地震波形包含了關於震源、傳遞介質及相關孕震地體構造的寶貴訊息。近年來，密集地震網的部署增強了偵測微震活動的能力，包括天然地震和地貌改變之過程，如山崩和落石。一般來說，地表震動事件之確認基於辨識單一測站記錄到的訊號並與測網內其他測站的辨識結果進行關聯性分析(association)。然而，大量收集而來的數據為手動數據處理和分析帶來了挑戰，尤其是像地震預警這樣的即時應用。開發可靠且高效率的地震監測算法對於研究孕震構造和減災至關重要。地震監測大致可以分為地震目錄編纂和地震預警。這兩者在反應時間上有所不同，取決於是否需要等待並使用地震S波的資訊。地震目錄編纂可以幫助探索微震活動及其與孕震構造的關係，其中地震S波的資訊有助於提高震源深度之解算精度。地震預警系統則僅使用地震P波到時的觀測數據發佈地震警報，在具有致災性的地震S波到達之前提供資訊給社會大眾，以減輕潛在的地震災害。當我開始攻讀博士學位時，深度學習已經成為解決地震偵測問題的主要算法之一，例如去除訊號背景噪音、挑選體波到時、事件確認的關聯性分析、規模與震度預估等。然而，地球物理的知識尚未整合到決策過程中，無法限制深度學習演算法潛在的錯誤輸出，因而使其說服力較低。我在地震監測中專注於三個方向，旨在結合深度學習與地球物理並探索其在地震學中的應用。首先，對於地震目錄編纂，我開發了一個用於挑選地震P和S波相到時的深度學習模型，並將其與基於已知速度模型解算之理論走時的反投影法(back projection)結合進行相位連結。其次，通過簡單的數據增強策略，我研究了深度學習算法在不完整地震波形（少於1到3秒）中即時辨識P波波相的方法，以用於地震預警。第三，我探索了在從單測站到多個測站的記錄中同時使用時間域與時頻域資料辨識落石事件的遷移學習(transfer learning)方法。訓練落石訊號辨識模型的問題在於落石波形的觀測和人工標記結果較少。然而，如果應用了適當的遷移學習技術，深度學習模型可以從大量標記的地震數據中學習如何解讀並辨識落石訊號。總結，深度學習應用在地震學中的發展在地震監測任務中顯示出其可靠並強大的能力。地震物理的整合可以進一步約束深度學習的輸出，並具有極大的潛力來改進常態性地震監測工作流程。

Seismic waveforms embody valuable information about seismic sources, propagation media, and source-induced structures. In recent years, the growth of dense seismic networks has enhanced the capability to detect microseismicity, including earthquakes and geomorphological processes like landslides and rockfalls. Generally, an event confirmation is based on signal identification at a single station and its association with multiple station observations. However, the massive amount of collected data poses manual data processing and analysis challenges, especially for real-time use cases such as earthquake early warning. Developing reliable and efficient algorithms for seismic monitoring is thus crucial for studying seismic-induced structures and hazard mitigation. Earthquake monitoring can be roughly separated into earthquake cataloging and earthquake early warning, which differ in reaction period and whether to wait for and utilize the S phase information. Earthquake cataloging could help explore microseismicity and its relationship with seismic-induced structures, in which the S phase information puts constraints to help solve focal depth precisely. The earthquake early warning system mitigates the potential hazard by reporting earthquake alerts with solely the observations of P arrivals preceding the most destructive S waves. When I started my Ph.D. program, deep learning emerged as one of the dominating algorithms in solving earthquake detection problems, such as signal denoising, body wave arrival-time picking, event determination, magnitude and ground motion prediction, etc. However, earthquake physics had not been integrated into the decision-making process, making it less convincing since there are no constraints for the potential error outputs from deep learning algorithms. I focused on three directions in seismic source monitoring to solve the problem and explore the utility of deep learning applications in seismology. First, for earthquake cataloging, I developed an advanced model for picking seismic P and S arrivals and integrated it with the back-projection algorithm for phase association. The back-projection algorithm sticks to the physics of theoretical travel time based on the known velocity model. Second, with a simple data augmentation strategy, I examined the deep learning algorithms' utility in identifying P arrivals with incomplete earthquake waveforms (less than 1 to 3 seconds) for earthquake early warning. Third, I explored the transfer learning method in identifying rockfall events from single to multiple-station recordings using time-series and time-frequency domain data. The problem is that the rockfall waveform is poorly observed and labeled. However, if the appropriate transfer learning technique has been applied, the deep learning model can recognize the rockfall waveform from the massively labeled earthquake data. In summary, the developments of deep learning applications in seismology hold promising results in earthquake monitoring tasks. Integrating earthquake physics could further constrain the deep learning outputs and has great potential to improve regular seismic monitoring workflow.

[1] Xiaohua Xu, David T. Sandwell, and Bridget Smith-Konter. Coseismic displacements and surface fractures from sentinel-1 insar: 2019 ridgecrest earthquakes. Seismological Research Letters, 91(4):1979–1985, January 2020.
[2] Rex Allen. Automatic phase pickers: Their present use and future prospects. Bulletin of the Seismological Society of America, 72(6B): S225–S242, December 1982.
[3] Hengchang Dai and Colin MacBeth. Automatic picking of seismic arrivals in local earthquake data using an artificial neural network. Geophysical journal international, 120(3):758–774, 1995.
[4] Hengchang Dai and Colin MacBeth. The application of back-propagation neural network to automatic picking seismic arrivals from single-component recordings. Journal of Geophysical Research: Solid Earth, 102(B7):15105–15113, 1997.
[5] Jin Wang and Ta-Liang Teng. Artificial neural network-based seismic detector. Bulletin of the Seismological Society of America, 85(1):308–319, 1995.
[6] Jin Wang and Ta-Liang Teng. Identification and picking of s phase using an artificial neural network. Bulletin of the Seismological Society of America, 87(5):1140–1149, 1997.
[7] Yue Zhao and Kiyoshi Takano. An artificial neural network approach for broadband seismic phase picking. Bulletin of the Seismological Society of America, 89(3):670–680, 1999.
[8] Zachary E. Ross, Men-Andrin Meier, Egill Hauksson, and Thomas H. Heaton. Generalized seismic phase detection with deep learning. Bulletin of the Seismological Society of America, 108(5A):2894–2901, August 2018.
[9] Zachary E Ross, Men-Andrin Meier, and Egill Hauksson. P wave arrival picking and first-motion polarity determination with deep learning. Journal of Geophysical Research: Solid Earth, 123(6):5120–5129, 2018.
[10] Weiqiang Zhu and Gregory C Beroza. Phasenet: a deep-neural-network-based seismic arrival-time picking method. Geophysical Journal International, 216(1):261–273, 2019.
[11] Yijian Zhou, Han Yue, Qingkai Kong, and Shiyong Zhou. Hybrid event detection and phase-picking algorithm using convolutional and recurrent neural networks. Seismological Research Letters, 90, 04 2019.
[12] S. Mostafa Mousavi, William L. Ellsworth, Weiqiang Zhu, Lindsay Y. Chuang, and Gregory C. Beroza. Earthquake transformer—an attentive deep-learning model for simultaneous earthquake detection and phase picking. Nature Communications, 11(1), August 2020.
[13] Wu-Yu Liao, En-Jui Lee, Dawei Mu, Po Chen, and Ruey-Juin Rau. ARRU Phase Picker: Attention Recurrent-Residual U-Net for Picking Seismic P- and S-Phase Arrivals. Seismological Research Letters, 92(4):2410–2428, 03 2021.
[14] Weiqiang Zhu, S. Mostafa Mousavi, and Gregory C. Beroza. Chapter four -seismic signal augmentation to improve generalization of deep neural networks. In Ben Moseley and Lion Krischer, editors, Machine Learning in Geosciences, volume 61 of Advances in Geophysics, pages 151–177. Elsevier, 2020.
[15] Wu-Yu Liao, En-Jui Lee, Dawei Mu, and Po Chen. Toward Fully Autonomous Seismic Networks: Backprojecting Deep Learning-Based Phase Time Functions for Earthquake Monitoring on Continuous Recordings. Seismological Research Letters, 93(3):1880–1894, 03 2022.
[16] W. Liao, E.-J. Lee, D. Chen, Po Chen, Dawei Mu, and Y.-M. Wu. RED-PAN: Real-Time Earthquake Detection and Phase-Picking With Multitask Attention Network. IEEE Transactions on Geoscience and Remote Sensing, 60:1–11, 2022.
[17] Ramin Dokht, Honn Kao, Ryan Visser, and Brindley Smith. Seismic event and phase detection using time-frequency representation and convolutional neural networks. Seismological Research Letters, 01 2019.
[18] S.Mostafa Mousavi, Zhu Weiqiang, Yixiao Sheng, and Gregory Beroza. Cred: A deep residual network of convolutional and recurrent units for earthquake signal detection. Scientific Reports, 9, 07 2019.
[19] Jannes Münchmeyer, Jack Woollam, Andreas Rietbrock, Frederik Tilmann, Dietrich Lange, Thomas Bornstein, Tobias Diehl, Carlo Giunchi, Florian Haslinger, Dario Jozinović, Alberto Michelini, Joachim Saul, and Hugo Soto. Which picker fits my data? a quantitative evaluation of deep learning based seismic pickers. Journal of Geophysical Research: Solid Earth, 127(1), January 2022.
[20] Jack Woollam, Jannes Münchmeyer, Frederik Tilmann, Andreas Rietbrock, Dietrich Lange, Thomas Bornstein, Tobias Diehl, Carlo Giunchi, Florian Haslinger, Dario Jozinović, Alberto Michelini, Joachim Saul, and Hugo Soto. SeisBench—A Toolbox for Machine Learning in Seismology. Seismological Research Letters, 03 2022.
[21] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional neworks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234–241. Springer, 2015.
[22] S. Mostafa Mousavi, Yixiao Sheng, Weiqiang Zhu, and Gregory C. Beroza. Stanford earthquake dataset (stead): A global data set of seismic signals for ai. IEEE Access, 7:179464–179476, 2019.
[23] Keiiti Aki and Paul G Richards. Quantitative seismology. University Science Books, 2002.
[24] HM Iyer and Kazuro Hirahara. Seismic tomography: Theory and practice. Springer Science & Business Media, 1993.
[25] Dmitry A Storchak, Johannes Schweitzer, and Peter Bormann. The iaspei standard seismic phase list. Seismological Research Letters, 74(6):761–772, 2003.
[26] Dmitry A Storchak, Johannes Schweitzer, and Peter Bormann. Seismic phase names: Iaspei standard. In Encyclopedia of Solid Earth Geophysics, pages 1162–1173. Springer, 2011.
[27] Tzay-Chyn Shin, Yi-Ben Tsai, and Yih-Min Wu. Rapid response of large earthquakes in taiwan using a real-time telemetered network of digital accelerographs. In Proc. 11th World Conf. Earthq. Eng., Paper, number 2137, 1996.
[28] Tzay-ChynShin, Chien-HsinChang, Hsin-ChiehPu, Hsiao-WeiLin, andPeih-Lin Leu. The geophysical database management system in taiwan. TAO: Terrestrial, Atmospheric and Oceanic Sciences, 24(1):11, 2013.
[29] M Baer and U Kradolfer. An automatic phase picker for local and teleseismic events. Bulletin of the Seismological Society of America, 77(4):1437–1445, 1987.
[30] Reinoud Sleeman and Torild Van Eck. Robust automatic p-phase picking: an online implementation in the analysis of broadband seismogram recordings. Physics of the earth and planetary interiors, 113(1-4):265–275, 1999.
[31] Christos D Saragiotis, Leontios J Hadjileontiadis, and Stavros M Panas. Pai-s/k: A robust automatic seismic p phase arrival identification scheme. IEEE Transactions on Geoscience and Remote Sensing, 40(6):1395–1404, 2002.
[32] C. Baillard, W. C. Crawford, V. Ballu, C. Hibert, and A. Mangeney. An automatic kurtosis-based p- and s-phase picker designed for local seismic networks. Bulletin of the Seismological Society of America, 104(1):394–409, November2013.
[33] SEJ Nippress, A Rietbrock, and AE Heath. Optimized automatic pickers: application to the ancorp data set. Geophysical Journal International, 181(2):911–925, 2010.
[34] Zachary E Ross and Yehuda Ben-Zion. Automatic picking of direct p, s seismic Phases and fault zone headwaves. Geophysical Journal International, 199(1):368-381, 2014.
[35] A. Lomax, C. Satriano, and M. Vassallo. Automatic picker developments and optimization: Filter picker–a robust, broadband picker for real-time seismic monitoring and earthquake early warning. Seismological Research Letters, 83(3):531–540, May 2012.
[36] Andy Jurkevics. Polarization analysis of three-component array data. Bulletin of the seismological society of America, 78(5):1725–1743, 1988.
[37] Artur Cichowicz. An automatic s-phase picker. Bulletin of the Seismological Society of America, 83(1):180–189, 1993.
[38] Isabelle Guyon and André Elisseeff. An introduction to variable and feature selection. Journal of machine learning research, 3(Mar):1157–1182, 2003.
[39] HDai and CDMacBeth. Identifying p- and s-waves using artificial neural network. In 57th EAGE Conference and Exhibition, pages cp–90. European Association of Geoscientists & Engineers, 1995.
[40] Sharan Chetlur, Cliff Woolley, Philippe Vandermersch, Jonathan Cohen, John Tran, Bryan Catanzaro, and Evan Shelhamer. cudnn: Efficient primitives for deep learning. arXiv preprint arXiv:1410.0759, 2014.
[41] Geoffrey Hinton, Li Deng, Dong Yu, George E Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara N Sainath, and Brian Kingsbury. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal processing magazine, 29(6):82–97, 2012.
[42] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097–1105, 2012.
[43] Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep learning. MIT press, 2016.
[44] Thibaut Perol, Michaël Gharbi, and Marine Denolle. Convolutional neural network for earthquake detection and location. Science Advances, 4(2):e1700578, 2018.
[45] Zachary E Ross, Men-Andrin Meier, Egill Hauksson, and Thomas H Heaton. Generalized seismic phase detection with deep learning. Bulletin of the Seismological Society of America, 108(5A):2894–2901, 2018.
[46] Qingkai Kong, Daniel T Trugman, Zachary E Ross, Michael J Bianco, Brendan J Meade, and Peter Gerstoft. Machine learning in seismology: Turning data into insights. Seismological Research Letters, 90(1):3–14, 2019.
[47] Lijun Zhu, Zhigang Peng, James McClellan, Chenyu Li, Dongdong Yao, Zefeng Li, and Lihua Fang. Deep learning for seismic phase detection and picking in the aftershock zone of 2008 mw7.9 wenchuan earthquake. Physics of the Earth and Planetary Interiors, 293:106261, 2019.
[48] Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence, 40(4):834–848, 2017.
[49] Jo Schlemper, Ozan Oktay, Michiel Schaap, Mattias Heinrich, Bernhard Kainz, Ben Glocker, and Daniel Rueckert. Attention gated networks: Learning to leverage salient regions in medical images. Medical image analysis, 53:197–207, 2019.
[50] Ming Liang and Xiaolin Hu. Recurrent convolutional neural network for object recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3367–3375, 2015.
[51] Benjamin Holtzman, Jason Candler, Matthew Turk, and Daniel Peter. Seismic sound lab: Sights, sounds and perception of the earth as an acoustic space. In International Symposium on Computer Music Multidisciplinary Research, pages 161–174. Springer, 2013.
[52] Arthur Paté, Lapo Boschi, Jean-Loic Le Carrou, and Benjamin Holtzman. Categorization of seismic sources by auditory display: A blind test. International journal of human-computer studies, 85:57–67, 2016.
[53] Arthur Paté, Lapo Boschi, Danièle Dubois, Jean-Loic Le Carrou, and Benjamin Holtzman. Auditory display of seismic data: On the use of experts’ categorizations and verbal descriptions as heuristics for geoscience. The Journal of the Acoustical Society of America, 141(3):2143–2162, 2017.
[54] LapoBoschi, LaurianneDelcor, Jean-LoïcLeCarrou, ClaudiaFritz, ArthurPaté, and Benjamin Holtzman. On the perception of audified seismograms. Seismological Research Letters, 88(5):1279–1289, 2017.
[55] Alex Graves, Santiago Fernández, Faustino Gomez, and Jürgen Schmidhuber. Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks. In Proceedings of the 23rd international conference on Machine learning, pages 369–376, 2006.
[56] Alex Graves, Abdel-rahman Mohamed, and Geoffrey Hinton. Speech recognition with deep recurrent neural networks. In 2013 IEEE international conference on acoustics, speech and signal processing, pages 6645–6649. IEEE, 2013.
[57] P. Foggia, N. Petkov, A. Saggese, N. Strisciuglio, and M. Vento. Audio surveillance of roads: A system for detecting anomalous sounds. IEEE Transactions on Intelligent Transportation Systems, 17(1):279–288, 2015. Conference Name: IEEE Transactions on Intelligent Transportation Systems.
[58] William Chan, Navdeep Jaitly, Quoc Le, and Oriol Vinyals. Listen, attend and spell: Aneuralnetworkforlargevocabularyconversationalspeechrecognition. In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 4960–4964. IEEE, 2016.
[59] Navdeep Jaitly, Quoc V Le, Oriol Vinyals, Ilya Sutskever, David Sussillo, and Samy Bengio. An online sequence-to-sequence model using partial conditioning. In Advances in Neural Information Processing Systems, pages 5067–5075, 2016.
[60] Chung-Cheng Chiu and Colin Raffel. Monotonic chunkwise attention. arXiv preprint arXiv:1712.05382, 2017.
[61] S. Adavanne, A. Politis, J. Nikunen, and T. Virtanen. Sound event localization and detection of overlapping sources using convolutional recurrent neural networks. IEEE Journal of Selected Topics in Signal Processing, 13(1):34–48, 2018. Conference Name: IEEE Journal of Selected Topics in Signal Processing.
[62] En-Jui Lee, Po Chen, Thomas H Jordan, Phillip B Maechling, Marine AM Denolle, and Gregory C Beroza. Full-3-d tomography for crustal structure in southern california based on the scattering-integral and the adjoint-wavefield methods. Journal of Geophysical Research: Solid Earth, 119(8):6421–6451, 2014.
[63] En-Jui Lee, Po Chen, and Thomas H Jordan. Testing waveform predictions of 3d velocity models against two recent los angeles earthquakes. Seismological Research Letters, 85(6):1275–1284, 2014.
[64] Po Chen and En-Jui Lee. Full-3D seismic waveform inversion: theory, software and practice. Springer, 2015.
[65] En-Jui Lee and Po Chen. Automating seismic waveform analysis for full 3-d waveform inversions. Geophysical Journal International, 194(1):572–589, 2013.
[66] Luc Vincent and Pierre Soille. Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Transactions on Pattern Analysis & Machine Intelligence, 13(6):583–598, 1991.
[67] Md Zahangir Alom, Mahmudul Hasan, Chris Yakopcic, Tarek M Taha, and Vijayan K Asari. Recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation. arXiv preprint arXiv:1802.06955, 2018.
[68] Kaiming He,Xiangyu Zhang, Shaoqing Ren,and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016.
[69] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473, 2014.
[70] Haijiang Zhang, Clifford H. Thurber, and Charlotte Rowe. Automatic p-wave arrival detection and picking with multiscale wavelet analysis for single-component recordings. Bulletin of the Seismological Society of America, 93(5):1904–1912, 2003.
[71] Juan J Galiana-Merino, Julio Luis Rosa-Herranz, and Stefano Parolai. Seismic p phase picking using a kurtosis-based criterion in the stationary wavelet domain. IEEE Transactions on Geoscience and Remote Sensing, 46(11):3815–3826, 2008.
[72] En-Jui Lee, Wu-Yu Liao, Guan-Wei Lin, Po Chen, Dawei Mu, and Ching-Weei Lin. Towards automated real-time detection and location of large-scale landslides through seismic waveform back projection. Geofluids, 2019, 2019.
[73] Shui-Beih Yu, Horng-Yue Chen, and Long-Chen Kuo. Velocity field of gps stations in the taiwan area. Tectonophysics, 274(1):41–59, 1997.
[74] Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2818–2826, 2016.
[75] Lion Krischer, Tobias Megies, Robert Barsch, Moritz Beyreuther, Thomas Lecocq, Corentin Caudron, and Joachim Wassermann. Obspy: A bridge for seismology into the scientific python ecosystem. Computational Science & Discovery, 8(1):014003, 2015.
[76] SCEDC. Southern california earthquake data center, 2013.
[77] Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE international conference on computer vision, pages 618–626, 2017.
[78] Peter M Shearer. Improving local earthquake locations using the l1 norm and waveform cross correlation : Application to the whittier narrows, california, aftershock sequence. Journal of Geophysical Research: Solid Earth, 102:8269–8283, 1997.
[79] Felix Waldhauser and William L. Ellsworth. A double-difference earthquake location algorithm: Methodandapplicationtothenorthernhaywardfault, california. Bulletin of the seismological society of America, 90(6):1353–1368, 2003.
[80] Haijiang Zhang and Clifford H. Thurber. Double-difference tomography: The method and its application to the hayward fault, california. Bulletin of the seismological society of America, 93:1875–1889, 2003.
[81] Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, and Ameet Talwalkar. Hyperband: a novel bandit-based approach to hyperparameter optimization. The Journal of Machine Learning Research, 18(1):6765–6816, 2017.
[82] Daniel Golovin, Benjamin Solnik, Subhodeep Moitra, Greg Kochanski, John Karro, and D. Sculley. Google vizier: A service for black-box optimization. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 1487–1495. ACM, 2017.
[83] D Sculley, Jasper Snoek, Ali Rahimi, and Alex Wiltschko. ON PACE, PROGRESS, AND EMPIRICAL RIGOR. page 4, 2018.
[84] En-Jui Lee, Wu-Yu Liao, Dawei Mu, Wei Wang, and Po Chen. Gpu-accelerated automatic microseismic monitoring algorithm (gamma) and its application to the 2019 ridgecrest earthquake sequence. Seismological Research Letters, 91(4):2062–2074, March 2020.
[85] Zhigang Peng and Peng Zhao. Migration of early aftershocks following the 2004 parkfield earthquake. Nature Geoscience, 2(12):877–881, November 2009.
[86] David R. Shelly and David P. Hill. Migrating swarms of brittle-failure earthquakes in the lower crust beneath mammoth mountain, california: Lower-crustal earthquake swarms. Geophysical Research Letters, 38(20), October 2011.
[87] Zachary E. Ross, Benjamín Idini, Zhe Jia, Oliver L. Stephenson, Minyan Zhong, Xin Wang, Zhongwen Zhan, Mark Simons, Eric J. Fielding, Sang-Ho Yun, Egill Hauksson, Angelyn W. Moore, Zhen Liu, and Jungkyo Jung. Hierarchical interlocked orthogonal faulting in the 2019 ridgecrest earthquake sequence. Science, 366(6463):346–351, October 2019.
[88] Zachary E. Ross, Daniel T. Trugman, Egill Hauksson, and Peter M. Shearer. Searching for hidden earthquakes in southern california. Science, 364(6442):767–771, May 2019.
[89] Aitaro Kato and Yehuda Ben-Zion. The generation of large earthquakes. Nature Reviews Earth & amp; Environment, 2(1):26–39, November 2020.
[90] Dawei Mu, En-Jui Lee, and Po Chen. Rapid earthquake detection through gpu-based template matching. Computers &; Geosciences, 109:305–314, December 2017.
[91] En-Jui Lee, Dawei Mu, Wei Wang, and Po Chen. Weighted template-matching algorithm (wtma) for improved foreshock detection of the 2019 ridgecrest earthquake sequence. Bulletin of the Seismological Society of America, 110(4):1832–1844, June 2020.
[92] Fengzhou Tan, Honn Kao, Edwin Nissen, and David Eaton. Seismicity-scanning based on navigated automatic phase-picking. Journal of Geophysical Research: Solid Earth, 124(4):3802–3818, April 2019.
[93] Jacob I. Walter, Paul Ogwari, Andrew Thiel, Fernando Ferrer, and Isaac Woelfel. easyQuake: Putting Machine Learning to Work for Your Regional Seismic Network or Local Earthquake Study. Seismological Research Letters, 92(1):555–563, 11 2020.
[94] Yijian Zhou, Han Yue, Lihua Fang, Shiyong Zhou, Li Zhao, and Abhijit Ghosh. An earthquake detection and location architecture for continuous seismograms: Phase picking, association, location, and matched filter (palm). Seismological Research Letters, 93(1):413–425, September 2021.
[95] Jubran Akram and David W. Eaton. A review and appraisal of arrival-time picking methods for downhole microseismic data. GEOPHYSICS, 81(2):KS71–KS91, March 2016.
[96] B. Weber, J. Becker, W. Hanka, A. Heinloo, M. Hoffmann, T. Kraft, D. Pahlke, J. Reinhardt, J. Saul, and H. Thoms. Seiscomp3 — automatic and interactive realtime data processing. InGeophysical Research Abstracts, number9, page219, Vienna, Austria, 2007. General Assembly European Geosciences Union (EGU).
[97] William L. Yeck, John M. Patton, Caryl E. Johnson, David Kragness, Harley M. Benz, Paul S. Earle, Michelle R. Guy, and Nicholas B. Ambruz. Glass3: A standalone multiscale seismic detection associator. Bulletin of the Seismological Society of America, 109(4):1469–1478, May 2019.
[98] Kiwamu NISHIDA. Ambient seismic wave field. Proceedings of the Japan Academy, Series B, 93(7):423–448, 2017.
[99] Katrin Löer, Nima Riahi, and Erik H Saenger. Three-component ambient noise beamforming in the parkfield area. Geophysical Journal International, 213(3):1478–1491, February 2018.
[100] Miaki Ishii, Peter M. Shearer, Heidi Houston, and John E. Vidale. Extent, duration and speed of the 2004 sumatra–andaman earthquake imaged by the hi-net array. Nature, 435(7044):933–936, June 2005.
[101] Eric Kiser and Miaki Ishii. Back-projection imaging of earthquakes. Annual Review of Earth and Planetary Sciences, 45(1):271–299, August 2017.
[102] N. Langet, A. Maggi, A. Michelini, and F. Brenguier. Continuous kurtosis-based migration for seismic event detection and location, with application to piton de la fournaise volcano, la reunion. Bulletin of the Seismological Society of America, 104(1):229–246, January 2014.
[103] Weiqiang Zhu and Gregory C Beroza. PhaseNet: a deep-neural-network-based seismic arrival-time picking method. Geophysical Journal International, 216(1):261–273, 10 2018.
[104] Yen-Che Liao, Honn Kao, Andreas Rosenberger, Shu-Kun Hsu, and Bor-Shouh Huang. Delineating complex spatiotemporal distribution of earthquake aftershocks: an improved source-scanning algorithm: Improved source-scanning algorithm. Geophysical Journal International, 189(3):1753–1770, April 2012.
[105] Julian Drew, Robert S. White, Frederik Tilmann, and Jon Tarasewicz. Coalescence microseismic mapping. Geophysical Journal International, 195(3):1773–1785, September 2013.
[106] Clifford Thurber and Donna Eberhart-Phillips. Local earthquake tomography with flexible gridding. Computers &; Geosciences, 25(7):809–818, August 1999.
[107] Fred W. Klein. User’s guide to HYPOINVERSE-2000, a Fortran program to solve for earthquake locations and magnitudes. 2002.
[108] Charles F. Richter. An instrumental earthquake magnitude scale*. Bulletin of the Seismological Society of America, 25(1):1–32, January 1935.
[109] David M. Boore. The richter scale: its development and use for determining earthquake source parameters. Tectonophysics, 166(1–3):1–14, September 1989.
[110] Seth Stein and Michael Wysession. An Introduction to Seismology, Earthquakes, and Earth Structure. Wiley-Blackwell, 2002.
[111] Elizabeth S. Cochran, Emily Wolin, Daniel E. McNamara, Alan Yong, David Wilson, Marcos Alvarez, Nicholas van der Elst, Adria McClain, and Jamison Steidl. The u.s. geological survey’s rapid seismic array deployment for the 2019 ridgecrest earthquake sequence. Seismological Research Letters, 91(4):1952–1960, January 2020.
[112] Laura Gulia, Stefan Wiemer, and Gianfranco Vannucci. Pseudoprospective evaluation of the foreshock traffic-light system in ridgecrest and implications for aftershock hazard assessment. Seismological Research Letters, 91(5):2828–2842, July 2020.
[113] Hiroo Kanamori. Real-time seismology and earthquake damage mitigation. Annual Review of Earth and Planetary Sciences, 33(1):195–214, 2005.
[114] Tsuneo Katayama and Shigeki Horiuchi. Early Earthquake Warning (EEW) System: Overview, pages 621–629. Springer Berlin Heidelberg, Berlin, Heidelberg,
2015.
[115] Richard M. Allen and Diego Melgar. Earthquake early warning: Advances, scientific challenges, and societal needs. Annual Review of Earth and Planetary Sciences, 47(1):361–388, 2019.
[116] Richard M. Allen and Hiroo Kanamori. The potential for earthquake early warning in southern california. Science, 300(5620):786–789, 2003.
[117] H. Serdar Kuyuk, Richard M. Allen, Holly Brown, Margaret Hellweg, Ivan Henson, and Douglas Neuhauser. Designing a Network-Based Earthquake Early Warning Algorithm for California: ElarmS-2. Bulletin of the Seismological Society of America, 104(1):162–173, 12 2013.
[118] Angela Chung, Ivan Henson, and Richard Allen. Optimizing earthquake early warning performance: Elarms-3. Seismological Research Letters, 90, 01 2019.
[119] Nai-Chi Hsiao, Y. Wu, Tzay-Chyn Shin, Li Zhao, and Ta-Liang Teng. Development of earthquake early warning system in taiwan. Geophysical Research Letters - GEOPHYS RES LETT, 36, 01 2009.
[120] Da-Yi Chen, Nai-Chi Hsiao, and Yih-Min Wu. The Earthworm Based Earthquake Alarm Reporting System in Taiwan. Bulletin of the Seismological Society of America, 105(2A):568–579, 02 2015.
[121] Y. Wu and Li Zhao. Magnitude estimation using the first three second p-wave amplitude in earthquake early warning. Geophysical Research Letters - GEOPHYS RES LETT, 331, 01 2006.
[122] Y. Wu, Hsin-Yi Yen, Li Zhao, Bor-Shouh Huang, and Wen-Tzong Liang. Magnitude determination using initial p waves: A single-station approach. Geophysical Research Letters - GEOPHYS RES LETT, 330, 03 2006.
[123] M. Böse, Egill Hauksson, Krupali Solanki, H. Kanamori, Y. Wu, and T. Heaton. A new trigger criterion for improved real-time performance of onsite earthquake early warning in southern california. Bulletin of the Seismological Society of America, 99, 04 2009.
[124] Ting-Yu Hsu, Pei-Yang Lin, H. Wang, H. Chiang, Y. Chang, Chun-Hsiang Kuo, C. Lin, and K. Wen. Comparing the performance of the neews earthquake early warning system against the cwb system during the February 6 2018 mw6.2 hualien earthquake. Geophysical Research Letters, 45, 06 2018.
[125] Fuyuki Hirose, Kazuki Miyaoka, Naoki Hayashimoto, Takayuki Yamazaki, and Masaki Nakamura. Outline of the 2011 off the pacific coast of tohoku earthquake (mw 9.0) - seismicity: foreshocks, mainshock, aftershocks, and induced activity. Earth, Planets, and Space, 63:513–518, 07 2011.
[126] Shota Hara, Yukitoshi Fukahata, and Yoshihisa Iio. P-wave first-motion polarity determination of waveform data in western japan using deep learning. Earth, Planets and Space, 71, 12 2019.
[127] Laura Gulia and Stefan Wiemer. Real-time discrimination of earthquake foreshocks and aftershocks. Nature, 574:193–199, 10 2019.
[128] Sebastian Ruder. An overview of multi-task learning in deep neural networks. arXiv preprint:1706.05098, 2017.
[129] Michael Crawshaw. Multi-task learning with deep neural networks: A survey. arXiv preprint:2009.09796, 2020.
[130] Roberto Cipolla, Yarin Gal, and Alex Kendall. Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages7482–7491, 2018.
[131] C. Doersch and A. Zisserman. Multi-task self-supervised visual learning. In 2017 IEEE International Conference on Computer Vision (ICCV), pages 2070–2079, Los Alamitos, CA, USA, oct 2017. IEEE Computer Society.
[132] Xiaodong Liu, Pengcheng He, Weizhu Chen, and Jianfeng Gao. Multi-task deep neural networks for natural language understanding. arXiv preprint:1901.11504, 2019.
[133] Shikun Liu, Edward Johns, and Andrew J Davison. End-to-end multi-task learning with attention. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1871–1880, 2019.
[134] Zhao Chen, Vijay Badrinarayanan, Chen-Yu Lee, andAndrew Rabinovich. Grad-norm: Gradient normalization for adaptive loss balancing in deep multitask networks. arXiv preprint:1711.02257, 11 2017.
[135] Institute of Earth Sciences Academia Sinica. Broadband array in taiwan for seismology, 1994.
[136] Yih-Min Wu, Da-Yi Chen, Ting-Li Lin, Chih-Yih Hsieh, Tai-Lin Chin, Wen-Yen Chang, Wei-Sen Li, and Shaw-Hsung Ker. A High-Density Seismic Network for Earthquake Early Warning in Taiwan Based on Low Cost Sensors. Seismological Research Letters, 84(6):1048–1054, 11 2013.
[137] Kun-Sung Liu, Tzay-Chyn Shin, and Yi-Ben Tsai. A Free-Field Strong Motion Network in Taiwan: TSMIP. Terrestrial, Atmospheric and Oceanic Sciences (TAO), 10(2):377–396, 11 1999.
[138] Antonino D’Alessando, Salvatore Scudero, and Giovanni Vitale. A review of the capacitive mems for seismology. Sensors, 19:3093, 07 2019.
[139] M. Krautblatter and M. Moser. A nonlinear model coupling rockfall and rainfall intensity based on a four year measurement in a high alpine rock wall (reintal, german alps). Natural Hazards and Earth System Sciences, 9(4):1425–1432, 2009.
[140] Agnès Helmstetter and Stéphane Garambois. Seismic monitoring of séchilienne rockslide (french alps): Analysis of seismic signals and their correlation with rainfalls. Journal of Geophysical Research, 115(F3), August 2010.
[141] A. Delonca, Y. Gunzburger, and T. Verdel. Statistical correlation between meteorological and rockfall databases. Natural Hazards and Earth System Sciences, 14(8):1953–1964, 2014.
[142] J. D’Amato, D. Hantz, A. Guerin, M. Jaboyedoff, L. Baillet, and A. Mariscal. Influence of meteorological factors on rockfall occurrence in a middle mountain limestone cliff. Natural Hazards and Earth System Sciences, 16(3):719–735, 2016.
[143] M.Lato, J.Hutchinson, M.Diederichs, D.Ball, and R.Harrap. Engineering monitoring of rockfall hazards along transportation corridors: using mobile terrestrial lidar. Natural Hazards and Earth System Sciences, 9(3):935–946, 2009.
[144] Hengxing Lan, C. Derek Martin, Chenghu Zhou, and Chang Ho Lim. Rockfall hazard analysis using lidar and spatial modeling. Geomorphology, 118(1):213–223, 2010.
[145] Matthew J. Lato, Mark S. Diederichs, D. Jean Hutchinson, and Rob Harrap. Evaluating roadside rockmasses for rockfall hazards using LiDAR data: optimizing data collection and processing protocols. Natural Hazards, 60(3):831–864, June 2011.
[146] Manuel Jesús Royán, Antonio Abellán, Michel Jaboyedoff, Joan Manuel Vilaplana, and Jaume Calvet. Spatio-temporal analysis of rockfall pre-failure deformation using terrestrial LiDAR. Landslides, 11(4):697–709, November 2013.
[147] Ali Mutar Fanos, Biswajeet Pradhan, Shattri Mansor, Zainuddin Md Yusoff, and Ahmad Fikri bin Abdullah. A hybrid model using machine learning methods and gis for potential rockfall source identification from airborne laser scanning data. Landslides, 15(9):1833–1850, April 2018.
[148] Norikazu Matsuoka. A multi-method monitoring of timing, magnitude and origin of rockfall activity in the japanese alps. Geomorphology, 336:65–76, 2019.
[149] C. Hibert, A. Mangeney, G. Grandjean, and N. M. Shapiro. Slope instabilities in dolomieu crater, réunion island: From seismic signals to rockfall characteristics. Journal of Geophysical Research: Earth Surface, 116(F4), 2011.
[150] Franziska Dammeier, Jeffrey R. Moore, Florian Haslinger, and Simon Loew. Characterization of alpine rockslides using statistical analysis of seismic signals. Journal of Geophysical Research, 116(F4), November 2011.
[151] Valerie L. Zimmer and Nicholas Sitar. Detection and location of rock falls using seismic and infrasound sensors. Engineering Geology, 193:49–60, 2015.
[152] C. Hammer, M. Ohrnberger, and D. Fäh. Classifying seismic waveforms from scratch: a case study in the alpine environment. Geophysical Journal International, 192(1):425–439, November 2012.
[153] C. Hibert, A. Mangeney, G. Grandjean, C. Baillard, D. Rivet, N. M. Shapiro, C. Satriano, A. Maggi, P. Boissier, V. Ferrazzini, and W. Crawford. Automated identification, location, and volume estimation of rockfalls at piton de la fournaise volcano. Journal of Geophysical Research: Earth Surface, 119(5):1082–1105, May 2014.
[154] Alessia Maggi, Valérie Ferrazzini, Clément Hibert, François Beauducel, Patrice Boissier, and Amandine Amemoutou. Implementation of a multistation approach for automated event classification at piton de la fournaise volcano. Seismological Research Letters, 88(3):878–891, March 2017.
[155] Clément Hibert, Floriane Provost, Jean-Philippe Malet, Alessia Maggi, André Stumpf, and Valérie Ferrazzini. Automatic identification of rockfalls and volcano-tectonic earthquakes at the piton de la fournaise volcano using a random forest algorithm. Journal of Volcanology and Geothermal Research, 340:130–142, 2017.
[156] F. Provost, C. Hibert, and J.-P. Malet. Automatic classification of endogenous landslide seismicity using the random forest supervised classifier. Geophysical Research Letters, 44(1):113–120, 2017.
[157] Marielle Malfante, Mauro Dalla Mura, Jerome I. Mars, Jean-Philippe Métaxian, Orlando Macedo, and Adolfo Inza. Automatic classification of volcano seismic signatures. Journal of Geophysical Research: Solid Earth, 123(12):10,645–10,658, 2018.
[158] M. Wenner, C. Hibert, A. van Herwijnen, L. Meier, and F. Walter. Near-real-time automated classification of seismic signals of slope failures with continuous random forests. Natural Hazards and Earth System Sciences, 21(1):339–361, 2021.
[159] Millaray Curilem, Fernando Huenupan, Daniel Beltrán, Cesar San Martin, Gustavo Fuentealba, Luis Franco, Carlos Cardona, Gonzalo Acuña, Max Chacón, M. Salman Khan, and Nestor Becerra Yoma. Pattern recognition applied to seismic signals of llaima volcano (chile): An evaluation of station-dependent classifiers. Journal of Volcanology and Geothermal Research, 315:15–27, April 2016.
[160] Marielle Malfante, Mauro Dalla Mura, Jean-Philippe Metaxian, Jerome I. Mars, Orlando Macedo, and Adolfo Inza. Machine learning for volcano-seismic signals: Challenges and perspectives. IEEE Signal Processing Magazine, 35(2):20–30, March 2018.
[161] P. Bottelin, D. Jongmans, D. Daudon, A. Mathy, A. Helmstetter, V. Bonilla- Sierra, H. Cadet, D. Amitrano, V. Richefeu, L. Lorier, L. Baillet, P. Villard, and F. Donzé. Seismic and mechanical studies of the artificially triggered rockfall at mount néron (french alps, december 2011). Natural Hazards and Earth System Sciences, 14(12):3175–3193, dec 2014.
[162] Liang Feng, Veronica Pazzi, Emanuele Intrieri, Teresa Gracchi, and Giovanni Gigli. Rockfall seismic features analysis based on in situ tests: frequency, amplitude, and duration. Journal of Mountain Science, 16(5):955–970, may 2019.
[163] C. Hibert, F. Noël, D. Toe, M. Talib, M. Desrues, E. Wyser, O. Brenguier, F. Bourrier, R. Toussaint, J.-P. Malet, and M. Jaboyedoff. Machine learning prediction of the mass and the velocity of controlled single-block rockfalls from the seismic waves they generate. EGUsphere, 2022:1–25, 2022.
[164] Andrew Ng. Machine learning yearning. URL: http://www. mlyearning.org/(96), 139, 2017.
[165] Alberto Michelini, Spina Cianetti, Sonja Gaviano, Carlo Giunchi, Dario Jozinović, and Valentino Lauciani. Instance the italian seismic dataset for machine learning, 2021.
[166] Zhiheng Huang, Wei Xu, and Kai Yu. Bidirectional lstm-crf models for sequence tagging. ArXiv, abs/1508.01991, 2015.
[167] DiederikP.KingmaandJimmyBa. Adam: Amethodforstochasticoptimization, 2014.
[168] Jiaxin Jiang, Vladimir Stankovic, Lina Stankovic, Emmanouil Parastatidis, and Stella Pytharouli. Microseismic event classification with time-, frequency-, and wavelet-domainconvolutionalneuralnetworks. IEEE Transactions on Geoscience and Remote Sensing, 61:1–14, 2023.
[169] F. Provost, J.-P. Malet, C. Hibert, A. Helmstetter, M. Radiguet, D. Amitrano, N.Langet, E.Larose, C.Abancó, M.Hürlimann, T.Lebourg, C.Levy, G.LeRoy, P. Ulrich, M. Vidal, and B. Vial. Towards a standard typology of endogenous landslide seismic sources. Earth Surface Dynamics, 6(4):1059–1088, 2018.
[170] Daesoo Lee, Erlend Aune, Nadège Langet, and Jo Eidsvik. Ensemble and self-supervised learning for improved classification of seismic signals from the Åknes rockslope. Mathematical Geosciences, 55(3):377–400, November 2022.
[171] Nadège Langet and Fred Marcus John Silverberg. Automated classification of seismic signals recorded on the Åknes rock slope, western norway, using a convolutional neural network. Earth Surface Dynamics, 11(1):89–115, February 2023.
[172] Chung-Ching Wang, En-Jui Lee, Wu-Yu Liao, Po Chen, Ruey-Juin Rau, Guan-Wei Lin, and Chung-Ray Chu. Cluster Analysis of Slope Hazard Seismic Recordings Based Upon Unsupervised Deep Embedded Clustering. Seismological Research Letters, 04 2023.
[173] M. Raissi, P. Perdikaris, and G.E. Karniadakis. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 378:686–707, February 2019.
[174] M.A.Ganaie, MinghuiHu, A.K.Malik, M.Tanveer, andP.N.Suganthan. Ensemble deep learning: A review. Engineering Applications of Artificial Intelligence, 115:105151, October 2022.
[175] Jia-Xiang Lian, Wu-Yu Liao, En-Jui Lee, Da-Yi Chen, and Po Chen. Integration of machine learning and equal differential time method for enhanced hypocenter localization in earthquake early warning systems: application to dense seismic arrays in taiwan. Earth, Planets and Space, 76(1), July 2024.