Hydromechanical characterization of a fractured crystalline rock volume during multi-stage hydraulic stimulations at the BedrettoLab

Bröker, Kai; Ma, Xiaodong; Gholizadeh Doonechaly, Nima; Rosskopf, Martina; Obermann, Anne; Rinaldi, Antonio Pio; Hertrich, Marian; Serbeto, Francisco; Maurer, Hansruedi; Wiemer, Stefan; Giardini, Domenico

doi:10.1016/j.geothermics.2024.103126

Geothermics

2024

DOI: 10.1016/j.geothermics.2024.103126

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Hydromechanical characterization of a fractured crystalline rock volume during multi-stage hydraulic stimulations at the BedrettoLab

Kai Bröker,

Xiaodong Ma,

Nima Gholizadeh Doonechaly

et al.

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Cited by 2 publications

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Seismic Response of Hectometer‐Scale Fracture Systems to Hydraulic Stimulation in the Bedretto Underground Laboratory, Switzerland

Obermann,

Rosskopf,

Durand

et al. 2024

JGR Solid Earth

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We performed a series of hydraulic stimulations at 1.1 km depth in the Bedretto underground laboratory, Switzerland, as part of an overall research strategy attempting to understand induced seismicity on different scales. Using an ultra‐high frequency seismic network we detect seismic events as small as Mw < −4, revealing intricate details of a complex fracture network extending over 100 m from the injection sites. Here, we outline the experimental approach and present seismic catalogs as well as a comparative analysis of event number per injection, magnitudes, b‐values, seismogenic index and reactivation pressures. In our first‐order seismicity analysis, we could make the following observations: The rock volume impacted by the stimulations in different intervals differs significantly with a lateral extent from a few meters to more than 150 m. In most intervals multiple fractures were reactivated. The seismicity typically propagates upwards toward shallower depth on parallel oriented planes that are consistent with the stress field and seem to a large extent associated with preexisting open fractures. This experiment confirms the diversity in seismic behavior independent from the injection protocol. The overall seismicity patterns demonstrate that multi‐stage stimulations using zonal isolation allow developing an extended fracture network in a 3D rock volume, which is necessary for enhanced geothermal systems. Our stimulations covering two orders of magnitude in terms of injected volume will give insights into upscaling of induced seismicity from underground laboratory scale to field scale.

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Seismic Response of Hectometer‐Scale Fracture Systems to Hydraulic Stimulation in the Bedretto Underground Laboratory, Switzerland

Obermann,

Rosskopf,

Durand

et al. 2024

JGR Solid Earth

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From Labquakes to Megathrusts: Scaling Deep Learning Based Pickers Over 15 Orders of Magnitude

Shi,

Meier,

Villiger

et al. 2024

Journal of Geophysical Research: Machine Learning and Computati

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The application of machine learning techniques in seismology has greatly advanced seismological analysis, especially for earthquake detection and seismic phase picking. However, machine learning approaches still face challenges in generalizing to data sets that differ from their original training setting. Previous studies focused on retraining or transfer‐learning models for these scenarios, but require high‐quality labeled data sets. This paper demonstrates a new approach for augmenting already trained models without the need for additional training data. We propose four strategies—rescaling, model aggregation, shifting, and filtering—to enhance the performance of pre‐trained models on out‐of‐distribution data sets. We further devise various methodologies to ensemble the individual predictions from these strategies to obtain a final unified prediction result featuring prediction robustness and detection sensitivity. We develop an open‐source Python module quakephase that implements these methods and can flexibly process input continuous seismic data of any sampling rate. With quakephase and pre‐trained ML models from SeisBench, we perform systematic benchmark tests on data recorded by different types of instruments, ranging from acoustic emission sensors to distributed acoustic sensing, and collected at different scales, spanning from laboratory acoustic emission events to major tectonic earthquakes. Our tests highlight that rescaling is essential for dealing with small‐magnitude seismic events recorded at high sampling rates as well as larger magnitude events having long coda and remote events with long wave trains. Our results demonstrate that the proposed methods are effective in augmenting pre‐trained models for out‐of‐distribution data sets, especially in scenarios with limited labeled data for transfer learning.

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Hydromechanical characterization of a fractured crystalline rock volume during multi-stage hydraulic stimulations at the BedrettoLab

Cited by 2 publications

References 43 publications

Seismic Response of Hectometer‐Scale Fracture Systems to Hydraulic Stimulation in the Bedretto Underground Laboratory, Switzerland

Seismic Response of Hectometer‐Scale Fracture Systems to Hydraulic Stimulation in the Bedretto Underground Laboratory, Switzerland

From Labquakes to Megathrusts: Scaling Deep Learning Based Pickers Over 15 Orders of Magnitude

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