<figure><img src="image_1.png" /></figure>

HAL
open science

# A Global-scale Database of Seismic Phases from Cloud-based Picking at Petabyte Scale

Yiyu Ni, Marine Denolle, Amanda Thomas, Alex Hamilton, Jannes Münchmeyer, Yinzhi Wang, Loïc Bachelot, Chad Trabant, David Mencin

<figure><img src="image_2.png" /></figure>

## To cite this version:

Yiyu Ni, Marine Denolle, Amanda Thomas, Alex Hamilton, Jannes Münchmeyer, et al. A Global-scale Database of Seismic Phases from Cloud-based Picking at Petabyte Scale. Seismica, 2025, 4 (2), 10.26443/seismica.v4i2.1738. hal-05350500

HAL Id: hal-05350500

https://hal.science/hal-05350500v1

Submitted on 6 Nov 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_3.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# A Global-scale Database of Seismic Phases from Cloud-based Picking at Petabyte Scale

Yiyu Ni ID* 1, Marine A. Denolle ID 1, Amanda M. Thomas ID 2, Alex Hamilton ID 3, Jannes Münchmeyer ID 4,
Yinzhi Wang ID 5, Loïc Bachelot ID 6, Chad Trabant ID 3, David Mencin ID 3

¹Department of Earth and Space Sciences, University of Washington, Seattle, WA, USA, ²Department of Earth and Planetary Sciences, University of California, Davis, CA, USA, ³EarthScope Consortium, Washington, DC, USA, ⁴Université Grenoble Alpes, Université Savoie Mont Blanc, CNRS, IRD, Université Gustave Eiffel, ISTerre, Grenoble, France, ⁵Texas Advanced Computing Center, University of Texas, Austin, TX, USA, ⁶Cascadia Region Earthquake Science Center, University of Oregon, Eugene, OR, USA

**Author contributions:** Conceptualization: MD, AMT, JM, YW, YN, LB. Methodology: MD, JM, YW, YN, AMT. Software: YN, JM, YW. Validation: YN, AMT. Formal Analysis: YN, JM, YW, AMT. Investigation: YN, JM, YW, MD, AMT. Resources: MD, AMT. Writing - Original draft: AMT, MD, YN. Writing - Review & Editing: AMT, MD, YN, LB, JM, CT, DM. Visualization: MD, YN. Supervision: MD, AMT. Project Administration: MD, AMT. Funding Acquisition: MD, YW.

**Abstract** 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.

Production Editor:

Gareth Funning

Handling Editor:

Hongyu Sun

Copy & Layout Editor:

Hannah F. Mark

Signed reviewer(s):

Piero Poli

Received:

May 25, 2025

Accepted:

August 24, 2025

Published:

September 6, 2025

## 1 Introduction

Detecting earthquakes by picking P- and S-wave arrival times is fundamental to seismology. It enables rapid estimation of earthquake source properties, provides early warning for potential ground shaking, and supports seismic hazard assessment. Picking the arrival time is also the first step in building earthquake catalogs. Traditional earthquake detection methods are typically unsupervised and rely on signal characteristics such as impulsivity, using techniques like the short-term average/long-term average (STA/LTA) filter (e.g., Allen, 1982) or kurtosis-based approaches (Hibert et al., 2014). However, these methods are highly sensitive to background seismic noise, limiting their effectiveness to small-magnitude events or recordings from particularly quiet stations.

Recent advances in seismic data processing techniques demonstrate that artificial intelligence (AI) and machine learning (ML) overcome these limitations and have shown high performance in earthquake detection (Perol et al., 2018; Ross et al., 2018; Mousavi et al., 2019b), phase picking (Zhu and Beroza, 2019; Zhu et al., 2022; Mousavi et al., 2020; Ross et al., 2020; Michelini et al., 2021), and phase association (Ross et al., 2019b; Mousavi et al., 2020; McBrearty and Beroza, 2023). Su-

pervised deep learning approaches to earthquake detection and phase identification require and have benefited from large, labeled datasets (e.g., several hundred thousand examples of P-waves, S-waves, and noise, along with the timing of body wave arrivals) for model training. These datasets are often compiled from analyst-reviewed phase picks cataloged by regional seismic networks and compiled by researchers for AI-readiness (Zhu and Beroza, 2019; Mousavi et al., 2019a; Yeck et al., 2021; Ni et al., 2023; Zhu et al., 2025). Once trained, deep-learning-based detection and phase picking frameworks have significantly outperformed more conventional approaches in sensitivity and timing accuracy. They can detect arrivals in data with low signal-to-noise ratios and reliably pick arrivals to within less than 0.1 s (Zhu and Beroza, 2019; Mousavi et al., 2020), without requiring manual intervention for hyperparameters. Additionally, once trained, these models are inference-only, making them extremely fast and scalable. These successes, combined with the abundance of readily available continuous seismic data, facilitate large-scale regional and global data mining efforts.

Data volumes in seismology are expanding rapidly, with researchers now typically tackling datasets of TBs in size that require advanced computing strategies for analysis. Cloud computing is a promising infrastructure that can support this by enhancing the reliability, scalability, and accessibility of data (Gentemann

<footnote>*Corresponding author: niyiyu@uw.edu</footnote>
<figure><img src="image_4.png" /><figcaption>**Figure 1** Map of stations, displayed as triangles and color coded according to the Data availability in station-years included in EarthScope, NCEDC, and SCEDC archives. Panel (a) shows the distribution of global station data availability. Panel (b) shows a detailed view of stations in the United States, while Panel (c) shows data availability in California.</figcaption></figure>

et al., 2021), promoting reproducible and open science. Most importantly for users, cloud computing provides large-scale computing power close to the data archives, eliminating the need to download massive datasets and enabling users to run analyses in-place on the cloud using cloud-native tools (e.g., elastic computing, batch computing, scalable storage, and database services). We refer the readers to Ni et al. (2025) for comprehensive descriptions of various AWS resources commonly used for scientific computing, including the ones used in our experiment. The Southern California Seismic Network (SCEDC) has been hosting a copy of its entire data archive on the Amazon Web Services (AWS) Simple Storage Service (S3) since 2019 (Yu et al., 2021). The Northern California Earthquake Data Center (NCEDC) followed a similar model and architecture in 2024. The EarthScope Consortium is a non-profit organization that supports Earth science research by collecting, managing, and providing access to seismic and geodetic data collected worldwide from all United States (US) NSF-supported seismic experiments, a subset of the US regional seismic networks, and a selection of stations from global seismic networks (Zawacki et al., 2023). It serves the scientific community by operating and maintaining networks of instruments, curating data and metadata, and delivering these to end-users. In recent years, the EarthScope Consortium decided to migrate data collection, archiving, and delivery services to the cloud.

Here we present results from one of the first large-scale, cloud-based seismic data-mining efforts. We processed approximately 1.3 PB of continuous seismic data from stations worldwide (see Figure 1). Leveraging modern cloud infrastructure, we developed a scalable cloud-native workflow to efficiently manage and analyze this extensive dataset. The sections below describe the waveform formats, cloud architecture, and com-

pute resource utilization. We also describe the structure and contents of the resulting phase-pick database and provide public access through a web service. This work demonstrates the feasibility and advantages of using cloud computing for robust, high-throughput seismic waveform analysis. At the same time, the resulting database can serve as a starting point for the community to study seismicity globally. As in a typical catalog workflow, phase picking is the most runtime-intensive step. We believe that our database, providing global-scale, high-quality phase picks, has the potential to substantially accelerate seismicity studies worldwide.

# 2 Methods

## 2.1 Data

The NCEDC provides public access to continuous seismic waveform data through AWS, utilizing the AWS Open Data Sponsorship program. NCEDC continuous waveform data are hosted in the ncedc-pds S3 bucket in the us-east-2 region (Ohio, United States). Files are organized by network, year, and day of year, and are stored in miniSEED format, with each file representing one day of data from a single channel. Similarly, the SCEDC offers public access through the scedc-pds S3 bucket in the us-west-2 region (Oregon, United States). The EarthScope Consortium provides data access to credentialed users through an S3 Access Point, which gives fine-grained control over permissions and network access. While users interact with the Access Point much like a standard S3 bucket, internally, requests are routed through a Lambda function that handles S3 requests, enabling dynamic access management and custom logic. The combined archives represent 47,354 stations recording data between January 1, 2002, and March 31, 2025, for a total of more than 1.3 PB of
<figure><img src="image_5.png" /><figcaption>**Figure 2** The scalable cloud-native workflow for seismic phase picking. Containerized jobs are submitted to AWS Batch, which loads miniSEED seismic waveforms directly from AWS S3 buckets. Phase arrivals were identified with PhaseNet through the SeisBench implementation. A DocumentDB cluster is employed to store job metadata, picks, and checkpoints. Finally, an EC2 instance is used to provide a public database query service.</figcaption></figure>

continuous seismic data (Figure 1).

## 2.2 Workflow

Several earthquake catalog building workflows exist (Walter et al., 2021; Zhang et al., 2022; Retailleau et al., 2022; Sun et al., 2024), some of which have focused on cloud deployments (Zhu et al., 2023; Krauss et al., 2023). Here, we develop a scalable cloud-native workflow for seismic data processing designed for large-scale data mining using AWS services, which we illustrate in Figure 2.

The workflow begins with a user-specified list of station codes and a defined time range. A unique combination of 40 stations and a 20-day time window is paired as one job, which is submitted to an AWS Batch computing queue. We request 8 vCPU and 16 GB RAM for each job, with interruptible SPOT instances enabled. In contrast to the on-demand counterpart, SPOT instances utilize unused AWS capacity with a discount (up to 90%) but can be arbitrarily recalled. With appropriate retrial and checkpoint mechanisms implemented, SPOT instances effectively optimize the cost efficiency of our workflow. The submitted jobs stay pending until requested resources are supplied, automatically elevating queued jobs into the running state until the account quota is reached. With a 12,000 vCPU account quota, the computing queue allows 1,500 jobs to run in parallel. A running job first retrieves instrument response information from the EarthScope International Federation of Digital Seismograph Stations

(FDSN) fdsnws-station service via the ObsPy library (Beyreuther et al., 2010). A temporary user credential is requested from EarthScope to specifically enable EarthScope S3 access, while no credential is required for SCEDC and NCEDC. Waveforms are loaded directly from the S3 buckets by mapping the network code to the appropriate data center's bucket, bypassing the need for middleware returning waveforms such as the FDSN fdsnws-datasetselect service. For this project, we processed all available seismic data from January 1, 2002, to March 31, 2025, across channels with the following codes: EH?, HH?, BH?, HN?, EP?, DP?, EL?, SL?, SH?, CN?. When stations host multiple location codes or channel types, we pick and record them separately in the workflow. We skip waveforms that are empty, embargoed, or contain over 50 gaps per component.

Once data is acquired, phase arrivals are identified using PhaseNet (Zhu and Beroza, 2019), a deep learning-based algorithm for automatic P- and S-wave detection, trained on the INSTANCE dataset (Michelini et al., 2021), implemented through the SeisBench framework (Münchmeyer et al., 2022; Woollam et al., 2022). We extract ground velocity estimates around each peak if horizontal components exist. Each job's output includes phase picks, job metadata, and checkpoint information during processing. These are stored in an AWS DocumentDB server, which functions as a NoSQL database for tracking job progress and storing results. This cloud-native architecture supports high throughput, fault-tolerant processing of large-scale seismic datasets, leveraging the scalability and modularity of
<table><caption>**Table 1** DocumentDB Collections and Schema Summary</caption><thead><tr><th></th><th>Field</th><th>Description</th><th>Type</th><th>Example</th></tr></thead><tbody><tr><td rowspan="10">8*stations</td><td>trace_id</td><td>Station identifier</td><td>str</td><td>UW.SHW.01</td></tr><tr><td>network_code</td><td>Network code</td><td>str</td><td>UW</td></tr><tr><td>station_code</td><td>Station code</td><td>str</td><td>SHW</td></tr><tr><td>location_code</td><td>Location code (if any)</td><td>str</td><td>01</td></tr><tr><td>channels</td><td>Available channels</td><td>str</td><td>SH</td></tr><tr><td>latitude</td><td>Latitude of station</td><td>float</td><td>46.19364</td></tr><tr><td>longitude</td><td>Longitude of station</td><td>float</td><td>-122.23492</td></tr><tr><td>elevation</td><td>Elevation in meters</td><td>float</td><td>1442.0</td></tr><tr><td>start_date</td><td>Operational start date</td><td>str</td><td>1972.275</td></tr><tr><td>end_date</td><td>Operational end date</td><td>str</td><td>3000.001</td></tr><tr><td rowspan="7">7*picks</td><td>trace_id</td><td>Station identifier</td><td>str</td><td>CI.SVD.</td></tr><tr><td>channel</td><td>Channel code</td><td>str</td><td>HH</td></tr><tr><td>start_time</td><td>Start of pick window</td><td>datetime</td><td>2012-01-01T02:45:37.400</td></tr><tr><td>peak_time</td><td>Peak amplitude time</td><td>datetime</td><td>2012-01-01T02:45:38.270</td></tr><tr><td>end_time</td><td>End of pick window</td><td>datetime</td><td>2012-01-01T02:45:38.610</td></tr><tr><td>confidence</td><td>ML confidence score</td><td>float</td><td>0.48456</td></tr><tr><td>amplitude</td><td>Signal amplitude</td><td>float</td><td>0.00027764</td></tr><tr><td rowspan="3">6*picks_record</td><td>phase</td><td>Phase type (P/S)</td><td>str</td><td>P</td></tr><tr><td>run_id</td><td>Associated run ID</td><td>ObjectId</td><td>ObjectId(...)</td></tr><tr><td>channel</td><td>Station identifier</td><td>str</td><td>CI.SVD.</td></tr><tr><td rowspan="5">6*sb_runs</td><td>year</td><td>Channel code</td><td>str</td><td>HH</td></tr><tr><td>doy</td><td>Year of record</td><td>int</td><td>2012</td></tr><tr><td>n_picks</td><td>Day of year</td><td>int</td><td>1</td></tr><tr><td>run_id</td><td>Number of picks</td><td>int</td><td>12</td></tr><tr><td>run_id</td><td>Associated run ID</td><td>ObjectId</td><td>ObjectId(...)</td></tr><tr><td rowspan="8">6*sb_runs</td><td>run_id</td><td>Run ID</td><td>ObjectId</td><td>ObjectId(...)</td></tr><tr><td>model</td><td>ML model used</td><td>str</td><td>PhaseNet</td></tr><tr><td>weight</td><td>Model weight name</td><td>str</td><td>instance</td></tr><tr><td>p_threshold</td><td>P-phase detection threshold</td><td>float</td><td>0.2</td></tr><tr><td>s_threshold</td><td>S-phase detection threshold</td><td>float</td><td>0.2</td></tr><tr><td>components_load</td><td>Input component configuration</td><td>str</td><td>ZNE12</td></tr><tr><td>seisbench_ver</td><td>SeisBench version</td><td>str</td><td>0.8.2</td></tr><tr><td>weight_ver</td><td>Weight version</td><td>str</td><td>2</td></tr></tbody></table>

AWS cloud services — separating data storage (S3), compute (Batch), and output management (DocumentDB).

## 2.3 Database

We employ the AWS DocumentDB to store intermediate and final data products. The DocumentDB service is a NoSQL database that manages and stores data in a structured, flexible format. The database is organized into several collections, each representing a different category of data relevant to the seismic data processing workflows we've implemented here. Each collection contains documents (JSON-like records) with a defined set of fields representing specific information pieces. The collections are also indexed to improve performance and to avoid duplicate picks.

The DocumentDB server organizes station metadata and processing outputs into distinct collections (Table 1). This design enables efficient querying and auditing of picking results and the exact software and settings used to generate them. The schema is flexible enough to query independent information, as station information,

job configuration, and analysis results are stored independently but connected through unique IDs and timestamps. During this experiment, an I/O-optimized class instance (db.r6g.2xlarge, 8 vCPUs, 64 GB RAM) was employed as the DocumentDB server.

# 3 Results

## 3.1 Job Statistics

Mining the EarthScope dataset took less than three days, while the combined NCEDC and SCEDC datasets took less than 16 hours. Given the configurations detailed in Section 2.2, this experiment results in a total cost of ~$15,000. Since this was the first major data mining exercise on the EarthScope archive, job sets were launched manually, progressively, and actively monitored. Each set of jobs was intended to process a year of data recorded on all stations with channels matching those listed above. Figure 3a shows the progression of the jobs mining the EarthScope dataset, color-coded by
<figure><img src="image_6.png" /><figcaption>**Figure 3** Detailed job run history for the EarthScope, NCEDC, and SCEDC dataset. Panel (a) shows the Job ID as a function of time, color-coded by year the data was recorded, for the EarthScope dataset. Panel (b) shows the job progression for the NCEDC and SCEDC datasets. Panels (c) and (d) show the pending jobs as a function of time. Panels (e) and (f) show the running jobs as a function of time. The horizontal dashed line represents the job quota.</figcaption></figure>

the year the data was recorded. Figure 3c and e show the pending and running jobs as a function of time. Time periods where jobs were manually launched manifest as increases in the number of pending and running jobs. Since we used a quota of 1500 running jobs, the pending jobs decreased at an approximately constant rate. There are occasional abrupt decreases in the number of running jobs, for example, the one near 60 hours in Figure 3c, that correspond to times when stations are listed as available in the metadata service, but no data is actually loaded. Figure 3b, d, and f show the job ID, pending, and running as a function of time. For the NCEDC and SCEDC datasets, all jobs were submitted and queued simultaneously and not parsed by year, as they were with the EarthScope dataset.

## 3.2 Phase Picking

The mining exercise resulted in a total of 4.3 billion picks (2.8 billion P-wave picks, 1.5 billion S-wave picks), with the EarthScope, NCEDC, and SCEDC datasets containing 2.8, 1.1, and 0.4 billion picks, respectively. Figure S1 shows the total number of picks broken down by the dominant network codes in each dataset. The NC network has the largest number of picks at nearly 1 billion, followed by UW at 0.5 billion, and CI at 0.4 billion. These differences in the number of picks among networks may be factorized into multiple aspects: 1) the amount of data available, 2) the tectonic settings where stations are installed, 3) non-tectonic noise signals that could be incorrectly picked, and 4) noise level that affects the performance of the phase picker

Figure 4 shows examples of the daily number of picks as a function of time for ten stations around the world. Station UW.BVW is a station near Beverly, WA, atop the Saddle Mountains. UW.BVW displays a strong seasonality of detections, with detection rates peaking in the late summer. IU.FURI is a station in southern Adis Ababa, Ethiopia. This station shows a large number of detections beginning in late 2024 and extending into early 2025. These detections likely correspond to a swarm of earthquakes that began in late September and produced 19 M5+ earthquakes, the largest of which was a M5.9 on February 14th, 2025. IU.MAJO in Matsushiro, Japan, clearly recorded aftershocks from the 2011 M9 Tohoku-oki, 2014 M6.2 Hakuba, and 2024 M7.5 Noto earthquakes, as well as many others. AK.MCK in McKinley Park, AK shows strong seasonality in detections and recorded the 2002 M7.9 Denali earthquake. HV.KKO in Hawai'i records seismicity from the 2018 M6.9 earthquake and ongoing eruptive activity from the Kilauea volcano. NZ.KHZ in Kahutara, New Zealand, recorded the 2013 M6.5 Blenheim and the 2016 M7.8 Kaikōura earthquakes and aftershocks. IU.QSPA at the South Pole shows a strong seasonality of detections and also records increased detections beginning in late 2024, possibly due to a series of M5+ events near the Balleny Islands and on the Pacific-Antarctic Ridge. C.GO04 at the Tololo Observatory, Vicuna, Chile, records the 2015 M8.3 Illapel and the M6.7 Coquimbo earthquakes. II.NNA records the 2007 M8.0 Pisco, Peru earthquake. Finally, CI.CGO records the 2009 M4.7 Inglewood, 2009 M5.8 Northern Baja California, the M6.4 and M7.1 2019
<figure><img src="image_7.png" /><figcaption>**Figure 4** Daily picks for selected stations. Stations are indicated by triangles on the central map, annotated with location and channel codes. For each of the ten example stations, the detail plots show a time series of the number of picks per day and a 28-day moving average in red.</figcaption></figure>

Ridgecrest, and the 2020 M5.8 Lone Pine earthquakes.

Figure 5 shows the number of picks and earthquakes after three selected large earthquakes: (a) 2015 M8.3 Illapel earthquake, (b) 2016 M7.8 Kaikōura earthquake, and (c) 2002 M7.9 Denali earthquake. In each case, the daily pick count follows approximately an Omori decay law (Utsu, 1961). While the Omori decay in the event count can only be observed for a short period in the regional and global reference catalogs used, they are stable over longer durations in the pick count. For the Kaikōura earthquake, the Omori decay in pick counts is stable over more than 1000 days. Notably, the Omori *p* values, describing the decay rate, differ between picks and events with systematically higher *p* values for events, indicating a faster decay. For example, for the Illapel earthquake, we estimate *p* = 0.87 for the event counts, a typical value, yet *p* = 0.49 for the pick counts, a surprisingly low value. We suggest this difference originates from the joint effects of event rate and event magnitude on the pick counts. This highlights the value of analyzing pick count dynamics to study earthquake statistics. Several time series of picks shown in Figure 4 exhibit seasonality, demonstrating the potential effects of the noise floor.

# 4 Discussion

To estimate the total number of earthquakes we can identify from the 4.3 billion seismic phase picks, we begin by assessing how picks are distributed across different magnitude bins. Based on estimates from McBrearty and Beroza (2023), we expect an average of

approximately 20 P-wave picks per M1 earthquake, 60 picks per M2, and 100 picks per M3. Because earthquake frequency decreases with increasing magnitude, for each M1 event (20 picks), we expect roughly 0.1 M2 events (6 picks) and 0.01 M3 events (1 pick). This implies that M2s contribute only about 30% as many picks as M1s, and M3s contribute just 5%. While these estimates are for the dense network in California, the same trends, even though with shifted magnitude values, are observed in deep learning-based catalogs from other regions (e.g., Münchmeyer et al., 2025). This suggests that the dataset is overwhelmingly composed of picks associated with earthquakes near the regional detectability threshold, e.g., in densely instrumented regions like California M1 and smaller. Using this assumption, and taking 20 picks per event as a working average, along with an estimated association rate of 25%, based on typical values from literature (McBrearty and Beroza, 2023; Münchmeyer, 2024; Münchmeyer et al., 2025; Journeau et al., 2025), we arrive at a rough estimate of more than 54 million earthquakes that could be associated with the data. We note that many further picks will correspond to actual events that just lack sufficiently many detections to successfully associate them. For context, in the time period of interest, the Southern California Seismic Network (SCSN) catalog contains approximately 450,000 events, while the Advanced National Seismic System (ANSS) Comprehensive Earthquake Catalog (ComCat) includes about 4 million events for the United States and larger global events. Deep learning methods have already demonstrated the ability to increase catalog completeness by up to an order of magnitude in sparsely
<figure><img src="image_8.png" /><figcaption>**Figure 5** Omori-type decays of the number of picks per day (blue) and the number of events per day (red) after the main-shock. We use picks at the reference station provided in the figure title. Event counts for the Illapel earthquake are from the Chilean Seismic Network (CSN) catalog, for the other two examples from the International Seismological Centre (ISC) and United States Geological Survey (USGS) catalog. In each case, we count all events with at most a 1.5-degree difference in latitude and longitude from the reference station.</figcaption></figure>

instrumented regions (Park et al., 2022). Given the un-precedented scale of this pick database, we fully expect it to significantly expand the number of earthquakes identified globally, particularly in regions that have historically been under-detected.

We anticipate that the archive of picks will be of use to many researchers in the field of earthquake science. As such, we have made it available for public query using web services and URL builders (see Data and Code Availability). A dedicated EC2 instance receives HTTPS requests and directly returns query results. As an example, one can query the picking results for AK.MCK. station for one month of data using the Python script in Listing 1. The script uses the pandas package (https://pandas.pydata.org) and loads the query results as a data frame. We also offer a binary dump of the entire database for download.

Our demonstration is simply a starting point for future and more focused exploration. For instance, we did omit the NCEDC SP? channels that had a name change at some time during the network operation. We also omitted using specialized base models, such as PickBlue, that include hydrophone data and would be more appropriate for picking ocean-bottom seismometer (OBS) data (Bornstein et al., 2024). There are also opportunities to identify non-traditional seismicity, such as volcanic earthquakes (Zhong and Tan, 2024) or low-frequency earthquakes associated with slow slip events (Münchmeyer et al., 2024; Lin et al., 2024). The fully open-source and modular design of the workflow ensures reproducibility while allowing flexibility to incorporate different models and pre-trained weights optimized for different applications. This enables other researchers to easily adopt and extend the cloud-native workflow for their own analyses. Future work might consider using template matching to enhance our detections, as done in California (Ross et al., 2019a).

Moreover, we did not use data from other seismic data providers. For instance, we omitted the non-FDSN seismic networks, the Observatories and Research Facilities for European Seismology (ORFEUS) federated networks, and the AusPass networks. Pulling data from these data centers can be done using their FDSN web services; however, care must be taken not to overload the data service (e.g., MacCarthy et al., 2020). Because of the resiliency of the commercial cloud providers to spiked demand in data access, a strategy to improve the stability of the non-cloud-hosted archive is to use cloud storage as a backup in case of service interruption and route users toward the cloud-hosted archive instead of the local seismic network's data servers.

# 5 Conclusions

In conclusion, our data mining experiment plants the seed for impactful advances in geophysics. Cloud-based and AI-aided picking of P- and S-waves can be used to retrain neural networks and improve the rapid and precise assessment of earthquake hazards, such as earthquake early warning (e.g., Zhang and Zhang, 2024). These newly detected potential earthquakes and waveforms may be valuable to build foundational models for seismology that learn fundamental seismic signal patterns from massive waveform libraries (e.g., Wang et al., 2025; Liu et al., 2024), and then be fine-tuned for specific tasks (e.g., picking, polarity, backazimuth, etc.). Running this petabyte-scale workflow on the cloud also provides a testbed for greener computing with tools that can allow researchers to deploy carbon-aware computing jobs (e.g., West et al., 2025).
## Listing 1 Python query of the picking database

```python
import pandas as pd
base_url = "https://dasway.ess.washington.edu/quakescope/service/picks/"
url = f"{base_url}query?tid=AK.MCK.&start_time=2010-01-01&end_time=2010-02-01&limit=1000"
pick = pd.read_csv(url, delimiter="|")
```

## Acknowledgements

This work is supported by the Seismic Computational Platform for Empowering Discovery (SCOPED) project under the National Science Foundation (award numbers OAC-2103701 (UW), OAC-2103494 (UT)). YN and MD are also partially supported by the EarthScope Consortium through a Pass-Through Entity (PTE) Federal award no 2310069. The computing resources presented in this paper were obtained using CloudBank (Norman et al., 2021), which is supported by the National Science Foundation (award number CNS-1925001). Data were accessed from the NSF SAGE data repository operated by EarthScope Consortium (award number 1724509). JM has been funded by the European Union under the grant agreement n°101104996 (DECODE). The Cascadia Region Earthquake Science Center (CRESCENT), funded by National Science Foundation Cooperative Agreement #2225286, partially supported this project through funding to AMT and LB. AMT was also supported by the National Science Foundation (award number 1848302). We thank Dr. Piero Poli and an anonymous reviewer for their comments and suggestions that improve this manuscript.

## Data and Code Availability

Data from SCEDC and NCEDC is publicly available on AWS S3, while data hosted at EarthScope could be accessed through the EarthScope FDSN web service. The DOIs of FDSN seismic networks used in this study are listed at https://dasway.ess.washington.edu/quakescope/doi.csv and added as supplementary materials. All data products are hosted at https://dasway.ess.washington.edu/quakescope. URL builders are available for database queries. The database snapshots are also dumped as BSON (binary JSON) files. Our code base is hosted on GitHub at https://github.com/SeisSCOPED/QuakeScope.

## Competing Interests

The authors declare no competing interests.

## References

* Allen, R. Automatic phase pickers: Their present use and future prospects. *Bulletin of the Seismological Society of America*, 72 (6B):S225-S242, 12 1982. doi: 10.1785/BSSA07206B0225.

* Beyreuther, M., Barsch, R., Krischer, L., Megies, T., Behr, Y., and Wassermann, J. ObsPy: A Python toolbox for seismology. *Seismological Research Letters*, 81(3):530-533, 2010. doi: 10.1785/gssrl.81.3.530.

* Bornstein, T., Lange, D., Münchmeyer, J., Woollam, J., Rietbrock, A., Barcheck, G., Grevemeyer, I., and Tilmann, F. PickBlue: Seismic phase picking for ocean bottom seismometers with deep learning. *Earth and Space Science*, 11(1):e2023EA003332, 2024. doi: 10.1029/2023EA003332.

* Gentemann, C. L., Holdgraf, C., Abernathey, R., Crichton, D., Colliander, J., Kearns, E. J., Panda, Y., and Signell, R. P. Science storms the cloud. *AGU Advances*, 2(2):e2020AV000354, 2021. doi: 10.1029/2020AV000354.

* Hibert, C., Mangeney, A., Grandjean, G., Baillard, C., Rivet, D., Shapiro, N. M., Satriano, C., Maggi, A., Boissier, P., Ferrazzini, V., et al. Automated identification, location, and volume estimation of rockfalls at Piton de la Fournaise volcano. *Journal of Geophysical Research: Earth Surface*, 119(5):1082-1105, 2014. doi: 10.1002/2013JF002970.

* Journeau, C., Thomas, A., Abercrombie, R., Hirao, B., Toomey, D., Hooft, E., Liu, M., Barbot, S., and Kuna, V. OBS Data Mining and Earthquake Swarms Analysis Reveal the Complex Structure and Dynamics of the Blanco Fracture Zone. Technical report, Copernicus Meetings, 2025. doi: 10.5194/egusphere-egu25-14331.

* Krauss, Z., Ni, Y., Henderson, S., and Denolle, M. Seismology in the cloud: guidance for the individual researcher. *Seismica*, 2(2), 08 2023. doi: 10.26443/seismica.v2i2.979.

* Lin, J.-T., Thomas, A. M., Bachelot, L., Toomey, D., Searcy, J., and Melgar, D. Detection of Hidden Low-Frequency Earthquakes in Southern Vancouver Island with Deep Learning. *Seismica*, 2(4), 10 2024. doi: 10.26443/seismica.v2i4.1134.

* Liu, T., Münchmeyer, J., Laurenti, L., Marone, C., de Hoop, M. V., and Dokmanić, I. SeisLM: a Foundation Model for Seismic Waveforms. *arXiv preprint arXiv:2410.15765*, 2024. doi: 10.48550/arXiv.2410.15765.

* MacCarthy, J., Marcillo, O., and Trabant, C. Seismology in the Cloud: A New Streaming Workflow. *Seismological Research Letters*, 91(3):1804-1812, 03 2020. doi: 10.1785/0220190357.

* McBrearty, I. W. and Beroza, G. C. Earthquake phase association with graph neural networks. *Bulletin of the Seismological Society of America*, 113(2):524-547, 2023. doi: 10.1785/0120220182.

* Michelini, A., Cianetti, S., Gaviano, S., Giunchi, C., Jozinović, D., and Lauciani, V. INSTANCE-the Italian seismic dataset for machine learning. *Earth System Science Data*, 13(12):5509-5544, 2021. doi: 10.5194/essd-13-5509-2021.

* Mousavi, S. M., Sheng, Y., Zhu, W., and Beroza, G. C. STanford Earthquake Dataset (STEAD): A global data set of seismic signals for AI. *IEEE Access*, 7:179464-179476, 2019a. doi: 10.1109/ACCESS.2019.2947848.

* Mousavi, S. M., Zhu, W., Sheng, Y., and Beroza, G. C. CRED: A deep residual network of convolutional and recurrent units for earthquake signal detection. *Scientific reports*, 9(1):10267, 2019b. doi: 10.1038/s41598-019-45748-1.

* Mousavi, S. M., Ellsworth, W. L., Zhu, W., Chuang, L. Y., and Beroza, G. C. Earthquake transformer-an attentive deep-learning model for simultaneous earthquake detection and phase picking. *Nature Communications*, 11(1):3952, 2020. doi: 10.1038/s41467-020-17591-w.

* Münchmeyer, J. PyOcto: A high-throughput seismic phase associator. *Seismica*, 3(1), 2024. doi: 10.26443/seismica.v3i1.1130.
Münchmeyer, J., Woollam, J., Rietbrock, A., Tilmann, F., Lange, D., Bornstein, T., Diehl, T., Giunchi, C., Haslinger, F., Jozinović, D., et al. Which picker fits my data? A quantitative evaluation of deep learning based seismic pickers. *Journal of Geophysical Research: Solid Earth*, 127(1):e2021JB023499, 2022. doi: 10.1029/2021JB023499.

Münchmeyer, J., Molina-Ormazabal, D., Marsan, D., Langlais, M., Baez, J.-C., Heit, B., González-Vidal, D., Moreno, M., Tilmann, F., Lange, D., et al. Characterizing the Atacama segment of the Chile subduction margin (24 S–31 S) with> 165,000 earthquakes. *Journal of Geophysical Research: Solid Earth*, 130(7): e2025JB031256, 2025. doi: 10.1029/2025JB031256.

Münchmeyer, J., Giffard-Roisin, S., Malfante, M., Frank, W., Poli, P., Marsan, D., and Socquet, A. Deep learning detects uncataloged low-frequency earthquakes across regions. *Seismica*, 3(1), 05 2024. doi: 10.26443/seismica.v3i1.1185.

Ni, Y., Hutko, A., Skene, F., Denolle, M., Malone, S., Bodin, P., Hartog, R., and Wright, A. Curated Pacific Northwest Al-ready Seismic Dataset. *Seismica*, 2(1), May 2023. doi: 10.26443/seismica.v2i1.368.

Ni, Y., Denolle, M. A., Münchmeyer, J., Wang, Y., Feng, K.-F., Suarez, C. G. J., Thomas, A. M., Trabant, C., Hamilton, A., and Mencin, D. A Review of Cloud Computing and Storage in Seismology. *Geophysical Journal International*, page ggaf322, 2025. doi: 10.1093/gji/ggaf322.

Norman, M., Kellen, V., Smallen, S., DeMeulle, B., Strande, S., Lazowska, E., Alterman, N., Fatland, R., Stone, S., Tan, A., Yelick, K., Van Dusen, E., and Mitchell, J. CloudBank: Managed Services to Simplify Cloud Access for Computer Science Research and Education. In *Practice and Experience in Advanced Research Computing 2021: Evolution Across All Dimensions*, PEARC '21. Association for Computing Machinery, 2021. doi: 10.1145/3437359.3465586.

Park, Y., Beroza, G. C., and Ellsworth, W. L. Basement Fault Activation before Larger Earthquakes in Oklahoma and Kansas. *The Seismic Record*, 2(3):197–206, 08 2022. doi: 10.1785/0320220020.

Perol, T., Gharbi, M. J., and Denolle, M. Convolutional neural network for earthquake detection and location. *Science Advances*, 4(2):e1700578, 2018. doi: 10.1126/sciadv.1700578.

Retailleau, L., Saurel, J.-M., Zhu, W., Satriano, C., Beroza, G. C., Isartel, S., Boissier, P., Team, O., Team, O., et al. A wrapper to use a machine-learning-based algorithm for earthquake monitoring. *Seismological Research Letters*, 93(3):1673–1682, 2022. doi: 10.1785/0220210279.

Ross, Z. E., Meier, M.-A., Hauksson, E., and Heaton, T. H. Generalized seismic phase detection with deep learning. *Bulletin of the Seismological Society of America*, 108(5A):2894–2901, 2018. doi: 10.1785/0120180080.

Ross, Z. E., Trugman, D. T., Hauksson, E., and Shearer, P. M. Searching for hidden earthquakes in Southern California. *Science*, 364 (6442):767–771, 2019a. doi: 10.1126/science.aaw6888.

Ross, Z. E., Yue, Y., Meier, M.-A., Hauksson, E., and Heaton, T. H. PhaseLink: A deep learning approach to seismic phase association. *Journal of Geophysical Research: Solid Earth*, 124(1): 856–869, 2019b. doi: 10.1029/2018JB016674.

Ross, Z. E., Meier, M.-A., Hauksson, E., and Heaton, T. H. P-wave arrival picking and first-motion polarity determination with deep learning. *Journal of Geophysical Research: Solid Earth*, 125(4): e2019JB018663, 2020. doi: 10.1029/2017JB015251.

Sun, W.-F., Pan, S.-Y., Huang, C.-M., Guan, Z.-K., Yen, I.-C., Ho, C.-W., Chi, T.-C., Ku, C.-S., Huang, B.-S., Fu, C.-C., et al. Deep learning-based earthquake catalog reveals the seismogenic structures of the 2022 MW 6.9 Chihshang earthquake sequence. *Terres-

trial, Atmospheric and Oceanic Sciences, 35(1):5, 2024. doi: 10.1007/s44195-024-00063-9.

Utsu, T. A statistical study on the occurrence of aftershocks. *Geo-phys. Mag.*, 30:521–605, 1961.

Walter, J. I., Ogwari, P., Thiel, A., Ferrer, F., and Woelfel, I. easyQuake: Putting machine learning to work for your regional seismic network or local earthquake study. *Seismological Society of America*, 92(1):555–563, 2021. doi: 10.1785/0220200226.

Wang, X., Liu, F., Su, R., Wang, Z., Bai, L., and Ouyang, W. Seis-MoLLM: Advancing Seismic Monitoring via Cross-modal Transfer with Pre-trained Large Language Model. *arXiv preprint arXiv:2502.19960*, 2025. doi: 10.48550/arXiv:2502.19960.

West, K., Lehmann, F., Bountris, V., Leser, U., Elkhatib, Y., and Thamsen, L. Exploring the Potential of Carbon-Aware Execution for Scientific Workflows. *arXiv preprint arXiv:2503.13705*, 2025. doi: 10.48550/arXiv:2503.13705.

Woollam, J., Münchmeyer, J., Tilmann, F., Rietbrock, A., Lange, D., Bornstein, T., Diehl, T., Giunchi, C., Haslinger, F., Jozinović, D., et al. SeisBench—A toolbox for machine learning in seismology. *Seismological Society of America*, 93(3):1695–1709, 2022. doi: 10.1785/0220210324.

Yeck, W. L., Patton, J. M., Ross, Z. E., Hayes, G. P., Guy, M. R., Ambruz, N. B., Shelly, D. R., Benz, H. M., and Earle, P. S. Leveraging deep learning in global 24/7 real-time earthquake monitoring at the National Earthquake Information Center. *Seismological Society of America*, 92(1):469–480, 2021. doi: 10.1785/0220200178.

Yu, E., Bhaskaran, A., Chen, S., Ross, Z. E., Hauksson, E., and Clayton, R. W. Southern California Earthquake Data Now Available in the AWS Cloud. *Seismological Research Letters*, 92(5): 3238–3247, 06 2021. doi: 10.1785/0220210039.

Zawacki, E. E., Bendick, R., and Woodward, R. L. Advancing geophysics: IRIS and UNAVCO merge to form EarthScope Consortium, 2023. doi: 10.1029/2023CN000227.

Zhang, M., Liu, M., Feng, T., Wang, R., and Zhu, W. LOC-FLOW: An end-to-end machine learning-based high-precision earthquake location workflow. *Seismological Society of America*, 93 (5):2426–2438, 2022. doi: 10.1785/0220220019.

Zhang, X. and Zhang, M. Universal neural networks for real-time earthquake early warning trained with generalized earthquakes. *Communications Earth & Environment*, 5(1):528, 2024. doi: 10.1038/s43247-024-01718-8.

Zhong, Y. and Tan, Y. J. Deep-Learning-Based Phase Picking for Volcano-Tectonic and Long-Period Earthquakes. *Geophysical Research Letters*, 51(12):e2024GL108438, 2024. doi: 10.1029/2024GL108438.

Zhu, W. and Beroza, G. C. Phasenet: a deep-neural-network-based seismic arrival time picking method. *Geophysical Journal International*, 216(1):261–273, 2019. doi: 10.1093/gji/ggy423.

Zhu, W., McBrearty, I. W., Mousavi, S. M., Ellsworth, W. L., and Beroza, G. C. Earthquake phase association using a Bayesian Gaussian mixture model. *Journal of Geophysical Research: Solid Earth*, 127(5):e2021JB023249, 2022. doi: 10.1029/2021JB023249.

Zhu, W., Hou, A. B., Yang, R., Datta, A., Mousavi, S. M., Ellsworth, W. L., and Beroza, G. C. QuakeFlow: a scalable machine-learning-based earthquake monitoring workflow with cloud computing. *Geophysical Journal International*, 232(1):684–693, 2023. doi: 10.1093/gji/ggac355.

Zhu, W., Wang, H., Rong, B., Yu, E., Zuzlewski, S., Tepp, G., Taira, T., Marty, J., Husker, A., and Allen, R. M. California Earthquake Dataset for Machine Learning and Cloud Computing, 2025. doi: 10.48550/arXiv:2502.11500.
The article *A Global-scale Database of Seismic Phases from Cloud-based Picking at Petabyte Scale* © 2025 by Yiyu Ni is licensed under CC BY 4.0.