Human Induced Earthquakes, Naturally Triggered Seismicity, and Their Interactions
Bibliographic record
Abstract
Earthquakes have affected humanity for centuries. Nowdays, we know that earthquakes can interact with each other not only at short distances but also at distances as far as hundreds even thousands of kilometers. In a similar manner, industrial anthropogenic activities, such as injecting fluids underground, mining, and reservoir impoundment, are capable of interacting with pre-existing fault structures and triggering earthquakes with magnitudes as high as 5. This thesis aims to investigate such complex earthquake-earthquake interactions and human-earthquake interactions using observational approaches.First, I provide (Chapter 1) an introduction in the mechanisms of earthquake interactions and anthropogenic induced seismicity.Second, I study (Chapter 2) how the seismic waves from remote earthquakes with large magnitudes can trigger seismicity in Oklahoma (USA), where the occurrence of earthquakes has been linked withthe injection of water under the surface. Using statistical tests, I find that small stresses generated by the passage of seismic waves, are capable of triggering events in Oklahoma with some delay time.Third, I investigate (Chapter 3) a case of induced seismicity by hydraulic fracturing in the Western Canada Sedimentary Basin. Specifically, an earthquake with ML = 4:5 occurred on November 2018 inclose proximity to a horizontal injection well. The seismicity during the 20 day period surrounding the mainshock can be explained as a two step process: (i) fluid migration into the basement through afracture network or a nascent fault that trigger the large event, and (ii) the stress changes generated by the coseismic deformation of the mainshock trigger events at shallower depths close to the injectionwell.Motivated to study other types of induced seismicity different from the one examined in Chapter 3, I analyze a case of how underground coal mining in Germany, triggered seismic events (Chapter 4). Earthquakes in this area are low magnitude, but still felt by the population because they tend to occur at depths of 1 km or less. I find that earthquakes are generated by two main mechanisms: reactivation ofold fault structures and mine-collapse.Additionally, I compare (Chapter 5) three different earthquake detections methods using three test data sets in distinctive seismic zones in Canada: the Fort St. John area, the Charlevoix Seismic Zone, andthe Lower St. Lawrence Seismic Zone. Each of the seismic zones have different seismic background rates and inter-station distances that allow me to present improvements to the current catalog developed by Natural Resources Canada (NRCan). In some cases, I am able to double the number of events than the NRCan while other times the detections increase by nearly a factor of 10.Finally, I summarize (Chapter 6) final remarks and possible futurescopes about earthquake interactions
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".