scholarly journals LAPPED TRANSFORMS AND HIDDEN MARKOV MODELS FOR SEISMIC DATA FILTERING

2004 ◽  
Vol 02 (04) ◽  
pp. 455-476
Author(s):  
LAURENT DUVAL ◽  
CAROLINE CHAUX
Keyword(s):  
Hidden Markov Models ◽  
Seismic Data ◽  
Markov Models ◽  
Hidden Markov ◽  
Image Features ◽  
Seismic Exploration ◽  
Pass Band ◽  
Markov Modelling ◽  
Noise Sources ◽  
Block Transforms

Seismic exploration provides information about the ground substructures. Seismic images are generally corrupted by several noise sources. Hence, efficient denoising procedures are required to improve the detection of essential geological information. Wavelet bases provide sparse representation for a wide class of signals and images. This property makes them good candidates for efficient filtering tools, allowing the separation of signal and noise coefficients. Recent works have improved their performance by modelling the intra- and inter-scale coefficient dependencies using hidden Markov models, since image features tend to cluster and persist in the wavelet domain. This work focuses on the use of lapped transforms associated with hidden Markov modelling. Lapped transforms are traditionally viewed as block-transforms, composed of M pass-band filters. Seismic data present oscillatory patterns and lapped transforms oscillatory bases have demonstrated good performances for seismic data compression. A dyadic like representation of lapped transform coefficient is possible, allowing a wavelet-like modelling of coefficients dependencies. We show that the proposed filtering algorithm often outperforms the wavelet performance both objectively (in terms of SNR) and subjectively: lapped transform better preserve the oscillatory features present in seismic data at low to moderate noise levels.

scholarly journals Automatic detection of alpine rockslides in continuous seismic data using hidden Markov models

2016 ◽  
Vol 121 (2) ◽  
pp. 351-371 ◽  
Author(s):  
Franziska Dammeier ◽  
Jeffrey R. Moore ◽  
Conny Hammer ◽  
Florian Haslinger ◽  
Simon Loew
Keyword(s):  
Hidden Markov Models ◽  
Seismic Data ◽  
Markov Models ◽  
Hidden Markov ◽  
Automatic Detection

scholarly journals Automatic detection of snow avalanches in continuous seismic data using hidden Markov models

2018 ◽  
Vol 18 (1) ◽  
pp. 383-396 ◽  
Author(s):  
Matthias Heck ◽  
Conny Hammer ◽  
Alec van Herwijnen ◽  
Jürg Schweizer ◽  
Donat Fäh
Keyword(s):  
Hidden Markov Models ◽  
Seismic Data ◽  
Markov Models ◽  
Hidden Markov ◽  
Winter Season ◽  
Automatic Detection ◽  
Seismic Signals ◽  
Snow Avalanches ◽  
Data Set ◽  
Processing Steps

Abstract. Snow avalanches generate seismic signals as many other mass movements. Detection of avalanches by seismic monitoring is highly relevant to assess avalanche danger. In contrast to other seismic events, signals generated by avalanches do not have a characteristic first arrival nor is it possible to detect different wave phases. In addition, the moving source character of avalanches increases the intricacy of the signals. Although it is possible to visually detect seismic signals produced by avalanches, reliable automatic detection methods for all types of avalanches do not exist yet. We therefore evaluate whether hidden Markov models (HMMs) are suitable for the automatic detection of avalanches in continuous seismic data. We analyzed data recorded during the winter season 2010 by a seismic array deployed in an avalanche starting zone above Davos, Switzerland. We re-evaluated a reference catalogue containing 385 events by grouping the events in seven probability classes. Since most of the data consist of noise, we first applied a simple amplitude threshold to reduce the amount of data. As first classification results were unsatisfying, we analyzed the temporal behavior of the seismic signals for the whole data set and found that there is a high variability in the seismic signals. We therefore applied further post-processing steps to reduce the number of false alarms by defining a minimal duration for the detected event, implementing a voting-based approach and analyzing the coherence of the detected events. We obtained the best classification results for events detected by at least five sensors and with a minimal duration of 12 s. These processing steps allowed identifying two periods of high avalanche activity, suggesting that HMMs are suitable for the automatic detection of avalanches in seismic data. However, our results also showed that more sensitive sensors and more appropriate sensor locations are needed to improve the signal-to-noise ratio of the signals and therefore the classification.

SHAPE TRACKING AND PRODUCTION USING HIDDEN MARKOV MODELS

2001 ◽  
Vol 15 (01) ◽  
pp. 197-221 ◽  
Author(s):  
TERRY CAELLI ◽  
ANDREW MCCABE ◽  
GARRY BRISCOE
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hamming Distance ◽  
Hidden Markov ◽  
Performance Measure ◽  
Image Features ◽  
Image Feature ◽  
Beam Search ◽  
Proposed Model ◽  
Symbol Sequences

This paper is concerned with an application of Hidden Markov Models (HMMs) to the generation of shape boundaries from image features. In the proposed model, shape classes are defined by sequences of "shape states" each of which has a probability distribution of expected image feature types (feature "symbols").The tracking procedure uses a generalization of the well-known Viterbi method by replacing its search by a type of "beam-search" so allowing the procedure, at any time, to consider less likely features (symbols) as well the search for an instantiable optimal state sequences. We have evaluated the model's performance on a variety of image and shape types and have also developed a new performance measure defined by an expected Hamming distance between predicted and observed symbol sequences. Results point to the use of this type of model for the depiction of shape boundaries when it is necessary to have accurate boundary annotations as, for example, occurs in Cartography.

scholarly journals Automatic detection of snow avalanches in continuous seismic data using hidden Markov models

2017 ◽  
Author(s):  
Matthias Heck ◽  
Conny Hammer ◽  
Alec van Herwijnen ◽  
Jürg Schweizer ◽  
Donat Fäh
Keyword(s):  
Hidden Markov Models ◽  
Seismic Data ◽  
Markov Models ◽  
Hidden Markov ◽  
Automatic Detection ◽  
Seismic Signals ◽  
Snow Avalanches ◽  
Data Set ◽  
Temporal Behaviour ◽  
Processing Steps

Abstract. Snow avalanches generate seismic signals as many other mass movements. Detection of avalanches by seismic monitoring is highly relevant to assess avalanche danger. In contrast to other seismic events, signals generated by avalanches do not have a characteristic first arrival nor is it possible to detect different wave phases. In addition, the moving source character of avalanches increases the intricacy of the signals. Although it is possible to visually detect seismic signals produced by avalanches, reliable automatic detection methods for all types of avalanches do not exist yet. We therefore evaluate whether hidden Markov models (HMMs) are suitable for the automatic detection of avalanches in continuous seismic data. We analyzed data recorded during the winter season 2010 by a seismic array deployed in an avalanche starting zone above Davos, Switzerland. We first visually inspected the data and identified more than 200 events we assume to be generated by avalanches. Since most of the data consists of noise we first applied a simple amplitude threshold to reduce the amount of data. As first classification results were unsatisfying, we analyzed the temporal behaviour of the seismic signals for the whole data set and found that there is a high variability in the seismic signals. We therefore applied further post-processing steps to reduce the number of false alarms by defining a minimal duration for the detected event, implementing a voting based approach and analyzing the coherence of the detected events. We obtained the best classification results for events detected by at least 5 sensors and with a minimal duration of 12 s. These processing steps allowed identifying two known periods of high avalanche activity, suggesting that HMMs are suitable for the automatic detection of avalanches in seismic data. However our results also showed that more sensitive sensors and more appropriate sensor locations are needed to improve the signal-to-noise ratio of the signals and therefore the classification.

Estimating Personality Impression from Speech Record Using Hidden Markov Models

2015 ◽  
Vol 135 (12) ◽  
pp. 1517-1523 ◽  
Author(s):  
Yicheng Jin ◽  
Takuto Sakuma ◽  
Shohei Kato ◽  
Tsutomu Kunitachi
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov

Hidden Markov Processes

2014 ◽  
Author(s):  
M. Vidyasagar
Keyword(s):  
Hidden Markov Models ◽  
Markov Processes ◽  
Viterbi Algorithm ◽  
Markov Models ◽  
Hidden Markov ◽  
Local Alignment ◽  
Biological Applications ◽  
Standard Material ◽  
Hidden Markov Processes ◽  
Genomics And Proteomics

This book explores important aspects of Markov and hidden Markov processes and the applications of these ideas to various problems in computational biology. It starts from first principles, so that no previous knowledge of probability is necessary. However, the work is rigorous and mathematical, making it useful to engineers and mathematicians, even those not interested in biological applications. A range of exercises is provided, including drills to familiarize the reader with concepts and more advanced problems that require deep thinking about the theory. Biological applications are taken from post-genomic biology, especially genomics and proteomics. The topics examined include standard material such as the Perron–Frobenius theorem, transient and recurrent states, hitting probabilities and hitting times, maximum likelihood estimation, the Viterbi algorithm, and the Baum–Welch algorithm. The book contains discussions of extremely useful topics not usually seen at the basic level, such as ergodicity of Markov processes, Markov Chain Monte Carlo (MCMC), information theory, and large deviation theory for both i.i.d and Markov processes. It also presents state-of-the-art realization theory for hidden Markov models. Among biological applications, it offers an in-depth look at the BLAST (Basic Local Alignment Search Technique) algorithm, including a comprehensive explanation of the underlying theory. Other applications such as profile hidden Markov models are also explored.

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