Application of machine learning algorithms for microseismic detection close to the Costa Rica Rift, Panama Basin
The application of automatic learning algorithms machine learning awoke great interest in the area of Geosciences. Currently, the use of these algorithms is quite common in many investigations, particularly in the branch of seismology. In this work we use an earthquake detection method based on a de...
- Autores:
-
Lozada Artunduaga, Santiago
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2022
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/60384
- Acceso en línea:
- http://hdl.handle.net/1992/60384
- Palabra clave:
- Machine learning
Panama Basin
Seismology
Microseismic
Geociencias
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
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Application of machine learning algorithms for microseismic detection close to the Costa Rica Rift, Panama Basin |
| title |
Application of machine learning algorithms for microseismic detection close to the Costa Rica Rift, Panama Basin |
| spellingShingle |
Application of machine learning algorithms for microseismic detection close to the Costa Rica Rift, Panama Basin Machine learning Panama Basin Seismology Microseismic Geociencias |
| title_short |
Application of machine learning algorithms for microseismic detection close to the Costa Rica Rift, Panama Basin |
| title_full |
Application of machine learning algorithms for microseismic detection close to the Costa Rica Rift, Panama Basin |
| title_fullStr |
Application of machine learning algorithms for microseismic detection close to the Costa Rica Rift, Panama Basin |
| title_full_unstemmed |
Application of machine learning algorithms for microseismic detection close to the Costa Rica Rift, Panama Basin |
| title_sort |
Application of machine learning algorithms for microseismic detection close to the Costa Rica Rift, Panama Basin |
| dc.creator.fl_str_mv |
Lozada Artunduaga, Santiago |
| dc.contributor.advisor.none.fl_str_mv |
Tary, Jean Baptiste |
| dc.contributor.author.none.fl_str_mv |
Lozada Artunduaga, Santiago |
| dc.contributor.jury.none.fl_str_mv |
Poveda Núñez, Hugo Esteban |
| dc.subject.keyword.none.fl_str_mv |
Machine learning Panama Basin Seismology Microseismic |
| topic |
Machine learning Panama Basin Seismology Microseismic Geociencias |
| dc.subject.themes.es_CO.fl_str_mv |
Geociencias |
| description |
The application of automatic learning algorithms machine learning awoke great interest in the area of Geosciences. Currently, the use of these algorithms is quite common in many investigations, particularly in the branch of seismology. In this work we use an earthquake detection method based on a deep learning approach called SCALODEEP, including two essential parts: the continuous wavelet transform (CWT) and a convolutional neural network (CNN). This method will be used to detect microseismic activity near the Costa Rica Rift (CRR) using Ocean Bottom Seismometer (OBS) signals from the OSCAR program (Oceanographic and Seismic Characterization of heat dissipation and alteration by hydrothermal fluids at an Axial Ridge). Due to the lack of generalization of the SCALODEEP model a new model was built with 3360 microseismic events and 3360 background noise time series. To set the output threshold, it was evaluated according to the behavior of the results, which were chaotic with minuscule changes in the threshold. The accuracy on the training dataset peaks at 86.57%, and on the validation set, it peaks at a maximum of 76.62%. This possibly comes out from an limited training dataset. Hence, to perform general results is required to enlarge the learning dataset, modify the training dataset or apply an alternative machine learning algorithm. |
| publishDate |
2022 |
| dc.date.accessioned.none.fl_str_mv |
2022-09-01T21:26:32Z |
| dc.date.available.none.fl_str_mv |
2022-09-01T21:26:32Z |
| dc.date.issued.none.fl_str_mv |
2022-08-24 |
| dc.type.es_CO.fl_str_mv |
Trabajo de grado - Pregrado |
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info:eu-repo/semantics/bachelorThesis |
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info:eu-repo/semantics/acceptedVersion |
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http://purl.org/coar/resource_type/c_7a1f |
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Text |
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http://purl.org/redcol/resource_type/TP |
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http://hdl.handle.net/1992/60384 |
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reponame:Repositorio Institucional Séneca |
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| dc.language.iso.es_CO.fl_str_mv |
eng |
| language |
eng |
| dc.relation.references.es_CO.fl_str_mv |
Krogh, A. (2008). What are artificial neural networks?. Nature Biotechnology, 26 (2), 195-197. Lv, Z., Hu, Y., Zhong, H., Wu, J., Li, B., & Zhao, H. (2010, October). Parallel k-means clustering of remote sensing images based on mapreduce [Paper presentation]. International Conference on Web Information Systems and Mining. Berlin, Germany. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. Cambridge: MIT press. Valueva, M. V., Nagornov, N. N., Lyakhov, P. A., Valuev, G. V., & Chervyakov, N. I. (2020). Application of the residue number system to reduce hardware costs of the convolutional neural network implementation. Mathematics and Computers in Simulation, 177, 232-243. Lary, D. J. (2010). Artificial Intelligence in Geoscience and Remote Sensing. In P. Imperatore, & D. Riccio (Eds.), Geoscience and Remote Sensing New Achievements. IntechOpen. Lary, D. J., Remer, L. A., MacNeill, D., Roscoe, B., & Paradise, S. (2009). Machine learning and bias correction of MODIS aerosol optical depth. IEEE Geoscience and Remote Sensing Letters, 6 (4), 694-698. Brown, M. E., Lary, D. J., Vrieling, A., Stathakis, D., & Mussa, H. (2008). Neural networks as a tool for constructing continuous NDVI time series from AVHRR and MODIS. International Journal of Remote Sensing, 29 (24), 7141-7158. Lary, D. J., Müller, M. D., & Mussa, H. Y. (2004). Using neural networks to describe tracer correlations. Atmospheric Chemistry and Physics, 4 (1), 143-146. Poulton, M. M. (2001). Computational neural networks for geophysical data processing. Elsevier. Gutenberg, B., & Richter, C. (1954). Seismicity of the Earth. Hafner Publishing Company. Ross, Z. E., Meier, M. A., Hauksson, E., & Heaton, T. H. (2018). Generalized seismic phase detection with deep learning short note. Bulletin of the Seismological Society of America, 108 (5A), 2894-2901. Mousavi, S. M., Ellsworth, W. L., Zhu, W., Chuang, L. Y., & Beroza, G. C. (2020). Earthquake transformer-an attentive deep-learning model for simultaneous earthquake detection and phase picking. Nature communications, 11 (1), 1-12. Yoon, C. E., O'Reilly, O., Bergen, K. J., & Beroza, G. C. (2015). Earthquake detection through computationally efficient similarity search. Science advances, 1 (11), e1501057. Baluja, S., & Covell, M. (2008). Waveprint: Efficient wavelet-based audio fingerprinting. Pattern recognition, 41 (11), 3467-3480. Woollam, J., Münchmeyer, J., Tilmann, F., Rietbrock, A., Lange, D., Bornstein, T., Diehl, T., Giunchi, C., Haslinger, F., Jozinovic, D., Michelini, A., Saul, J., & Soto, H. (2022). SeisBench-A toolbox for machine learning in seismology. Seismological Society of America, 93 (3), 1695-1709. Mousavi, S. M., Zhu, W., Sheng, Y., & Beroza, G. C. (2019). CRED: A deep residual network of convolutional and recurrent units for earthquake signal detection. Scientific reports, 9 (1), 1-14. Saad, O. M., Huang, G., Chen, Y., Savvaidis, A., Fomel, S., Pham, N., & Chen, Y. (2021). Scalodeep: A highly generalized deep learning framework for real-time earthquake detection. Journal of Geophysical Research: Solid Earth, 126 (4), e2020JB021473. Hobbs, R. W. & Peirce, C. (2015). Cruise Report. Durham University, Department of Earth Sciences. Tary, J. B., Hobbs, R. W., Peirce, C., Lesmes, C. L., & Funnell, M. J. (2021). Local rift and intraplate seismicity reveal shallow crustal fluid-related activity and sub-crustal faulting. Earth and Planetary Science Letters, 562, 1-3. Banyte, D., Morales Maqueda, M., Hobbs, R., Smeed, D. A., Megann, A., & Recalde, S. (2018). Geothermal heating in the Panama Basin: 1. Hydrography of the basin. Journal of Geophysical Research: Oceans, 123 (10), 7382-7392. Banyte, D., Morales Maqueda, M., Smeed, D. A., Megann, A., Hobbs, R., & Recalde, S. (2018). Geothermal heating in the Panama Basin. Part II: Abyssal water mass transformation. Journal of Geophysical Research: Oceans, 123 (10), 7393-7406. Kolandaivelu, K. P., Harris, R. N., Lowell, R. P., Robinson, A. H., Wilson, D. J., & Hobbs, R. W. (2020). Evolution of heat flow, hydrothermal circulation and permeability on the young southern flank of the Costa Rica Rift. Geophysical Journal International, 220 (1), 278-295. Lowell, R. P., Zhang, L., Maqueda, M. A. M., Banyte, D., Tong, V. C. H., Johnston, R. E. R., Harris, R.N., Hobbs, R.W., Peirce, C., Robinson, A.H., & Kolandaivelu, K. (2020). Magma-hydrothermal interactions at the Costa Rica Rift from data collected in 1994 and 2015. Earth and Planetary Science Letters, 531. Gregory, E., & Philippa, M. (2018). The seismic characterisation of layer 2 in oceanic crust around ODP borehole 504B [Doctoral dissertation]. Durham University. Robinson, A. H., Zhang, L., Hobbs, R. W., Peirce, C., & Tong, V. C. H. (2020). Magmatic and tectonic segmentation of the intermediate-spreading Costa Rica Rift-a fine balance between magma supply rate, faulting and hydroth6ermal circulation. Geophysical Journal International, 222 (1), 132-152. Haughton, G. (2019). Controls of brittle extension on the large scale at mid-ocean ridges [Doctoral dissertation]. University of Southampton. Wilson, J. D., Robinson, A. H., Hobbs, R. W., Peirce, C. & Funnell, M. (2019). Does intermediate spreading-rate oceanic crust result from episodic transition between magmatic and magma-dominated, faulting-enhanced spreading?-The Costa Rica Rift. Geophysical Journal International, 218 (3), 1617-1641. Lonsdale, P. (2005). Creation of the Cocos and Nazca plates by fission of the Farallon plate. Tectonophysics, 404 (3-4), 237-264. Vargas, C. A., Pulido, J. E. & Hobbs, R. W. (2018). Thermal structrure of the Panama Basin by analysis of seismic attenuation. Tectonophysics, 730, 81-99. Tarbuck, E. J., Lutgens, F. K., Tasa, D., & Cientficias, A. T. (2005). Ciencias de la Tierra. Madrid: Pearson Educación. Grevemeyer, I., Hayman, N., Lange, D., Peirce, C., Papenberg, C., Van Avendonk, H., Schimd, F., Gomez de La Peña, L. & Dannowski, A. (2019). Constraining the maximum depth of brittle deformation at slow-and ultraslow-spreading ridges using microseismicity. Geology, 47 (11), 1069-1073. Herron, E. M., & Heirtzler, J. R. (1967). Sea-floor spreading near the Galapagos. Science, 158 (3802), 775-780. Raff, A. D. (1968). Sea-floor spreading-Another rift. Journal of Geophysical Research, 73 (12), 3699-3705. Grim, P. J. (1970). Connection of the Panama fracture zone with the Galapagos rift zone, eastern tropical Pacific. Marine Geophysical Researches, 1 (1), 85-90. Molnar, P., & Sykes, L. R. (1969). Tectonics of the Caribbean and Middle America regions from focal mechanisms and seismicity. Geological Society of America Bulletin, 80 (9), 1639-1684. Vogt, P. R., & De Boer, J. (1976). Morphology, magnetic anomalies and basalt magnetization at the ends of the Galapagos high-amplitude zone. Earth and Planetary Science Letters, 33 (1), 145-163. Lonsdale, P. & Klitgord, K. (1978). Structure and tectonic history of the eastern Panama Basin. Geological Society of America Bulletin, 89, 981-999. Fisher, A., Becker, K., Narasimhan, T. N., Langseth, M. & Mottl, M. (1990). Passive, off-axis convection through the southern flank of the Costa Rica Rift. Journal of Geophysical Research, 95, 9343-9370. Wilson, D. S., & Hey, R. N. (1995). History of rift propagation and magnetization intensity for the Cocos-Nazca spreading Center. Journal of Geophysical Research: Solid Earth, 100 (B6), 10041-10056. Roger Buck, W., Carbotte, S. M., & Mutter, C. (1997). Controls on extrusion at mid-ocean ridges. Geology, 25 (10), 935-938. Macdonald, K. C., & Fox, P. J. (1983). Overlapping spreading centres: New accretion geometry on the East Pacific Rise. Nature, 302 (5903), 55-58. Acocella, V. (2008). Transform faults or overlapping spreading centers? Oceanic ridge interactions revealed by analogue models. Earth and Planetary Science Letters, 265 (3-4), 379-385. Rundquist, D. V., & Sobolev, P. O. (2002). Seismicity of mid-oceanic ridges and its geodynamic implications: a review. Earth-Science Reviews, 58 (1-2), 143-161. Burr, N. C., & Solomon, S. C. (1978). The relationship of source parameters of oceanic transform earthquakes to plate velocity and transform length. Journal of Geophysical Research: Solid Earth, 83 (B3), 1193-1205. Solomon, S. C., & Burr, N. C. (1979). The relationship of source parameters of ridge-crest and transform earthquakes to the thermal structure of oceanic lithosphere. Tectonophysics, 55 (1-2), 107-126. Vassallo, M., Satriano, C., & Lomax, A. (2012). Automatic picker developments and optimization: A strategy for improving the performances of automatic phase pickers. Seismological Research Letters, 83 (3), 541. Allen, R. (1982). Automatic phase pickers: Their present use and future prospects. Bulletin of the Seismological Society of America, 72 (6B), S225-S242. Sleeman, R., & Van Eck, T. (1999). Robust automatic P-phase picking: an on-line implementation in the analysis of broadband seismogram recordings. Physics of the earth and planetary interiors, 113 (1-4), 265-275. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521 (7553), 436-444. Mitchell, T. M. (1997). Machine learning. New York: McGraw-hill. Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349 (6245), 255-260. Sandberg, I. W., Lo, J. T., Fancourt, C. L., Principe, J. C., Katagiri, S., & Haykin, S. (2001). Nonlinear dynamical systems: feedforward neural network perspectives (Vol. 21). John Wiley & Sons. Ross, Z. E., & Ben-Zion, Y. (2014). Automatic picking of direct P, S seismic phases and fault zone head waves. Geophysical Journal International, 199 (1), 368-381. Mathworks. (2020). Continuous Wavelet Transform and Scale-Based Analysis. https://la.mathworks.com/help/wavelet/gs/continuous-wavelet-transform-and-scale-based-analysis.html Mathworks. (2020). Continuous and Discrete Wavelet Transforms. https://la.mathworks.com/help/wavelet/gs/continuous-and-discrete-wavelet-transforms.html Olhede, S. C., & Walden, A. T. (2002). Generalized morse wavelets. IEEE Transactions on Signal Processing, 50 (11), 2661-2670. |
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Atribución-NoComercial 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Tary, Jean Baptistevirtual::7926-1Lozada Artunduaga, Santiago820e4f34-3413-4b55-90d7-c340b7f7d693600Poveda Núñez, Hugo Esteban2022-09-01T21:26:32Z2022-09-01T21:26:32Z2022-08-24http://hdl.handle.net/1992/60384instname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/The application of automatic learning algorithms machine learning awoke great interest in the area of Geosciences. Currently, the use of these algorithms is quite common in many investigations, particularly in the branch of seismology. In this work we use an earthquake detection method based on a deep learning approach called SCALODEEP, including two essential parts: the continuous wavelet transform (CWT) and a convolutional neural network (CNN). This method will be used to detect microseismic activity near the Costa Rica Rift (CRR) using Ocean Bottom Seismometer (OBS) signals from the OSCAR program (Oceanographic and Seismic Characterization of heat dissipation and alteration by hydrothermal fluids at an Axial Ridge). Due to the lack of generalization of the SCALODEEP model a new model was built with 3360 microseismic events and 3360 background noise time series. To set the output threshold, it was evaluated according to the behavior of the results, which were chaotic with minuscule changes in the threshold. The accuracy on the training dataset peaks at 86.57%, and on the validation set, it peaks at a maximum of 76.62%. This possibly comes out from an limited training dataset. Hence, to perform general results is required to enlarge the learning dataset, modify the training dataset or apply an alternative machine learning algorithm.GeocientíficoPregradoMachine learningSeimology33 páginasapplication/pdfengUniversidad de los AndesGeocienciasFacultad de CienciasDepartamento de GeocienciasApplication of machine learning algorithms for microseismic detection close to the Costa Rica Rift, Panama BasinTrabajo de grado - Pregradoinfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_7a1fTexthttp://purl.org/redcol/resource_type/TPMachine learningPanama BasinSeismologyMicroseismicGeocienciasKrogh, A. (2008). What are artificial neural networks?. Nature Biotechnology, 26 (2), 195-197.Lv, Z., Hu, Y., Zhong, H., Wu, J., Li, B., & Zhao, H. (2010, October). Parallel k-means clustering of remote sensing images based on mapreduce [Paper presentation]. International Conference on Web Information Systems and Mining. Berlin, Germany.Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. Cambridge: MIT press.Valueva, M. V., Nagornov, N. N., Lyakhov, P. A., Valuev, G. V., & Chervyakov, N. I. (2020). Application of the residue number system to reduce hardware costs of the convolutional neural network implementation. Mathematics and Computers in Simulation, 177, 232-243.Lary, D. J. (2010). Artificial Intelligence in Geoscience and Remote Sensing. In P. Imperatore, & D. Riccio (Eds.), Geoscience and Remote Sensing New Achievements. IntechOpen.Lary, D. J., Remer, L. A., MacNeill, D., Roscoe, B., & Paradise, S. (2009). Machine learning and bias correction of MODIS aerosol optical depth. IEEE Geoscience and Remote Sensing Letters, 6 (4), 694-698.Brown, M. E., Lary, D. J., Vrieling, A., Stathakis, D., & Mussa, H. (2008). Neural networks as a tool for constructing continuous NDVI time series from AVHRR and MODIS. International Journal of Remote Sensing, 29 (24), 7141-7158.Lary, D. J., Müller, M. D., & Mussa, H. Y. (2004). Using neural networks to describe tracer correlations. Atmospheric Chemistry and Physics, 4 (1), 143-146.Poulton, M. M. (2001). Computational neural networks for geophysical data processing. Elsevier.Gutenberg, B., & Richter, C. (1954). Seismicity of the Earth. Hafner Publishing Company.Ross, Z. E., Meier, M. A., Hauksson, E., & Heaton, T. H. (2018). Generalized seismic phase detection with deep learning short note. Bulletin of the Seismological Society of America, 108 (5A), 2894-2901.Mousavi, S. M., Ellsworth, W. L., Zhu, W., Chuang, L. Y., & Beroza, G. C. (2020). Earthquake transformer-an attentive deep-learning model for simultaneous earthquake detection and phase picking. Nature communications, 11 (1), 1-12.Yoon, C. E., O'Reilly, O., Bergen, K. J., & Beroza, G. C. (2015). Earthquake detection through computationally efficient similarity search. Science advances, 1 (11), e1501057.Baluja, S., & Covell, M. (2008). Waveprint: Efficient wavelet-based audio fingerprinting. Pattern recognition, 41 (11), 3467-3480.Woollam, J., Münchmeyer, J., Tilmann, F., Rietbrock, A., Lange, D., Bornstein, T., Diehl, T., Giunchi, C., Haslinger, F., Jozinovic, D., Michelini, A., Saul, J., & Soto, H. (2022). SeisBench-A toolbox for machine learning in seismology. Seismological Society of America, 93 (3), 1695-1709.Mousavi, S. M., Zhu, W., Sheng, Y., & Beroza, G. C. (2019). CRED: A deep residual network of convolutional and recurrent units for earthquake signal detection. Scientific reports, 9 (1), 1-14.Saad, O. M., Huang, G., Chen, Y., Savvaidis, A., Fomel, S., Pham, N., & Chen, Y. (2021). Scalodeep: A highly generalized deep learning framework for real-time earthquake detection. Journal of Geophysical Research: Solid Earth, 126 (4), e2020JB021473.Hobbs, R. W. & Peirce, C. (2015). Cruise Report. Durham University, Department of Earth Sciences.Tary, J. B., Hobbs, R. W., Peirce, C., Lesmes, C. L., & Funnell, M. J. (2021). Local rift and intraplate seismicity reveal shallow crustal fluid-related activity and sub-crustal faulting. Earth and Planetary Science Letters, 562, 1-3.Banyte, D., Morales Maqueda, M., Hobbs, R., Smeed, D. A., Megann, A., & Recalde, S. (2018). Geothermal heating in the Panama Basin: 1. Hydrography of the basin. Journal of Geophysical Research: Oceans, 123 (10), 7382-7392.Banyte, D., Morales Maqueda, M., Smeed, D. A., Megann, A., Hobbs, R., & Recalde, S. (2018). Geothermal heating in the Panama Basin. Part II: Abyssal water mass transformation. Journal of Geophysical Research: Oceans, 123 (10), 7393-7406.Kolandaivelu, K. P., Harris, R. N., Lowell, R. P., Robinson, A. H., Wilson, D. J., & Hobbs, R. W. (2020). Evolution of heat flow, hydrothermal circulation and permeability on the young southern flank of the Costa Rica Rift. Geophysical Journal International, 220 (1), 278-295.Lowell, R. P., Zhang, L., Maqueda, M. A. M., Banyte, D., Tong, V. C. H., Johnston, R. E. 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