Search for: All records

Abstract This article introduces a general processing framework to effectively utilize waveform data stored on modern cloud platforms. The focus is hybrid processing schemes for which a local system drives processing. We show that downloading files and doing all processing locally is problematic even when the local system is a high-performance computing (HPC) cluster. Benchmark tests with parallel processing show that approach always creates a bottleneck as the volume of data being handled increases with more processes pulling data. We find a hybrid model for which processing to reduce the volume of data transferred from the cloud servers to the local system can dramatically improve processing time. Tests implemented with the Massively Parallel Analysis System for Seismology (MsPASS) utilizing Amazon Web Service’s (AWS) Lambda service yield throughput comparable to processing day files on a local HPC file system. Given the ongoing migration of seismology data to cloud storage, our results show doing some or all processing on the cloud will be essential for any processing involving large volumes of data. 

We present the first global-scale database of 4.3 billion P- and S-wave picks extracted from 1.3 PB continuous seismic data via a cloud-native workflow. Using cloud computing services on Amazon Web Services, we launched ~145,000 containerized jobs on continuous records from 47,354 stations spanning 2002-2025, completing in under three days. Phase arrivals were identified with a deep learning model, PhaseNet, through an open-source Python ecosystem for deep learning, SeisBench. To visualize and gain a global understanding of these picks, we present preliminary results about pick time series revealing Omori-law aftershock decay, seasonal variations linked to noise levels, and dense regional coverage that will enhance earthquake catalogs and machine-learning datasets. We provide all picks in a publicly queryable database, providing a powerful resource for researchers studying seismicity around the world. This report provides insights into the database and the underlying workflow, demonstrating the feasibility of petabyte-scale seismic data mining on the cloud and of providing intelligent data products to the community in an automated manner. 

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. 

Abstract With the rise of data volume and computing power, seismological research requires more advanced skills in data processing, numerical methods, and parallel computing. We present the experience of conducting training workshops in various forms of delivery to support the adoption of large-scale high-performance computing (HPC) and cloud computing, advancing seismological research. The seismological foci were on earthquake source parameter estimation in catalogs, forward and adjoint wavefield simulations in 2D and 3D at local, regional, and global scales, earthquake dynamics, ambient noise seismology, and machine learning. This contribution describes the series of workshops delivered as part of research projects, the learning outcomes for participants, and lessons learned by the instructors. Our curriculum was grounded on open and reproducible science, large-scale scientific computing and data mining, and computing infrastructure (access and usage) for HPC and the cloud. We also describe the types of teaching materials that have proven beneficial to the instruction and the sustainability of the program. We propose guidelines to deliver future workshops on these topics. 

A burst buffer is a common method to bridge the performance gap between the I/O needs of modern supercomputing applications and the performance of the shared file system on large-scale supercomputers. However, existing I/O sharing methods require resource isolation, offline profiling, or repeated execution that significantly limit the utilization and applicability of these systems. Here we present ThemisIO, a policy-driven I/O sharing framework for a remote-shared burst buffer: a dedicated group of I/O nodes, each with a local storage device. ThemisIO preserves high utilization by implementing opportunity fairness so that it can reallocate unused I/O resources to other applications. ThemisIO accurately and efficiently allocates I/O cycles among applications, purely based on real-time I/O behavior without requiring user-supplied information or offline-profiled application characteristics. ThemisIO supports a variety of fair sharing policies, such as user-fair, size-fair, as well as composite policies, e.g., group-then-user-fair. All these features are enabled by its statistical token design. ThemisIO can alter the execution order of incoming I/O requests based on assigned tokens to precisely balance I/O cycles between applications via time slicing, thereby enforcing processing isolation. Experiments using I/O benchmarks show that ThemisIO sustains 13.5--13.7% higher I/O throughput and 19.5--40.4% lower performance variation than existing algorithms. For real applications, ThemisIO significantly reduces the slowdown by 59.1--99.8% caused by I/O interference. 

This article introduces a new framework for seismic data processing and management we call the Massive Parallel Analysis System for Seismologists (MsPASS). The framework was designed to enable new scientific frontiers in seismology by providing a means to more effectively utilize massively parallel computers to handle the increasingly large data volume available today. MsPASS leverages several existing technologies: (1) scalable parallel processing frameworks, (2) NoSQL database management system, and (3) containers. The system leans heavily on the widely used ObsPy toolkit. It automates many database operations and provides a mechanism to automatically save the processing history for reproducibility. The synthesis of these components can provide flexibility to adapt to a wide range of data processing workflows. We demonstrate the system with a basic data processing workflow applied to USArray data. Through extensive documentation and examples, we aim to make this system a sustainable, open‐source framework for the community.