Monitoring Seismo-volcanic and Infrasonic Signals at Volcanoes: Mt. Etna Case Study

Volcanoes generate a broad range of seismo-volcanic and infrasonic signals, whose features and variations are often closely related to volcanic activity. The study of these signals is hence very useful in the monitoring and investigation of volcano dynamics. The analysis of seismo-volcanic and infrasonic signals requires specifically developed techniques due to their unique characteristics, which are generally quite distinct compared with tectonic and volcano-tectonic earthquakes. In this work, we describe analysis methods used to detect and locate seismo-volcanic and infrasonic signals at Mt. Etna. Volcanic tremor sources are located using a method based on spatial seismic amplitude distribution, assuming propagation in a homogeneous medium. The tremor source is found by calculating the goodness of the linear regression fit (R ²) of the log-linearized equation of the seismic amplitude decay with distance. The location method for long-period events is based on the joint computation of semblance and R ² values, and the location method of very long-period events is based on the application of radial semblance. Infrasonic events and tremor are located by semblance–brightness- and semblance-based methods, respectively. The techniques described here can also be applied to other volcanoes and do not require particular network geometries (such as arrays) but rather simple sparse networks. Using the source locations of all the considered signals, we were able to reconstruct the shallow plumbing system (above sea level) during 2011.     

Infrasonic Signal Detection and Source Location at the Prototype International Data Centre

—?This paper describes an automatic and interactive data processing system designed to locate impulsive atmospheric sources with a yield of at least one kiloton by detecting and characterizing the airborne infrasound radiated by the source. The infrasonic processing subsystem forms part of a larger system currently under development at the Prototype International Data Center (PIDC) in Arlington, Virginia where seismic, hydroacoustic, radionuclide and infrasonic methods are used to detect and locate impulsive sources in any terrestrial environment. Infrasonic signal detection is achieved via a coincidence detector which requires both the normalized cross correlation and the short-term-average/long-term-average ratio of a beam in the direction of maximum correlation to exceed predetermined threshold values simultaneously before a detection is declared. The infrasound propagation model currently used to infer travel-time information assumes the horizontal sound speed across the ground to be 320.0?m/s. This crude model is currently being replaced by a model which predicts travel-time information through a ray-tracing algorithm for acoustic waves in an atmosphere with seasonal representations for temperature and wind. A novel feature of the source location process is the fusion of all available arrival information, whether it be seismic, hydroacoustic or infrasonic to locate a single source where it is reasonable to hypothesize a common source. In its final configuration the infrasonic subsystem will routinely process data from the global 60-station International Monitoring System (IMS) infrasonic network currently under development.     

Backward probabilistic model of groundwater contamination in non-uniform and transient flow

Backward location and travel time probabilities, which provide information about the former location of contamination in an aquifer, can be used to identify unknown contamination sources. Backward location probability describes the possible upgradient positions of contamination at a known time in the past, and backward travel time probability describes the time required for contamination to travel from a known upgradient location to an observation point. These probabilities are related to adjoint states of resident concentration, and their governing equation is the adjoint of a forward contaminant transport model. Using adjoint theory to obtain the appropriate governing equation, we extend the backward probability model for conservative solutes to more general non-uniform and transient flow fields. In particular, we address three important extensions, spatially-varying porosity, transient flow and temporally-varying porosity, and internal distributed sources and sinks of solute and water. For the first time we learn that forward and backward location and travel time probabilities are not necessarily equivalent to adjoint states, but are related to them. The extensions are illustrated using a vertically-integrated groundwater model, creating transient flow by a step change in pumping and using areal recharge as an internal distributed source. Both the movement and spread of probabilities are affected. With internal sources of water, there are two interpretations of backward probability, depending on whether or not the source of water is also a source of solute. The results demonstrate how the backward probability model can be applied to other, perhaps more important, non-uniform and transient flow conditions, with time- and space-varying water storage, such as time-varying pumping or unsaturated (or saturated–unsaturated) flow and transport with spatially- and temporally-varying moisture content.     

Surveying Infrasonic Noise on Oceanic Islands

v--vAn essential step in the establishment of an International Monitoring System (IMS) infrasound station is the site survey. The survey seeks a location with relatively low infrasonic noise and the necessary logistical support. This paper reports results from our surveys of two of the oceanic sites in the IMS - the Azores and Cape Verde. Each survey sampled infrasonic noise, wind velocity, air temperature and humidity for ~3 weeks at 4 sites near the nominal IMS locations. The surveys were conducted on Sao Miguel (the main island in the Azores) and Maio (Cape Verde). Infrasonic noise was measured using the French MB2000 microbarometer.¶During our 3-week experiment in January the trade winds at Cape Verde varied little from an azimuth of 63°. Because of the unvarying wind azimuth, the experiment gave us an opportunity to examine the effectiveness of a forest at reducing both wind speed and infrasonic noise. We find that the thick Acacia forest on Maio reduces wind speeds at a 2 m elevation by more than 50% but does not reduce infrasonic noise at frequencies below 0.25 Hz. This forest serves as a high-frequency filter and clearly does not reduce long-period noise levels which are due to large-scale turbulence in the atmospheric boundary layer above the forest. This is consistent with our observations in the Azores where the relationship between infrasonic noise and wind speed is more complex due to frequent changes in wind azimuth.¶In Cape Verde, wind speed and infrasonic noise are relatively constant. The diurnal variations are clearly seen however the microbarom is only rarely sensed. In the Azores, during our 3-week experiment in November and December of 1998, wind speed and infrasonic noise change rapidly. At this location, daily noise level swings of 40 to 50 dB at 0.1 Hz are not uncommon in the early winter and are due to changes in wind speed and atmospheric turbulence. The effectiveness of an infrasound station in the Azores will be strongly dependent on time during the winter season.¶The two surveys illustrate some of the difficulties inherent in the selection of sites for 1 to 3 km aperture arrays on oceanic islands. Due to elevated noise levels at these sites, 8 element, 2 km aperture arrays are strongly preferred.     

Numerical implementation of a backward probabilistic model of ground water contamination

Backward location and travel time probabilities can be used to characterize known and unknown sources or prior positions of ground water contamination. Backward location probability describes the position of the observed contamination at some time in the past; backward travel time probability describes the amount of time prior to observation that the contamination was released from its source or was at a particular upgradient location. The governing equation for backward probabilities is the adjoint of the governing equation for contaminant transport, but with new load terms. Numerical codes that have been written to solve the forward equations of contaminant transport, e.g., the advection-dispersion equation, can also be used to solve the adjoint equation for location and travel time probabilities; however, the interpretation of the results is different and some new approximations must be made for the load terms. We present the governing equations for backward location and travel time probabilities, and provide appropriate numerical approximations for these load terms using the cell-centered finite difference method, one of the most popular numerical methods in ground water hydrology. We discuss some additional numerical considerations for the backward model including boundary conditions, reversal of the flow field, and interpretation of the results. We illustrate the implementation of the backward probability model using hypothetical examples in one- and two-dimensional domains. We also present a three-dimensional application of a pump-and-treat remediation capture zone delineation at the Massachusetts Military Reservation. The illustrations are performed using MODFLOW-96 for flow simulations and MT3DMS for transport simulations.     

Reliability‐based control algorithms for nonlinear hysteretic systems based on enhanced stochastic averaging of energy envelope

Current reliability‐based control techniques have been successfully applied to linear systems; however, incorporation of stochastic nonlinear behavior of systems in such control designs remains a challenge. This paper presents two reliability‐based control algorithms that minimize failure probabilities of nonlinear hysteretic systems subjected to stochastic excitations. The proposed methods include constrained reliability‐based control (CRC) and unconstrained reliability‐based control (URC) algorithms. Accurate probabilistic estimates of nonlinear system responses to stochastic excitations are derived analytically using enhanced stochastic averaging of energy envelope proposed previously by the authors. Convolving these demand estimates with capacity models yields the reliability of nonlinear systems in the control design process. The CRC design employs the first‐level and second‐level optimizations sequentially where the first‐level optimization solves the Hamilton–Jacobi–Bellman equation and the second‐level optimization searches for optimal objective function parameters to minimize the probability of failure. In the URC design, a single optimization minimizes the probability of failure by directly searching for the optimal control gain. Application of the proposed control algorithms to a building on nonlinear foundation has shown noticeable improvements in system performance under various stochastic excitations. The URC design appears to be the most optimal method as it reduced the probability of slight damage to 8.7%, compared with 11.6% and 19.2% for the case of CRC and a stochastic linear quadratic regulator, respectively. Under the Kobe ground motion, the normalized peak drift displacement with respect to stochastic linear quadratic regulator is reduced to 0.78 and 0.81 for the URC and CRC cases, respectively, at comparable control force levels. Copyright © 2017 John Wiley & Sons, Ltd.     

The synthesis of strong ground motion accelerograms from existing records

The paper discusses the synthesis of acceleration time histories primarily for use in structural response estimation. A proposed model for the acceleration time history is based on the summation of dispersive wave modes over the range of frequencies of typical interest to structural response. The values of the modal amplitudes and the travel times (or group velocities) are, for cases examined here, extracted from a target earthquake (or an ensemble of them). The synthetic accelerograms are constructed from these parameters with the inclusion of a probabilistic definition of those or other characteristics of the strong ground shaking and, in particular, the modal phase angle. The probability distributions of the peak acceleration and the integral of the square acceleration of the synthetic records are examined along with comparisons of the pseudo spectral velocity (PSV) response.     

Simultaneous infrasonic,seismic, magnetic and ionospheric observations in an earthquake epicentre

Various pre-seismic and co-seismic effects have been reported in the literature in the solid Earth, hydrosphere, atmosphere, electric/magnetic field and in the ionosphere. Some of the effects observed above the surface, particularly some of the pre-seismic effects, are still a matter of debate. Here we analyze the co-seismic effects of a relatively weak earthquake of 28 October 2008, which was a part of an earthquake swarm in the westernmost region of the Czech Republic. Special attention is paid to unique measurements of infrasonic phenomena. As far as we know, these have been the first infrasonic measurements during earthquake in the epicentre zone. Infrasonic oscillations (～1–12 Hz) in the epicentre region appear to be excited essentially by the vertical seismic oscillations. The observed oscillations are real epicentral infrasound not caused by seismic shaking of the instruments or by meteorological phenomena. Seismo-infrasonic oscillations observed 155 km apart from the epicentre were excited in situ by seismic waves. No earthquake-related infrasonic effects have been observed in the ionosphere. Necessity to make vibration tests of instruments is pointed out in order to be sure that observed effects are not effects of mechanical shaking of the instrument.     

Probabilistic sensitivity and modeling of two-dimensional transport in porous media

A reliability approach is used to develop a probabilistic model of two-dimensional non-reactive and reactive contaminant transport in porous media. The reliability approach provides two important quantitative results: an estimate of the probability that contaminant concentration is exceeded at some location and time, and measures of the sensitivity of the probabilistic outcome to likely changes in the uncertain variables. The method requires that each uncertain variable be assigned at least a mean and variance; in this work we also incorporate and investigate the influence of marginal probability distributions. Uncertain variables includex andy components of average groundwater flow velocity,x andy components of dispersivity, diffusion coefficient, distribution coefficient, porosity and bulk density. The objective is to examine the relative importance of each uncertain variable, the marginal distribution assigned to each variable, and possible correlation between the variables. Results utilizing a two-dimensional analytical solution indicate that the probabilistic outcome is generally very sensitive to likely changes in the uncertain flow velocity. Uncertainty associated with dispersivity and diffusion coefficient is often not a significant issue with respect to the probabilistic analysis; therefore, dispersivity and diffusion coefficient can often be treated for practical analysis as deterministic constants. The probabilistic outcome is sensitive to the uncertainty of the reaction terms for early times in the flow event. At later times, when source contaminants are released at constant rate throughout the study period, the probabilistic outcome may not be sensitive to changes in the reaction terms. These results, although limited at present by assumptions and conceptual restrictions inherent to the closed-form analytical solution, provide insight into the critical issues to consider in a probabilistic analysis of contaminant transport. Such information concerning the most important uncertain parameters can be used to guide field and laboratory investigations.     

Stochastic modeling of solute transport in 3-D heterogeneous porous media with random source condition

During probabilistic analysis of flow and transport in porous media, the uncertainty due to spatial heterogeneity of governing parameters are often taken into account. The randomness in the source conditions also play a major role on the stochastic behavior in distribution of the dependent variable. The present paper is focused on studying the effect of both uncertainty in the governing system parameters as well as the input source conditions. Under such circumstances, a method is proposed which combines with stochastic finite element method (SFEM) and is illustrated for probabilistic analysis of concentration distribution in a 3-D heterogeneous porous media under the influence of random source condition. In the first step SFEM used for probabilistic solution due to spatial heterogeneity of governing parameters for a unit source pulse. Further, the results from the unit source pulse case have been used for the analysis of multiple pulse case using the numerical convolution when the source condition is a random process. The source condition is modeled as a discrete release of random amount of masses at fixed intervals of time. The mean and standard deviation of concentration is compared for the deterministic and the stochastic system scenarios as well as for different values of system parameters. The effect of uncertainty of source condition is also demonstrated in terms of mean and standard deviation of concentration at various locations in the domain.     

Assessment and management of risk in subsurface hydrology: A review and perspective

Uncertainty plagues every effort to model subsurface processes and every decision made on the basis of such models. Given this pervasive uncertainty, virtually all practical problems in hydrogeology can be formulated in terms of (ecologic, monetary, health, regulatory, etc.) risk. This review deals with hydrogeologic applications of recent advances in uncertainty quantification, probabilistic risk assessment (PRA), and decision-making under uncertainty. The subjects discussed include probabilistic analyses of exposure pathways, PRAs based on fault tree analyses and other systems-based approaches, PDF (probability density functions) methods for propagating parametric uncertainty through a modeling process, computational tools (e.g., random domain decompositions and transition probability based approaches) for quantification of geologic uncertainty, Bayesian algorithms for quantification of model (structural) uncertainty, and computational methods for decision-making under uncertainty (stochastic optimization and decision theory). The review is concluded with a brief discussion of ways to communicate results of uncertainty quantification and risk assessment.     

DYNAMIC RESPONSE VARIABILITY OF STRUCTURES WITH UNCERTAIN PROPERTIES

A modal-based analysis of the dynamic response variability of multiple degree-of-freedom linear structures with uncertain parameters subjected to either deterministic or stochastic excitations is considered. A probabilistic methodology is presented in which random variables with specified probability distributions are used to quantify the parameter uncertainties. The uncertainty in the response due to uncertainties in the structural modelling and loading is quantified by various probabilistic measures such as mean, variance and coefficient of excess. The computation of these probabilistic measures is addressed. A series expansion involving orthogonal polynomials in terms of the system parameters is first used to model the response variability of each contributing mode. Linear equations for the coefficients of each series expansion are derived using the weighted residual method. Mode superposition is then used to derive analytical expressions for the variability and statistics of the uncertain response in terms of the coefficients of the series expansions for all contributing modes. A primary–secondary system and a ten-story building subjected to deterministic and stochastic loads are used to demonstrate the methodology, as well as evaluate its performance by comparing it to existing methods, including the computationally cost-efficient perturbation method.     

Accuracy analysis of PPDF-based method to parameterize aerosol scattering effect

We introduce the path length probability density function (PPDF) method, which is based on an equivalence theorem and parameterizes the aerosol scattering effect by adding four factors to the atmospheric transmittance model. Using simulated observations in the O₂-A band, we examined the utility of the PPDF-based method to account for the aerosol scattering effect. First, observations were simulated using a forward model under different aerosol conditions; PPDF factors were then retrieved using an optimal estimation method; PPDF factors were used to reconstruct the observations; and finally, simulated true observations and reconstructions were compared. Analysis of the difference between the true observations and reconstructions confirmed the utility of the PPDF-based method. Additionally, the O₂ band was demonstrated to be an efficient observing band for assisting the remote sensing of atmospheric trace gases in the near-infrared band.     

Laboratory sandbox validation of pollutant source location methods

Inverse methods can be used to recover the pollutant source location from concentration data. In this paper, the relative effectiveness of two proposed methods, simultaneous release function and source location identification (SRSI) and backward probability model based on adjoint state method (BPM-ASM) are evaluated using real data collected by using experimental equipment. The device is a sandbox that reproduces an unconfined aquifer in which all the variables are controlled. A numerical model was calibrated using experimental observations. The SRSI is a stochastic procedure which finds the source location and the release history by means of a Bayesian geostatistical approach (GA). The BPM-ASM provides the backward probability location of the pollutant detected at a monitoring point by means of a reverse transport simulation. The results show that both methods perform well. While the simultaneous release function and SRSI method requires a preliminary delineation of a probable source area and some weak hypotheses about the statistical structure of the unknown release function, the backward probability model requires some hypothesis about the contaminant release time. A case study was performed using two observation points only, and despite the scarcity of data, both methodologies were able to accurately reconstruct the true source location. The GA has the advantage to recover the release history function too, whilst the backward probability model works well with fewer data. If there are many observations, both methodologies may be computationally heavy. A transfer function approach has been adopted for the numerical definition of the sensitivity matrix in the SRSI method. The reliability of the experimental equipment was tested in previous laboratory works, conducted under several different conditions.     

Probabilistic permanent fault displacement hazard via Monte Carlo simulation and its consideration for the probabilistic risk assessment of buried continuous steel pipelines

Permanent fault displacements (PFDs) because of fault ruptures emerging at the surface are critical for seismic design and risk assessment of continuous pipelines. They impose significant compressive and tensile strains to the pipe cross‐section at pipe‐fault crossings. The complexity of fault rupture, inaccurate mapping of fault location and uncertainties in fault‐pipe crossing geometries require probabilistic approaches for assessing the PFD hazard and mitigating pipeline failure risk against PFD. However, the probabilistic approaches are currently waived in seismic design of pipelines. Bearing on these facts, this paper first assesses the probabilistic PFD hazard by using Monte Carlo‐based stochastic simulations whose theory and implementation are given in detail. The computed hazard is then used in the probabilistic risk assessment approach to calculate the failure probability of continuous pipelines under different PFD levels as well as pipe cross‐section properties. Our probabilistic pipeline risk computations consider uncertainties arising from complex fault rupture and geomorphology that result in inaccurate mapping of fault location and fault‐pipe crossings. The results presented in this paper suggest the re‐evaluation of design provisions in current pipeline design guidelines to reduce the seismic risk of these geographically distributed structural systems. Copyright © 2016 John Wiley & Sons, Ltd.     

Source constraints of Tungurahua volcano explosion events

The most recent eruptive cycle of Tungurahua volcano began in May 2004, and reached its highest level of activity in July 2004. This activity cycle is the last one of a series of four cycles that followed the reawakening and major eruption of Tungurahua in 1999. Between June 30 and August 12, 2004, three temporary seismic and infrasonic stations were installed on the flanks of the volcano and recorded over 2,000 degassing events. The events are classified by waveform character and include: explosion events (the vast majority, spanning three orders of pressure amplitudes at 3.5 km from the vent, 0.1–180 Pa), jetting events, and sequences of repetitive infrasonic pulses, called chugging events. Travel-time analysis of seismic first arrivals and infrasonic waves indicates that explosions start with a seismic event at a shallow depth (<200 m), followed ∼1 s later by an out-flux of gas, ash and solid material through the vent. Cluster analysis of infrasonic signals from explosion events was used to isolate four groups of similar waveforms without apparent correlation to event size, location, or time. The clustering is thus associated with source mechanism and probably spatial distribution. Explosion clusters do not exhibit temporal dependence.     

模拟退火算法在地震定位中的应用

阐述了模拟退火算法及其在地震定位中的原理,以震相观测到时与理论到时之差(到时残差)为目标函数,利用模拟退火算法求解目标函数的最小值以获得地震的震源参数。在模拟退火算法的地震定位中根据震相的可靠程度对不同震相设置了相应的定位权重;对震相的走时误差设置了3组不同的值,以分析走时误差对定位结果的影响,对随机生成的1000个模拟地震进行模拟退火算法再定位。结果表明,走时误差越小,定位结果越准确。最后,通过地震震例进一步验证了模拟退火算法在地震定位中的可用性。     

The New Algorithm for Fast Probabilistic Hypocenter Locations

The spatial location of sources of seismic waves is one of the first tasks when transient waves from natural (uncontrolled) sources are analysed in many branches of physics, including seismology, oceanology, to name a few. It is well recognised that there is no single universal location algorithm which performs equally well in all situations. Source activity and its spatial variability in time, the geometry of recording network, the complexity and heterogeneity of wave velocity distribution are all factors influencing the performance of location algorithms. In this paper we propose a new location algorithm which exploits the reciprocity and time-inverse invariance property of the wave equation. Basing on these symmetries and using a modern finite-difference-type eikonal solver, we have developed a new very fast algorithm performing the full probabilistic (Bayesian) source location. We illustrate an efficiency of the algorithm performing an advanced error analysis for 1647 seismic events from the Rudna copper mine operating in southwestern Poland.