# Machine Learning Applications in Seismology
## 2 Summary of the Published Articles  
The following overview of the published articles in this Special Issue is organized chronologically by publication date. [PERSON] et al. (Contribution 1) introduce an innovative approach to earthquake detection that integrates batch normalization with graph convolutional neural networks (BNGCNN). This study highlights the significance of hyper-parameter optimization in enhancing model performance and demonstrates that the BNGCNN model effectively amalgamates local and global features from seismic data, resulting in improved earthquake detection capabilities. The experimental findings indicate that the BNGCNN surpasses existing models, suggesting its potential utility in real-time earthquake monitoring systems. [PERSON] et al. (Contribution 2) investigate the use of non-destructive seismic wave velocity measurements to predict geotechnical parameters, such as water content and dry density, through artificial neural networks (ANNs). By incorporating seismic wave velocity data, the ANN models exhibit enhanced predictability compared to traditional multilinear regression models, thereby demonstrating the potential for increased efficiency and accuracy in geotechnical evaluations. [PERSON] et al. (Contribution 3) utilize convolutional neural networks for the automatic classification of filtered displacement time series derived from strong-motion records, thereby improving efficiency over conventional visual inspection techniques. By employing transfer learning with models such as VGG19 and ResNet50, this research work achieves enhanced accuracy in determining high-pass cut-off frequencies, attaining a maximum coefficient of determination (R\({}^{2}\)) of 0.82 with minimal prediction errors. [PERSON] et al. (Contribution 4) apply time series analysis to forecast earthquake frequency and magnitude in northeastern Algeria, utilizing both parametric (autoregressive integrated moving average, ARIMA) and non-parametric (singular spectrum analysis, SSA) methodologies. Analyzing data from 1910 to 2019, the findings reveal that the SSA model outperforms the ARIMA model. Their forecasts indicate that between 2020 and 2030, the annual maximum magnitude will range from Mw 4.8 to Mw 5.1, with an expectation of four to six earthquakes of at least Mw 4.0 occurring annually. [PERSON] et al. (Contribution 5) propose Fock-Flow, an automated workflow designed for determining P-wave first-motion polarity and focal mechanism inversion, applied to the 2021 Yangbi earthquake sequence. Utilizing the deep learning model DiTingMotion, the method achieves an accuracy of 98.49% in polarity detection and provides 112 focal mechanism solutions, thereby enhancing the understanding of fault structures and regional stress fields in small to moderate earthquakes. [PERSON] et al. (Contribution 6) present advancements in the FCAZ (fuzzy clustering and zoning) method for identifying earthquake-prone regions through enhancements to its mathematical algorithms and foundational principles. This study focuses on improving the precision and reliability of high-seismicity area identification, exemplified by a case study in California. By refining the FCAZ algorithm, closely linked small zones are consolidated into larger, high-seismicity areas, thereby enhancing the efficacy of earthquake hazard assessments. [PERSON] et al. (Contribution 7) explore the potential of machine learning techniques, specifically random forest and long short-term memory (LSTM) neural networks, to predict large earthquakes utilizing seismic catalog data from the Sichuan-Yunnan region. The research addresses two critical questions: the likelihood of a significant earthquake occurring within a year and the anticipated maximum magnitude. The results indicate that the random forest method excels in classifying large earthquake occurrences, while LSTM provides reasonable magnitude estimations.