DrumCorr program for selecting volcanic earthquake multiplets based on cross-correlation analysis

The DrumCorr program based on cross-correlation detection has been developed to identify multiplets of the volcanic earthquakes. The program is implemented in Python 3 and reads ASCII and MiniSEED seismic data formats. The article presents the algorithm of the program, describing the cross-correlation detector and an example of subsequent processing of seismic data. The program was applied to volcanic earthquakes of the «drumbeats» seismic regime and allowed to identify earthquake multiplets characterized by various wave forms. The article presents the algorithm of the program, describing the cross-correlation detector, the features of the weak volcanic earthquakes selection by the STA/LTA method. And the primary analysis of the values of the correlation coefficients with the calculation of their standard errors depending on different signal-to-noise ratios.

Application of Subspace-Based Detection Algorithm to Infrasound Signals in Volcanic Areas

Infrasonic signals investigation plays a fundamental role for both volcano monitoring purpose and the study of the explosion dynamics. Proper and reliable detection of weak signals is a critical issue in active volcano monitoring. In particular, in volcanic acoustics, it has direct consequences in pinpointing the real number of generated events (amplitude transients), especially when they exhibit low amplitude, are close in time to each other, and/or multiple sources exist. To accomplish this task, several algorithms have been proposed in literature; in particular, to overcome limitations of classical approaches such as short-time average/long-time average and cross-correlation detector, in this paper a subspace-based detection technique has been implemented. Results obtained by applying subspace detector on real infrasound data highlight that this method allows sensitive detection of lower energy events. This method is based on a projection of a sliding window of signal buffer onto a signal subspace that spans a collection of reference signals, representing similar waveforms from a particular infrasound source. A critical point is related to subspace design. Here, an empirical procedure has been applied to build the signal subspace from a set of reference waveforms (templates). In addition, in order to determine detectors parameters, such as subspace dimension and detection threshold, even in presence of overlapped noise such as infrasonic tremor, a statistical analysis of noise has been carried out. Finally, the subspace detector reliability and performance, have been assessed by performing a comparison among subspace approach, cross-correlation detector and short-time average/long-time average detector. The obtained confusion matrix and extrapolated performance indices have demonstrated the potentiality, the advantages and drawbacks of the subspace method in tracking volcanic activity producing infrasound events. This method revealed to be a good compromise in detecting low-energy and very close in time events recorded during Strombolian activity.

Multisensory correlation computations in the human brain uncovered by a time-resolved encoding model

ABSTRACTNeural mechanisms that arbitrate between integrating and segregating multisensory information are essential for complex scene analysis and for the resolution of the multisensory correspondence problem. However, these mechanisms and their dynamics remain largely unknown, partly because classical models of multisensory integration are static. Here, we used the Multisensory Correlation Detector, a model that provides a good explanatory power for human behavior while incorporating dynamic computations. Participants judged whether sequences of auditory and visual signals originated from the same source (causal inference) or whether one modality was leading the other (temporal order), while being recorded with magnetoencephalography. To test the match between the Multisensory Correlation Detector dynamics and the magnetoencephalographic recordings, we developed a novel dynamic encoding-model approach of electrophysiological activity, which relied on temporal response functions. First, we confirm that the Multisensory Correlation Detector explains causal inference and temporal order patterns well. Second, we found strong fits of brain activity to the two outputs of the Multisensory Correlation Detector in temporo-parietal cortices, a region with known multisensory integrative properties. Finally, we report an asymmetry in the goodness of the fits, which were more reliable during the causal inference than during the temporal order judgment task. Overall, our results suggest the plausible existence of multisensory correlation detectors in the human brain, which explain why and how causal inference is strongly driven by the temporal correlation of multisensory signals.