Processed Lab Data for Neural Network-Based Shear Stress Level Prediction

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Machine learning can be used to predict fault properties such as shear stress, friction, and time to failure using continuous records of fault zone acoustic emissions. The files are extracted features and labels from lab data (experiment p4679). The features are extracted with a non-overlapping window from the original acoustic data. The first column is the time of the window. The second and third columns are the mean and the variance of the acoustic data in this window, respectively. The 4th-11th column is the the power spectrum density ranging from low to high frequency. And the last column is the corresponding label (shear stress level). The name of the file means which driving velocity the sequence is generated from. Data were generated from laboratory friction experiments conducted with a biaxial shear apparatus. Experiments were conducted in the double direct shear configuration in which two fault zones are sheared between three rigid forcing blocks. Our samples consisted of two 5-mm-thick layers of simulated fault gouge with a nominal contact area of 10 by 10 cm^2. Gouge material consisted of soda-lime glass beads with initial particle size between 105 and 149 micrometers. Prior to shearing, we impose a constant fault normal stress of 2 MPa using a servo-controlled load-feedback mechanism and allow the sample to compact. Once the sample has reached a constant layer thickness, the central block is driven down at constant rate of 10 micrometers per second. In tandem, we collect an AE signal continuously at 4 MHz from a piezoceramic sensor embedded in a steel forcing block about 22 mm from the gouge layer The data from this experiment can be used with the deep learning algorithm to train it for future fault property prediction.

Citation Formats

Pennsylvania State University. (2021). Processed Lab Data for Neural Network-Based Shear Stress Level Prediction [data set]. Retrieved from https://dx.doi.org/10.15121/1787545.
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Marone, Chris, Elsworth, Derek, and Yang, Jing. Processed Lab Data for Neural Network-Based Shear Stress Level Prediction. United States: N.p., 14 May, 2021. Web. doi: 10.15121/1787545.
Marone, Chris, Elsworth, Derek, & Yang, Jing. Processed Lab Data for Neural Network-Based Shear Stress Level Prediction. United States. https://dx.doi.org/10.15121/1787545
Marone, Chris, Elsworth, Derek, and Yang, Jing. 2021. "Processed Lab Data for Neural Network-Based Shear Stress Level Prediction". United States. https://dx.doi.org/10.15121/1787545. https://gdr.openei.org/submissions/1312.
@div{oedi_1312, title = {Processed Lab Data for Neural Network-Based Shear Stress Level Prediction}, author = {Marone, Chris, Elsworth, Derek, and Yang, Jing.}, abstractNote = {Machine learning can be used to predict fault properties such as shear stress, friction, and time to failure using continuous records of fault zone acoustic emissions. The files are extracted features and labels from lab data (experiment p4679). The features are extracted with a non-overlapping window from the original acoustic data. The first column is the time of the window. The second and third columns are the mean and the variance of the acoustic data in this window, respectively. The 4th-11th column is the the power spectrum density ranging from low to high frequency. And the last column is the corresponding label (shear stress level). The name of the file means which driving velocity the sequence is generated from. Data were generated from laboratory friction experiments conducted with a biaxial shear apparatus. Experiments were conducted in the double direct shear configuration in which two fault zones are sheared between three rigid forcing blocks. Our samples consisted of two 5-mm-thick layers of simulated fault gouge with a nominal contact area of 10 by 10 cm^2. Gouge material consisted of soda-lime glass beads with initial particle size between 105 and 149 micrometers. Prior to shearing, we impose a constant fault normal stress of 2 MPa using a servo-controlled load-feedback mechanism and allow the sample to compact. Once the sample has reached a constant layer thickness, the central block is driven down at constant rate of 10 micrometers per second. In tandem, we collect an AE signal continuously at 4 MHz from a piezoceramic sensor embedded in a steel forcing block about 22 mm from the gouge layer The data from this experiment can be used with the deep learning algorithm to train it for future fault property prediction.}, doi = {10.15121/1787545}, url = {https://gdr.openei.org/submissions/1312}, journal = {}, number = , volume = , place = {United States}, year = {2021}, month = {05}}
https://dx.doi.org/10.15121/1787545

Details

Data from May 14, 2021

Last updated Jun 10, 2021

Submitted May 14, 2021

Organization

Pennsylvania State University

Contact

Duo Cheng

Authors

Chris Marone

Pennsylvania State University

Derek Elsworth

Pennsylvania State University

Jing Yang

Pennsylvania State University

DOE Project Details

Project Name Machine Learning Approaches to Predicting Induced Seismicity and Imaging Geothermal Reservoir Properties

Project Lead Mike Weathers

Project Number EE0008763

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