Ranking and Selection of Earthquake Ground-Motion Models Using the Stochastic Area Metric

We introduce the cumulative-distribution-based area metric (AM)—also known as stochastic AM—as a scoring metric for earthquake ground-motion models (GMMs). The AM quantitatively informs the user of the degree to which observed or test data fit with a given model, providing a rankable absolute measure of misfit. The AM considers underlying data distributions and model uncertainties without any assumption of form. We apply this metric, along with existing testing methods, to four GMMs in order to test their performance using earthquake ground-motion data from the Preston New Road (United Kingdom) induced seismicity sequences in 2018 and 2019. An advantage of the proposed approach is its applicability to sparse datasets. We, therefore, focus on the ranking of models for discrete ranges of magnitude and distance, some of which have few data points. The variable performance of models in different ranges of the data reveals the importance of considering alternative models. We extend the ranking of GMMs through analysis of intermodel variations of the candidate models over different ranges of magnitude and distance using the AM. We find the intermodel AM can be a useful tool for selection of models for the logic-tree framework in seismic-hazard analysis. Overall, the AM is shown to be efficient and robust in the process of selection and ranking of GMMs for various applications, particularly for sparse and small-sized datasets.

SCU-Net: A deep learning method for segmentation and quantification of breast arterial calcifications on mammograms

PurposeMeasurements of breast arterial calcifications (BAC) can offer a personalized, noninvasive approach to risk-stratify women for cardiovascular disease such as heart attack and stroke. We aim to detect and segment breast arterial calcifications in mammograms accurately and suggest novel measurements to quantify detected BAC for future clinical applications.MethodsTo separate BAC in mammograms, we propose a light-weight fine vessel segmentation method Simple Context U-Net (SCU-Net). Due to the large image size of mammograms, we adopt a patch-based way to train SCU-Net and obtain the final whole-image-size results by stitching patch-wise results together. To further quantify calcifications, we test five quantitative metrics to inspect the progression of BAC for subjects: Sum of Mask Probability Metric (𝒫ℳ), Sum of Mask Area Metric (𝒜ℳ), Sum of Mask Intensity Metric (𝒮ℐℳ), Sum of Mask Area with Threshold Intensity Metric (𝒯𝒜ℳX) and Sum of Mask Intensity with Threshold X Metric (𝒯 𝒮ℐℳX). Finally, we demonstrate the ability of the metrics to longitudinally measure calcifications in a group of 26 subjects and evaluate our quantification metrics compared to calcified voxels and calcium mass on breast CT for 10 subjects.ResultsOur segmentation results are compared with state-of-the-art network architectures based on recall, precision, accuracy, F1-score/Dice Score and Jaccard Index evaluation metrics and achieve corresponding values of 0.789, 0.708, 0.997, 0.729, and 0.581 for whole-image-size results. The quantification results all show >95% correlation between quantification measures on predicted masks of SCU-Net as compared to the groundtruth and measurement of calcification on breast CT. For the calcifications quantification measurement, our calcification volume (voxels) results yield R2-correlation values of 0.834, 0.843, 0.832, 0.798, and 0.800 for the 𝒫ℳ, 𝒜ℳ, 𝒮ℐℳ, 𝒯𝒜ℳ100, 𝒯 𝒮ℐℳ100 metrics, respectively; our calcium mass results yield comparable R2-correlation values of 0.866, 0.873, 0.840, 0.774, and 0.798 for the same metrics.ConclusionsSCU-Net is a simple method to accurately segment arterial calcification retrospectively on routine mammograms. Quantification of the calcifications based on this segmentation in the retrospective cohort study has sufficient sensitivity to detect the normal progression over time and should be useful for future research and clinical applications.

Which Green Space Metric Best Predicts a Lowered Odds of Type 2 Diabetes?

The choice of a green space metric may affect what relationship is found with health outcomes. In this research, we investigated the relationship between percent green space area, a novel metric developed by us (based on the average contiguous green space area a spatial buffer has contact with), in three different types of buffers and type 2 diabetes (T2D). We obtained information about diagnosed T2D and relevant covariates at the individual level from the large and representative 45 and Up Study. Average contiguous green space and the percentage of green space within 500 m, 1 km, and 2 km of circular buffer, line-based road network (LBRN) buffers, and polygon-based road network (PBRN) buffers around participants’ residences were used as proxies for geographic access to green space. Generalized estimating equation regression models were used to determine associations between access to green space and T2D status of individuals. It was found that 30%–40% green space within 500 m LBRN or PBRN buffers, and 2 km PBRN buffers, but not within circular buffers, significantly reduced the risk of T2D. The novel average green space area metric did not appear to be particularly effective at measuring reductions in T2D. This study complements an existing research body on optimal buffers for green space measurement.

Ranking and calibration of ground-motion models using the stochastic area metric.

<p>The selection and ranking of  Ground Motion Models (GMMs) for scenario earthquakes is a crucial element in seismic hazard analysis. Typically model testing and ranking do not appropriately account for uncertainties, thus leading to improper ranking. We introduce the stochastic area metric (AM) as a scoring metric for GMMs, which not only informs the analyst of the degree to which observed or test data fit the model but also considers the uncertainties without the assumption of how data are distributed. The AM can be used as a scoring metric or cost function, whose minimum value identifies the model that best fits a given dataset. We apply this metric along with existing testing methods to recent and commonly used European ground motion prediction equations: Bindi et al. (2014, B014), Akkar et al. (2014, A014) and Cauzzi et al. (2015, C015). The GMMs are ranked and their performance analysed against the European Engineering Strong Motion (ESM) dataset. We focus on the ranking of models for ranges of magnitude and distance with sparse data, which pose a specific problem with other statistical testing methods. The performance of models over different ranges of magnitude and distance were analysed using AM, revealing the importance of considering different models for specific applications (e.g., tectonic, induced). We find the A014 model displays good performance with complete dataset while B014 appears to be best for small magnitudes and distances. In addition, we calibrated GMMs derived from a compendium of data and generated a suite of models for the given region through an optimisation technique utilising the concept of AM and ground motion variability. This novel framework for ranking and calibration guides the informed selection of models and helps develop regionally adjusted and application-specific GMMs for better prediction. </p><p> </p>

A Power Performance Online Assessment Method of a Wind Turbine Based on the Probabilistic Area Metric

This paper presents an approach for creating online assessment power curves by calculating the variations between the baseline and actual power curves. The actual power curve is divided into two regions based on the operation rules of a wind turbine, and the regions are individually assessed. The raw data are filtered using the control command, and outliers are detected using the density-based spatial clustering of applications with noise clustering method. The probabilistic area metric is applied to quantify the variations of the two power curves in the two regions. Based on this result, the variation in the power curves can be calculated, and the results can be used to dynamically evaluate the power performance of a wind turbine. The proposed method is verified against the derivation of secondary principal component method and traditional statistical methods. The potential applications of the proposed method in wind turbine maintenance activities are discussed.

A New Interval Area Metric for Model Validation With Limited Experimental Data

Various stochastic validation metrics have been developed for validating models, among which area metric is frequently used in many practical problems. However, the existing area metric does not consider experimental epistemic uncertainty caused by lack of sufficient physical observations. Therefore, it cannot provide a confidence level associated with the amount of experimental data, which is a desired characteristic of validation metric. In this paper, the concept of area metric is extended to a new metric, namely interval area metric, for single-site model validation with limited experimental data. The kernel of the proposed metric is defining two boundary distribution functions based on Dvoretzky–Kiefer–Wolfowitz inequality, so as to provide an interval at a given confidence level, which covers the true cumulative distribution function (CDF) of physical observations. Based on this interval area metric, the validity of a model can be quantitatively measured with the specific confidence level in association with consideration of the lack of experiment information. The new metric is examined and compared with the existing metrics through numerical case studies to demonstrate its validity and discover its properties. Furthermore, an engineering example is provided to illustrate the effectiveness of the proposed metric in practical satellite structure engineering application.