Ensemble Riemannian Data Assimilation: Towards High-dimensional Implementation

This paper presents the results of the Ensemble Riemannian Data Assimilation for relatively high-dimensional nonlinear dynamical systems, focusing on the chaotic Lorenz-96 model and a two-layer quasi-geostrophic (QG) model of atmospheric circulation. The analysis state in this approach is inferred from a joint distribution that optimally couples the background probability distribution and the likelihood function, enabling formal treatment of systematic biases without any Gaussian assumptions. Despite the risk of the curse of dimensionality in the computation of the coupling distribution, comparisons with the classic implementation of the particle filter and the stochastic ensemble Kalman filter demonstrate that with the same ensemble size, the presented methodology could improve the predictability of dynamical systems. In particular, under systematic errors, the root mean squared error of the analysis state can be reduced by 20 % (30 %) in Lorenz-96 (QG) model.

Long- and Short-Term Stress Interaction of the 2019 Ridgecrest Sequence and Coulomb-Based Earthquake Forecasts

We first explore a series of retrospective earthquake interactions in southern California. We find that the four Mw≥7 shocks in the past 150 yr brought the Ridgecrest fault ∼1 bar closer to failure. Examining the 34 hr time span between the Mw 6.4 and Mw 7.1 events, we calculate that the Mw 6.4 event brought the hypocentral region of the Mw 7.1 earthquake 0.7 bars closer to failure, with the Mw 7.1 event relieving most of the surrounding stress that was imparted by the first. We also find that the Mw 6.4 cross-fault aftershocks shut down when they fell under the stress shadow of the Mw 7.1. Together, the Ridgecrest mainshocks brought a 120 km long portion of the Garlock fault from 0.2 to 10 bars closer to failure. These results motivate our introduction of forecasts of future seismicity. Most attempts to forecast aftershocks use statistical decay models or Coulomb stress transfer. Statistical approaches require simplifying assumptions about the spatial distribution of aftershocks and their decay; Coulomb models make simplifying assumptions about the geometry of the surrounding faults, which we seek here to remove. We perform a rate–state implementation of the Coulomb stress change on focal mechanisms to capture fault complexity. After tuning the model through a learning period to improve its forecast ability, we make retrospective forecasts to assess model’s predictive ability. Our forecast for the next 12 months yields a 2.3% chance of an Mw≥7.5 Garlock fault rupture. If such a rupture occurred and reached within 45 km of the San Andreas, we calculate it would raise the probability of a San Andreas rupture on the Mojave section by a factor of 150. We therefore estimate the net chance of large San Andreas earthquake in the next 12 months to be 1.15%, or about three to five times its background probability.

Study of Motion Detection Technology Based on Davinci Platform

A motion object detection method is presented based on Davinci platform. This paper adopts color histogram algorithm to detect moving target, which is operated on TMS320DM6446. In continuous frames, probability distributions of both foreground pixel and background pixel are counted separately to build color histogram. Background probability can be computed based on gauss model. After background separation, we can use median filtering to suppress image noise, detect connected domain, converge foreground pixels to make up of moving object regions and mark them. The results show that the method presented has good accuracy and quick speed for realtime application.

Optimization of the decision threshold for single radioactive counting

When the activity of a sample is close to the background level, the decision threshold is classically defined by considering distributions of sample and background as being equal. Recently, the Bayesian approach has been considered in the standard ISO to refine the determination of the decision threshold by taking into account all accessible information prior to measurement such as type A and type B uncertainties. However, simplifications using Gaussian approximation and experimental values instead of true means are often used to facilitate calculations. In this paper, we develop a complete treatment without simplification, based on the Bayesian approach and Poisson distribution. Minimal informations have been considered: one single raw counting for the sample and one previously acquired background. From one single background counting, the net background probability law is calculated and then a decision threshold is deduced. In particular, we demonstrate that the decision threshold is defined for any case including very low background or even null event. Comparisons with classical approach as well as the Bayesian treatment in the new ISO 11929 have been carried out. Applications of this decision threshold for the optimisation of radioactive measurement or in case of a set of minimal detectable activities used to determine average releases are given. Bayesian treatment also gives relevant informations such as the probability for a source to be radioactive when the net number of counts is below the decision threshold.

CNS Lupus Presenting As Encephalitis : A Case Report

An 18 years old female presented with several episodes of convulsions followed by unconsciousness. On admission, her Glasgow Coma Scale was 3, and other neurological and systemic examination revealed no abnormality. Initially it appeared to be a case of viral encephalitis with a background probability of glomerulonephritis. Differential diagnosis was uraemic encephalopathy and seizure disorder. However, finally it was diagnosed as a case of CNS Lupus - a rare presentation of Systemic Lupus Erythematosus (SLE). Key words: Convulsion; glomerulonephritis; encephalitis; lupus. DOI: http://dx.doi.org/10.3329/jdmc.v20i1.8588 J Dhaka Med Coll. 2011; 20(1) :82-85