Doppler radar monitoring of lava dome processes at Merapi Volcano, Indonesia

Merapi volcano in Central Java, Indonesia, is considered one of the most dangerous volcanoes worldwide. Due to the high viscosity of its magma, the lava emerging at the top the volcano cannot flow silently down the flanks of the volcano but builds a lava dome. An indicator for the stability of the lava dome are rockfalls and block and ash flows, which are caused by local instabilities at the dome. When the lava dome reaches a critical size, it collapses. This results in dangerous block and ash flows, which can reach several kilometers into the proximity of the volcano. In the past rockfall and block and ash flow activity has been observed visually or by seismic networks. However, visual observations are often impossible due to bad visibility conditions and until now seismic measurements allow only few insights into the dynamic processes that are involved in instability events, i.e. events of material breaks off the lava dome. In order to enhance monitoring of lava dome activity, a first prototype Doppler radar system has been installed at the western of the Merapi in October 2001. This system consists of a frequency modulated continuous wave (FMCW) 24GHz Doppler radar. The Doppler spectra recorded by the system give a relative measure of the amount of material moving through the beam as well as information about its velocities. Because the radar system is insensitive for clouds, the system provides first continuous "quasi-visual" observations of dome instabilities...thesi

Classifying seismic waveforms from scratch: a case study in the alpine environment

Nowadays, an increasing amount of seismic data is collected by daily observatory routines. The basic step for successfully analyzing those data is the correct detection of various event types. However, the visually scanning process is a time-consuming task. Applying standard techniques for detection like the STA/LTA trigger still requires the manual control for classification. Here, we present a useful alternative. The incoming data stream is scanned automatically for events of interest. A stochastic classifier, called hidden Markov model, is learned for each class of interest enabling the recognition of highly variable waveforms. In contrast to other automatic techniques as neural networks or support vector machines the algorithm allows to start the classification from scratch as soon as interesting events are identified. Neither the tedious process of collecting training samples nor a time-consuming configuration of the classifier is required. An approach originally introduced for the volcanic task force action allows to learn classifier properties from a single waveform example and some hours of background recording. Besides a reduction of required workload this also enables to detect very rare events. Especially the latter feature provides a milestone point for the use of seismic devices in alpine warning systems. Furthermore, the system offers the opportunity to flag new signal classes that have not been defined before. We demonstrate the application of the classification system using a data set from the Swiss Seismological Survey achieving very high recognition rates. In detail we document all refinements of the classifier providing a step-by-step guide for the fast set up of a well-working classification syste

A Neural-based Algorithm for Landslide Detection at Stromboli Volcano: Preliminary Results.

This study presents a neural-based algorithm for the automatic detection of landslides on Stromboli volcano (Italy). It has been shown that landslides are an important short-term precursor of effusive eruptions of Stromboli. In particular, an increase in the occurrence rate of landslides was observed a few hours before the beginning of the February 2007 effusive eruption. Automating the process of detection of these signals will help analysts and represents a useful tool for the monitoring of the stability of the Sciara del Fuoco flank of Stromboli volcano. A multi-layer perceptron neural network is here applied to continuously discriminate landslides from other signals recorded at Stromboli (e.g., explosion quakes, tremor signals), and its output is used by an automatic system for the detection task. To correctly represent the seismic data, coefficients are extracted from both the frequency domain, using the linear predictive coding technique, and the time domain, using temporal waveform parameterization. The network training and testing was carried out using a dataset of 537 signals, from 267 landslides and 270 records that included explosion quakes and tremor signals. The classification results were 99.5% predictive for the best net performance, and 98.7% when the performance was averaged over the different net configurations. Thus, this detection system was effective when tested on the 2007 effusive eruption period. However, continuing investigations into different time intervals are needed, to further define and optimize the algorithm

Unfolding the procedure of characterizing recorded ultra low frequency, kHZ and MHz electromagetic anomalies prior to the L'Aquila earthquake as pre-seismic ones. Part I

Ultra low frequency, kHz and MHz electromagnetic anomalies were recorded prior to the L'Aquila catastrophic earthquake that occurred on April 6, 2009. The main aims of this contribution are: (i) To suggest a procedure for the designation of detected EM anomalies as seismogenic ones. We do not expect to be possible to provide a succinct and solid definition of a pre-seismic EM emission. Instead, we attempt, through a multidisciplinary analysis, to provide elements of a definition. (ii) To link the detected MHz and kHz EM anomalies with equivalent last stages of the L'Aquila earthquake preparation process. (iii) To put forward physically meaningful arguments to support a way of quantifying the time to global failure and the identification of distinguishing features beyond which the evolution towards global failure becomes irreversible. The whole effort is unfolded in two consecutive parts. We clarify we try to specify not only whether or not a single EM anomaly is pre-seismic in itself, but mainly whether a combination of kHz, MHz, and ULF EM anomalies can be characterized as pre-seismic one

Recognition and reconstruction of coherent energy with application to deep seismic reflection data

Reflections in deep seismic reflection data tend to be visible on only a limited number of traces in a common midpoint gather. To prevent stack degeneration, any noncoherent reflection energy has to be removed. In this paper, a standard classification technique in remote sensing is presented to enhance data quality. It consists of a recognition technique to detect and extract coherent energy in both common shot gathers and fi- nal stacks. This technique uses the statistics of a picked seismic phase to obtain the likelihood distribution of its presence. Multiplication of this likelihood distribution with the original data results in a “cleaned up” section. Application of the technique to data from a deep seismic reflection experiment enhanced the visibility of all reflectors considerably. Because the recognition technique cannot produce an estimate of “missing” data, it is extended with a reconstruction method. Two methods are proposed: application of semblance weighted local slant stacks after recognition, and direct recognition in the linear tau-p domain. In both cases, the power of the stacking process to increase the signal-to-noise ratio is combined with the direct selection of only specific seismic phases. The joint application of recognition and reconstruction resulted in data images which showed reflectors more clearly than application of a single technique