, achieving notable success. The application of machine learning within seismology has also garnered increasing attention. This collection of articles in this Special Issue contribute to the field of seismology by presenting views and research examples that illustrate the integration of machine learning into seismology. The topics covered in these articles include seismic inversion, earthquake detection, focal mechanism analysis, ground motion simulation, earthquake early warning systems, and earthquake forecasting, utilizing a diverse array of machine learning methods. Moreover, these articles highlight interdisciplinary research that bridges seismology and machine learning, offering innovative solutions to challenges associated with seismic data and advancements in model interpretability. While limitations exist, there is a strong expectation for future work to focus on enhancing model accuracy and generalizability, the development of real-time applications in seismology and the exploration of the physical mechanisms underlying earthquakes through machine learning methodologies. In conclusion, we extend our heartfelt appreciation to the contributors, reviewers, and the editorial team for their dedicated efforts in bringing this Special Issue to fruition. Their contributions have significantly enriched the discourse on the application of machine learning in seismology, paving the way for future advancements in the field. The authors declare no conflict of interest. List of Contributions  
[PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Early Earthquake Detection Using Batch Normalization Graph Convolutional Neural Network (BNGCNN). _Appl. Sci._**2022**, _12_, 7548. [[https://doi.org/10.3390/app12157548](https://doi.org/10.3390/app12157548)]([https://doi.org/10.3390/app12157548](https://doi.org/10.3390/app12157548)). * [PERSON]; [PERSON]; [PERSON] Predicting Geotechnical Parameters from Seismic Wave Velocity Using Artificial Neural Networks. _Appl. Sci._**2022**, _12_, 12815. [[https://doi.org/10.3390/app122412815](https://doi.org/10.3390/app122412815)]([https://doi.org/10.3390/app122412815](https://doi.org/10.3390/app122412815)). * [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] The Cut-Off Frequency of High-Pass Filtering of Strong-Motion Records Based on Transfer Learning. _Appl. Sci._**2023**, _13_, 1500. [[https://doi.org/10.3390/app13031500](https://doi.org/10.3390/app13031500)]([https://doi.org/10.3390/app13031500](https://doi.org/10.3390/app13031500)). * [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Earthquake Magnitude and Frequency Forecasting in Northeastern Algeria using Time Series Analysis. _Appl. Sci._**2023**, _13_, 1566. [[https://doi.org/10.3390/app13031566](https://doi.org/10.3390/app13031566)]([https://doi.org/10.3390/app13031566](https://doi.org/10.3390/app13031566)). * [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Fock-Flow: Automatic Determination of P-Wave First-Motion Polarity and Focal Mechanism Inversion and Application to the 2021 Yangbi Earthquake Sequence. _Appl. Sci._**2023**, _13_, 2233. [[https://doi.org/10.3390/app13042233](https://doi.org/10.3390/app13042233)]([https://doi.org/10.3390/app13042233](https://doi.org/10.3390/app13042233)). * [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Development of the Algorithmic Basis of the FCAZ Method for Earthquake-Prone Area Recognition. _Appl. Sci._**2023**, _13_, 2496. [[https://doi.org/10.3390/app13042496](https://doi.org/10.3390/app13042496)]([https://doi.org/10.3390/app13042496](https://doi.org/10.3390/app13042496)). * [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Small Earthquakes Can Help Predict Large Earthquakes: A Machine Learning Perspective. _Appl. Sci._**2023**, _13_, 6424.