Journal: Seismological Research Letters

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Seismological Society of America

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0895-0695
1938-2057

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Publications 1 - 10 of 82
  • Variability of Seismicity Rates and Maximum Magnitude for Adjacent Hydraulic Stimulations
    Item type: Journal Article
    Kwiatek, Grzegorz; Grigoratos, Iason; Wiemer, Stefan (2025)
    Seismological Research Letters
    We hindcasted the seismicity rates and the next largest earthquake magnitude using seismic and hydraulic data from two hydraulic stimulation campaigns carried out in adjacent (500 m apart) ultra‐deep wells in Finland. The two campaigns performed in 2018 and 2020 took place in the frame of the St1 Helsinki project producing stable, pressure‐controlled induced seismic activity with the maximum magnitudes of 1.7 and 1.2, respectively. The seismicity rates were modeled using simplified physics‐based approaches tailored to varying injection rates. This is the first time that this framework was applied to a cyclical injection protocol. The next largest earthquake magnitude was estimated using several existing models from the literature. Despite the close proximity of the two hydraulic stimulations and associated seismicity, we obtained strongly different parameterizations of the critical model components, questioning the usefulness of a priori seismic hazard modeling parameters for neighboring stimulation. The differences in parameterization were attributed to the contrasting hydraulic energy rates observed in each stimulation, small differences in the fracture network characteristics of the reservoir and resulting seismic injection efficiency, and potentially to variations in the injection protocol itself. As far as the seismicity rate model is concerned, despite a good performance during the 2018 campaign, the fit during the 2020 stimulation was suboptimal. Forecasting the next largest magnitude using different models led to a very wide range of outcomes. Moreover, their relative ranking across stimulations was inconsistent, including the situation when the best‐performing model in the 2018 stimulation turned out to be the worst one in the 2020 stimulation.
  • Searching the InSight Seismic Data for Mars’s Background-Free Oscillations
    Item type: Journal Article
    Duran, Andrea Cecilia; Khan, Amir; Kemper, Johannes Maximilian; et al. (2025)
    Seismological Research Letters
    Mars’s atmosphere has theoretically been predicted to be strong enough to continuously excite Mars’s background-free oscillations, potentially providing an independent means of verifying radial seismic body-wave models of Mars determined from marsquakes and meteorite impacts recorded during the Interior Exploration using Seismic Investigations, Geodesy, and Heat Transport (InSight) mission. To extract the background-free oscillations, we processed and analyzed the continuous seismic data, consisting of 966 Sols (a Sol is equivalent to a Martian day), collected by the Mars InSight mission using both automated and manual deglitching schemes to remove nonseismic disturbances. We then computed 1-Sol-long autocorrelations for the entire data set and stacked these to enhance any normal-mode peaks present in the spectrum. We find that while peaks in the stacked spectrum in the 2–4 mHz frequency band align with predictions based on seismic body-wave models and appear to be consistent across the different processing and stacking methods applied, unambiguous detection of atmosphere-induced free oscillations in the Martian seismic data nevertheless remains difficult. This possibly relates to the limited number of Sols of data that stack coherently and the continued presence of glitch-related signal that affects the seismic data across the normal-mode frequency range (∼1–10 mHz). Improved deglitching schemes may allow for clearer detection and identification in the future.
  • MALMI: An Automated Earthquake Detection and Location Workflow Based on Machine Learning and Waveform Migration
    Item type: Journal Article
    Shi, Peidong; Grigoli, Francesco; Lanza, Federica; et al. (2022)
    Seismological Research Letters
    Robust automatic event detection and location is central to real-Time earthquake monitoring. With the increase of computing power and data availability, automated workflows that utilize machine learning (ML) techniques have become increasingly popular; however, ML-based classical workflows still face challenges when applied to the analysis of microseismic data. These seismic sequences are often characterized by short interevent times and/or low signal-To-noise ratio (SNR). Full waveform methods that do not rely on phase picking and association are suitable for processing such datasets, but are computationally costly and lack clear event identification criteria, which is not ideal for real-Time processing. To leverage the advantages of both the methods, we propose a new workflow-MAchine Learning aided earthquake MIgration location (MALMI), which integrates ML and waveform migration to perform automated event detection and location. The new workflow uses a pretrained ML model to generate continuous phase probabilities that are then backprojected and stacked to locate seismic sources using migration. We applied the workflow to one month of continuous data collected in the Hengill geothermal area of Iceland to monitor induced earthquakes around two geothermal production sites. With a ML model (EQ-Transformer) pretrained using a global distribution of earthquakes, the proposed workflow automatically detects and locates 250 additional seismic events (accounting for 36% events in the obtained catalog) compared to a reference catalog generated using the SeisComP software. Most of the new events are microseismic events with a magnitude less than 0. Visual inspection of the waveforms of the newly detected events indicates that they are real seismic events of low SNR and are only reliably recorded by very few stations in the array. Further comparison with the conventional migration method based on short-Term average over long-Term average confirms that MALMI can produce much clearer stacked images with higher resolution and reliability, especially for events with low SNR. The workflow is freely available on GitHub, providing an automated tool for simultaneous event detection and location from continuous seismic data.
  • Seismic Source Inversion Using Hamiltonian Monte Carlo and a 3D Earth Model for the Japanese Islands
    Item type: Other Conference Item
    Simutė, Saulė; Fichtner, Andreas (2019)
    Seismological Research Letters ~ SSA Annual Meeting: Book of Abstracts
  • Preparing for InSight: Evaluation of the Blind Test for Martian Seismicity
    Item type: Journal Article
    van Driel, Martin; Ceylan, Savas; Clinton, John Francis; et al. (2019)
    Seismological Research Letters
  • On-demand custom broadband synthetic seismograms
    Item type: Journal Article
    Krischer, Lion; Hutko, Alexander R.; van Driel, Martin; et al. (2017)
    Seismological Research Letters
  • Preparing for InSight: An Invitation to Participate in a Blind Test for Martian Seismicity
    Item type: Journal Article
    Clinton, John Francis; Giardini, Domenico; Lognonne, Philippe; et al. (2017)
    Seismological Research Letters
  • A Self‐Noise Model for the German DEPAS OBS Pool
    Item type: Journal Article
    Stähler, Simon Christian; Schmidt-Aursch, Mechita C.; Hein, Gerrit; et al. (2018)
    Seismological Research Letters
  • Counterfactual Analysis of Runaway Earthquakes
    Item type: Journal Article
    Woo, Gordon; Mignan, Arnaud (2018)
    Seismological Research Letters
  • An Almost Fair Comparison Between Earthworm and SeisComp3
    Item type: Journal Article
    Olivieri, Marco; Clinton, John Francis (2012)
    Seismological Research Letters
Publications 1 - 10 of 82