RUSSIAN JOURNAL OF EARTH SCIENCES VOL. 10, ES1001, doi:10.2205/2007ES000278, 2008
2. Detection of Anomalies
[3] Using the monograph
[Kedrov, 2005],
we present here a review of actual systems
of detection of anomalies (applied mainly in seismology). The goal of this far
from being exhaustive overview is to compare FCARS with the difference
recognition algorithm for signals (DRAS) and with the fuzzy logic algorithm for
recognition of signals (FLARS)
[Gvishiani et al., 2003, 2004],
implementing the difference-from-moving average (DMA) approach.
[4] According to
[Kedrov, 2005],
the complete cycle of the procedure of detecting
anomalies from signal records is divided into three stages: predetection,
discovery (detection), and processing of the anomaly discovered. Algorithms of
anomaly detection are mostly based on a combination of the statistical approach
and the spectral-time analysis (STA). The latter is a method of statistical
analysis designed for the study of frequency characteristics of a stationary
random process with discrete time or a time series. The STA is based on a
combination of diverse spectral, asymptotic, and functional techniques that is
often strongly constrained by the physical essence of events studied and, for
this reason, is fairly illustrative
[Prokhorov, 1999].
We give brief characterization of some of these systems.
2.1. System SESMO1
[5]
[Kedrov, 2005]
is intended for real-time detection of short-period
seismic anomalies in time and frequency domains. The algorithm uses eight
Butterworth filters encompassing with overlap the entire frequency response band
of an anomaly. This algorithm uses three-component polarization analysis.
2.2. Autoregressive moving average (ARMA) models
[6]
[Kedrov et al., 2000].
This type of algorithms of
anomaly detection is based on the use of adaptive and matching filters. The
algorithm of such a detector is constructed in terms of an autoregressive
description (ARMA models) of seismic analyses and noise. The related filtering
consists in a continuous analysis of noise. Based on this, the type of data is
predicted that should be recorded in a subsequent moment. If the prediction of
noise accumulated until the current time moment fails, there is made a
suggestion that a desired anomaly is recorded.
2.3. Maximum likelihood
[7]
[Kushnir and Mostovoi, 1990].
Methods of anomaly detection based on the use of maximum likelihood filters are
difficult to be utilized for real-time detection of anomalies. In this case,
the detection procedure involves real-time continuous estimation of spectral
properties of noise under the condition that parameters of an anomaly are known
either completely or partially.
2.4. Recognition with training
[8]
[Haries and Joswig, 1985].
For detecting local or regional low-amplitude anomalies in a region of interest,
known anomalies are STA-analyzed to construct a set of typical patterns. A new
anomaly in the given region is detected by comparing it with the available patterns.
This comparison is based on a coherence value specified for several levels of the
signal intensity.
2.5. Neural networks
[9]
[Romeo, 1994].
This approach consists in the modeling of researcher's capabilities by means of
a specially constructed neural network. The network is a multilayer perceptron.
Input parameters that were used in this network are absolute values of seismic
wave spectral amplitudes in nine frequency bands. At the output, the discovered
anomalies are classified as local, regional, or teleseismic anomalies or as noise
of two types.

Citation: Gvishiani, A. D., S. M. Agayan, Sh. R. Bogoutdinov, E. M. Graeva, J. Zlotnicki, and J. Bonnin (2008), Recognition of anomalies from time series by fuzzy logic methods, Russ. J. Earth Sci., 10, ES1001, doi:10.2205/2007ES000278.
Copyright 2008 by the Russian Journal of Earth Sciences
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