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Laboratory data for characterizing acoustic emissions during differential stress-driven and pore-pressure-driven failure in granite

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This dataset contains acoustic-emission (AE), ultrasonic-transmission, permeability, and hydromechanical measurements from triaxial laboratory experiments comparing differential stress-driven and pore-pressure-driven failure in saturated, thermally cracked Barre granite at 80 degree C. Cylindrical samples approximately 40 mm in diameter and 80 mm long were tested at confining pressures of 40 or 70 MPa and an initial pore pressure of 30 MPa using two loading paths: increasing differential stress at constant pore pressure and increasing pore pressure at constant differential stress. AE waveforms were recorded in triggered mode at 50 MS/s and continuously at 10 MS/s, producing more than 1,000 triggered and 10,000 continuous AEs per experiment. Supporting and derived products include stress, strain, pressure, permeability, active-ultrasonic measurements, waveform clusters based on dynamic time warping, AE source locations, spatial-temporal event patterns, and post-failure micro-CT fracture networks where available. The dataset supports investigation of rupture signatures under stress and fluid-driven loading and development of AE classification, geothermal seismic-monitoring, and adaptive traffic-light methods.

Citation Formats

TY - DATA AB - This dataset contains acoustic-emission (AE), ultrasonic-transmission, permeability, and hydromechanical measurements from triaxial laboratory experiments comparing differential stress-driven and pore-pressure-driven failure in saturated, thermally cracked Barre granite at 80 degree C. Cylindrical samples approximately 40 mm in diameter and 80 mm long were tested at confining pressures of 40 or 70 MPa and an initial pore pressure of 30 MPa using two loading paths: increasing differential stress at constant pore pressure and increasing pore pressure at constant differential stress. AE waveforms were recorded in triggered mode at 50 MS/s and continuously at 10 MS/s, producing more than 1,000 triggered and 10,000 continuous AEs per experiment. Supporting and derived products include stress, strain, pressure, permeability, active-ultrasonic measurements, waveform clusters based on dynamic time warping, AE source locations, spatial-temporal event patterns, and post-failure micro-CT fracture networks where available. The dataset supports investigation of rupture signatures under stress and fluid-driven loading and development of AE classification, geothermal seismic-monitoring, and adaptive traffic-light methods. AU - Nakata, Nori A2 - Pec, Matej DB - Open Energy Data Initiative (OEDI) DP - Open EI | National Laboratory of the Rockies DO - KW - geothermal KW - energy KW - AE KW - Granite KW - laboratory experiment KW - permeability KW - fracture networks KW - stress KW - strain KW - seismic monitoring KW - raw data KW - traffic light LA - English DA - 2026/09/20 PY - 2026 PB - Lawrence Berkeley National Laboratory T1 - Laboratory data for characterizing acoustic emissions during differential stress-driven and pore-pressure-driven failure in granite UR - https://data.openei.org/submissions/8790 ER -
Export Citation to RIS
Nakata, Nori, and Matej Pec. Laboratory data for characterizing acoustic emissions during differential stress-driven and pore-pressure-driven failure in granite. Lawrence Berkeley National Laboratory, 20 September, 2026, GDR. https://gdr.openei.org/submissions/1874.
Nakata, N., & Pec, M. (2026). Laboratory data for characterizing acoustic emissions during differential stress-driven and pore-pressure-driven failure in granite. [Data set]. GDR. Lawrence Berkeley National Laboratory. https://gdr.openei.org/submissions/1874
Nakata, Nori and Matej Pec. Laboratory data for characterizing acoustic emissions during differential stress-driven and pore-pressure-driven failure in granite. Lawrence Berkeley National Laboratory, September, 20, 2026. Distributed by GDR. https://gdr.openei.org/submissions/1874
@misc{OEDI_Dataset_8790, title = {Laboratory data for characterizing acoustic emissions during differential stress-driven and pore-pressure-driven failure in granite}, author = {Nakata, Nori and Pec, Matej}, abstractNote = {This dataset contains acoustic-emission (AE), ultrasonic-transmission, permeability, and hydromechanical measurements from triaxial laboratory experiments comparing differential stress-driven and pore-pressure-driven failure in saturated, thermally cracked Barre granite at 80 degree C. Cylindrical samples approximately 40 mm in diameter and 80 mm long were tested at confining pressures of 40 or 70 MPa and an initial pore pressure of 30 MPa using two loading paths: increasing differential stress at constant pore pressure and increasing pore pressure at constant differential stress. AE waveforms were recorded in triggered mode at 50 MS/s and continuously at 10 MS/s, producing more than 1,000 triggered and 10,000 continuous AEs per experiment. Supporting and derived products include stress, strain, pressure, permeability, active-ultrasonic measurements, waveform clusters based on dynamic time warping, AE source locations, spatial-temporal event patterns, and post-failure micro-CT fracture networks where available. The dataset supports investigation of rupture signatures under stress and fluid-driven loading and development of AE classification, geothermal seismic-monitoring, and adaptive traffic-light methods.}, url = {https://gdr.openei.org/submissions/1874}, year = {2026}, howpublished = {GDR, Lawrence Berkeley National Laboratory, https://gdr.openei.org/submissions/1874}, note = {Accessed: 2026-10-01} }

Details

Data from Sep 20, 2026

Last updated Sep 30, 2026

Submitted Sep 21, 2026

Organization

Lawrence Berkeley National Laboratory

Contact

Nori Nakata

510.685.5494

Authors

Nori Nakata

Lawrence Berkeley National Laboratory

Matej Pec

Massachusetts Institute of Technology

Research Areas

DOE Project Details

Project Name Utah FORGE 6-3656: Real-Time Traffic Light System and Reservoir Engineering with Seismicity Forecasting and Ground Motion Prediction

Project Lead Lauren Boyd

Project Number EE0007080

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