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1. Seismology in the cloud: guidance for the individual researcher

   https://doi.org/10.26443/seismica.v2i2.979  

   Krauss, Zoe; Ni, Yiyu; Henderson, Scott; Denolle, Marine (August 2023, Seismica)

   The commercial cloud offers on-demand computational resources that could be revolutionary for the seismological community, especially as seismic datasets continue to grow. However, there are few educational examples for cloud use that target individual seismological researchers. Here, we present a reproducible earthquake detection and association workflow that runs on Microsoft Azure. The Python-based workflow runs on continuous time-series data using both template matching and machine learning. We provide tutorials for constructing cloud resources (both storage and computing) through a desktop portal and deploying the code both locally and remotely on the cloud resources. We report on scaling of compute times and costs to show that CPU-only processing is generally inexpensive, and is faster and simpler than using GPUs. When the workflow is applied to one year of continuous data from a mid-ocean ridge, the resulting earthquake catalogs suggest that template matching and machine learning are complementary methods whose relative performance is dependent on site-specific tectonic characteristics. Overall, we find that the commercial cloud presents a steep learning curve but is cost-effective. This report is intended as an informative starting point for any researcher considering migrating their own processing to the commercial cloud. 

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2. A review of cloud computing and storage in seismology

   https://doi.org/10.1093/gji/ggaf322  

   Ni, Yiyu; Denolle, Marine A; Münchmeyer, Jannes; Wang, Yinzhi; Feng, Kuan-Fu; Garcia Jurado Suarez, Carlos; Thomas, Amanda M; Trabant, Chad; Hamilton, Alex; Mencin, David (August 2025, Geophysical Journal International)

   SUMMARY Seismology has entered the petabyte era, driven by decades of continuous recordings of broad-band networks, the increase in nodal seismic experiments and the recent emergence of distributed acoustic sensing (DAS). This review explains how cloud platforms, by providing object storage, elastic compute and managed data bases, enable researchers to ‘bring the code to the data,’ thereby providing a scalable option to overcome traditional HPC solutions’ bandwidth and capacity limitations. After literature reviews of cloud concepts and their research applications in seismology, we illustrate the capacities of cloud-native workflows using two canonical end-to-end demonstrations: (1) ambient noise seismology that calculates cross-correlation functions at scale, and (2) earthquake detection and phase picking. Both workflows utilize Amazon Web Services, a commercial cloud platform for streaming I/O and provenance, demonstrating that cloud throughput can rival on-premises HPC at comparable costs, scanning 100 TBs to 1.3 PBs of seismic data in a few hours or days of processing. The review also discusses research and education initiatives, the reproducibility benefits of containers and cost pitfalls (e.g. egress, I/O fees) of energy-intensive seismological research computing. While designing cloud pipelines remains non-trivial, partnerships with research software engineers enable converting domain code into scalable, automated and environmentally conscious solutions for next-generation seismology. We also outline where cloud resources fall short of specialized HPC—most notably for tightly coupled petascale simulations and long-term, PB-scale archives—so that practitioners can make informed, cost-effective choices. 

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3. Deep learning for deep earthquakes: insights from OBS observations of the Tonga subduction zone

   https://doi.org/10.1093/gji/ggae200  

   Xi, Ziyi; Wei, S Shawn; Zhu, Weiqiang; Beroza, Gregory C; Jie, Yaqi; Saloor, Nooshin (June 2024, Geophysical Journal International)

   SUMMARY Applications of machine learning in seismology have greatly improved our capability of detecting earthquakes in large seismic data archives. Most of these efforts have been focused on continental shallow earthquakes, but here we introduce an integrated deep-learning-based workflow to detect deep earthquakes recorded by a temporary array of ocean-bottom seismographs (OBSs) and land-based stations in the Tonga subduction zone. We develop a new phase picker, PhaseNet-TF, to detect and pick P- and S-wave arrivals in the time–frequency domain. The frequency-domain information is critical for analysing OBS data, particularly the horizontal components, because they are contaminated by signals of ocean-bottom currents and other noise sources in certain frequency bands. PhaseNet-TF shows a much better performance in picking S waves at OBSs and land stations compared to its predecessor PhaseNet. The predicted phases are associated using an improved Gaussian Mixture Model Associator GaMMA-1D and then relocated with a double-difference package teletomoDD. We further enhance the model performance with a semi-supervised learning approach by iteratively refining labelled data and retraining PhaseNet-TF. This approach effectively suppresses false picks and significantly improves the detection of small earthquakes. The new catalogue of Tonga deep earthquakes contains more than 10 times more events compared to the reference catalogue that was analysed manually. This deep-learning-enhanced catalogue reveals Tonga seismicity in unprecedented detail, and better defines the lateral extent of the double-seismic zone at intermediate depths and the location of four large deep-focus earthquakes relative to background seismicity. It also offers new potential for deciphering deep earthquake mechanisms, refining tomographic models, and understanding of subduction processes. 

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4. ARRU Phase Picker: Attention Recurrent-Residual U-Net for Picking Seismic P - and S -Phase Arrivals

   https://doi.org/10.1785/0220200382  

   Liao, Wu-Yu; Lee, En-Jui; Mu, Dawei; Chen, Po; Rau, Ruey-Juin (March 2021, Seismological Research Letters)

   null (Ed.)

   Abstract Seismograms are convolution results between seismic sources and the media that seismic waves propagate through, and, therefore, the primary observations for studying seismic source parameters and the Earth interior. The routine earthquake location and travel-time tomography rely on accurate seismic phase picks (e.g., P and S arrivals). As data increase, reliable automated seismic phase-picking methods are needed to analyze data and provide timely earthquake information. However, most traditional autopickers suffer from low signal-to-noise ratio and usually require additional efforts to tune hyperparameters for each case. In this study, we proposed a deep-learning approach that adapted soft attention gates (AGs) and recurrent-residual convolution units (RRCUs) into the backbone U-Net for seismic phase picking. The attention mechanism was implemented to suppress responses from waveforms irrelevant to seismic phases, and the cooperating RRCUs further enhanced temporal connections of seismograms at multiple scales. We used numerous earthquake recordings in Taiwan with diverse focal mechanisms, wide depth, and magnitude distributions, to train and test our model. Setting the picking errors within 0.1 s and predicted probability over 0.5, the AG with recurrent-residual convolution unit (ARRU) phase picker achieved the F1 score of 98.62% for P arrivals and 95.16% for S arrivals, and picking rates were 96.72% for P waves and 90.07% for S waves. The ARRU phase picker also shown a great generalization capability, when handling unseen data. When applied the model trained with Taiwan data to the southern California data, the ARRU phase picker shown no cognitive downgrade. Comparing with manual picks, the arrival times determined by the ARRU phase picker shown a higher consistency, which had been evaluated by a set of repeating earthquakes. The arrival picks with less human error could benefit studies, such as earthquake location and seismic tomography. 

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5. Comparison of Automatic Event Detection Algorithms: A Case Study in East Antarctica

   Ho, Long M; Walter, Jacob I; Hansen, Samantha E; Sánchez-Roldán, José; Peng, Zhigang (December 2023, American Geophysical Union)

   As seismic data availability increases, the necessity for automated processing techniques has become increasingly evident. Expanded geophysical datasets collected over the past several decades across Antarctica provide excellent resources to evaluate different event detection approaches. We have used the traditional Short-Term Average/Long-Term Average (STA/LTA) algorithm to catalogue seismic data recorded by 19 stations in East Antarctica between 2012 and 2015. However, the complexities of the East Antarctic dataset, including low magnitude earthquakes and other types of seismic events such as icequakes or firnquakes, warrant more advanced automated detection techniques. Therefore, we have also applied template matching as well as several deep learning algorithms, including Generalized Phase Detection (GPD), PhaseNet, BasicPhaseAE, and EQTransformer (EQT), to identify seismic phases within our dataset. Our goal is not only to increase the volume of detectable seismic events but also to gain insights into the effectiveness of these different automated approaches. Our assessment evaluates the completeness of the newly generated catalogs, the precision of identified event locations, and the quality of the picks. The performance of these different event detection techniques applied to continuous seismic data from a polar environment will be highlighted. We will also identify potential limitations and necessary adjustments for deep learning algorithm training, which is essential for their reliable application to specific datasets. 

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