Clustering characteristics of gas-extraction induced seismicity in the Groningen gas field
Bibliographic record
Abstract
SUMMARY The Groningen gas field in the north of the Netherlands is one of the largest gas fields in the world. Since the early 1990s induced seismicity has been recorded. The largest magnitude event observed so far was a Mw = 3.6 event at the town of Huizinge in 2012. The risk posed by the induced events urged the necessity to build comprehensive seismological models capable of explaining the spatial-temporal distribution of the recorded seismicity and evaluating the regional seismic hazard and risk. The link between the occurrence of the seismicity and pressure depletion due to the production of the gas has been firmly established. However, the construction of comprehensive seismological models as well as hazard assessment is complicated by the fact that it is difficult to distinguish between induced and clustered events (events triggered by stress transfer of preceding, neighbouring events). This paper explores the contribution of clustered populations (i.e. aftershocks) to the Groningen induced seismic catalogue based on a statistical methodology in the time–space–magnitude domain. Specifically, the distributions of space–time distances between pairs of nearest-neighbour earthquakes, referred to as cluster style, is analysed. The cluster style of the Groningen induced seismicity is found to be very diffuse and characterized by a very low proportion of fore-/aftershock sequences and swarms (∼5 per cent) and a large proportion of repeater events (∼10 per cent). In contrast to human-induced seismicity in other regions, the background seismicity rate of Groningen is very low. Temporal variations in background seismicity rates can be related to changes in fault loading rates induced by gas production. Furthermore, a significant amount of independent, coincidental events (events occurring very close in time, but long distances apart) are observed. As the large gas field is fully connected, loading of the faults occurs roughly simultaneously throughout the field. Hence, the statistical probability of events occurring very close in time, but spatially far apart is significantly larger than in areas of fluid-injection induced seismicity The significant amount of repeaters and coincidental events cause an overabundance of events at intermediate time- and space-distances. This is further enhanced by the larger location errors in the catalogue increasing the estimated space-distance for non-relocated events. The diffusivity due to this overabundance of events at intermediate time- and space-distances, and the low-proportion of true fore-/aftershocks renders the statistical method used incapable of deriving a proper mode-separation value. However, this is not unique to this method. Any statistical method aimed at resolving two populations will break down if one of the populations analysed is too small. Hence, it is advisable to use caution when distinguishing fore-/aftershocks sequences or swarms for induced seismicity where the relative proportion of clustered events may be significantly lower than for tectonic events. In addition, given the small proportion of clustering and the general uncertainty in earthquake statistics, the results of this paper indicate that a distinction for earthquake risk modelling in Groningen is unnecessary.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".