Accurate Uncertainty Estimates Research Articles

The MapCHECK Measurement Uncertainty function and its effect on planar dose pass rates.

Our study aimed to quantify the effect of the Measurement Uncertainty function on planar dosimetry pass rates, as measured and analyzed with the Sun Nuclear Corporation MapCHECK 2 array and its associated software. This optional function is toggled in the program preferences of the software (though turned on by default upon installation), and automatically increases the dose difference tolerance defined by the user for each planar dose comparison. Dose planes from 109 static‐gantry IMRT fields and 40 VMAT arcs, of varying modulation complexity, were measured at 5 cm water‐equivalent depth in the MapCHECK 2 diode array, and respective calculated dose planes were exported from a commercial treatment planning system. Planar dose comparison pass rates were calculated within the Sun Nuclear Corporation analytic software using a number of calculation parameters, including Measurement Uncertainty on and off. By varying the percent difference (%Diff) criterion for similar analyses performed with Measurement Uncertainty turned off, an effective %Diff criterion was defined for each field/arc corresponding to the pass rate achieved with Measurement Uncertainty turned on. On average, the Measurement Uncertainty function increases the user‐defined %Diff criterion by 0.8%‐1.1% for 3%/3 mm analysis, depending on plan type and calculation technique (corresponding to an average change in pass rate of 1.0%‐3.5%, and a maximum change of 8.7%). At the 2%/2 mm level, the Measurement Uncertainty function increases the user‐defined %Diff criterion by 0.7%‐1.2% on average, again depending on plan type and calculation technique (corresponding to an average change in pass rate of 3.5%‐8.1%, and a maximum change of 14.2%). The largest increases in pass rate due to the Measurement Uncertainty function are generally seen with poorly matched planar dose comparisons, while the function has a notably smaller effect as pass rates approach 100%. The Measurement Uncertainty function, then, may substantially increase the pass rates for planar dose comparisons. Meanwhile, the types of uncertainties incorporated into the function (and their associated quantitative estimates, as described in the software user's manual) may not be an accurate estimation of actual measurement uncertainty, depending on the user's measurement conditions. Pass rates listed in published reports, comparisons between institutions or simply separate workstations, or comparisons with the calculation methods of other vendors, should clearly indicate whether or not the Measurement Uncertainty function is used, since it has the potential to substantially inflate pass rates for typical IMRT and VMAT dose planes.PACS number(s): 87.55.Qr, 87.56.Fc

Modeling and quantifying uncertainty in the product design phase for effects of user preference changes

Purpose– The purpose of this paper is to quantify external and internal uncertainties in product design process. The research addresses the measure of product future changes.Design/methodology/approach– Two methods are proposed to model and quantify uncertainty in the product life cycle. Changes of user preferences are considered as the external uncertainty. Changes stemming from dependencies between components are addressed as the internal uncertainty. Both methods use developed mechanisms to capture and treat changes of user preferences. An agent-based model is developed to simulate sociotechnical events in the product life cycle for the external uncertainty. An innovative application of Big Data Analytics (BDA) is proposed to evaluate the external and internal uncertainties in product design. The methods can identify the most affected product components under uncertainty.Findings– The results show that the proposed method could identify product changes during its life cycle, particularly using the proposed BDA method.Practical implications– It is essential for manufacturers in the competitive market to know their product changes under uncertainty. Proposed methods have potential to optimize design parameters in complex environments.Originality/value– This research bridges the gap of literature in the accurate estimation of uncertainty. The research integrates the change prediction and change transferring, applies data management methods innovatively, and utilizes the proposed methods practically.

SU‐E‐T‐561: Monte Carlo‐Based Organ Dose Reconstruction Using Pre‐Contoured Human Model for Hodgkins Lymphoma Patients Treated by Cobalt‐60 External Beam Therapy

Purpose:Organ doses for the Hodgkin's lymphoma patients treated with cobalt‐60 radiation were estimated using an anthropomorphic model and Monte Carlo modeling.Methods:A cobalt‐60 treatment unit modeled in the BEAMnrc Monte Carlo code was used to produce phase space data. The Monte Carlo simulation was verified with percent depth dose measurement in water at various field sizes. Radiation transport through the lung blocks were modeled by adjusting the weights of phase space data. We imported a precontoured adult female hybrid model and generated a treatment plan. The adjusted phase space data and the human model were imported to the XVMC Monte Carlo code for dose calculation. The organ mean doses were estimated and dose volume histograms were plotted.Results:The percent depth dose agreement between measurement and calculation in water phantom was within 2% for all field sizes. The mean organ doses of heart, left breast, right breast, and spleen for the selected case were 44.3, 24.1, 14.6 and 3.4 Gy, respectively with the midline prescription dose of 40.0 Gy.Conclusion:Organ doses were estimated for the patient group whose threedimensional images are not available. This development may open the door to more accurate dose reconstruction and estimates of uncertainties in secondary cancer risk for Hodgkin's lymphoma patients.This work was partially supported by the intramural research program of the National Institutes of Health, National Cancer Institute, Division of Cancer Epidemiology and Genetics.

SU-E-J-92: Validating Dose Uncertainty Estimates Produced by AUTODIRECT, An Automated Program to Evaluate Deformable Image Registration Accuracy

Purpose: Deformable image registration (DIR) is a powerful tool with the potential to deformably map dose from one computed-tomography (CT) image to another. Errors in the DIR, however, will produce errors in the transferred dose distribution. We have proposed a software tool, called AUTODIRECT (automated DIR evaluation of confidence tool), which predicts voxel-specific dose mapping errors on a patient-by-patient basis. This work validates the effectiveness of AUTODIRECT to predict dose mapping errors with virtual and physical phantom datasets. Methods: AUTODIRECT requires 4 inputs: moving and fixed CT images and two noise scans of a water phantom (for noise characterization). Then, AUTODIRECT uses algorithms to generate test deformations and applies them to the moving and fixed images (along with processing) to digitally create sets of test images, with known ground-truth deformations that are similar to the actual one. The clinical DIR algorithm is then applied to these test image sets (currently 4) . From these tests, AUTODIRECT generates spatial and dose uncertainty estimates for each image voxel based on a Student's t distribution. This work compares these uncertainty estimates to the actual errors made by the Velocity Deformable Multi Pass algorithm on 11 virtual and 1 physical phantom datasets. Results: For 11 of the 12 tests, the predicted dose error distributions from AUTODIRECT are well matched to the actual error distributions within 1–6% for 10 virtual phantoms, and 9% for the physical phantom. For one of the cases though, the predictions underestimated the errors in the tail of the distribution. Conclusion: Overall, the AUTODIRECT algorithm performed well on the 12 phantom cases for Velocity and was shown to generate accurate estimates of dose warping uncertainty. AUTODIRECT is able to automatically generate patient-, organ-, and voxel-specific DIR uncertainty estimates. This ability would be useful for patient-specific DIR quality assurance.

Accurate calibration and uncertainty estimation of the normal spring constant of various AFM cantilevers.

Measurement of force on a micro- or nano-Newton scale is important when exploring the mechanical properties of materials in the biophysics and nanomechanical fields. The atomic force microscope (AFM) is widely used in microforce measurement. The cantilever probe works as an AFM force sensor, and the spring constant of the cantilever is of great significance to the accuracy of the measurement results. This paper presents a normal spring constant calibration method with the combined use of an electromagnetic balance and a homemade AFM head. When the cantilever presses the balance, its deflection is detected through an optical lever integrated in the AFM head. Meanwhile, the corresponding bending force is recorded by the balance. Then the spring constant can be simply calculated using Hooke’s law. During the calibration, a feedback loop is applied to control the deflection of the cantilever. Errors that may affect the stability of the cantilever could be compensated rapidly. Five types of commercial cantilevers with different shapes, stiffness, and operating modes were chosen to evaluate the performance of our system. Based on the uncertainty analysis, the expanded relative standard uncertainties of the normal spring constant of most measured cantilevers are believed to be better than 2%.

Blank corrections for ramped pyrolysis radiocarbon dating of sedimentary and soil organic carbon.

Ramped pyrolysis (RP) targets distinct components of soil and sedimentary organic carbon based on their thermochemical stabilities and allows the determination of the full spectrum of radiocarbon ((14)C) ages present in a soil or sediment sample. Extending the method into realms where more precise ages are needed or where smaller samples need to be measured involves better understanding of the blank contamination associated with the method. Here, we use a compiled data set of RP measurements of samples of known age to evaluate the mass of the carbon blank and its associated (14)C signature, and to assess the performance of the RP system. We estimate blank contamination during RP using two methods, the modern-dead and the isotope dilution method. Our results indicate that during one complete RP run samples are contaminated by 8.8 ± 4.4 μg (time-dependent) of modern carbon (MC, fM ∼ 1) and 4.1 ± 5.5 μg (time-independent) of dead carbon (DC, fM ∼ 0). We find that the modern-dead method provides more accurate estimates of uncertainties in blank contamination; therefore, the isotope dilution method should be used with caution when the variability of the blank is high. Additionally, we show that RP can routinely produce accurate (14)C dates with precisions ∼100 (14)C years for materials deposited in the last 10,000 years and ∼300 (14)C years for carbon with (14)C ages of up to 20,000 years.

Estimating continuous body size-based shifts in δ15N–δ13C space using multivariate hierarchical models

Stable isotopes (δ15N and δ13C) offer one representation of an individual’s trophic niche and are important tools for elucidating ecological patterns and testing a diversity of hypotheses. Because δ15N and δ13C values are often obtained from the same sample, they compose a bivariate response that researchers commonly analyze using multivariate statistical methods. However, stable isotope data sets often exhibit hierarchical structure whereby samples may be clustered or grouped at multiple levels either as an artifact of sampling design or due to structure inherent in the sampled population (e.g., samples from individuals grouped according to life history stages, social groups, ages, or sizes classes). Ignoring such structure can result in overly optimistic confidence intervals and heighten the risk of observing significant differences where none exist. To address these issues, we suggest researchers utilize multivariate hierarchical models, which are a simple extension of univariate hierarchical methods. The models account for potential dependencies between δ15N and δ13C values, permit valid predictions of shifts in δ15N–δ13C space related to predictor variables, provide more accurate estimates of parameter uncertainty, and improved inferences on coefficients that correspond to groups with small to moderate quantities of data. We demonstrate advantages of multivariate hierarchical models by examining size-dependent shifts in δ15N–δ13C space in outmigrating post-smolt Chinook salmon sampled from an estuarine habitat. Given the prevalence of complex structure in ecological stable isotope data sets, multivariate hierarchical models should hold considerable value to food web and stable isotope ecologists.

The Open-Faced Sandwich Adjustment for MCMC Using Estimating Functions

A situation frequently arises where working with the likelihood function is problematic. This can happen for several reasons—perhaps the likelihood is prohibitively computationally expensive, perhaps it lacks some robustness property, or perhaps it is simply not known for the model under consideration. In these cases, it is often possible to specify alternative functions of the parameters and the data that can be maximized to obtain asymptotically normal estimates. However, these scenarios present obvious problems if one is interested in applying Bayesian techniques. This article describes open-faced sandwich adjustment, a way to incorporate a wide class of nonlikelihood objective functions within Bayesian-like models to obtain asymptotically valid parameter estimates and inference via MCMC. Two simulation examples show that the method provides accurate frequentist uncertainty estimates. The open-faced sandwich adjustment is applied to a Poisson spatio-temporal model to analyze an ornithology dataset from the citizen science initiative eBird. An online supplement contains an appendix with additional figures, tables, and discussion, as well as R code.

Bayesian lensing shear measurement

We derive an estimator of weak gravitational lensing shear from background galaxy images that avoids noise-induced biases through a rigorous Bayesian treatment of the measurement. The derived shear estimator disposes with the assignment of ellipticities to individual galaxies that is typical of previous approaches to galaxy lensing. Shear estimates from the mean of the Bayesian posterior are unbiased in the limit of large number of background galaxies, regardless of the noise level on individual galaxies. The Bayesian formalism requires a prior describing the (noiseless) distribution of the target galaxy population over some parameter space; this prior can be constructed from low-noise images of a subsample of the target population, attainable from long integrations of a fraction of the survey field. We find two ways to combine this exact treatment of noise with rigorous treatment of the effects of the instrumental point-spread function and sampling. The Bayesian model fitting (BMF) method assigns a likelihood of the pixel data to galaxy models (e.g. Sersic ellipses). The Bayesian Fourier domain (BFD) method compresses the pixel data to a small set of weighted moments calculated after PSF correction in Fourier space. A numerical test using a simplified model of a biased galaxy measurement process demonstrates that the Bayesian formalism recovers applied shears to $<1$ part in $10^3$ accuracy and provides accurate uncertainty estimates. BFD is the first shear measurement algorithm that is model-free and requires no approximations or ad hoc assumptions in correcting for the effects of PSF, noise, or sampling on the galaxy images. These algorithms are good candidates for attaining the part-per-thousand shear inference required for hemisphere-scale weak gravitational lensing surveys. (abridged)

THE DYNAMICS OF MERGING CLUSTERS: A MONTE CARLO SOLUTION APPLIED TO THE BULLET AND MUSKET BALL CLUSTERS

Merging galaxy clusters have become one of the most important probes of dark matter, providing evidence for dark matter over modified gravity and even constraints on the dark matter self-interaction cross-section. To properly constrain the dark matter cross-section it is necessary to understand the dynamics of the merger, as the inferred cross-section is a function of both the velocity of the collision and the observed time since collision. While the best understanding of merging system dynamics comes from N-body simulations, these are computationally intensive and often explore only a limited volume of the merger phase space allowed by observed parameter uncertainty. Simple analytic models exist but the assumptions of these methods invalidate their results near the collision time, plus error propagation of the highly correlated merger parameters is unfeasible. To address these weaknesses I develop a Monte Carlo method to discern the properties of dissociative mergers and propagate the uncertainty of the measured cluster parameters in an accurate and Bayesian manner. I introduce this method, verify it against an existing hydrodynamic N-body simulation, and apply it to two known dissociative mergers: 1ES 0657-558 (Bullet Cluster) and DLSCL J0916.2+2951 (Musket Ball Cluster). I find that this method surpasses existing analytic models - providing accurate (10% level) dynamic parameter and uncertainty estimates throughout the merger history. This coupled with minimal required a priori information (subcluster mass, redshift, and projected separation) and relatively fast computation (~6 CPU hours) makes this method ideal for large samples of dissociative merging clusters.

Sensitivity and Uncertainty Analysis for the Annual Phosphorus Loss Estimator Model

Models are often used to predict phosphorus (P) loss from agricultural fields. Although it is commonly recognized that model predictions are inherently uncertain, few studies have addressed prediction uncertainties using P loss models. In this study we assessed the effect of model input error on predictions of annual P loss by the Annual P Loss Estimator (APLE) model. Our objectives were (i) to conduct a sensitivity analyses for all APLE input variables to determine which variables the model is most sensitive to, (ii) to determine whether the relatively easy-to-implement first-order approximation (FOA) method provides accurate estimates of model prediction uncertainties by comparing results with the more accurate Monte Carlo simulation (MCS) method, and (iii) to evaluate the performance of the APLE model against measured P loss data when uncertainties in model predictions and measured data are included. Our results showed that for low to moderate uncertainties in APLE input variables, the FOA method yields reasonable estimates of model prediction uncertainties, although for cases where manure solid content is between 14 and 17%, the FOA method may not be as accurate as the MCS method due to a discontinuity in the manure P loss component of APLE at a manure solid content of 15%. The estimated uncertainties in APLE predictions based on assumed errors in the input variables ranged from ±2 to 64% of the predicted value. Results from this study highlight the importance of including reasonable estimates of model uncertainty when using models to predict P loss.

Wide-Angle X-Ray Solution Scattering for Protein-Ligand Binding: Multivariate Curve Resolution with Bayesian Confidence Intervals

A new way to use wide-angle x-ray solution scattering to study protein-ligand binding is presented. First, scattering patterns are measured at different protein and ligand concentrations. Multivariate curve resolution based on singular value decomposition and global analysis is applied to estimate the binding affinities and reference patterns (i.e., the scattering patterns of individual components). As validated by simulation, Bayesian confidence intervals provide accurate uncertainty estimates for the binding free energies and reference patterns. Experimental results from several protein-ligand systems demonstrate the feasibility of the approach, which promises to expand the role of wide-angle x-ray scattering as a quantitative biophysical tool.

Use of the data depth function to differentiate between case of interpolation and extrapolation in hydrological model prediction

Summary Hydrological models are subject to significant sources of uncertainty including input data, model structure and parameter uncertainty. A key requirement for an operational flow forecasting model is therefore to give accurate estimates of model uncertainty. This estimate is often presented in terms of confidence bounds. The quality and quantity of observed rainfall and flow data available for calibration has a great influence on the identification of hydrological model parameters, and hence the model error distribution and width of the confidence bounds. The information contained in the observed time series is not uniformly distributed, and may not represent all types of behaviour or activation of flow pathways that could occur in the catchment. A model calibrated with data from a given time period could therefore perform well or poorly when evaluated over a new time period, depending on the information content and variability of the calibration data, in relation to the validation period. Our hypothesis is that we can improve the estimate of hydrological predictive uncertainty, based on our knowledge of the range of data available for calibration. If the characteristics of the validation data are similar in information content and variability to those in the calibration period, we term this an “interpolation case”, and expect the model errors during calibration to be similar to those in validation. Otherwise, it is an “extrapolation case”, where we may expect model errors to be greater. In this study, we developed an algorithm to differentiate cases of ‘interpolation’ versus ‘extrapolation’ in the prediction time period. The algorithm is based on the concept of ‘data depth’, i.e. the location of new data in relation to the convex hull of the calibration data set. Using a case study, we calculated uncertainty bounds for the predictive time period using methods with/without differentiation of interpolation and extrapolation cases. The performance of the resulting confidence bounds in accurately representing model error was evaluated using both visual inspection for specific events, and rank histograms and range statistics for the simulated model error quantiles. We show that the proposed algorithm enables us to give differentiated predictive uncertainty bounds which represent model error in more realistic ways.

Spatial regression methods capture prediction uncertainty in species distribution model projections through time

AbstractAimThe uncertainty associated with species distribution model (SDM) projections is poorly characterized, despite its potential value to decision makers. Error estimates from most modelling techniques have been shown to be biased due to their failure to account for spatial autocorrelation (SAC) of residual error. Generalized linear mixed models (GLMM) have the ability to account for SAC through the inclusion of a spatially structured random intercept, interpreted to account for the effect of missing predictors. This framework promises a more realistic characterization of parameter and prediction uncertainty. Our aim is to assess the ability of GLMMs and a conventional SDM approach, generalized linear models (GLM), to produce accurate projections and estimates of prediction uncertainty.InnovationWe employ a unique historical dataset to assess the accuracy of projections and uncertainty estimates from GLMMs and GLMs. Models were trained using historical (1928–1940) observations for 99 woody plant species in California, USA, and assessed using temporally independent validation data (2000–2005).Main conclusionsGLMMs provided a closer fit to historic data, had fewer significant covariates, were better able to eliminate spatial autocorrelation of residual error, and had larger credible intervals for projections than GLMs. The accuracy of projections was similar between methods but GLMMs better quantified projection uncertainty. Additionally, GLMMs produced more conservative estimates of species range size and range size change than GLMs. We conclude that the GLMM error structure allows for a more realistic characterization of SDM uncertainty. This is critical for conservation applications that rely on honest assessments of projection uncertainty.

The impact of model and rainfall forcing errors on characterizing soil moisture uncertainty in land surface modeling

Abstract. The contribution of rainfall forcing errors relative to model (structural and parameter) uncertainty in the prediction of soil moisture is investigated by integrating the NASA Catchment Land Surface Model (CLSM), forced with hydro-meteorological data, in the Oklahoma region. Rainfall-forcing uncertainty is introduced using a stochastic error model that generates ensemble rainfall fields from satellite rainfall products. The ensemble satellite rain fields are propagated through CLSM to produce soil moisture ensembles. Errors in CLSM are modeled with two different approaches: either by perturbing model parameters (representing model parameter uncertainty) or by adding randomly generated noise (representing model structure and parameter uncertainty) to the model prognostic variables. Our findings highlight that the method currently used in the NASA GEOS-5 Land Data Assimilation System to perturb CLSM variables poorly describes the uncertainty in the predicted soil moisture, even when combined with rainfall model perturbations. On the other hand, by adding model parameter perturbations to rainfall forcing perturbations, a better characterization of uncertainty in soil moisture simulations is observed. Specifically, an analysis of the rank histograms shows that the most consistent ensemble of soil moisture is obtained by combining rainfall and model parameter perturbations. When rainfall forcing and model prognostic perturbations are added, the rank histogram shows a U-shape at the domain average scale, which corresponds to a lack of variability in the forecast ensemble. The more accurate estimation of the soil moisture prediction uncertainty obtained by combining rainfall and parameter perturbations is encouraging for the application of this approach in ensemble data assimilation systems.

RIGOROUS IMAGE FORMATION FROM AIRBORNE AND SPACEBORNE DIGITAL ARRAY SCANNERS

Abstract. Sensor builders in the digital era have design limitations due to the constraint of maximum available digital array size. A straightforward solution exists, for example, when four cameras that each simultaneously captures an image from essentially the same perspective centre; they can be re-sampled to form a virtual large format image that can be exploited using a single (instead of four separate) instantiation of a frame model. The purpose of this paper is to address the less trivial time-dependent cases where the sensor scans the ground and the detector arrays obtain chips of imagery that need to be stitched together to form a single conveniently exploitable image. Many operational techniques warp the imagery to form a mosaic, or ortho-rectify it using an imperfect digital surface model (DSM), thus eliminating the possibility for accurate geolocation and uncertainty estimation. This algorithm, however, forms a single virtual image with associated smooth metadata, which can be exploited using a simple physical sensor model. The algorithm consists of four main steps: 1) automated tie point matching; 2) camera calibration (once per sensor); 3) block adjustment; and 4) pixel re-sampling based on an "idealized" virtual model. The same geometry model used to form the image, or its true replacement, must be used to exploit it. This paper verifies the algorithm using real imagery acquired from the Global Hawk (GH) UAV. Registration of the virtual image to a WorldView1 stereopair using four tie points yielded an RMS below 0.6 meters per horizontal axis.

Bayesian Neural Networks for Uncertainty Analysis of Hydrologic Modeling: A Comparison of Two Schemes

Bayesian Neural Networks (BNNs) have been shown as useful tools to analyze modeling uncertainty of Neural Networks (NNs). This research focuses on the comparison of two BNNs. The first BNNs (BNN-I) use statistical methods to describe the characteristics of different uncertainty sources (input, parameter, and model structure) and integrate these uncertainties into a Markov Chain Monte Carlo (MCMC) framework to estimate total uncertainty. The second BNNs (BNN-II) lump all uncertainties into a single error term (i.e. the residual between model prediction and measurement). In this study, we propose a simple BNN-II, which uses Genetic Algorithms (GA) and Bayesian Model Averaging (BMA) to calibrate Neural Networks with different structures (number of hidden units) and combine the predictions from different NNs to derive predictions and uncertainty estimation. We tested these two BNNs in two watersheds for daily and monthly hydrologic simulations. The BMA based BNNs (BNN-II) developed here outperforms BNN-I in the two watersheds in terms of both accurate prediction and uncertainty estimation. These results indicate that, given incomplete understanding of the characteristics associated with each uncertainty source and their interactions, the simple lumped error approach may yield better prediction and uncertainty estimation.

Classification of newborn EEG maturity with Bayesian averaging over decision trees

EEG experts can assess a newborn’s brain maturity by visual analysis of age-related patterns in sleep EEG. It is highly desirable to make the results of assessment most accurate and reliable. However, the expert analysis is limited in capability to provide the estimate of uncertainty in assessments. Bayesian inference has been shown providing the most accurate estimates of uncertainty by using Markov Chain Monte Carlo (MCMC) integration over the posterior distribution. The use of MCMC enables to approximate the desired distribution by sampling the areas of interests in which the density of distribution is high. In practice, the posterior distribution can be multimodal, and so that the existing MCMC techniques cannot provide the proportional sampling from the areas of interest. The lack of prior information makes MCMC integration more difficult when a model parameter space is large and cannot be explored in detail within a reasonable time. In particular, the lack of information about EEG feature importance can affect the results of Bayesian assessment of EEG maturity. In this paper we explore how the posterior information about EEG feature importance can be used to reduce a negative influence of disproportional sampling on the results of Bayesian assessment. We found that the MCMC integration tends to oversample the areas in which a model parameter space includes one or more features, the importance of which counted in terms of their posterior use is low. Using this finding, we proposed to cure the results of MCMC integration and then described the results of testing the proposed method on a set of sleep EEG recordings.

Improved location procedures at the International Seismological Centre

SUMMARY The International Seismological Centre (ISC) is a non-governmental, non-profit organization with the primary mission of producing the definitive account of the Earth's seismicity. The ISC Bulletin covers some 50 yr (1960–2011) of seismicity. The recent years have seen a dramatic increase both in the number of reported events and especially in the number of reported phases, owing to the ever-increasing number of stations worldwide. Similar ray paths will produce correlated traveltime prediction errors due to unmodelled heterogeneities in the Earth, resulting in underestimated location uncertainties, and for unfavourable network geometries, location bias. Hence, the denser and more unbalanced the global seismic station coverage becomes, the less defensible is the assumption (that is the observations are independent), which is made by most location algorithms. To address this challenge we have developed a new location algorithm for the ISC that accounts for correlated error structure, and uses all IASPEI standard phases with a valid ak135 traveltime prediction to obtain more accurate event locations. In this paper we describe the new ISC locator, and present validation tests by relocating the ground truth events in the IASPEI Reference Event List, as well as by relocating the entire ISC Bulletin. We show that the new ISC location algorithm provides small, but consistent location improvements, considerable improvements in depth determination and significantly more accurate formal uncertainty estimates. We demonstrate that the new algorithm, through the use of later phases and testing for depth resolution, considerably clusters event locations more tightly, thus providing an improved view of the seismicity of the Earth.

Smoothing Population Size Estimates for Time-Stratified Mark-Recapture Experiments Using Bayesian P-Splines

Petersen-type mark-recapture experiments are often used to estimate the number of fish or other animals in a population moving along a set migration route. A first sample of individuals is captured at one location, marked, and returned to the population. A second sample is then captured farther along the route, and inferences are derived from the numbers of marked and unmarked fish found in this second sample. Data from such experiments are often stratified by time (day or week) to allow for possible changes in the capture probabilities, and previous methods of analysis fail to take advantage of the temporal relationships in the stratified data. We present a Bayesian, semiparametric method that explicitly models the expected number of fish in each stratum as a smooth function of time. Results from the analysis of historical data from the migration of young Atlantic salmon (Salmo salar) along the Conne River, Newfoundland, and from a simulation study indicate that the new method provides more precise estimates of the population size and more accurate estimates of uncertainty than the currently available methods.