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.

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.

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 A Test for the Underlying State-Structure of Hidden Markov Models: Partially Observed Capture-Recapture Data

2021 ◽  
Vol 9 ◽  
Author(s):  
Anita Jeyam ◽  
Rachel S. McCrea ◽  
Roger Pradel
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Canada Geese ◽  
State Structure ◽  
Data Set ◽  
Partial Observations ◽  
Partially Observed ◽  
Capture Recapture ◽  
Special Case

Hidden Markov models (HMMs) are being widely used in the field of ecological modeling, however determining the number of underlying states in an HMM remains a challenge. Here we examine a special case of capture-recapture models for open populations, where some animals are observed but it is not possible to ascertain their state (partial observations), whilst the other animals' states are assigned without error (complete observations). We propose a mixture test of the underlying state structure generating the partial observations, which assesses whether they are compatible with the set of states observed in the complete observations. We demonstrate the good performance of the test using simulation and through application to a data set of Canada Geese.

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 Linkage Analysis and Haplotype Phasing in Experimental Autopolyploid Populations with High Ploidy Level Using Hidden Markov Models

2019 ◽  
Vol 9 (10) ◽  
pp. 3297-3314 ◽  
Author(s):  
Marcelo Mollinari ◽  
Antonio Augusto Franco Garcia
Keyword(s):  
Linkage Analysis ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Snp Markers ◽  
Genetic Linkage Analysis ◽  
Linkage Phase ◽  
Data Set ◽  
Ploidy Levels ◽  
Allele Dosage

Modern SNP genotyping technologies allow measurement of the relative abundance of different alleles for a given locus and consequently estimation of their allele dosage, opening a new road for genetic studies in autopolyploids. Despite advances in genetic linkage analysis in autotetraploids, there is a lack of statistical models to perform linkage analysis in organisms with higher ploidy levels. In this paper, we present a statistical method to estimate recombination fractions and infer linkage phases in full-sib populations of autopolyploid species with even ploidy levels for a set of SNP markers using hidden Markov models. Our method uses efficient two-point procedures to reduce the search space for the best linkage phase configuration and reestimate the final parameters by maximizing the likelihood of the Markov chain. To evaluate the method, and demonstrate its properties, we rely on simulations of autotetraploid, autohexaploid and autooctaploid populations and on a real tetraploid potato data set. The results show the reliability of our approach, including situations with complex linkage phase scenarios in hexaploid and octaploid populations.

Variational Learning for Switching State-Space Models

2000 ◽  
Vol 12 (4) ◽  
pp. 831-864 ◽  
Author(s):  
Zoubin Ghahramani ◽  
Geoffrey E. Hinton
Keyword(s):  
Time Series ◽  
Dynamical Systems ◽  
State Space ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
State Space Models ◽  
Data Set ◽  
Linear Dynamical Systems ◽  
Switching State

We introduce a new statistical model for time series that iteratively segments data into regimes with approximately linear dynamics and learns the parameters of each of these linear regimes. This model combines and generalizes two of the most widely used stochastic time-series models—hidden Markov models and linear dynamical systems—and is closely related to models that are widely used in the control and econometrics literatures. It can also be derived by extending the mixture of experts neural network (Jacobs, Jordan, Nowlan, & Hinton, 1991) to its fully dynamical version, in which both expert and gating networks are recurrent. Inferring the posterior probabilities of the hidden states of this model is computationally intractable, and therefore the exact expectation maximization (EM) algorithm cannot be applied. However, we present a variational approximation that maximizes a lower bound on the log-likelihood and makes use of both the forward and backward recursions for hidden Markov models and the Kalman filter recursions for linear dynamical systems. We tested the algorithm on artificial data sets and a natural data set of respiration force from a patient with sleep apnea. The results suggest that variational approximations are a viable method for inference and learning in switching state-space models.

Sign in / Sign up

Export Citation Format

Share Document