scholarly journals Interevent-time distribution and aftershock frequency in non-stationary induced seismicity

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Richard A. J. Post ◽  
Matthias A. J. Michels ◽  
Jean-Paul Ampuero ◽  
Thibault Candela ◽  
Peter A. Fokker ◽  
...  
Keyword(s):  
Induced Seismicity ◽  
Time Distribution ◽  
Failure Time ◽  
Accelerated Failure Time Model ◽  
Operational Parameters ◽  
Gas Field ◽  
Time Model ◽  
The North ◽  
Interevent Time ◽  
Background Events

AbstractThe initial footprint of an earthquake can be extended considerably by triggering of clustered aftershocks. Such earthquake–earthquake interactions have been studied extensively for data-rich, stationary natural seismicity. Induced seismicity, however, is intrinsically inhomogeneous in time and space and may have a limited catalog of events; this may hamper the distinction between human-induced background events and triggered aftershocks. Here we introduce a novel Gamma Accelerated-Failure-Time model for efficiently analyzing interevent-time distributions in such cases. It addresses the spatiotemporal variation and quantifies, per event, the probability of each event to have been triggered. Distentangling the obscuring aftershocks from the background events is a crucial step to better understand the causal relationship between operational parameters and non-stationary induced seismicity. Applied to the Groningen gas field in the North of the Netherlands, our model elucidates geological and operational drivers of seismicity and has been used to test for aftershock triggering. We find that the hazard rate in Groningen is indeed enhanced after each event and conclude that aftershock triggering cannot be ignored. In particular we find that the non-stationary interevent-time distribution is well described by our Gamma model. This model suggests that 27.0(± 8.5)% of the recorded events in the Groningen field can be attributed to triggering.

Dependability Analysis of Homogeneous Distributed Software/Hardware Systems

2015 ◽  
Vol 22 (02) ◽  
pp. 1550007
Author(s):  
G. Vijayalakshmi
Keyword(s):  
Reliability Analysis ◽  
Distributed System ◽  
Failure Time ◽  
Load Sharing ◽  
Accelerated Failure Time Model ◽  
Time To Failure ◽  
Critical Systems ◽  
Time Model ◽  
Distributed Software ◽  
Dependability Analysis

With the increasing demand for high availability in safety-critical systems such as banking systems, military systems, nuclear systems, aircraft systems to mention a few, reliability analysis of distributed software/hardware systems continue to be the focus of most researchers. The reliability analysis of a homogeneous distributed software/hardware system (HDSHS) with k-out-of-n : G configuration and no load-sharing nodes is analyzed. However, in practice the system load is shared among the working nodes in a distributed system. In this paper, the dependability analysis of a HDSHS with load-sharing nodes is presented. This distributed system has a load-sharing k-out-of-(n + m) : G configuration. A Markov model for HDSHS is developed. The failure time distribution of the hardware is represented by the accelerated failure time model. The software faults are detected during software testing and removed upon failure. The Jelinski–Moranda software reliability model is used. The maintenance personal can repair the system up on both software and hardware failure. The dependability measures such as reliability, availability and mean time to failure are obtained. The effect of load-sharing hosts on system hazard function and system reliability is presented. Furthermore, an availability comparison of our results and the results in the literature is presented.

An analysis of timing decision in venture capital staged financing: evidence from India

2020 ◽  
Vol 43 (12) ◽  
Author(s):  
Satya Narayan Panda ◽  
Arun Kumar Gopalaswamy
Keyword(s):  
Venture Capital ◽  
Capital Investment ◽  
Failure Time ◽  
Agency Problems ◽  
Policy Implications ◽  
Accelerated Failure Time Model ◽  
Market Uncertainty ◽  
Time Model ◽  
Content Type ◽  
Staged Financing

Purpose Staged financing is a prominent feature of the venture capital investment process. With staged financing, venture capitalists (VCs) may choose to either make an investment or delay it at each round. The purpose of this paper is to investigate the influence of market uncertainty, project-specific uncertainty and agency problems on these decisions. Design/methodology/approach The study uses data from Indian firms that received venture capital funding between 2000 and 2017. The duration between funding rounds is analysed using survival analysis. An accelerated failure time model is used to estimate the influence of market uncertainty, project-specific uncertainty and agency problems on the length of time between funding rounds. Findings VCs delay investment when there are high levels of uncertainty in the market; if market uncertainty increases by 1%, delay in funding increases by more than 6% (almost a month) on average. There is no statistically significant relationship found between the funding duration and project-specific uncertainty. Agency problems motivate VCs to invest sooner. An increase in agency problems results in a reduction of 55% (almost five months) in the length of time before the next funding round. Practical implications This study has useful business policy implications. It provides VCs with real option value drivers such as market uncertainty, agency problems, which influence the timing of decisions in staged investment processes. It will help to make the choice between investing and delaying at each round of financing more robust. Further, it is useful for VCs to differentiate between market uncertainty and agency problems against the backdrop of their different implications for staging decisions. Originality/value Few studies have examined staging decisions from a real options perspective in the context of a developed economy and very few from a developing economy perspective. This study increases understanding of staging decisions in the Indian context.

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