Deterrence and false alarms in seismic discrimination

1973 ◽  
Vol 63 (3) ◽  
pp. 1119-1132
Author(s):  
D. H. Weichert ◽  
P. W. Basham

abstract The statistical capabilities of Ms:mb earthquake-explosion discrimination are derived from cumulative distributions of a linear discrimination parameter with confidence limits estimated by a distribution-free method. Cumulative discriminant distributions are shown to be preferable to previously employed “operating characteristics” because of difficulties of construction and interpretation of the latter. Three sets of Ms:mb discrimination data, two regional (North American and Eurasian) and one global, are employed to estimate false alarm probabilities at given “deterrence” (probability of correct identification of an explosion) for situations of both a ban and no ban on underground testing. In the hypothetical situation of monitoring a test ban using the Ms:mb criterion, reasonable deterrence (nominally 30 per cent, with a 95 per cent confidence that it is greater than 10 per cent) will be accompanied by a false alarm probability estimated from global data of about 0.1 per cent.

Author(s):  
С.Б. Егоров ◽  
Р.И. Горбачев

«Выбросовая» вероятностная модель работы обнаружителя в режиме ожидания сигнала, предложенная авторами в [1], использована для оценки влияния селекции выбросов по длительности на вероятность ложной тревоги. Флюктуационные выбросы помехового индикаторного процесса, превысившие пороги селекции по уровню и длительности, трактуются как редкие события на интервале ожидания сигнала, подчиняющиеся вероятностному закону Пуассона. При условии, что средний период следования ложных выбросов превышает интервал корреляции индикаторного процесса, получено соотношение между средним числом выбросов любой длительности и средним числом выбросов, превысивших пороговую длительность. На основании известных числовых и вероятностных характеристик выбросов нормального стационарного случайного процесса получен уравнения, связывающие относительные пороги селекции по уровню и длительности с вероятностью ложной тревоги на интервале ожидания сигнала. Предложена методика определения порога селекции по длительности для снижения порога селекции по уровню до заданной величины. «Emissional» probability model of the detector in stand-by mode proposed by the authors in [1], is intended for estimation of false alarm rate dependence from the value of time-selection threshold. Fluctuation emissions of the noise indicator process are interpreted as rare events correspond to Poisson distribution. Assuming that average rate of false alarms exceeds the correlation interval of indicator process, obtained equation between average number of false alarms of any duration and average number of false alarms exceed the time threshold. Based on known numerical and statistical characteristics of emissions of normal stationary random process obtained equations, relating time and level thresholds with false alarm probability on stand-by mode time interval. Also suggested a method of determining time threshold intended to reduce level threshold.


2019 ◽  
Author(s):  
Chrystinne Fernandes ◽  
Simon Miles ◽  
Carlos José Pereira Lucena

BACKGROUND Although alarm safety is a critical issue that needs to be addressed to improve patient care, hospitals have not given serious consideration about how their staff should be using, setting, and responding to clinical alarms. Studies have indicated that 80%-99% of alarms in hospital units are false or clinically insignificant and do not represent real danger for patients, leading caregivers to miss relevant alarms that might indicate significant harmful events. The lack of use of any intelligent filter to detect recurrent, irrelevant, and/or false alarms before alerting health providers can culminate in a complex and overwhelming scenario of sensory overload for the medical team, known as <i>alarm fatigue</i>. OBJECTIVE This paper’s main goal is to propose a solution to mitigate <i>alarm fatigue</i> by using an automatic reasoning mechanism to decide how to calculate false alarm probability (FAP) for alarms and whether to include an indication of the FAP (ie, FAP_LABEL) with a notification to be visualized by health care team members designed to help them prioritize which alerts they should respond to next. METHODS We present a new approach to cope with the <i>alarm fatigue</i> problem that uses an automatic reasoner to decide how to notify caregivers with an indication of FAP. Our reasoning algorithm calculates FAP for alerts triggered by sensors and multiparametric monitors based on statistical analysis of false alarm indicators (FAIs) in a simulated environment of an intensive care unit (ICU), where a large number of warnings can lead to <i>alarm fatigue</i>. RESULTS The main contributions described are as follows: (1) a list of FAIs we defined that can be utilized and possibly extended by other researchers, (2) a novel approach to assess the probability of a false alarm using statistical analysis of multiple inputs representing alarm-context information, and (3) a reasoning algorithm that uses alarm-context information to detect false alarms in order to decide whether to notify caregivers with an indication of FAP (ie, FAP_LABEL) to avoid <i>alarm fatigue</i>. CONCLUSIONS Experiments were conducted to demonstrate that by providing an intelligent notification system, we could decide how to identify false alarms by analyzing alarm-context information. The reasoner entity we described in this paper was able to attribute FAP values to alarms based on FAIs and to notify caregivers with a FAP_LABEL indication without compromising patient safety.


10.2196/15407 ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. e15407 ◽  
Author(s):  
Chrystinne Fernandes ◽  
Simon Miles ◽  
Carlos José Pereira Lucena

Background Although alarm safety is a critical issue that needs to be addressed to improve patient care, hospitals have not given serious consideration about how their staff should be using, setting, and responding to clinical alarms. Studies have indicated that 80%-99% of alarms in hospital units are false or clinically insignificant and do not represent real danger for patients, leading caregivers to miss relevant alarms that might indicate significant harmful events. The lack of use of any intelligent filter to detect recurrent, irrelevant, and/or false alarms before alerting health providers can culminate in a complex and overwhelming scenario of sensory overload for the medical team, known as alarm fatigue. Objective This paper’s main goal is to propose a solution to mitigate alarm fatigue by using an automatic reasoning mechanism to decide how to calculate false alarm probability (FAP) for alarms and whether to include an indication of the FAP (ie, FAP_LABEL) with a notification to be visualized by health care team members designed to help them prioritize which alerts they should respond to next. Methods We present a new approach to cope with the alarm fatigue problem that uses an automatic reasoner to decide how to notify caregivers with an indication of FAP. Our reasoning algorithm calculates FAP for alerts triggered by sensors and multiparametric monitors based on statistical analysis of false alarm indicators (FAIs) in a simulated environment of an intensive care unit (ICU), where a large number of warnings can lead to alarm fatigue. Results The main contributions described are as follows: (1) a list of FAIs we defined that can be utilized and possibly extended by other researchers, (2) a novel approach to assess the probability of a false alarm using statistical analysis of multiple inputs representing alarm-context information, and (3) a reasoning algorithm that uses alarm-context information to detect false alarms in order to decide whether to notify caregivers with an indication of FAP (ie, FAP_LABEL) to avoid alarm fatigue. Conclusions Experiments were conducted to demonstrate that by providing an intelligent notification system, we could decide how to identify false alarms by analyzing alarm-context information. The reasoner entity we described in this paper was able to attribute FAP values to alarms based on FAIs and to notify caregivers with a FAP_LABEL indication without compromising patient safety.


2021 ◽  
Vol 13 (19) ◽  
pp. 3856
Author(s):  
Xiaolong Chen ◽  
Jian Guan ◽  
Xiaoqian Mu ◽  
Zhigao Wang ◽  
Ningbo Liu ◽  
...  

Traditional radar target detection algorithms are mostly based on statistical theory. They have weak generalization capabilities for complex sea clutter environments and diverse target characteristics, and their detection performance would be significantly reduced. In this paper, the range-azimuth-frame information obtained by scanning radar is converted into plain position indicator (PPI) images, and a novel Radar-PPInet is proposed and used for marine target detection. The model includes CSPDarknet53, SPP, PANet, power non-maximum suppression (P-NMS), and multi-frame fusion section. The prediction frame coordinates, target category, and corresponding confidence are directly given through the feature extraction network. The network structure strengthens the receptive field and attention distribution structure, and further improves the efficiency of network training. P-NMS can effectively improve the problem of missed detection of multi-targets. Moreover, the false alarms caused by strong sea clutter are reduced by the multi-frame fusion, which is also a benefit for weak target detection. The verification using the X-band navigation radar PPI image dataset shows that compared with the traditional cell-average constant false alarm rate detector (CA-CFAR) and the two-stage Faster R-CNN algorithm, the proposed method significantly improved the detection probability by 15% and 10% under certain false alarm probability conditions, which is more suitable for various environment and target characteristics. Moreover, the computational burden is discussed showing that the Radar-PPInet detection model is significantly lower than the Faster R-CNN in terms of parameters and calculations.


1970 ◽  
Vol 60 (5) ◽  
pp. 1521-1546 ◽  
Author(s):  
Ulf A. Ericsson

Abstract This paper describes a method to determine the usefulness of seismological event identification criteria for underground test ban control with or without on-site inspection. The method rests upon the notion that such control should be deterrent, by confronting a prospective violator with a certain disclosure probability at a politically determined level. Simple decision theory is then applied to the statistical properties of identification measures, to find the required compromise between a sufficient probability to disclose explosions and a not too high incidence of false alarms about earthquakes. A clean separation between the political requirements and the seismological capabilities is obtained. The latter are expressed by identification curves similar to the receiver operating characteristics curves employed in telecommunications analysis. The political requirements appear as geometrical conditions on the identification curves, expressing the required deterrence, the number of yearly explosions and earthquakes to be considered and the permitted number and quality of inspections, in the case of control with inspection. In the case of control without inspection, the acceptable rate of false alarms is included. The method also shows how identification criteria are most efficiently exploited. Application to some published observations shows identification by short-period body-wave magnitudes and long-period surface-wave magnitudes to be most efficient. It remains, however, to extend the analysis to weak events and low signal-to-noise ratios. For applications the acquisition of proper statistics is essential.


Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1643
Author(s):  
Ming Liu ◽  
Shichao Chen ◽  
Fugang Lu ◽  
Mengdao Xing ◽  
Jingbiao Wei

For target detection in complex scenes of synthetic aperture radar (SAR) images, the false alarms in the land areas are hard to eliminate, especially for the ones near the coastline. Focusing on the problem, an algorithm based on the fusion of multiscale superpixel segmentations is proposed in this paper. Firstly, the SAR images are partitioned by using different scales of superpixel segmentation. For the superpixels in each scale, the land-sea segmentation is achieved by judging their statistical properties. Then, the land-sea segmentation results obtained in each scale are combined with the result of the constant false alarm rate (CFAR) detector to eliminate the false alarms located on the land areas of the SAR image. In the end, to enhance the robustness of the proposed algorithm, the detection results obtained in different scales are fused together to realize the final target detection. Experimental results on real SAR images have verified the effectiveness of the proposed algorithm.


2021 ◽  
Vol 503 (4) ◽  
pp. 5223-5231
Author(s):  
C F Zhang ◽  
J W Xu ◽  
Y P Men ◽  
X H Deng ◽  
Heng Xu ◽  
...  

ABSTRACT In this paper, we investigate the impact of correlated noise on fast radio burst (FRB) searching. We found that (1) the correlated noise significantly increases the false alarm probability; (2) the signal-to-noise ratios (S/N) of the false positives become higher; (3) the correlated noise also affects the pulse width distribution of false positives, and there will be more false positives with wider pulse width. We use 55-h observation for M82 galaxy carried out at Nanshan 26m radio telescope to demonstrate the application of the correlated noise modelling. The number of candidates and parameter distribution of the false positives can be reproduced with the modelling of correlated noise. We will also discuss a low S/N candidate detected in the observation, for which we demonstrate the method to evaluate the false alarm probability in the presence of correlated noise. Possible origins of the candidate are discussed, where two possible pictures, an M82-harboured giant pulse and a cosmological FRB, are both compatible with the observation.


2020 ◽  
Vol 501 (1) ◽  
pp. 701-712
Author(s):  
N Yonemaru ◽  
S Kuroyanagi ◽  
G Hobbs ◽  
K Takahashi ◽  
X-J Zhu ◽  
...  

ABSTRACT Cosmic strings are potential gravitational-wave (GW) sources that can be probed by pulsar timing arrays (PTAs). In this work we develop a detection algorithm for a GW burst from a cusp on a cosmic string, and apply it to Parkes PTA data. We find four events with a false alarm probability less than 1 per cent. However further investigation shows that all of these are likely to be spurious. As there are no convincing detections we place upper limits on the GW amplitude for different event durations. From these bounds we place limits on the cosmic string tension of Gμ ∼ 10−5, and highlight that this bound is independent from those obtained using other techniques. We discuss the physical implications of our results and the prospect of probing cosmic strings in the era of Square Kilometre Array.


2018 ◽  
Vol 33 (6) ◽  
pp. 1501-1511 ◽  
Author(s):  
Harold E. Brooks ◽  
James Correia

Abstract Tornado warnings are one of the flagship products of the National Weather Service. We update the time series of various metrics of performance in order to provide baselines over the 1986–2016 period for lead time, probability of detection, false alarm ratio, and warning duration. We have used metrics (mean lead time for tornadoes warned in advance, fraction of tornadoes warned in advance) that work in a consistent way across the official changes in policy for warning issuance, as well as across points in time when unofficial changes took place. The mean lead time for tornadoes warned in advance was relatively constant from 1986 to 2011, while the fraction of tornadoes warned in advance increased through about 2006, and the false alarm ratio slowly decreased. The largest changes in performance take place in 2012 when the default warning duration decreased, and there is an apparent increased emphasis on reducing false alarms. As a result, the lead time, probability of detection, and false alarm ratio all decrease in 2012. Our analysis is based, in large part, on signal detection theory, which separates the quality of the warning system from the threshold for issuing warnings. Threshold changes lead to trade-offs between false alarms and missed detections. Such changes provide further evidence for changes in what the warning system as a whole considers important, as well as highlighting the limitations of measuring performance by looking at metrics independently.


2020 ◽  
Vol 642 ◽  
pp. A157 ◽  
Author(s):  
N. Meunier ◽  
A.-M. Lagrange

Context. The detectability of exoplanets and the determination of their projected mass in radial velocity are affected by stellar magnetic activity and photospheric dynamics. Among those processes, the effect of granulation, and even more so of supergranulation, has been shown to be significant in the solar case. The impact for other spectral types has not yet been characterised. Aims. Our study is aimed at quantifying the impact of these flows for other stars and estimating how such contributions affect their performance. Methods. We analysed a broad array of extended synthetic time series that model these processes to characterise the impact of these flows on exoplanet detection for main sequence stars with spectral types from F6 to K4. We focussed on Earth-mass planets orbiting within the habitable zone around those stars. We estimated the expected detection rates and detection limits, tested the tools that are typically applied to such observations, and performed blind tests. Results. We find that both granulation and supergranulation on these stars significantly affect planet mass characterisation in radial velocity when performing a follow-up of a transit detection: the uncertainties on these masses are sometimes below 20% for a 1 MEarth (for granulation alone or for low-mass stars), but they are much larger in other configurations (supergranulation, high-mass stars). For granulation and low levels of supergranulation, the detection rates are good for K and late G stars (if the number of points is large enough), but poor for more massive stars. The highest level of supergranulation leads to a very poor performance, even for K stars; this is both due to low detection rates and to high levels of false positives, even for a very dense temporal sampling over 10 yr. False positive levels estimated from standard false alarm probabilities sometimes significantly overestimate or underestimate the true level, depending on the number of points: it is, therefore, crucial to take this effect into account when analysing observations. Conclusions. We conclude that granulation and supergranulation significantly affect the performance of exoplanet detectability. Future works will focus on improving the following three aspects: decreasing the number of false positives, increasing detection rates, and improving the false alarm probability estimations from observations.


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