Exploratory Data Analyses

To analyze recorded seismic data sets to find patterns and summarize their main characteristics, often using clustering approaches or other data visualization methods.

Used ML Approaches:

• Gaussian Naıve Bayes

• Logistic Regression

• Linear Discriminant Analysis

• Random Forest

• Support Vector Machine

• K-Nearest Neighbour

• Sequencing

• Decision Tree

• Gaussian Mixture Model

• K‐Means

• Hidden Markov Model

• Bayesian Network

• Regression Trees

• Artificial Neural Networks

Used Neural Networks:

• SOM

• CNN

• FC

• Autoencoder

Used Learning Procedures:

• Supervised Learning

• Unsupervised Learning

References:

1. Johnson, C. W., Ben‐Zion, Y., Meng, H., & Vernon, F. (2020). Identifying different classes of seismic noise signals using unsupervised learning. Geophysical Research Letters, 47(15), e2020GL088353.

2. Kim, D., Lekić, V., Ménard, B., Baron, D., & Taghizadeh-Popp, M. (2020). Sequencing seismograms: A panoptic view of scattering in the core-mantle boundary region. Science, 368(6496), 1223-1228.

3. Ren, C. X., Peltier, A., Ferrazzini, V., Rouet‐Leduc, B., Johnson, P. A., & Brenguier, F. (2020). Machine learning reveals the seismic signature of eruptive behavior at Piton de la Fournaise volcano. Geophysical research letters, 47(3), e2019GL085523.

4. McKean, S. H., Priest, J. A., Dettmer, J., & Eaton, D. W. (2019). Quantifying fracture networks inferred from microseismic point clouds by a Gaussian mixture model with physical constraints. Geophysical Research Letters, 46(20), 11008-11017.

5. Seydoux, L., Balestriero, R., Poli, P., De Hoop, M., Campillo, M., & Baraniuk, R. (2020). Clustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning. Nature communications, 11(1), 1-12.

6. Witsil, A. J., & Johnson, J. B. (2020). Analyzing continuous infrasound from Stromboli volcano, Italy using unsupervised machine learning. Computers & Geosciences, 140, 104494.

7. Bolton, D. C., Shokouhi, P., Rouet‐Leduc, B., Hulbert, C., Rivière, J., Marone, C., & Johnson, P. A. (2019). Characterizing acoustic signals and searching for precursors during the laboratory seismic cycle using unsupervised machine learning. Seismological Research Letters, 90(3), 1088-1098.

8. Chamarczuk, M., Nishitsuji, Y., Malinowski, M., & Draganov, D. (2020). Unsupervised learning used in automatic detection and classification of ambient‐noise recordings from a large‐N array. Seismological Research Letters, 91(1), 370-389.

9. Ross, Z. E., Trugman, D. T., Azizzadenesheli, K., & Anandkumar, A. (2020). Directivity modes of earthquake populations with unsupervised learning. Journal of Geophysical Research: Solid Earth, 125(2), e2019JB018299.

10. Holtzman, B. K., Paté, A., Paisley, J., Waldhauser, F., & Repetto, D. (2018). Machine learning reveals cyclic changes in seismic source spectra in Geysers geothermal field. Science advances, 4(5), eaao2929.

11. Hincks, T., Aspinall, W., Cooke, R., & Gernon, T. (2018). Oklahoma's induced seismicity strongly linked to wastewater injection depth. Science, 359(6381), 1251-1255.

12. Larson, J., Kramar, D., & Leonard, K. (2020). A geostatistical analysis of seismicity in Oklahoma using regression trees and neural networks. Physical Geography, 1-17.

13. Strecker, U., & Uden, R. (2002). Data mining of 3D poststack seismic attribute volumes using Kohonen self-organizing maps. The Leading Edge, 21(10), 1032-1037.

14. Bauer, K., Pratt, R. G., Haberland, C., & Weber, M. (2008). Neural network analysis of crosshole tomographic images: The seismic signature of gas hydrate bearing sediments in the Mackenzie Delta (NW Canada). Geophysical Research Letters, 35(19).

15. Köhler, A., Ohrnberger, M., & Scherbaum, F. (2009). Unsupervised feature selection and general pattern discovery using Self-Organizing Maps for gaining insights into the nature of seismic wavefields. Computers & Geosciences, 35(9), 1757-1767.

16. Braeuer, B., & Bauer, K. (2015). A new interpretation of seismic tomography in the southern Dead Sea basin using neural network clustering techniques. Geophysical Research Letters, 42(22), 9772-9780.

17. Unglert, K., & Jellinek, A. M. (2017). Feasibility study of spectral pattern recognition reveals distinct classes of volcanic tremor. Journal of Volcanology and Geothermal Research, 336, 219-244.

18. Snover, D., Johnson, C. W., Bianco, M. J., & Gerstoft, P. (2021). Deep clustering to identify sources of urban seismic noise in Long Beach, California. Seismological Society of America, 92(2A), 1011-1022.