Related Experiment Video
Updated: Oct 5, 2025

Setting Limits on Supersymmetry Using Simplified Models
Published on: November 15, 2013
Statistical bounds on how induced seismicity stops
Ryan Schultz1, William L Ellsworth2, Gregory C Beroza2
1Department of Geophysics, Stanford University, Stanford, CA, USA. rjs10@stanford.edu.
Abstract:
Earthquakes caused by human activities receive scrutiny due to the risks and hazards they pose. Seismicity that occurs after the causative anthropogenic operation stops has been particularly problematic-both because of high-profile cases of damage caused by this trailing seismicity and due to the loss of control for risk management. With this motivation, we undertake a statistical examination of how induced seismicity stops. We borrow the concept of Båth's law from tectonic aftershock sequences. Båth's law anticipates the difference between magnitudes in two subsets of seismicity as dependent on their population count ratio. We test this concept for its applicability to induced seismicity, including ~ 80 cases of earthquakes caused by hydraulic fracturing, enhanced geothermal systems, and other fluid-injections with clear operational end points. We find that induced seismicity obeys Båth's law: both in terms of the magnitude-count-ratio relationship and the power law distribution of residuals. Furthermore, the distribution of count ratios is skewed and heavy-tailed, with most earthquakes occurring during stimulation/injection. We discuss potential models to improve the characterization of these count ratios and propose a Seismogenic Fault Injection Test to measure their parameters in situ. We conclude that Båth's law quantifies the occurrence of earthquake magnitudes trailing anthropogenic operations.
More Related Videos
08:28Methods for ECG Evaluation of Indicators of Cardiac Risk, and Susceptibility to Aconitine-induced Arrhythmias in Rats Following Status Epilepticus
Published on: April 5, 2011
06:55Kinematic History of a Salient-recess Junction Explored through a Combined Approach of Field Data and Analog Sandbox Modeling
Published on: August 5, 2016
Related Concept Videos
Censoring Survival Data
Uncertainty: Confidence Intervals
Interpretation of Confidence Intervals
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
Propagation of Uncertainty from Systematic Error
BIBO stability of continuous and discrete -time systems
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
Critical Region, Critical Values and Significance Level
In hypothesis testing, a sample statistic is converted to a test statistic using z, t, or chi-square distribution. A critical region is an area under the curve in probability distributions demarcated by the critical value. When the test statistic falls in this region, it suggests that the null hypothesis must be rejected. As this region contains all those values of the...