Accurate Determination of Local Magnitude for Earthquakes in the Western Canada Sedimentary Basin
Bibliographic record
Abstract
Research Article| November 07, 2018 Accurate Determination of Local Magnitude for Earthquakes in the Western Canada Sedimentary Basin Alireza Babaie Mahani; Alireza Babaie Mahani aGeoscience BC, Vancouver, British Columbia, Canada V6C 2T7, ali.mahani@mahangeo.com Search for other works by this author on: GSW Google Scholar Honn Kao Honn Kao bPacific Geoscience Centre, Geological Survey of Canada, Sidney, British Columbia, Canada V8L 4B2 Search for other works by this author on: GSW Google Scholar Seismological Research Letters (2019) 90 (1): 203–211. https://doi.org/10.1785/0220180264 Article history first online: 07 Nov 2018 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Alireza Babaie Mahani, Honn Kao; Accurate Determination of Local Magnitude for Earthquakes in the Western Canada Sedimentary Basin. Seismological Research Letters 2018;; 90 (1): 203–211. doi: https://doi.org/10.1785/0220180264 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietySeismological Research Letters Search Advanced Search ABSTRACT In this study, we take a close look at the constituents of the Richter (1935) relation for calculation of local magnitude (ML), which is the basis for magnitude determination by Natural Resources Canada (NRCan) in the western Canada sedimentary basin (WCSB). Using a comprehensive catalog of Wood–Anderson amplitudes from earthquakes in northeast British Columbia and western Alberta, we first compare the distance correction terms −log(A0) for the Richter magnitude scale previously obtained for WCSB and several other regions. We also formulate a new correction term specifically for NRCan's routine ML calculation that better accounts for the attenuation of direct and refracted waves from events within WCSB. Based on a bilinear model for ground‐motion attenuation, our −log(A0) is {0.7974×log(Rhypo100)+0.0016×(Rhypo−100)+3.0 Rhypo≤85 km−0.1385×log(Rhypo100)+0.0016×(Rhypo−100)+3.0 Rhypo>85 km,in which Rhypo is the hypocentral distance. Our −log(A0) results in lower ML by an average of 0.29, 0.27, 0.12, and 0.34 units, respectively, compared with those obtained by Richter (1958; California), Hutton and Boore (1987; California), Brazier et al. (2008; Ethiopian plateau), and Bona (2016; Italy) over all distances, but gives higher ML values than those obtained by Yenier (2017; WCSB), with an average of 0.12 unit over all distances. The difference between our ML calculation and Yenier (2017) is more significant for Rhypo≤50 km (0.27 unit) and varies slightly for larger Rhypo: 0.08 unit for 50 km 200 km. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".