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Inferring maximum magnitudes from the ordered sequence of large earthquakes.

Authors :
Schultz, Ryan
Source :
Philosophical Transactions of the Royal Society A: Mathematical, Physical & Engineering Sciences. 8/9/2024, Vol. 382 Issue 2276, p1-19. 19p.
Publication Year :
2024

Abstract

The largest magnitude earthquake in a sequence is often used as a proxy for hazard estimates, as consequences are often predominately from this single event (in small seismic zones). In this article, the concept of order statistics is adapted to infer the maximum magnitude (MMAX) of an earthquake catalogue. A suite tools developed here can discern MMAX influences through hypothesis testing, quantify MMAX through maximum likelihood estimation (MLE) or select the best MMAX prediction amongst several models. The efficacy of these tools is benchmarked against synthetic and real-data tests, demonstrating their utility. Ultimately, 13 cases of induced seismicity spanning wastewater disposal, hydraulic fracturing and enhanced geothermal systems are tested for volume-based MMAX. I find that there is no evidence of volume-based processes influencing any of these cases. On the contrary, all these cases are adequately explained by an unbounded magnitude distribution. This is significant because it suggests that induced earthquake hazards should also be treated as unbounded. On the other hand, if bounded cases exist, then the tools developed here will be able to discern them, potentially changing how an operator mitigates these hazards. Overall, this suite of tools will be important for better-understanding earthquakes and managing their risks. This article is part of the theme issue 'Induced seismicity in coupled subsurface systems'. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
1364503X
Volume :
382
Issue :
2276
Database :
Academic Search Index
Journal :
Philosophical Transactions of the Royal Society A: Mathematical, Physical & Engineering Sciences
Publication Type :
Academic Journal
Accession number :
178977113
Full Text :
https://doi.org/10.1098/rsta.2023.0185