Geodetic displacement data near North Brawley Geothermal Field, 2009-2019
Bibliographic record
Abstract
Supplementary Dataset for Materna, Barbour, Jiang, and Eneva (2022), "Detection of aseismic slip and poroelastic reservoir deformation at the North Brawley Geothermal Field from 2009–2019". The 2009-2019 downsampled and processed displacements for modeling, shown in their Figure 3, are presented in this repository. Processing methods are included in detail in the paper. Also included are the fault geometries used for modeling and the surface rupture trace of the M4.7 normal faulting earthquake in the 2012 Brawley Swarm, traced from a UAVSAR interferogram. TerraSAR-X data were ordered from the German Space Agency (DLR), using funding from grant GEO-10-001 awarded to Imageair Inc. by the California Energy Commission (CEC). InSAR/SqueeSAR processing of these data was done by TRE Altamira in Canada and Italy under CEC grant GEO-16-003 to Imageair Inc. Leveling data were obtained from the Imperial County Department of Public Works (https://publicworks.imperialcounty.org) and processed under the same grant. The Copernicus Sentinel-1 data were processed by the European Space Agency (ESA) and retrieved from the Alaska Satellite Facility (ASF) (https://search.asf.alaska.edu/). Sentinel-1 displacement time series were derived from interferograms processed in Jiang and Lohman (2021) through the support of Southern California Earthquake Center (SCEC) award 20139. SCEC is funded by NSF Cooperative Agreement EAR-1600087 & USGS Cooperative Agreement G17AC00047. UAVSAR data can be downloaded at https://uavsar.jpl.nasa.gov/. Quadtree downsampling was performed with the Kite library (Isken et al., 2017).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.008 | 0.017 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.085 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".