Guillermo Cortés

Continuous HMM-Based Seismic-Event Classification at Deception Island, Antarctica

This paper shows a complete seismic-event classification and monitoring system that has been developed based on the seismicity observed during three summer Antarctic surveys at the Deception Island Volcano, Antarctica. The system is based on the state of the art in hidden Markov modeling (HMM) techniques successfully applied to other scenarios. A database that contains a representative set of different seismic events including volcano-tectonic earthquakes, long period (LP) events, volcanic tremor, and hybrid events that were recorded during the 1994-1995 and 1995-1996 seismic surveys was collected for training and testing. Simple left-to-right HMMs and multivariate Gaussian probability density functions with a diagonal covariance matrix were used. The feature vector consists of the log-energies of a filter bank that consists of 16 triangular weighting functions that were uniformly spaced between 0 and 20 Hz and the first- and second-order derivatives. The system is suitable to operate in real time, and its accuracy for this task is about 90%. On the other hand, when the system was tested with a different data set including mainly LP events that were registered during several seismic swarms during the 2001-2002 field survey, more than 95% of the recognized events were marked by the recognition system

Real‐time eruption forecasting using the material Failure Forecast Method with a Bayesian approach

Many attempts for deterministic forecasting of eruptions and landslides have been performed using the material Failure Forecast Method (FFM). This method consists in adjusting an empirical power law on precursory patterns of seismicity or deformation. Until now, most of the studies have presented hindsight forecasts based on complete time series of precursors and do not evaluate the ability of the method for carrying out real-time forecasting with partial precursory sequences. In this study, we present a rigorous approach of the FFM designed for real-time applications on volcano-seismic precursors. We use a Bayesian approach based on the FFM theory and an automatic classification of seismic events. The probability distributions of the data deduced from the performance of this classification are used as input. As output, it provides the probability of the forecast time at each observation time before the eruption. The spread of the a posteriori probability density function of the prediction time and its stability with respect to the observation time are used as criteria to evaluate the reliability of the forecast. We test the method on precursory accelerations of long-period seismicity prior to vulcanian explosions at Volcan de Colima (Mexico). For explosions preceded by a single phase of seismic acceleration, we obtain accurate and reliable forecasts using approximately 80% of the whole precursory sequence. It is, however, more difficult to apply the method to multiple acceleration patterns.

Volcano-seismic signal detection and classification processing using hidden Markov models. Application to San Cristóbal volcano, Nicaragua

We present a method for automatic seismic event detection and classification, focusing on volcanic-seismic signals by means of the validity of the hidden Markov modeling (HMM) method in active volcanoes. Recordings of different seismic event types are studied at one active volcano; San Cristobal in Nicaragua. We use data from one field surveys carried out in February to March 2006. More than 600 hours of data in San Cristobal volcano were analyzed and 1098 seismic events were registered at short period stations. These events were manually labelled by a single expert technicians and identified three types classes of signals (S1, S2, S3) and tremor background seismic noise (NS). The method analyzes the seismograms comparing the characteristics of the data to a number of event classes defined beforehand. If a signal is present, the method detects its occurrence and produces a classification. From the application performed over our data set, we have demonstrated that in order to have a reliable result, a careful and adequate segmentation process is crucial. Also, each type of signals requires its own characterization. That is, each signal type must be represented by its own specific model, which would include the effects of source, path and sites. Once we have built this model, the success level of the system is high. Extensive performance evaluation is conducted to derive the optimal configuration of the different parameters Correct classification rates of up to 80% are achieved. The high success rates obtained imply that the method is fully able to detect, isolate, and identify seismic signals on raw seismic data. These results imply that, once an adequate training process has been used, the present method is particularly appropriate to work in real time, and in parallel to the data acquisition.

Discriminative Feature Selection for Automatic Classification of Volcano-Seismic Signals

Feature extraction is a critical element in automatic pattern classification. In this letter, we propose different sets of parameters for classification of volcano-seismic signals, and the discriminative feature selection (DFS) method is applied for selecting the minimum number of features containing most of the discriminative information. We have applied DFS to a conventional cepstral-based parameterization (with 39 features) and to an extended set of parameters (including 84 features). Classification experiments using seismograms recorded at Colima Volcano (Mexico) show that, for the most complex classifier and using the cepstral-based parameterization, DFS provided a reduction of the error rate from 24.3% (using 39 features) to 15.5% (ten components). When DFS is applied to the extended parameterization, the error rate decreased from 27.9% (84 features) to 13.8% (14 features). These results show the utility of DFS for identifying the best components from the original feature vector and for exploring new parameterizations for the classification of volcano-seismic signals.

An Automatic P-Phase Picking Algorithm Based on Adaptive Multiband Processing

This letter presents a novel picking algorithm which allows an automated determination of the P-phase onset time. The algorithm includes an adaptive multiband processing and noise-reduction techniques to allow a confident onset time estimation in signals strongly affected by background and/or nonstationary noise processes. Results using a set of 3780 computer-generated earthquake-like signals show that the accuracy is much better than that achieved by conventional STA/LTA algorithm. In addition, the accuracy of the proposed method is improved when it is combined with an autoregressive method. An application of the algorithm to a set of 400 natural earthquakes confirms that the combination of both algorithms provides a precise P-phase onset time estimation in real environments, overcoming the limitations associated with the autoregressive method.

A Comparative Study of Dimensionality Reduction Algorithms Applied to Volcano-Seismic Signals

Detection and classification of the different seismic events are important tasks in volcanological observatories. Trying to make these an automatic process is fundamental for the volcanological community. It is crucial to choose how the seismic signal is represented in terms of parameters or features useful for dealing with the automatic classification problem, since the number and type of parameters could be really large leading to the curse of dimensionality issue. Machine learning theory establishes that in order to build a classifier from a labeled database, there should be a compromise between the complexity of the classifier and the size of the database. Since generating a manually labeled database is a tedious work performed by specialists in volcanology, the size of the databases limits the complexity of the classification systems built by them. On the other hand, if the databases could be represented by a reduced, but relevant, number of features, the complexity of the classifier would be simplified. In order to study the problem just described, this paper performs a comparative study of different classical techniques of dimensionality reduction (DR) of the feature set. The algorithms implemented include feature selection techniques as wrappers and filters and methods which directly transform the original feature space into another with lower dimension. All algorithms have been tested using an automatic classification system of volcano-seismic events. The best results have been obtained with the discriminative feature selection (DFS) algorithm which belongs to the set of wrapper methods.

Evaluating robustness of a HMM-based classification system of volcano-seismic events at colima and popocatepetl volcanoes

This work presents a continuous volcano-seismic classification system based in the Hidden Markov Models as solution to recently strong needs for automatic event detection and recognition methods in early warning and monitoring scenarios. Furthermore, our system includes a reliable method to assign confidence measures to the recognized signals in order to evaluate the robustness of the results. Data from the two most active volcanoes have been used to probe the system reliability on a complex joint corpus achieving a recognition accuracy higher than 78% in blind recognition tests.

Improving feature extraction in the automatic classification of seismic events. Application to Colima and Arenal volcanoes

Monitoring of precursory seismicity in volcanoes is the most reliable and widely used technique in volcano monitoring. Since a visual inspection by human operators is a tedious task in a non-stop monitoring process, Hidden Markov Models have been previously proposed to automatically classify the different types of volcano-seismic events. Mel Frequency Cepstral Coefficients were successfully used as feature vector in this continuous classification system. In this paper seven novel features to be included in the MFCC feature vector are proposed. A very elementary GMM-based classifier has been implemented in order to assess the efficiency of the proposed parameters. Results using hundreds of events recorded from stations situated at Colima (Mexico) and Arenal (Costa Rica) volcanoes show that the proposed features improve the recognition accuracy and therefore they may be relevant in continuous volcano-seismic event automatic classification.