Lessons Learned from Two Earthquakes That Afflicted Mexico City—After a Pause of 32 Years
Bibliographic record
Abstract
As history would have it, the recent Mw7.1 Puebla-Morelos intraplate event occurred on September 19, 2017, coincidental to the 32nd anniversary of the 1985 Mw8.0 Michoacan interplate event. The former had less impact both in affected areas and in severity than the latter which had profound impacts on the nation. Factors affecting the respective impacts include: ground motion input, site setting of Mexico City with its thick lake-sediment deposits, and structural types of affected building stocks. The interplay of these factors at the two events shared general similarities with specific nuances, and the author participated in the Canadian reconnaissance teams after both events. This paper discusses the vulnerability of Mexico City to the two events that are representative of two types of source mechanisms: the recent event being moderate yet closer, while the earlier one being major but further away. In addition to the difference in earthquake magnitude and epicentral distance, they also generate ground motions of different frequency contents. Thus, site amplification differs in the two events depending on thickness of lake sediments in various seismic zones within the city, and the paper first presents an overview of the physical and seismo-tectonic setting of Mexico City. The earthquake and damage data for this pair of events are summarized in a table. Four aspects of the general earthquake impacts are then discussed: ground failure, building performance, building retrofit, and lifelines performance. Moreover, the earthquake early warning system, having its origin because of the 1985 event, has been fully operative since 1993. Up to the 2017 event, the system had recorded 6,896 earthquakes and issued 158 early warnings. Finally, a succinct summary highlights key lessons learned from this pair of seismic events.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".