High-Resolution Seismic Catalog Expansion Using a Single Deep Borehole Sensor: A Confidence-Based, Network-Informed Framework
Abstract
High-resolution seismic catalogs are essential for understanding induced seismicity, reservoir dynamics, and improving seismic hazard assessment, yet conventional surface and shallow-borehole networks often fail to detect low-magnitude events. Here, we demonstrate how a single deep-borehole sensor can enhance catalog resolution using a confidence-based framework integrating machine-learning phase picking, waveform similarity, and a site-specific data-quality metric into a unified detection score. The approach combines sensitive single-station detection with network-informed relative location and empirical magnitude estimation to generate a consistent high-resolution catalog. Applied to continuous data from a 2052-m-deep sensor at the Flemish Institute for Technological Research geothermal site in Belgium during the 2021–2022 circulation test, the framework detects and locates approximately 10,000 events, compared with 242 events in the operational network-based catalog over the same period. The resulting catalog achieves a completeness magnitude of Mc≈−1.95, extending the observable magnitude range by more than 1.5 units relative to the network catalog (Mc≈−0.4). The expanded catalog reveals earlier and more continuous reservoir activation and enables robust Gutenberg–Richter analysis over an extended magnitude range. These results show that the primary gain arises from improved signal conditions at depth, whereas the confidence-based framework enables reliable extraction and interpretation of this information. Single-station deep-borehole observations, combined with network-informed constraints, provide a computationally efficient and transferable strategy for enhancing seismic monitoring in settings where dense instrumentation is impractical.
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BibTeX
@article{gautam2026highresolution,
title = {{High-Resolution Seismic Catalog Expansion Using a Single Deep Borehole Sensor: A Confidence-Based, Network-Informed Framework}},
author = {Rachit Gautam and Jannes Kinscher and Jean Schmittbuhl and Matsen Broothaers and Ben Laenen},
journal = {Seismological Research Letters},
year = {2026},
month = sep,
doi = {10.1785/0220260103},
url = {https://ineris.hal.science/ineris-05790131v1},
note = {CC BY},
}
RIS
TY - JOUR
TI - High-Resolution Seismic Catalog Expansion Using a Single Deep Borehole Sensor: A Confidence-Based, Network-Informed Framework
AU - Rachit Gautam
AU - Jannes Kinscher
AU - Jean Schmittbuhl
AU - Matsen Broothaers
AU - Ben Laenen
PY - 2026
DA - 2026/09/08
JO - Seismological Research Letters
DO - 10.1785/0220260103
UR - https://ineris.hal.science/ineris-05790131v1
AB - High-resolution seismic catalogs are essential for understanding induced seismicity, reservoir dynamics, and improving seismic hazard assessment, yet conventional surface and shallow-borehole networks often fail to detect low-magnitude events. Here, we demonstrate how a single deep-borehole sensor can enhance catalog resolution using a confidence-based framework integrating machine-learning phase picking, waveform similarity, and a site-specific data-quality metric into a unified detection score. The approach combines sensitive single-station detection with network-informed relative location and empirical magnitude estimation to generate a consistent high-resolution catalog. Applied to continuous data from a 2052-m-deep sensor at the Flemish Institute for Technological Research geothermal site in Belgium during the 2021–2022 circulation test, the framework detects and locates approximately 10,000 events, compared with 242 events in the operational network-based catalog over the same period. The resulting catalog achieves a completeness magnitude of Mc≈−1.95, extending the observable magnitude range by more than 1.5 units relative to the network catalog (Mc≈−0.4). The expanded catalog reveals earlier and more continuous reservoir activation and enables robust Gutenberg–Richter analysis over an extended magnitude range. These results show that the primary gain arises from improved signal conditions at depth, whereas the confidence-based framework enables reliable extraction and interpretation of this information. Single-station deep-borehole observations, combined with network-informed constraints, provide a computationally efficient and transferable strategy for enhancing seismic monitoring in settings where dense instrumentation is impractical.
SN - 0895-0695
ER -