| 參考文獻 |
Abdulhafedh, A. (2021). Incorporating k-means, hierarchical clustering and pca in customer segmentation. Journal of City and Development, 3(1), 12-30.
Aden‐Antoniów, F., Frank, W., & Seydoux, L. (2022). An adaptable random forest model for the declustering of earthquake catalogs. Journal of Geophysical Research: Solid Earth, 127(2), e2021JB023254.
Akhoondzadeh, M. (2013). A MLP neural network as an investigator of TEC time series to detect seismo-ionospheric anomalies. Advances in Space Research, 51(11), 2048-2057.
Allen, R. M., Kong, Q., & Martin-Short, R. (2020). The myshake platform: a global vision for earthquake early warning. Pure and Applied Geophysics, 177, 1699-1712.
Andajani, R. D., Tsuji, T., Snieder, R., & Ikeda, T. (2020). Spatial and temporal influence of rainfall on crustal pore pressure based on seismic velocity monitoring. Earth, Planets and Space, 72(1), 1-17.
Ao, Y., Li, H., Zhu, L., Ali, S., & Yang, Z. (2019). The linear random forest algorithm and its advantages in machine learning assisted logging regression modeling. Journal of Petroleum Science and Engineering, 174, 776-789.
Asim, K. M., Idris, A., Martínez-Álvarez, F., & Iqbal, T. (2016). Short term earthquake prediction in Hindukush region using tree based ensemble learning. Paper presented at the 2016 International conference on frontiers of information technology (FIT).
Benjamin, D. J., Berger, J. O., Johannesson, M., Nosek, B. A., Wagenmakers, E.-J., Berk, R., . . . Camerer, C. (2018). Redefine statistical significance. Nature human behaviour, 2(1), 6-10.
Biau, G., & Scornet, E. (2016). A random forest guided tour. Test, 25, 197-227.
Blanpied, M., Lockner, D., & Byerlee, J. (1991). Fault stability inferred from granite sliding experiments at hydrothermal conditions. Geophysical Research Letters, 18(4), 609-612.
Bowles‐Martinez, E., & Schultz, A. (2020). Composition of magma and characteristics of the hydrothermal system of Newberry volcano, Oregon, from Magnetotellurics. Geochemistry, Geophysics, Geosystems, 21(3), e2019GC008831.
Breiman, L. (2001). Random forests. Machine learning, 45, 5-32.
Budiman, K., & Ifriza, Y. N. (2021). Analysis of earthquake forecasting using random forest. Journal of soft computing exploration, 2(2), 153-162.
Buforn, E., Bezzeghoud, M., UdÍas, A., & Pro, C. (2004). Seismic sources on the Iberia-African plate boundary and their tectonic implications. Pure and Applied Geophysics, 161, 623-646.
Buscema, M. (1998). Back propagation neural networks. Substance use & misuse, 33(2), 233-270.
Buturovic, L., & Citkusev, L. (1992). Back propagation and forward propagation. Paper presented at the [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.
Chaurasia, K., Kanse, S., Yewale, A., Singh, V. K., Sharma, B., & Dattu, B. (2019). Predicting damage to buildings caused by earthquakes using machine learning techniques. Paper presented at the 2019 IEEE 9th International Conference on Advanced Computing (IACC).
Chen, C.-H., Wang, C.-H., Wen, S., Yeh, T.-K., Lin, C.-H., Liu, J.-Y., . . . Lin, T.-W. (2013). Anomalous frequency characteristics of groundwater level before major earthquakes in Taiwan. Hydrology and Earth System Sciences, 17(5), 1693-1703.
Chen, Y., Zheng, W., Li, W., & Huang, Y. (2021). Large group activity security risk assessment and risk early warning based on random forest algorithm. Pattern Recognition Letters, 144, 1-5.
Chigira, M., Wu, X., Inokuchi, T., & Wang, G. (2010). Landslides induced by the 2008 Wenchuan earthquake, Sichuan, China. Geomorphology, 118(3-4), 225-238.
Cremen, G., & Galasso, C. (2020). Earthquake early warning: Recent advances and perspectives. Earth-Science Reviews, 205, 103184.
Cui, M. (2020). Introduction to the k-means clustering algorithm based on the elbow method. Accounting, Auditing and Finance, 1(1), 5-8.
De Winter, J. C. (2013). Using the Student′s t-test with extremely small sample sizes. Practical Assessment, Research, and Evaluation, 18(1), 10.
Derakhshani, A., & Foruzan, A. H. (2019). Predicting the principal strong ground motion parameters: A deep learning approach. Applied Soft Computing, 80, 192-201.
Dong, L., Wesseloo, J., Potvin, Y., & Li, X. (2016). Discrimination of mine seismic events and blasts using the fisher classifier, naive bayesian classifier and logistic regression. Rock Mechanics and Rock Engineering, 49, 183-211.
Einarsson, P. (2008). Plate boundaries, rifts and transforms in Iceland. Jökull, 58(12), 35-58.
Ellsworth, W. L. (2013). Injection-induced earthquakes. Science, 341(6142), 1225942.
Flinders, A. F., Shelly, D. R., Dawson, P. B., Hill, D. P., Tripoli, B., & Shen, Y. (2018). Seismic evidence for significant melt beneath the Long Valley Caldera, California, USA. Geology, 46(9), 799-802.
Foulger, G. R., Wilson, M. P., Gluyas, J. G., Julian, B. R., & Davies, R. J. (2018). Global review of human-induced earthquakes. Earth-Science Reviews, 178, 438-514.
Frades, I., & Matthiesen, R. (2010). Overview on techniques in cluster analysis. Bioinformatics methods in clinical research, 81-107.
Freed, A. M. (2005). Earthquake triggering by static, dynamic, and postseismic stress transfer. Annu. Rev. Earth Planet. Sci., 33, 335-367.
Frick, R. W. (1996). The appropriate use of null hypothesis testing. Psychological Methods, 1(4), 379.
Galkina, A., & Grafeeva, N. (2019). Machine learning methods for earthquake prediction: A survey. Paper presented at the Proceedings of the Fourth Conference on Software Engineering and Information Management (SEIM-2019), Saint Petersburg, Russia.
Gaucher, E., Schoenball, M., Heidbach, O., Zang, A., Fokker, P. A., van Wees, J.-D., & Kohl, T. (2015). Induced seismicity in geothermal reservoirs: A review of forecasting approaches. Renewable and Sustainable Energy Reviews, 52, 1473-1490.
Gerald, B. (2018). A brief review of independent, dependent and one sample t-test. International journal of applied mathematics and theoretical physics, 4(2), 50-54.
Ghojogh, B., & Crowley, M. (2019). The theory behind overfitting, cross validation, regularization, bagging, and boosting: tutorial. arXiv preprint arXiv:1905.12787.
Gibson, G., & Sandiford, M. (2013). Seismicity and induced earthquakes. Background paper to NSW Chief Scientist and Engineer (OCSE). Univ. Melbourne, 33.
Gomberg, J., Beeler, N., Blanpied, M., & Bodin, P. (1998). Earthquake triggering by transient and static deformations. Journal of Geophysical Research: Solid Earth, 103(B10), 24411-24426.
Gomberg, J., Reasenberg, P., Bodin, P. l., & Harris, R. (2001). Earthquake triggering by seismic waves following the Landers and Hector Mine earthquakes. Nature, 411(6836), 462-466.
Grigoli, F., Cesca, S., Priolo, E., Rinaldi, A. P., Clinton, J. F., Stabile, T. A., . . . Dahm, T. (2017). Current challenges in monitoring, discrimination, and management of induced seismicity related to underground industrial activities: A European perspective. Reviews of Geophysics, 55(2), 310-340.
Gubbins, D., Willis, A. P., & Sreenivasan, B. (2007). Correlation of Earth’s magnetic field with lower mantle thermal and seismic structure. Physics of the Earth and planetary Interiors, 162(3-4), 256-260.
Gupta, H. K. (1992). Reservoir induced earthquakes: Elsevier.
Hainzl, S., Kraft, T., Wassermann, J., Igel, H., & Schmedes, E. (2006). Evidence for rainfall‐triggered earthquake activity. Geophysical Research Letters, 33(19).
Hajeb, M., Karimzadeh, S., & Matsuoka, M. (2020). SAR and LIDAR datasets for building damage evaluation based on support vector machine and random forest algorithms—A case study of Kumamoto earthquake, Japan. Applied Sciences, 10(24), 8932.
Han, H., Guo, X., & Yu, H. (2016). Variable selection using mean decrease accuracy and mean decrease gini based on random forest. Paper presented at the 2016 7th ieee international conference on software engineering and service science (icsess).
Han, J., Park, S., Kim, S., Son, S., Lee, S., & Kim, J. (2019). Performance of logistic regression and support vector machines for seismic vulnerability assessment and mapping: a case study of the 12 September 2016 ML5. 8 Gyeongju Earthquake, South Korea. Sustainability, 11(24), 7038.
Hayat, M. J. (2010). Understanding statistical significance. Nursing research, 59(3), 219-223.
Hilbe, J. M. (2009). Logistic regression models: CRC press.
Hill, D. P., Bailey, R. A., & Ryall, A. S. (1985). Active tectonic and magmatic processes beneath Long Valley caldera, eastern California: An overview. Journal of Geophysical Research: Solid Earth, 90(B13), 11111-11120.
Hill, D. P., Pollitz, F., & Newhall, C. (2002). Earthquake-volcano interactions. Physics Today, 55(11), 41-47.
Hosmer Jr, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression (Vol. 398): John Wiley & Sons.
Huang, B. F., & Boutros, P. C. (2016). The parameter sensitivity of random forests. BMC bioinformatics, 17(1), 1-13.
Huang, J., Wang, X., Zhao, Y., Xin, C., & Xiang, H. (2018). Large earthquake magnitude prediction in Taiwan based on deep learning neural network. Neural Network World, 28(2), 149-160.
Hung, H. J., O′Neill, R. T., Bauer, P., & Kohne, K. (1997). The behavior of the p-value when the alternative hypothesis is true. biometrics, 11-22.
Ide, S., Yabe, S., & Tanaka, Y. (2016). Earthquake potential revealed by tidal influence on earthquake size–frequency statistics. Nature Geoscience, 9(11), 834-837.
Jairi, I., Fang, Y., & Pirhadi, N. (2021). Application of Logistic Regression Based on Maximum Likelihood Estimation to Predict Seismic Soil Liquefaction Occurrence. Human-Centric Intelligent Systems, 1(3-4), 98-104.
Johnson, C. W., Fu, Y., & Bürgmann, R. (2017). Seasonal water storage, stress modulation, and California seismicity. Science, 356(6343), 1161-1164.
Keiding, M., Lund, B., & Árnadóttir, T. (2009). Earthquakes, stress, and strain along an obliquely divergent plate boundary: Reykjanes Peninsula, southwest Iceland. Journal of Geophysical Research: Solid Earth, 114(B9).
Keranen, K. M., Weingarten, M., Abers, G. A., Bekins, B. A., & Ge, S. (2014). Sharp increase in central Oklahoma seismicity since 2008 induced by massive wastewater injection. Science, 345(6195), 448-451.
Kim, K.-H., Ree, J.-H., Kim, Y., Kim, S., Kang, S. Y., & Seo, W. (2018). Assessing whether the 2017 M w 5.4 Pohang earthquake in South Korea was an induced event. Science, 360(6392), 1007-1009.
Kim, T. K. (2015). T test as a parametric statistic. Korean journal of anesthesiology, 68(6), 540-546.
King, G. C., Stein, R. S., & Lin, J. (1994). Static stress changes and the triggering of earthquakes. Bulletin of the Seismological Society of America, 84(3), 935-953.
Kirasich, K., Smith, T., & Sadler, B. (2018). Random forest vs logistic regression: binary classification for heterogeneous datasets. SMU Data Science Review, 1(3), 9.
Kodinariya, T. M., & Makwana, P. R. (2013). Review on determining number of Cluster in K-Means Clustering. International Journal, 1(6), 90-95.
Kreemer, C., Holt, W. E., & Haines, A. J. (2003). An integrated global model of present-day plate motions and plate boundary deformation. Geophysical Journal International, 154(1), 8-34.
Kusky, T. M. (2008). Earthquakes: plate tectonics and earthquake hazards: Infobase Publishing.
Lai, T.-J., Chang, C.-M., Connor, K. M., Lee, L.-C., & Davidson, J. R. (2004). Full and partial PTSD among earthquake survivors in rural Taiwan. Journal of psychiatric research, 38(3), 313-322.
Li, L., Doroslovački, M., & Loew, M. H. (2020). Approximating the gradient of cross-entropy loss function. IEEE Access, 8, 111626-111635.
Li, N., Li, B., Chen, D., Wang, E., Tan, Y., Qian, J., & Jia, H. (2020). Waveform characteristics of earthquakes induced by hydraulic fracturing and mining activities: Comparison with those of natural earthquakes. Natural Resources Research, 29, 3653-3674.
Li, Z., Meier, M. A., Hauksson, E., Zhan, Z., & Andrews, J. (2018). Machine learning seismic wave discrimination: Application to earthquake early warning. Geophysical Research Letters, 45(10), 4773-4779.
Liang, J. (2022). Confusion Matrix: Machine Learning. POGIL Activity Clearinghouse, 3(4).
Livingston, E. H. (2004). Who was student and why do we care so much about his t-test? 1. Journal of Surgical Research, 118(1), 58-65.
Lixin, Y., Dong, Z., & Chenglong, L. (2012). Preliminary study of reservoir‐induced seismicity in the Three Gorges reservoir, China. Seismological research letters, 83(5), 806-814.
Mahmoudi, J., Arjomand, M. A., Rezaei, M., & Mohammadi, M. H. (2016). Predicting the earthquake magnitude using the multilayer perceptron neural network with two hidden layers. Civil engineering journal, 2(1), 1-12.
Mallouhy, R., Abou Jaoude, C., Guyeux, C., & Makhoul, A. (2019). Major earthquake event prediction using various machine learning algorithms. Paper presented at the 2019 International Conference on Information and Communication Technologies for Disaster Management (ICT-DM).
Mangalathu, S., Sun, H., Nweke, C. C., Yi, Z., & Burton, H. V. (2020). Classifying earthquake damage to buildings using machine learning. Earthquake Spectra, 36(1), 183-208.
Marone, C. (1998). Laboratory-derived friction laws and their application to seismic faulting. Annual Review of Earth and Planetary Sciences, 26(1), 643-696.
McGarr, A., & Barbour, A. J. (2017). Wastewater disposal and the earthquake sequences during 2016 near Fairview, Pawnee, and Cushing, Oklahoma. Geophysical Research Letters, 44(18), 9330-9336.
McGuire, J. J. (2008). Seismic cycles and earthquake predictability on East Pacific Rise transform faults. Bulletin of the Seismological Society of America, 98(3), 1067-1084.
McKnight, P. E., & Najab, J. (2010). Mann‐Whitney U Test. The Corsini encyclopedia of psychology, 1-1.
Meier, M. A., Ross, Z. E., Ramachandran, A., Balakrishna, A., Nair, S., Kundzicz, P., . . . Yue, Y. (2019). Reliable real‐time seismic signal/noise discrimination with machine learning. Journal of Geophysical Research: Solid Earth, 124(1), 788-800.
Menze, B. H., Kelm, B. M., Masuch, R., Himmelreich, U., Bachert, P., Petrich, W., & Hamprecht, F. A. (2009). A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data. BMC bioinformatics, 10, 1-16.
Midzi, V., Zulu, B., Manzunzu, B., Mulabisana, T., Pule, T., Myendeki, S., & Gubela, W. (2015). Macroseismic survey of the ML5. 5, 2014 Orkney earthquake. Journal of Seismology, 19, 741-751.
Mignan, A., & Broccardo, M. (2020). Neural network applications in earthquake prediction (1994–2019): Meta‐analytic and statistical insights on their limitations. Seismological research letters, 91(4), 2330-2342.
Mishra, P., Singh, U., Pandey, C. M., Mishra, P., & Pandey, G. (2019). Application of student′s t-test, analysis of variance, and covariance. Annals of cardiac anaesthesia, 22(4), 407.
Morales-Simfors, N., Wyss, R. A., & Bundschuh, J. (2020). Recent progress in radon-based monitoring as seismic and volcanic precursor: A critical review. Critical reviews in environmental science and technology, 50(10), 979-1012.
Mousavi, S. M., & Beroza, G. C. (2020). A machine‐learning approach for earthquake magnitude estimation. Geophysical Research Letters, 47(1), e2019GL085976.
Nick, T. G., & Campbell, K. M. (2007). Logistic regression. Topics in biostatistics, 273-301.
Olshen, R. A. (1973). The conditional level of the F—Test. Journal of the American Statistical Association, 68(343), 692-698.
Pail, R., Bingham, R., Braitenberg, C., Dobslaw, H., Eicker, A., Güntner, A., . . . Panet, I. (2015). Science and user needs for observing global mass transport to understand global change and to benefit society. Surveys in Geophysics, 36, 743-772.
Phung, V. H., & Rhee, E. J. (2019). A high-accuracy model average ensemble of convolutional neural networks for classification of cloud image patches on small datasets. Applied Sciences, 9(21), 4500.
Popescu, M.-C., Balas, V. E., Perescu-Popescu, L., & Mastorakis, N. (2009). Multilayer perceptron and neural networks. WSEAS Transactions on Circuits and Systems, 8(7), 579-588.
Rümpker, G., Ryberg, T., & Bock, G. (2003). Boundary-layer mantle flow under the Dead Sea transform fault inferred from seismic anisotropy. Nature, 425(6957), 497-501.
Roberts, D. R., Bahn, V., Ciuti, S., Boyce, M. S., Elith, J., Guillera‐Arroita, G., . . . Thuiller, W. (2017). Cross‐validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography, 40(8), 913-929.
Roeloffs, E. (1996). Poroelastic techniques in the study of earthquake-related hydrologic phenomena. In Advances in geophysics (Vol. 37, pp. 135-195): Elsevier.
Ross, A., Willson, V. L., Ross, A., & Willson, V. L. (2017). Independent samples T-test. Basic and Advanced Statistical Tests: Writing Results Sections and Creating Tables and Figures, 13-16.
Rynkiewicz, J. (2012). General bound of overfitting for MLP regression models. Neurocomputing, 90, 106-110.
Saini, A. (2021). Conceptual understanding of logistic regression for data science beginners. web-article), analyticsvidhya. com.
SAPUTRA, D. M., SAPUTRA, D., & OSWARI, L. D. (2020). Effect of distance metrics in determining k-value in k-means clustering using elbow and silhouette method. Paper presented at the Sriwijaya International Conference on Information Technology and Its Applications (SICONIAN 2019).
Schultz, R., Skoumal, R. J., Brudzinski, M. R., Eaton, D., Baptie, B., & Ellsworth, W. (2020). Hydraulic fracturing‐induced seismicity. Reviews of Geophysics, 58(3), e2019RG000695.
Scornet, E. (2017). Tuning parameters in random forests. ESAIM: Proceedings and Surveys, 60, 144-162.
Shah, H., Ghazali, R., & Nawi, N. M. (2011). Using artificial bee colony algorithm for MLP training on earthquake time series data prediction. arXiv preprint arXiv:1112.4628.
Shah, S. H., Angel, Y., Houborg, R., Ali, S., & McCabe, M. F. (2019). A random forest machine learning approach for the retrieval of leaf chlorophyll content in wheat. Remote Sensing, 11(8), 920.
Shaver, J. P. (1993). What statistical significance testing is, and what it is not. The Journal of Experimental Education, 61(4), 293-316.
Shen, Z., Pan, P., Zhang, D., & Huang, S. (2022). Rapid structural safety assessment using a deep neural network. Journal of Earthquake Engineering, 26(5), 2625-2641.
Shi, C., Wei, B., Wei, S., Wang, W., Liu, H., & Liu, J. (2021). A quantitative discriminant method of elbow point for the optimal number of clusters in clustering algorithm. EURASIP Journal on Wireless Communications and Networking, 2021(1), 1-16.
Stabile, T. A., Giocoli, A., Lapenna, V., Perrone, A., Piscitelli, S., & Telesca, L. (2014). Evidence of low‐magnitude continued reservoir‐induced seismicity associated with the Pertusillo Artificial Lake (southern Italy). Bulletin of the Seismological Society of America, 104(4), 1820-1828.
Stein, S., & Wysession, M. (2009). An introduction to seismology, earthquakes, and earth structure: John Wiley & Sons.
Sugioka, H., Okamoto, T., Nakamura, T., Ishihara, Y., Ito, A., Obana, K., . . . Fukao, Y. (2012). Tsunamigenic potential of the shallow subduction plate boundary inferred from slow seismic slip. Nature Geoscience, 5(6), 414-418.
Syakur, M., Khotimah, B., Rochman, E., & Satoto, B. D. (2018). Integration k-means clustering method and elbow method for identification of the best customer profile cluster. Paper presented at the IOP conference series: materials science and engineering.
Šílený, J., & Milev, A. (2008). Source mechanism of mining induced seismic events—Resolution of double couple and non double couple models. Tectonophysics, 456(1-2), 3-15.
Talwani, P. (1997). On the nature of reservoir-induced seismicity. Pure and Applied Geophysics, 150, 473-492.
Tao, W., Masterlark, T., Shen, Z. K., & Ronchin, E. (2015). Impoundment of the Zipingpu reservoir and triggering of the 2008 Mw 7.9 Wenchuan earthquake, China. Journal of Geophysical Research: Solid Earth, 120(10), 7033-7047.
Taud, H., & Mas, J. (2018). Multilayer perceptron (MLP). Geomatic approaches for modeling land change scenarios, 451-455.
Thisted, R. A. (1998). What is a P-value. Departments of Statistics and Health Studies.
Tiku, M. (1967). Tables of the power of the F-test. Journal of the American Statistical Association, 62(318), 525-539.
Toda, S., Stein, R. S., Richards‐Dinger, K., & Bozkurt, S. B. (2005). Forecasting the evolution of seismicity in southern California: Animations built on earthquake stress transfer. Journal of Geophysical Research: Solid Earth, 110(B5).
Vallejos, J., & McKinnon, S. (2013). Logistic regression and neural network classification of seismic records. International Journal of Rock Mechanics and Mining Sciences, 62, 86-95.
Vens, C., & Costa, F. (2011). Random forest based feature induction. Paper presented at the 2011 IEEE 11th international conference on data mining.
Visa, S., Ramsay, B., Ralescu, A. L., & Van Der Knaap, E. (2011). Confusion matrix-based feature selection. Maics, 710(1), 120-127.
Wang, C. Y., & Manga, M. (2010). Hydrologic responses to earthquakes and a general metric. Geofluids, 10(1‐2), 206-216.
Wang, F., Franco-Penya, H.-H., Kelleher, J. D., Pugh, J., & Ross, R. (2017). An analysis of the application of simplified silhouette to the evaluation of k-means clustering validity. Paper presented at the Machine Learning and Data Mining in Pattern Recognition: 13th International Conference, MLDM 2017, New York, NY, USA, July 15-20, 2017, Proceedings 13.
Wang, Q., Yu, S., Qi, X., Hu, Y., Zheng, W., Shi, J., & Yao, H. (2019). Overview of logistic regression model analysis and application. Zhonghua yu fang yi xue za zhi [Chinese journal of preventive medicine], 53(9), 955-960.
Wang, S., Xu, W., Xu, C., Yin, Z., Bürgmann, R., Liu, L., & Jiang, G. (2019). Changes in groundwater level possibly encourage shallow earthquakes in central Australia: The 2016 Petermann Ranges earthquake. Geophysical Research Letters, 46(6), 3189-3198.
Woolson, R. F. (2007). Wilcoxon signed‐rank test. Wiley encyclopedia of clinical trials, 1-3.
Wouters, B., Bonin, J. A., Chambers, D. P., Riva, R. E., Sasgen, I., & Wahr, J. (2014). GRACE, time-varying gravity, Earth system dynamics and climate change. Reports on Progress in Physics, 77(11), 116801.
Wu, J., & Wu, J. (2012). Cluster analysis and K-means clustering: an introduction. Advances in K-Means clustering: A data mining thinking, 1-16.
Wu, Y. M., Mittal, H., Huang, T. C., Yang, B. M., Jan, J. C., & Chen, S. K. (2019). Performance of a low‐cost earthquake early warning system (P‐Alert) and shake map production during the 2018 M w 6.4 Hualien, Taiwan, earthquake. Seismological Research Letters, 90(1), 19-29.
Xie, Y., Ebad Sichani, M., Padgett, J. E., & DesRoches, R. (2020). The promise of implementing machine learning in earthquake engineering: A state-of-the-art review. Earthquake Spectra, 36(4), 1769-1801.
Yan, R., Chen, X., Sun, H., Xu, J., & Zhou, J. (2022). A review of tidal triggering of global earthquakes. Geodesy and Geodynamics.
Yue, L.-F., Suppe, J., & Hung, J.-H. (2005). Structural geology of a classic thrust belt earthquake: the 1999 Chi-Chi earthquake Taiwan (Mw= 7.6). Journal of Structural Geology, 27(11), 2058-2083.
Zhang, W., & Goh, A. T. (2016). Evaluating seismic liquefaction potential using multivariate adaptive regression splines and logistic regression. Geomech Eng, 10(3), 269-284.
Zhang, Y., Guo, X., Zhong, M., Shen, W., Li, W., & He, B. (2010). Wenchuan earthquake: Brightness temperature changes from satellite infrared information. Chinese Science Bulletin, 55(18), 1917-1924.
Zhu, W., Mousavi, S. M., & Beroza, G. C. (2019). Seismic signal denoising and decomposition using deep neural networks. IEEE Transactions on Geoscience and Remote Sensing, 57(11), 9476-9488. |
[Endnote RIS 格式]
[相關文章]
[文章引用]
[完整記錄]
[館藏目錄]
至系統瀏覽論文 (2025-12-31以後開放)
plurk
funp
live
udn
HD
myshare
netvibes
friend
youpush
delicious
baidu
Google bookmarks
del.icio.us
hemidemi
facebook
twitter
google
reddit