Analysis of tremor at the San Andreas Fault at Parkfield

Emergent phase arrivals, low amplitude waveforms, and variable event durations make detection and location of tectonic tremor a non-trivial task. In this work I employ a new method to identify tremor in large datasets using a semi-automated technique, which is comprised of an envelope cross-correlation and a Self-Organizing Map (SOM) algorithm to identify and classify event types. Furthermore, I present a new tremor localization method based on time-reversal imaging techniques

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

Full Wave-equation Based Passive Seismic Imaging and Multispectral Seismic Geometric Attributes

Both passive (e.g., microseismic) and active seismic methods are used for the hydrocarbon exploration. For example, microseismic data analysis provides helpful information in not only mapping hydraulic fracture initiation, but also reservoir monitoring and even structural imaging. In 3D seismic interpretation, geometric attributes provide effective tools to map structure and stratigraphy. However, current microseismic events locating method faces challenge due to the poor resolution of passive-seismic imaging result and the dependency of the subsurface velocity model. Further, some stratigraphic features are buried in the conventional seismic geometric attributes. Focusing on these challenges, I develop new passive seismic imaging method and multispectral geometric attributes in this dissertation to provide more effective tools for hydrocarbon exploration. To improve the quality of passive seismic imaging, I have constructed an iterative approach to locate the passive source locations and estimate the background overburden velocity based on full wave-equation methods. Specifically, I use a high-resolution geometric-mean reverse-time migration (GmRTM) to provide source locations that are better focused compared to conventional time-reversal imaging method. I also use the passive-source full-waveform inversion (FWI) to optimize the overburden velocity model. Given this accurate velocity, I use passive-source reverse-time migration to provide a structural image. Numerical experiments on the Marmousi model dataset indicate that the proposed approach can handle complicated structures and noisy seismic recordings. Recent developments in multispectral coherence based on simple band-pass filters show improvements in fault and stratigraphic edge delineation. To further improve this technology, I evaluate several different spectral decomposition algorithms to determine which, if any provide superior coherence images, I find that exponentially-spaced spectral voices provide better coherence images than linearly-spaced spectral components for the same computation cost. I also find that multispectral coherence computed from generated using the high-resolution maximum entropy algorithm provides reduced noise and better resolution of thinner channels than the other spectral decomposition algorithms. Equally important, I analyze why multispectral coherence provides more continuous fault images where conventional coherence images often exhibit gaps in areas where a human interpreter would draw a single, unbroken fault. These coherence fault occur when the displacement across the fault aligns different stratigraphic reflectors, resulting in what appears to be a continuous reflector. This same alignment also confounds auto-pickers. Although two different broadband seismic reflectors may be aligned across faults, in general, the corresponding spectral voices are not, thereby reducing their cross-correlation, and for multispectral coherence, elements in their covariance matrices, across the fault. Considering that multispectral coherence provides a better delineation of the seismic discontinuities due to data quality, thin-bed tuning, or non-stratigraphic alignments, I further investigate multispectral dip attributes, which try to combine the benefits from different spectral voices. I illustrate the multispectral gradient structure tensor (GST) dip method, which helps improve the quality of dip attributes in the conventional broadband dips. Multispectral GST dip is performed by computing the eigenvectors and eigenvalues from the multispectral gradient structure tensor matrix. I use two 3D seismic surveys to indicate the improvement using the multispectral GST dip over conventional broadband dip attributes

Characterization of geohazards via seismic and acoustic waves

Thesis (Ph.D.) University of Alaska Fairbanks, 2023Earth processes, such as large landslides and volcanic eruptions, occur globally and can be hazardous to life and property. Geophysics -- the quantitative study of Earth processes and properties -- is used to monitor and rapidly respond to these geohazards. In particular, seismoacoustics, which is the joint study of seismic waves in the solid Earth and acoustic waves in Earth's atmosphere, has been proven effective for a variety of geophysical monitoring tasks. Typically, the acoustic waves studied are infrasonic: They have frequencies less than 20 hertz, which is below the threshold of human hearing. In this dissertation, we use seismic and acoustic waves and techniques to characterize geohazards, and we examine the propagation of the waves themselves to better understand how seismoacoustic energy is transformed on its path from a given source to the measurement location. Chapter 1 provides a broad overview of seismoacoustics tailored to this dissertation. In Chapter 2, we use seismic and acoustic waves to reconstruct the dynamics of two very large, and highly similar, ice and rock avalanches occurring in 2016 and 2019 on Iliamna Volcano (Alaska). We determine their trajectories using seismic data from distant stations, demonstrating the feasibility of remote seismic landslide characterization. Chapter 3 details the application of machine learning to a rich volcano infrasound dataset consisting of thousands of explosions recorded at Yasur Volcano (Vanuatu) over six days in 2016. We automatically generate a labeled catalog of infrasound waveforms associated to two different locations in Yasur's summit crater, and use this catalog to test different strategies for transforming the waveforms prior to classification model input. In Chapter 4, we use the coupling of atmospheric waves into the Earth to leverage a dense network of about 900 seismometers around Mount Saint Helens volcano (Washington state) as a quasi-infrasound network. We use buried explosions from a 2014 experiment as sources of infrasound. The dense spatial wavefield measurements permit detailed examination of the effects of wind and topography on infrasound propagation. Finally, in Chapter 5 we conclude with some discussion of future work and additional seismoacoustic topics

Hva kontrollerer kalving av breer? Fra observasjoner til prediksjoner

This thesis addresses the process of iceberg calving at the front of tidewater glaciers and tries to clarify what controls the calving of glaciers, from observations in the field to modeling and predictions. Iceberg calving is the detachment of ice from a parent glacier and it makes the glacier very sensitive to its local environment. In turn, calving at a glacier front has a strong impact on the glacier dynamics and can trigger and/or enhance glacier instabilities, acceleration and glacier retreat, making the calving process a crucial factor in glacier dynamics and hence in sea level rise. This thesis is based on field observations, collected throughout 4 years at the front of Kronebreen, Svalbard. A special emphasis has been given to trying various observation techniques: ground-based RADAR, direct observations, seismic monitoring, terrestrial photogrammetry and remote sensing. Using ground-based RADAR we were able to automatically detect 92% of the largest calving events. The percentage detected by seismic monitoring is lower (about 10%) but the technique allows for finer distinction between different calving types and glacier-related seismic events. Seismic equipment also requires less maintenance, less technical expertise and less funding, and can be left in the field for several months. Terrestrial photogrammetry is a very useful tool that can provide glacier dimensions and a continuous monitoring of the general conditions at the front. Finally, direct observations are recommended for the study of calving because it can provide, when used together with terrestrial photogrammetry, both qualitative and quantitative data. The qualitative aspect provides key information for understanding the calving process but is especially hard to obtain with technical methods. The question of seasonal calving variations is also addressed and we show that glacial seismic activity is highly variable throughout the year with recurrent increased activity in autumn, while velocity is low. However this thesis focuses on explaining very short-term variations: the individual calving events. Individual calving events have received so far very little attention in the field and no attention in modeling studies. This thesis was inspired by other studies of complex natural processes in which individual events are all equally considered, large and small, and which emphasize the value of understanding a process at the individual scale, for example the study of earthquakes or forest fires. We first show that general spatial patterns in calving activity can be explained by glacier characteristics like longitudinal stretching rate, which themselves are very linked to the glacier geometry. We then created a simple calving model with the object of understanding what controls the size and timing of calving events. Our simple model, focussing solely on the interplay between calving and its impact on the front stability, manages to reproduce the size and timing distribution of calving events as observed in the field. This result highlights the role of calving on front stability and on calving itself. Front stability is shown to be crucial in the control of calving. Implications of this new finding are that the size distribution of calving depends on the glacier stability: a glacier becoming unstable will produce a higher proportion of large calving events. Beyond a critical glacier stability, calving can become self-sustained and ongoing, leading to very rapid glacier retreat. We propose that the characteristics of the calving event sizes distribution indicate how close a glacier is to rapid retreat. One main point of this thesis is to show the importance of studying calving events at an individual scale to gain more understanding of the process.Denne avhandlingen omhandler kalvingsprosessen i fronten av en tidevannsbre og den forsøker å klargjøre hva som kontrollerer kalving av breer, ved hjelp av feltobservasjoner, modellering og prediksjon. Kalving av isfjell skjer når is brekker av fra en isbre, og kalving gjør breer svært sensitive til det lokale miljøet. Motsatt har også kalvingen ved brefronten en stor innflytelse på breens dynamikk, kalvingen kan initiere eller forsterke ustabilitet, akselerasjon eller tilbaketrekking av breen, hvilket gjør kalvingsprosessen til en sentral faktor for isdynamikken, og for havnivået. Denne avhandlingen er basert på feltobservasjoner som er samlet gjennom fire år ved fronten av Kronebreen på Svalbard. Det er blitt lagt spesielt vekt på å prøve ut forskjellige observasjonsteknikker, bakkebasert RADAR, direkte observasjoner, seismisk monitorering, terrestrisk fotogrammetri, og fjernanalyse. Ved hjelp av bakkebasert RADAR kunne vi detektere 92% av de storste kalvingsepisodene. Prosentandelen for seismisk monitorering er mye lavere, ca 10%, men denne monitoreringen tillater finere distinksjon av forskjellige kalvingsformer og brerelaterte seismiske episoder. Seismisk utstyr krever også mindre ettersyn, mindre teknisk ekspertise og lavere finansiering, og utstyret kan være utplassert i felt uten tilsyn i flere måneder. Terrestrisk fotogrammetri er et svært nyttig verktøy som kan fortelle om breens dimensjoner og som muliggjør en kontinuerlig monitorering av generelle forhold ved fronten. Tilsutt anbefales direkte observasjoner for å studere kalving, fordi disse i kombinasjon med terrestrisk fotogrammetri kan gi både kvalitative og kvantitative data. Det kvalitative aspektet gir essensiell informasjon for forståelsen av kalvingsprosessen, men er spesielt vanskelig å oppnå ved teknologiske metoder. Spørsmålet om sesongbaserte kalvingsvariasjoner er også undersøkt og vi viser at kalvingsaktiviteten er svært variabel gjennom året, med gjentagende økning i aktivitet på høsten når også hastigheten er på sitt laveste. Allikevel fokuserer denne avhandlingen på å forklare de svart raske variasjonene, nemlig individuelle kalvingshendelser. Så langt har det blitt viet svært lite oppmerksomhet mot individuelle kalvingshendelser i felt, og ingen oppmerksomhet innen modelleringsstudier. Denne avhandlingen er inspirert av studier av komplekse prosesser hvor individuelle hendelser er vurdert likeverdige, store som små, og som vektlegger verdien av å forstå prosessen på en skala på individuelt nivå, for eksempel for studier av jordskjelv. Vi viser først at generelle romlige monstre i kalvingsaktivitet kan forklares ved brekarakteristikker som longitudinell tøynings rate (stretching rate), som igjen er knyttet til breens geometri. Vi har laget en enkel kalvingsmodell hvor intensjonen er å forstå hva som kontrollerer størrelse og tidspunkt for kalvingshendelsen. Vår modell, som fokuserer kun på interaksjon mellom kalving og dennes innflytelse på frontstabiliteten, greier å reprodusere størrelses- og tidsfordeling av kalvingshendelser som observert i felt. Dette resultatet fremhever kalvingens rolle på frontstabiliteten og på kalvingen selv. Det viser seg at frontstabiliteten er en essensiell styringsmekanisme for kalvingen. Konsekvensene av dette nye funnet er at størrelsesfordelingen av kalvingshendelsene avhenger av breens stabilitet; en bre som blir ustabil produserer høyere proporsjon av større kalvingshendelser. Over en kritisk brestabilitet vil kalvingen bli selvopprettholdende og vedvarende, hvilket vil medføre en svart rask tilbaketrekning av brefronten. Vi fremsetter en påstand om at karakteristikken av fordelingen av størrelsene på kalvingshendelsene indikerer hvor nært forestående en rask tilbaketrekning er for breen. Et hovedpoeng ved denne avhandlingen er å vise hvor viktig det er å studere kalvingshendelser på en skala på individuelt nivå for å oppnå en bedre forståelse av prosessen.GLACIODY

Temporal and spectral pattern recognition for detection and combined network and array waveform coherence analysis for location of seismic events

The reliable automatic detection, location and classification of seismic events still poses great challenges if only few sensors record an event and/or the signal-to-noise ratio is very low. This study first examines, compares and evaluates the most widely used algorithms for automatic processing on a diverse set of seismic datasets (e.g. from induced seismicity and nuclear-test-ban verification experiments). A synthesis of state-of-the-art algorithms is given. Several single station event detection and phase picking algorithms are tested followed by a comparison of single station waveform cross-correlation and spectral pattern recognition. Coincidence analysis is investigated afterwards to demonstrate up to which level false alarms can be ruled out in sensor networks of multiple stations. It is then shown how the use of seismic (mini) arrays in diverse configurations can improve these results considerably through the use of waveform coherence. In a second step, two concepts are presented which combine the previously analysed algorithmic building blocks in a new way. The first concept is seismic event signal clustering by unsupervised learning which allows event identification with only one sensor. The study serves as a base level investigation to explore the limits of elementary seismic monitoring with only one single vertical-component seismic sensor and shows the level of information which can be extracted from a single station. It is investigated how single station event signal similarity clusters relate to geographic hypocenter regions and common source processes. Typical applications arise in local seismic networks where reliable ground truth by a dense temporal network precedes or follows a sparse (permanent) installation. The test dataset comprises a three-month subset from a field campaign to map subduction below northern Chile, project for the seismological investigation of the western cordillera (PISCO). Due to favourable ground noise conditions in the Atacama desert, the dataset contains an abundance of shallow and deep earthquakes, and many quarry explosions. Often event signatures overlap, posing a challenge to any signal processing scheme. Pattern recognition must work on reduced seismograms to restrict parameter space. Continuous parameter extraction based on noise-adapted spectrograms was chosen instead of discrete representation by, e.g. amplitudes, onset times, or spectral ratios to ensure consideration of potentially hidden features. Visualization of the derived feature vectors for human inspection and template matching algorithms was hereby possible. Because event classes shall comprise earthquake regions regardless of magnitude, signal clustering based on amplitudes is prevented by proper normalization of feature vectors. Principal component analysis (PCA) is applied to further reduce the number of features used to train a self-organizing map (SOM). The SOM arranges prototypes of each event class in a 2D map topologically. Overcoming the restrictions of this black-box approach, the arranged prototypes can be transformed back to spectrograms to allow for visualization and interpretation of event classes. The final step relates prototypes to ground-truth information, confirming the potential of automated, coarse-grain hypocenter clustering based on single station seismograms. The approach was tested by a two-fold cross-validation whereby multiple sets of feature vectors from half the events are compared by a one-nearest neighbour classifier in combination with an euclidean distance measure resulting in an overall correct geographic separation rate of 95.1% for coarse clusters and 80.5% for finer clusters (86.3% for a more central station). The second concept shows a new method to combine seismic networks of single stations and arrays for automatic seismic event location. After exploring capabilities of single station algorithms in the section before, this section explores capabilities of algorithms for small local seismic networks. Especially traffic light systems for induced seismicity monitoring rely on the real-time automated location of weak events. These events suffer from low signal-to-noise ratios and noise spikes due to the industrial setting. Conventional location methods rely on independent picking of first arrivals from seismic wave onsets at recordings of single stations. Picking is done separately and without feedback from the actual location algorithm. With low signal-to-noise ratios and local events, the association of onsets gets error prone, especially for S-phase onsets which are overlaid by coda from previous phases. If the recording network is small or only few phases can be associated, single wrong associations can lead to large errors in hypocenter locations and magnitude. Event location by source scanning which was established in the last two decades can provide more robust results. Source scanning uses maxima from a travel time corrected stack of a characteristic function of the full waveforms on a predefined location grid. This study investigates how source-scanning can be extended and improved by integrating information from seismic arrays, i.e. waveform stacking and Fisher ratio. These array methods rely on the coherency of the raw filtered waveforms while traditional source scanning uses a characteristic function to obtain coherency from otherwise incoherent waveforms between distant stations. The short term average to long term average ratio (STA/LTA) serves as the characteristic function and single station vertical-component traces for P-phases and radial and transverse components for S-phases are used. For array stations, the STA/LTA of the stacked vertical seismogram which is furthermore weighted by the STA/LTA of the Fisher ratio, dependent on back azimuth and slowness, is utilized for P-phases. In the chosen example, the extension by array-processing techniques can reduce the mean error in comparison to manually determined hypocenters by up to a factor of 2.9, resolve ambiguities and further restrain the location

Broadband Seismic Noise: Classification and Green\u27s Function Estimation

This thesis aims at a better understanding of the spatial and temporal variations of the amplitudes as well as the statistical properties of the (urban) seismic noise. A new statistical time series classification is presented which is used to conduct an analysis of the spatial and temporal variations of seismic noise between 8 mHz and 45 Hz in a metropolitan area. Furthermore, it is used to realise data selection approaches for the estimation of Green\u27s functions from seismic noise

Proceedings of the International Workshop on Medical Ultrasound Tomography: 1.- 3. Nov. 2017, Speyer, Germany

Ultrasound Tomography is an emerging technology for medical imaging that is quickly approaching its clinical utility. Research groups around the globe are engaged in research spanning from theory to practical applications. The International Workshop on Medical Ultrasound Tomography (1.-3. November 2017, Speyer, Germany) brought together scientists to exchange their knowledge and discuss new ideas and results in order to boost the research in Ultrasound Tomography