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Detection and diagnosis of model parameter and noise variance changes with application in seismic signal processing

机译：模型参数和噪声方差变化的检测与诊断在地震信号处理中的应用

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摘要

The change detection and diagnosis methods have gained considerable attention in scientific research and appears to be the central issue in various application areas. These applications need some robust change detection schemes to work well and separate the changes in the experimental conditions from the real changes in the system, especially for systems with arbitrary and non-stationary known or unknown inputs. The objective of the paper is to develop such kind of change detection and diagnosis scheme. In the first part of the paper we give the conceptual description of some classical change detection schemes based on sliding windows and likelihood techniques. Then, starting from these classical change detection schemes, a new algorithm able to discriminate between the model parameter and noise variance changes is presented. Finally, we include some Monte Carlo simulations for change detection in a second order FIR model and experimental results obtained in analysis of seismic signals, using the proposed approach.
机译：变化检测和诊断方法已在科学研究中引起了广泛关注，并且似乎已成为各个应用领域中的中心问题。这些应用需要一些健壮的变化检测方案才能正常工作，并将实验条件的变化与系统的实际变化分开，尤其是对于具有任意和非平稳已知或未知输入的系统。本文的目的是开发这种变化检测和诊断方案。在本文的第一部分，我们给出了一些基于滑动窗口和似然技术的经典变化检测方案的概念描述。然后，从这些经典的变化检测方案开始，提出了一种能够区分模型参数和噪声方差变化的新算法。最后，我们使用提议的方法包括了一些用于二阶FIR模型中的变化检测的蒙特卡洛模拟以及在地震信号分析中获得的实验结果。

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