The European Infrasound Bulletin highlights infrasound activity produced mostly by anthropogenic sources, recorded all over Europe and collected in the course of the ARISE and ARISE2 projects (Atmospheric dynamics Research InfraStructure in Europe). Data includes high-frequency (> 0.7 Hz) infrasound detections at 24 European infrasound arrays from nine different national institutions complemented with infrasound stations of the International Monitoring System for the Comprehensive Nuclear-Test-Ban Treaty (CTBT). Data were acquired during 16 years of operation (from 2000 to 2015) and processed to identify and locate ∼ 48,000 infrasound events within Europe. The source locations of these events were derived by combining at least two corresponding station detections per event. Comparisons with ground-truth sources, e.g., Scandinavian mining activity, are provided as well as comparisons with the CTBT Late Event Bulletin (LEB). Relocation is performed using ray-tracing methods to estimate celerity and back-azimuth corrections for source location based on meteorological wind and temperature values for each event derived from European Centre for Medium-range Weather Forecast (ECMWF) data. This study focuses on the analysis of repeating, man-made infrasound events (e.g., mining blasts and supersonic flights) and on the seasonal, weekly and diurnal variation of the infrasonic activity of sources in Europe. Drawing comparisons to previous studies shows that improvements in terms of detection, association and location are made within this study due to increasing the station density and thus the number of events and determined source regions. This improves the capability of the infrasound station network in Europe to more comprehensively estimate the activity of anthropogenic infrasound sources in Europe.

We report in this study the infrasound signal measured consistently at four stations in Fennoscandia, associated with the development of two intense cyclones, called polar lows, over the Norwegian Sea. When conditions of propagation are favourable, the infrasound signal comes from the direction of the polar lows, and it follows their track. The results thus, tend to confirm those of a previous study who claimed that an outbreak of three polar lows generated clear infrasound to distances up to 1000 km, according to measurements acquired in Northern Norway and on Svalbard. Because the conditions of propagation of infrasound depend on the state of the atmosphere between the sources and the receivers, signals may remain undetected, which limits the capability of a systematic early warning system, and also of a global monitoring of polar lows. However, the recorded signals might reflect on-going source processes, since convection associated with the polar lows is detected using microwave satellite observations in the areas from which the signals emanate. This suggests that at least part of the signal is due to turbulence induced by convection, in agreement with the earlier study. Nevertheless, more evidence of broadband infrasound measurements of polar low cases have to be examined in order to be able to fully assess the role of other possible sources (swell, surf, lightnings, …). The addition in Northern Norway in late 2013 of the IS37 infrasound station of the International Monitoring Network, developed for the verification of the Comprehensive nuclear-Test-ban Treaty, will provide new opportunities to further investigate this issue.

We describe the seismoacoustic monitoring network in Fennoscandia and North West Russia and present how it is being used to characterize infrasound studies in that part of the world. The history of the infrasound array network is presented, together with a description of array processing considerations, and examples of infrasound signals recorded from repeating explosions.

The International Monitoring System (IMS) for verifying compliance with the Comprehensive Nuclear‐Test‐Ban Treaty (CTBT) comprises sensors associated with four monitoring technologies: seismic, infrasound, hydroacoustic, and radionuclide. The so‐called waveform technologies (seismic, infrasound, and hydroacoustic) are used to detect and locate events that could constitute treaty violations. All four technologies may be employed to investigate the nature of events, with the network of radionuclide sensors in place to provide evidence of a nuclear explosion. Historical, political, and technical issues surrounding the CTBT are discussed by Dahlman et al. (2009, 2011). The global IMS infrasound network (Fig. 1) is primarily to detect signals generated by atmospheric nuclear tests. Figure 1. Status of the International Monitoring System infrasound network in August 2014. Filled symbols are certified stations sending data to the International Data Center (IDC) in Vienna. White symbols indicate the treaty coordinates of stations planned or under construction. The IMS infrasound network has been deployed over the last two decades (Brachet et al. , 2010; Christie and Campus, 2010), and only with the network approaching completion has a realistic picture of its detection capability emerged (Le Pichon et al. , 2009; Green and Bowers, 2010). The detectability of atmospheric signals is governed by a seasonally varying wind‐determined anisotropy. In the northern summer, the stratospheric winds blow predominantly east to west, facilitating the detection of infrasound at stations west of sources and inhibiting the detection at stations east of sources. In the northern winter, the winds blow in the opposite direction changing the sense of high and low detectability. The reverse patterns occur in the southern hemisphere. There is increasing interest in using infrasound for probing atmospheric structure (e.g., Lalande et al. , 2012), and the broader properties and applications of infrasound are discussed by Evers and Haak (2009) and Hedlin et al. (2012). In October …

Numerous objects, man-made, such as rockets and satellites, and natural, in the form of meteoroids, continuously enter the Earth's atmosphere. Most of these events occur over unpopulated areas, during unfavourable meteorological conditions or in daylight, without being observed. However all these objects, when passing through the atmosphere, generate infrasound. That infrasound may be detected at long distances, even when optical observations are impossible. The present report shows that it is even possible, by studying the signature of the wave field on the ground, to identify different types of entry events. Detection of Meteoroch Space Object Entries 2 
 Table of contents Introduction .................................................................................................................... 3 Infrasound arrays in Northern Scandinavia ................................................................... 4 Infrasonic observations of distant sources ..................................................................... 5 Identification of infrasound sources – statistical properties of the wave field ............... 6 Neural network tool for source identification ............................................................... 10 Sounding rockets: launch and reentry ........................................................................... 11 Recognition of meteoroid entries .................................................................................. 13 Recognition of five specific types of event ................................................................... 14 Estimation of meteoroid orbits ...................................................................................... 16 Future development: a meta-array for space debris/meteoroid detection ..................... 19 Acknowledgements ....................................................................................................... 20 References ..................................................................................................................... 21 Detection of Meteoroch Space Object Entries

The April-May 2010 summit eruption of Eyjafjallajokull, Iceland, was recorded by 14 atmospheric infrasound sensor arrays at ranges between 1,700 and 3,700 km, indicating that infrasound from modest-size eruptions can propagate for thousands of kilometers in atmospheric waveguides. Although variations in both atmospheric propagation conditions and background noise levels at the sensors generate fluctuations in signal-to-noise ratios and signal detectability, array processing techniques successfully discriminate between volcanic infrasound and ambient coherent and incoherent noise. The current global infrasound network is significantly more dense and sensitive than any previously operated network and signals from large volcanic explosions are routinely recorded. Because volcanic infrasound is generated during the explosive release of fluid into the atmosphere, it is a strong indicator that an eruption has occurred. Therefore, long-range infrasonic monitoring may aid volcanic explosion detection by complementing other monitoring technologies, especially in remote regions with sparse ground-based instrument networks. Citation: Matoza, R. S., et al. (2011), Long-range acoustic observations of the Eyjafjallajokull eruption, Iceland, April-May 2010, Geophys. Res. Lett., 38, L06308, doi:10.1029/2011GL047019.

Introduction The infrasound project with continuous recording of infrasound started in October 1972 at Kiruna Geophysical Observatory, which later became Kiruna Geophysical Institute and, finally, the Swedish Institute of Space Physics (IRF). The recording equipment evolved through different stages: from narrow band (2 Hz) film recordings (1972-82), through the computerized version of narrow band recordings (1982-94), to the present broad-band. Computerized broad-band equipment started its operation at all arrays in 1994. The infrasound recordings in Sweden are the longest continuous time series of its kind in the world.

Introduction Infrasound’s extraordinary propagation capability has been known for almost a century. In 1908 it was found that the low-frequency signal generated by the Tunguska Meteor event travelled more than once around the Earth and was detected by barographs around the world. It is generally assumed that the low-frequency acoustic waves—infrasound, is minimally attenuated in the atmosphere and propagates through reflections in temperature gradients in the upper atmosphere. That propagation mechanism is believed to be analogous to the propagation of radio waves in the atmosphere.

The aim of this study is to test whether it is possible to predict a Grand Minimum, assuming that there is, in the solar activity, precursor information about the approaching minimum. The monthly averages of sunspot numbers, covering the period from the year 1610 until the present, are used as input data. The time series is converted into a multivariate time series of indicators (the Multiple Indicator Model technique). The multivariate time series for periods including the Maunder and Dalton Minima is used to train a Neural Network model, which is later applied to recent solar sunspot data. The result shows a clear similarity between the periods before the Maunder and Dalton Minima and the period after the year 2000. Introduction Grand Minima in solar activity have occurred randomly in historical time. Since these periods of extremely low solar activity seem to have an important impact on the Earth’s climate, it would be interesting if a method to predict this phenomenon could be found. In this study monthly values of the solar group sunspot number are used as the input of information. The reason for this is that the above index has been scaled since 1610, and thus represents the longest time series directly describing the solar activity. Unfortunately, after December 1994, the group sunspot number is no longer scaled. After this date the time series is continued using monthly values of sunspot numbers. The input time series is shown in Fig. 1. Since there is no apparent prior information about the approaching Grand Minimum, the input time series has to be processed. An important processing tool is the wavelet transform and its applications: the ampligram and the time scale spectrum. Fig. 1. Monthly sunspot numbers 1610-2009. Prediction of Grand Minima 2 1. Wavelet transform The wavelet transform has become a powerful tool for frequency analysis, in particular for non-stationary time series. Discussions of the wavelet transform and its applications can be found in a number of recent books and review articles (Chui, 1992, Chui et al., 1994, Farge, 1992). The wavelet transform of a function y(t) is defined as (here * denotes complex conjugation): +! w(a,b) = a -1/2 " y(t) g*((t-b)/a) dt (2) -! where variable a is the scale dilation parameter and b the translation parameter. Both parameters are dimensionless. The realor complex-valued function g(t) is called a mother (or analyzing) wavelet. Here a particular wavelet transform, the Morlet wavelet, will be used. The Morlet wavelet, being a locally periodic wavetrain, is related to windowed Fourier analysis. It is obtained by taking a complex sine wave and localizing it with a Gaussian (bell-shaped) envelope. The Morlet wavelet is defined as: g(t) = exp(i#ot t 2 /2) (3) and its Fourier transform: G(#) = $2% exp[-(# #o) 2 /2] (4) The Morlet wavelet gives the smallest time-bandwidth product (Lagoutte et al. 1992). #o is a phase constant (in the present study #o = 5). For large #o the frequency resolution improves, though at the expense of decreased time resolution. The dilation parameter may be considered as equivalent to the frequency of the analyzed signal, while the translation parameter corresponds to the time elapsed along the analyzed sample. In the present study dilation #1 corresponds to the highest frequency (a half of sampling rate). The highest dilation # corresponds to the lowest observable frequency. 2. Time-series decomposition using wavelet transform Many time series observed in physics consist of a deterministic part with a superimposed stochastic component. A powerful technique to separate both components has been proposed by Farge (1993) and implemented in practically usable software by Wernik (1997). In that method, being a kind of non-linear filtering (also called the threshold filtering), a wavelet frequency spectrum of the time series is calculated. The time series is decomposed into two parts in the following way: A deterministic “strong” part is obtained by setting to zero all wavelet coefficients less than a certain threshold level. The inverse wavelet transform is used to calculate the corresponding time series. A stochastic “weak” part is obtained by setting to zero all wavelet coefficients greater than that threshold level. The inverse wavelet transform is also used here to calculate the corresponding time series. New wavelet spectra are calculated for each partial time series. Signal discrimination using the magnitude of wavelet coefficients as a discrimination criterion would correspond to discrimination with respect to the spectral density when using the Fourier transform. Prediction of Grand Minima 3 The stochastic part must follow a Gaussian probability distribution function. As a measure of departure from a Gaussian distribution the kurtosis is used. If the threshold is properly selected, the integral of the kurtosis of the stochastic part over the entire frequency range reaches a minimum. 3. The ampligram There is a straightforward generalisation of the above technique (Liszka and Holmström, 1999), which may be used to separate independent components of the signal, assuming that the different components are characterized by different wavelet coefficient magnitudes (spectral densities). Experience from studies of oscillations in complex mechanical systems indicates that a given oscillation mode usually occurs with a certain amplitude/spectral density. The amplitude ratios between possible modes are usually constant in such a system. That observation may be used to generalize the above non-linear filtering technique. For a time series of N values (N must be an integer power of 2) the following operations are performed: 1. A Morlet wavelet transform is performed with at least 128 dilations. Thus, three N x 128 matrices, A, R and I, are obtained. The matrix A is a matrix of magnitudes of wij: A = &'wij'( i=1, ...N j=1, ...128 (5) R and I contain respective real and imaginary parts of wij. 2. Instead of using the low-pass or high-pass filtering of wavelet coefficient magnitudes, as described in §2, a kind of band-pass filtering of wavelet coefficient magnitudes is used. The entire range of coefficient magnitudes: 0 to wmax, or its lowest 20%, is divided into M intervals such that the k-th interval is limited by: wmax * (k-1)/M and wmax * k/M where k= 1, ...M (6) Two sets of intervals are used in the present work: 10 equal intervals between 0 and 100% of wmax and in the other set 20 equal intervals between 0 and 20% of wmax. For each k the coefficients outside the range defined by (6) are identified and zeroed in matrices R and I, creating two new matrices Rk and Ik. The inverse wavelet transform is performed using Rk and Ik and a new version of the original time series, yk(ti), is created. yk(ti) is what the signal would look like if only a narrow range of wavelet coefficient amplitude was be present in the signal. 3. The operation is repeated M times at 1 or 10% intervals over the interesting range of coefficient magnitudes, usually 0 20% of the maximum wavelet coefficient magnitude. A real-valued matrix M, consisting of M columns and N rows is created: M = & yk(ti) ( Each column of the matrix corresponds to the time series that would be observed if only a narrow range of coefficient magnitudes contributed to the observed signal. A 3-D plot of the matrix M, called an ampligram, may be constructed. An ampligram covering 0 100% of coefficient magnitudes is called here the total ampligram. A total ampligram for the time series of monthly sunspot numbers in Fig. 1 is shown in Fig. 2. The summation of the matrix M over k should result in the original sample y(ti), if there was no energy leakage from outside the filter band (6). The ampligram demonstrates the amplitude and phase of components of the signal corresponding to different spectral densities. The ampligram is a useful method for presenting the physical properties of the signal. The vertical (colour) scale of the ampligram has been limited to ±5 in order to enhance the structure of the data around zero crossings. Prediction of Grand Minima 4 Fig. 2. The total ampligram of the time series of monthly values of the sunspot numbers in Fig. 1. 4. Time scale spectrum of an ampligram The ampligram may be used for calculation of average wavelet spectra, one for each coefficient magnitude. It is equivalent to performing, once again, the forward wavelet transform on the filtered, inverse transformed data, which constitute the milligram. The transformation is applied to each row of the milligram matrix. The procedure generates a 3-D graph showing the time scale of the signal on the x-axis, the wavelet coefficient magnitude of the original signal (in percent of its max value) on the y-axis and the wavelet coefficient magnitude (corresponding to the power spectral density) of the decomposed component as the colour scale. A graph of that kind will show the average properties of the different modes, if such exist, during the entire sample period. As an example the time scale spectrum of the time series in Fig. 1 is shown in Fig. 3. Only scales shorter than 5.5 years are displayed. It may be seen that, on average, for the whole period and magnitudes above 15% of the maximum, the spectrum is dominated by the 11-year solar cycle component. Fig. 3. The time scale spectrum (scales < 20 years) for the time series of Fig. 1. Prediction of Grand Minima 5 5. Data analysis The analysis of the sunspot number time series is performed using the Multiple Indicator Model (MIM) technique and Neural Network (NN) modelling. The methods have been described earlier (Liszka, 2003). The purpose of the MIM technique is to convert a univariate time series into a multivariate time series. This new multivariate time series consists of series of indicators describing the original time series. A suitable analysis window must be selected and properties of the original time series in the window are computed. The window is move

During the last 10 years, infrasonic chirps in the frequency range 0.5–8Hz were occasionally observed by arrays belonging to the Swedish Infrasonic Network (SIN). These chirps have been attributed to certain types of thunderstorm activity associated with the high-altitude discharges, sprites [Liszka, L., 2004. On the possible infrasound generation by sprites, Journal of Low Frequency Noise, Vibration and Active Control 23, 85–93]. A method for automatic detection of chirps in the recorded data has been developed and applied to 10 years of data from two arrays belonging to the SIN: Jamton and Lycksele. The temporal and directional distribution of the phenomenon is described. Also, long-term variability and possible relation to the solar cycle is studied.

The goal of the research presented in this thesis is to extract features, to filter and get fingerprints from signals detected by infrasound, seismic and magnetic sensors. If this can be achieved in a real time system, then signals from various events can be detected and identified in an otherwise torrent data. Several approaches have been analyzed. Wavelet transform methods are used together with ampligram and time scale spectrum to analyze infrasound, seismic and magnetic data. The energy distribution in the frequency domain may be seen in wavelet scalograms. A scalogram displays the wavelet coefficients as a function of the time scale and of the elapsed time. The ampligram is a useful method of presentation of the physical properties of the time series. The ampligram demonstrate the amplitude and phase of components of the signal corresponding to different spectral densities. The ampligram may be considered as an analogy to signal decomposition into Fourier components. In that case different components correspond to different frequencies. In the present case different components correspond to different wavelet coefficient magnitudes, being equivalent to spectral densities. The time scale spectrum is a forward wavelet transform of each row (wavelet coefficient magnitude) in the ampligram. The time scale spectrum reveals individual signal components and indicates the statistical properties of each component: deterministic or stochastic. Next step is to distinguish between different sources of infrasound on-line. This will require signal classification after detection is made. The implementation of wavelet – neural network in hardware may be a first choice. In this work the Independent Component Analysis is presented to improve the quality of the infrasonic signals by removing background noise before the hardware classification. The implementation of the discrete wavelet transform in a Field Programmable Gate Array (FPGA) is also included in this thesis using Xilinx System Generator and Simulink software. A study of using infrasound recordings together with a miniature 3-axis fluxgate magnetometer to find meteorites as soon as possible after hitting the earth is also presented in this work.

This paper reports work in characterization of infrasonic and seismic signals from mining explosions and an earthquake. Wavelet transform together with ampligram and time scale spectrum are used to address characterization issues for both infrasonic and seismic signals. The ampligram may be considered as an analogy to signal decomposition into Fourier components. In that case different components correspond to different frequencies. In the present case different components correspond to different wavelet coefficient magnitudes, being equivalent to spectral densities. The time scale spectrum is a forward wavelet transform of each row (wavelet coefficient magnitude) in the ampligram. The time scale spectrum reveals individual signal components and indicates the statistical properties of each component: deterministic or stochastic. This study focuses on infrasound and seismic data from the earthquake and the following type of mining operation: rock fragmentation for copper recovery (Cerro Verde mine) where moderate sized explosions are designed to break the rock for further processing. Infrasound and seismic signals have been recorded during three weeks at University of San Agustin observatory station (Arequipa, Perú) on January 2006. A single infrasound microphone, seismometers and accelerometers were used. From the recorded data two events were chosen to this study: an earthquake occurred 19th of January measuring 3.8 on the Richter scale and a mining explosion. Infrasonic signals from the mining area show arrivals a few milliseconds before the seismic signals. The infrasonic signal from the earthquake has the appearance of the z-component of seismic signals.