Matthias Ohrnberger | Universitaet Potsdam (original) (raw)

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Papers by Matthias Ohrnberger

Research paper thumbnail of Tracking the rupture of the Mw = 9.3 Sumatra earthquake over 1,150 km at teleseismic distance

Research paper thumbnail of Spatio-temporal source characteristics of the 26 December 2004 Sumatra earthquake as imaged by teleseismic broadband arrays

Geophysical Research Letters, 2005

Research paper thumbnail of Surface-wave inversion using a direct search algorithm and its application to ambient vibration measurements

Near Surface Geophysics, 2004

Research paper thumbnail of Improving the Accuracy of Automatic Detections at Seismic Stations via Machine Learning

EGU General Assembly …, 2010

We present a Machine Learning approach aiming for improving the accuracy of automatic detections ... more We present a Machine Learning approach aiming for improving the accuracy of automatic detections of noise and signal at 3-component seismic stations. Using supervised learning in conjunction with the multivariate framework of Dynamic Bayesian Networks (DBNs) we make use of historical data obtained from the LEB bulletin to train a classifier to capture the intrinsic characteristics of signal and noise patterns appearing in seismic data streams. On a per station basis this yields generative statistical models that essentially summarize and generalize the information implicitly contained in the LEB allowing for classifying future an previously unseen seismic data of the same kind. Also, the system provides a numerical value reflecting the classification confidence potentially aiding the analyst is correcting or identifying events that are non-typical. The system has the potential for being implemented in real time: both feature computation/extraction as well as classification work on data segments/windows and seismic patterns of varying length, e.g., 12 sec. Various features are considered including spectral features, polarization information and statistical moments and moment ratios. All features are derived from a time-frequency-(amplitude) decomposition of the raw waveform data for each component, taking the 6 frequency bands currently in use at IDC into account. These different feature sets give rise to different DBN structures (model-feature scenarios) that probabilistically relate the features to each other depending on empirical observations and physical knowledge available. 1 week of waveform data is considered for training both the signal and noise classes. The performance of the classifier is measured on a separate test set from the same week of data but also on a 1-month data set, where 4 weeks of data is distributed over a one year period. In the system evaluation both a static approach as well as a sliding-window approach is taken. Binary classification accuracy, sensitivity and specificity is measured as well as a comparison to the SEL3 and LEB bulletins. Our results suggest that a model-feature scenario with spectral features performs well, with a relatively high accuracy. Moreover, when using the DBN trained on 1 week data to classify noise throughout the year, our analysis suggests that there is a seasonal noise variation/dependence (the degree depending on the station in question). Spectral analysis also confirms this observation. In effect this means that an adaptive approach to training the generative DBN for seismic noise needs to be employed in order to correctly identify noise at any given time.

Research paper thumbnail of Probabilistic tsunami threat assessment of 10 recent earthquakes offshore Sumatra

Geophysical Journal …, 2012

Research paper thumbnail of Bayesian networks for tsunami early warning

Geophysical Journal …, 2011

Research paper thumbnail of Bayesian Belief Network for Tsunami Warning Decision Support

… to Reasoning with …, 2009

Early warning systems help to mitigate the impact of disastrous natural catastrophes on society b... more Early warning systems help to mitigate the impact of disastrous natural catastrophes on society by providing short notice of an imminent threat to geographical regions. For early tsunami warning, real-time observations from a seismic monitoring network can be used to estimate the severity of a potential tsunami wave at a specific site. The ability of deriving accurate estimates of tsunami impact is limited due to the complexity of the phenomena and the uncertainties in seismic source parameter estimates. Here we describe the use of a Bayesian belief network (BBN), capable of handling uncertain and even missing data, to support emergency managers in extreme time critical situations. The BBN comes about via model selection from an artifically generated database. The data is generated by ancestral sampling of a generative model defined to convey formal expert knowledge and physical/mathematical laws known to hold in the realm of tsunami generation. Hence, the database implicitly holds the information for learning a BBN capturing the required domain knowledge.

Research paper thumbnail of Scaling relations of earthquake source parameter estimates with special focus on subduction environment

Research paper thumbnail of Evaluation of the imminence of a tsunami based on real-time seismic source parameter estimates - a Bayesian network approach

EGU General Assembly …, 2010

Tsunami early warnings are based on co-seismic evidences being the earliest available information... more Tsunami early warnings are based on co-seismic evidences being the earliest available information from a hazardous earthquake with the potential of causing a tsunami. Evaluations are generally done by applying rules derived from historic observation and making use of seismological expertise regarding regional tectonic contexts, faulting styles, occurrence frequency of large earthquakes and more. However, the co-seismic generation of a tsunami as well as the estimation of a potentially tsunamigenic event is prone to various uncertainties. As Bayesian networks (BNs) allow for integration and quantification of the uncertainties within the framework of probabilistic graphical models, we propose the usage of BNs for evaluating the imminence of a tsunami based on real-time seismic source parameter estimates. Earthquake parameter estimates (including uncertainties) are evaluated in real-time and the probabilities of tsunami threat levels are calculated and updated whenever new co-seismic evidence is available. The fast and efficient method gives an important additional information for the staff members at tsunami warning centers as it provides a probabilistic overview on the imminence of a tsunami for some particular costal region. In our work, we have developed a preliminary BN tsunami warning system for the region of Sumatra by extracting knowledge from a set of formulas describing the physical process from earthquake rupture to sea-floor deformation to tsunami wave propagation and finally shoaling at the coast. The physical knowledge was transformed by ancestral sampling to a synthetic database and thereof BNs were learned for several sites of interest along the Sumatran coast and the fore-arc islands. To determine the conditional probability of the tsunami amplitude a set of seven co-seismic variables was defined: epicenter, centroid, magnitude, hypocentral depth, rupture direction, rupture length and width. We illustrate the advantages of this approach by case studies of recent tsunamigenic earhtquakes offshore Sumatra with particular focus on the probabilistically sound treatment of uncertainties relevant to tsunami early warning problem.

Research paper thumbnail of A Machine Learning Approach for Improving the Detection Capabilities at 3C Seismic Stations

Pure and Applied Geophysics, 2012

Research paper thumbnail of Dynamic bayesian networks for real-time classification of seismic signals

Knowledge Discovery in …, 2007

... 80(1), 170– 186 (1990) 7. Kulesh, M., Holschneider, M., Diallo, MS: Geophysics wavelet librar... more ... 80(1), 170– 186 (1990) 7. Kulesh, M., Holschneider, M., Diallo, MS: Geophysics wavelet library: Applications of the continuous wavelet transform to the polarization and dispersion analysis of signals. Comput-ers & Geoscience (Submitted 2007) 8. Kumar, P., Foufoula-Georgiou ...

Research paper thumbnail of Graphical models for automatic seismic signal classification

Geophysical Research Abstracts, 2007

Research paper thumbnail of Classifying seismic waveforms from scratch: a case study in the alpine environment

Geophysical Journal …, 2013

Research paper thumbnail of A Seismic‐Event Spotting System for Volcano Fast‐Response Systems

Research paper thumbnail of Signal classification by waveletbased hidden Markov models: application to seismic signals of volcanic origin

Statistics in Volcanology. …, 2006

Research paper thumbnail of Continuous automatic classification of seismic signals of volcanic origin at Mt. Merapi, Java, Indonesia

... Merapi volcano is one of the most active and dangerous volcanoes of the earth. ... Due to the... more ... Merapi volcano is one of the most active and dangerous volcanoes of the earth. ... Due to the close relationship between the volcanic unrest and the occurrence of seismic events at ... of Merapi's seismicity plays an important role for recognizing major changes in the volcanic activity. ...

Research paper thumbnail of Automatic hypocenter determination of volcano induced seismic transients based on wavefield coherence — an application to the 1998 eruption of Mt. Merapi, Indonesia

Journal of Volcanology and Geothermal Research, 2001

In order to monitor the seismic activity of Mt. Merapi (Indonesia) over a long period of time, we... more In order to monitor the seismic activity of Mt. Merapi (Indonesia) over a long period of time, we installed a permanent array of both broadband and short-period seismometers during the summer of 1997. Considering the requirements of an automatic classification and localization system for seismic monitoring and surveillance at active volcanoes, we split this network into three small-aperture arrays distributed around the volcano. We introduce here a newly developed method to determine the hypocenters in an automatic, non-linear manner using the coherence of seismic waves observed at the different arrays. To test this method, we analyze a swarm of VT-B events recorded by the network. The first step in this algorithm is based on a modified smoothed coherence transform. In the second step, we perform a semblance analysis applied to the 3D problem, evaluating the quality of the estimated relative onset-times. After more than one year of dormancy, Mt. Merapi renewed its activity at the end of June 1998. This gave us the opportunity to analyze all stages of dome growth, collapse and new intrusion of magma using the associated seismicity in a post-processing sense. This also allowed us to calibrate and test our newly developed automatic monitoring system using the more pronounced waveforms of VT-B events. By detecting and classifying different event types automatically, we are able to localize a large number of VT-B events, which occurred just before the initial eruption. We are also able to resolve some properties of the wavefield at Mt. Merapi, which are essential for further interpretations. Finally, the results show that the source region of the VT-B type seismicity just before the 1998 eruption is closely related to the region of subsequent high volcanic activity and therefore may represent a promising tool to forecast future eruptions.

Research paper thumbnail of Correlation of seismic activity and fumarole temperature at the Mt. Merapi volcano (Indonesia) in 2000

Journal of volcanology …, 2004

... Merapi volcano [Lat. ... high sampling rate of the fumarole temperature enables us to compare... more ... Merapi volcano [Lat. ... high sampling rate of the fumarole temperature enables us to compare the temperature variation directly with the seismic activity. ... monitoring of fumarole temperatures offers a powerful tool for monitoring variations in degassing behavior at active volcanoes. ...

Research paper thumbnail of Exploring the proximity of ground-motion models using high-dimensional visualization techniques

Earthquake …, 2010

Logic trees have become a popular tool to capture epistemic uncertainties in seismic hazard analy... more Logic trees have become a popular tool to capture epistemic uncertainties in seismic hazard analysis. They are commonly used by assigning weights to models on a purely descriptive basis (nominal scale). This invites the creation of unintended inconsistencies regarding ...

Research paper thumbnail of Unsupervised Feature Selection for Pattern Discovery in Seismic Wavefields

Research paper thumbnail of Tracking the rupture of the Mw = 9.3 Sumatra earthquake over 1,150 km at teleseismic distance

Research paper thumbnail of Spatio-temporal source characteristics of the 26 December 2004 Sumatra earthquake as imaged by teleseismic broadband arrays

Geophysical Research Letters, 2005

Research paper thumbnail of Surface-wave inversion using a direct search algorithm and its application to ambient vibration measurements

Near Surface Geophysics, 2004

Research paper thumbnail of Improving the Accuracy of Automatic Detections at Seismic Stations via Machine Learning

EGU General Assembly …, 2010

We present a Machine Learning approach aiming for improving the accuracy of automatic detections ... more We present a Machine Learning approach aiming for improving the accuracy of automatic detections of noise and signal at 3-component seismic stations. Using supervised learning in conjunction with the multivariate framework of Dynamic Bayesian Networks (DBNs) we make use of historical data obtained from the LEB bulletin to train a classifier to capture the intrinsic characteristics of signal and noise patterns appearing in seismic data streams. On a per station basis this yields generative statistical models that essentially summarize and generalize the information implicitly contained in the LEB allowing for classifying future an previously unseen seismic data of the same kind. Also, the system provides a numerical value reflecting the classification confidence potentially aiding the analyst is correcting or identifying events that are non-typical. The system has the potential for being implemented in real time: both feature computation/extraction as well as classification work on data segments/windows and seismic patterns of varying length, e.g., 12 sec. Various features are considered including spectral features, polarization information and statistical moments and moment ratios. All features are derived from a time-frequency-(amplitude) decomposition of the raw waveform data for each component, taking the 6 frequency bands currently in use at IDC into account. These different feature sets give rise to different DBN structures (model-feature scenarios) that probabilistically relate the features to each other depending on empirical observations and physical knowledge available. 1 week of waveform data is considered for training both the signal and noise classes. The performance of the classifier is measured on a separate test set from the same week of data but also on a 1-month data set, where 4 weeks of data is distributed over a one year period. In the system evaluation both a static approach as well as a sliding-window approach is taken. Binary classification accuracy, sensitivity and specificity is measured as well as a comparison to the SEL3 and LEB bulletins. Our results suggest that a model-feature scenario with spectral features performs well, with a relatively high accuracy. Moreover, when using the DBN trained on 1 week data to classify noise throughout the year, our analysis suggests that there is a seasonal noise variation/dependence (the degree depending on the station in question). Spectral analysis also confirms this observation. In effect this means that an adaptive approach to training the generative DBN for seismic noise needs to be employed in order to correctly identify noise at any given time.

Research paper thumbnail of Probabilistic tsunami threat assessment of 10 recent earthquakes offshore Sumatra

Geophysical Journal …, 2012

Research paper thumbnail of Bayesian networks for tsunami early warning

Geophysical Journal …, 2011

Research paper thumbnail of Bayesian Belief Network for Tsunami Warning Decision Support

… to Reasoning with …, 2009

Early warning systems help to mitigate the impact of disastrous natural catastrophes on society b... more Early warning systems help to mitigate the impact of disastrous natural catastrophes on society by providing short notice of an imminent threat to geographical regions. For early tsunami warning, real-time observations from a seismic monitoring network can be used to estimate the severity of a potential tsunami wave at a specific site. The ability of deriving accurate estimates of tsunami impact is limited due to the complexity of the phenomena and the uncertainties in seismic source parameter estimates. Here we describe the use of a Bayesian belief network (BBN), capable of handling uncertain and even missing data, to support emergency managers in extreme time critical situations. The BBN comes about via model selection from an artifically generated database. The data is generated by ancestral sampling of a generative model defined to convey formal expert knowledge and physical/mathematical laws known to hold in the realm of tsunami generation. Hence, the database implicitly holds the information for learning a BBN capturing the required domain knowledge.

Research paper thumbnail of Scaling relations of earthquake source parameter estimates with special focus on subduction environment

Research paper thumbnail of Evaluation of the imminence of a tsunami based on real-time seismic source parameter estimates - a Bayesian network approach

EGU General Assembly …, 2010

Tsunami early warnings are based on co-seismic evidences being the earliest available information... more Tsunami early warnings are based on co-seismic evidences being the earliest available information from a hazardous earthquake with the potential of causing a tsunami. Evaluations are generally done by applying rules derived from historic observation and making use of seismological expertise regarding regional tectonic contexts, faulting styles, occurrence frequency of large earthquakes and more. However, the co-seismic generation of a tsunami as well as the estimation of a potentially tsunamigenic event is prone to various uncertainties. As Bayesian networks (BNs) allow for integration and quantification of the uncertainties within the framework of probabilistic graphical models, we propose the usage of BNs for evaluating the imminence of a tsunami based on real-time seismic source parameter estimates. Earthquake parameter estimates (including uncertainties) are evaluated in real-time and the probabilities of tsunami threat levels are calculated and updated whenever new co-seismic evidence is available. The fast and efficient method gives an important additional information for the staff members at tsunami warning centers as it provides a probabilistic overview on the imminence of a tsunami for some particular costal region. In our work, we have developed a preliminary BN tsunami warning system for the region of Sumatra by extracting knowledge from a set of formulas describing the physical process from earthquake rupture to sea-floor deformation to tsunami wave propagation and finally shoaling at the coast. The physical knowledge was transformed by ancestral sampling to a synthetic database and thereof BNs were learned for several sites of interest along the Sumatran coast and the fore-arc islands. To determine the conditional probability of the tsunami amplitude a set of seven co-seismic variables was defined: epicenter, centroid, magnitude, hypocentral depth, rupture direction, rupture length and width. We illustrate the advantages of this approach by case studies of recent tsunamigenic earhtquakes offshore Sumatra with particular focus on the probabilistically sound treatment of uncertainties relevant to tsunami early warning problem.

Research paper thumbnail of A Machine Learning Approach for Improving the Detection Capabilities at 3C Seismic Stations

Pure and Applied Geophysics, 2012

Research paper thumbnail of Dynamic bayesian networks for real-time classification of seismic signals

Knowledge Discovery in …, 2007

... 80(1), 170– 186 (1990) 7. Kulesh, M., Holschneider, M., Diallo, MS: Geophysics wavelet librar... more ... 80(1), 170– 186 (1990) 7. Kulesh, M., Holschneider, M., Diallo, MS: Geophysics wavelet library: Applications of the continuous wavelet transform to the polarization and dispersion analysis of signals. Comput-ers & Geoscience (Submitted 2007) 8. Kumar, P., Foufoula-Georgiou ...

Research paper thumbnail of Graphical models for automatic seismic signal classification

Geophysical Research Abstracts, 2007

Research paper thumbnail of Classifying seismic waveforms from scratch: a case study in the alpine environment

Geophysical Journal …, 2013

Research paper thumbnail of A Seismic‐Event Spotting System for Volcano Fast‐Response Systems

Research paper thumbnail of Signal classification by waveletbased hidden Markov models: application to seismic signals of volcanic origin

Statistics in Volcanology. …, 2006

Research paper thumbnail of Continuous automatic classification of seismic signals of volcanic origin at Mt. Merapi, Java, Indonesia

... Merapi volcano is one of the most active and dangerous volcanoes of the earth. ... Due to the... more ... Merapi volcano is one of the most active and dangerous volcanoes of the earth. ... Due to the close relationship between the volcanic unrest and the occurrence of seismic events at ... of Merapi's seismicity plays an important role for recognizing major changes in the volcanic activity. ...

Research paper thumbnail of Automatic hypocenter determination of volcano induced seismic transients based on wavefield coherence — an application to the 1998 eruption of Mt. Merapi, Indonesia

Journal of Volcanology and Geothermal Research, 2001

In order to monitor the seismic activity of Mt. Merapi (Indonesia) over a long period of time, we... more In order to monitor the seismic activity of Mt. Merapi (Indonesia) over a long period of time, we installed a permanent array of both broadband and short-period seismometers during the summer of 1997. Considering the requirements of an automatic classification and localization system for seismic monitoring and surveillance at active volcanoes, we split this network into three small-aperture arrays distributed around the volcano. We introduce here a newly developed method to determine the hypocenters in an automatic, non-linear manner using the coherence of seismic waves observed at the different arrays. To test this method, we analyze a swarm of VT-B events recorded by the network. The first step in this algorithm is based on a modified smoothed coherence transform. In the second step, we perform a semblance analysis applied to the 3D problem, evaluating the quality of the estimated relative onset-times. After more than one year of dormancy, Mt. Merapi renewed its activity at the end of June 1998. This gave us the opportunity to analyze all stages of dome growth, collapse and new intrusion of magma using the associated seismicity in a post-processing sense. This also allowed us to calibrate and test our newly developed automatic monitoring system using the more pronounced waveforms of VT-B events. By detecting and classifying different event types automatically, we are able to localize a large number of VT-B events, which occurred just before the initial eruption. We are also able to resolve some properties of the wavefield at Mt. Merapi, which are essential for further interpretations. Finally, the results show that the source region of the VT-B type seismicity just before the 1998 eruption is closely related to the region of subsequent high volcanic activity and therefore may represent a promising tool to forecast future eruptions.

Research paper thumbnail of Correlation of seismic activity and fumarole temperature at the Mt. Merapi volcano (Indonesia) in 2000

Journal of volcanology …, 2004

... Merapi volcano [Lat. ... high sampling rate of the fumarole temperature enables us to compare... more ... Merapi volcano [Lat. ... high sampling rate of the fumarole temperature enables us to compare the temperature variation directly with the seismic activity. ... monitoring of fumarole temperatures offers a powerful tool for monitoring variations in degassing behavior at active volcanoes. ...

Research paper thumbnail of Exploring the proximity of ground-motion models using high-dimensional visualization techniques

Earthquake …, 2010

Logic trees have become a popular tool to capture epistemic uncertainties in seismic hazard analy... more Logic trees have become a popular tool to capture epistemic uncertainties in seismic hazard analysis. They are commonly used by assigning weights to models on a purely descriptive basis (nominal scale). This invites the creation of unintended inconsistencies regarding ...

Research paper thumbnail of Unsupervised Feature Selection for Pattern Discovery in Seismic Wavefields