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  • 2015-2019  (8)
  • 1990-1994  (8)
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  • 1
    Electronic Resource
    Electronic Resource
    Oxford, UK : Blackwell Publishing Ltd
    Geophysical journal international 102 (1990), S. 0 
    ISSN: 1365-246X
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Geosciences
    Notes: Analysis of global traveltime data has been formulated in terms of the stochastic properties of the Earth's heterogeneity pattern and random errors in the data. the formalism relates the coherency of traveltime residuals within bundles of rays (summary rays) of varying size to the spherical harmonic power spectrum of the slowness field of the medium. It has been applied to mantle P-wave data from the ISC catalogue. the measure of coherency is the variance within summary rays. It is estimated within bins in source depth, epicentral distance and the scale size of the area defining a summary ray. the variance at infinitesimal scale length represents the incoherent component of the data (random errors). the variation of the variance with scale length contains information about the autocorrelation function or power spectrum of slowness perturbations within the Earth. the variation with epicentral distance reflects the depth variation of the spectrum. the formalism accounts for the uneven distribution (clustering) of stations and events.We find that estimates of random errors correlate well with complexities on the traveltime curve of P-waves. the variance peaks at 1.0–2.0 s2 at δ20°, where triplications occur on the traveltime curve, drops to 0.15–0.8s2 at teleseismic distances, and rises to 0.4–1.3 s2 approaching the core shadow, where the traveltime curves of P-waves and PcP-waves merge. These estimates should be considered upper bounds for the random error variance of the data. the signal to random noise ratio in the teleseismic ISC P-wave data is about S/N= 2.Inversion of the scale-dependent structural signal in the data yields models that concentrate heterogeneity strongly in the upper mantle. the product of correlation length and power drops by about two orders of magnitude from the surface of the Earth to the lower mantle. About half of this quantity in the upper mantle is due to small-scale features (〈300km). the lower mantle is devoid of small-scale structure. It contains 0.1 per cent velocity variations at a characteristic scale of about 1000km. This corresponds to a spectral band-width of l= 7. the D″ layer at the bottom 100–200 km of the mantle shows up as a distinct layer in our results. It has 0.3 per cent velocity variations at a characteristic scale of 350km. the top of the lower mantle contains 0.3 per cent velocity variations on a scale of 500km and also contains some small-scale power.These results are compatible with previous deterministic lower mantle studies, although some details differ. the strength of heterogeneity in the upper mantle may obscure attempts to model the Earth's deep interior.
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    Oxford, UK : Blackwell Publishing Ltd
    Geophysical journal international 119 (1994), S. 0 
    ISSN: 1365-246X
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Geosciences
    Notes: Results of a 2-D, seismic undershooting experiment on the Katla central volcano in south Iceland are reported. Large localized traveltime anomalies (0.4s) are observed on an array within the Katla caldera. the traveltimes are forward modelled using a wavefront tracker developed in Appendix A. Thus, non-linear effects encountered in traveltime tomography are avoided as well as common problems with ray tracing in the presence of strong lateral heterogeneity. an extreme variation in compressional velocity is required to extend over a significant volume in order to model the data. the resulting model is not unique, but constraints on the allowable range of velocities (2.5-6.0 kms−1) render the basic features well constrained. A clear S-wave shadow is closely associated with delays in traveltime due to a shallow slow anomaly. Low-amplitude P waves go hand in hand with early arrivals due to thin structural features flanking the slow anomaly. the model is interpreted in terms of a magma chamber containing extensively molten rock. the magma chamber is shallow, with a bottom at a depth of about 1.5km below sea-level (3.0 km below surface), and measures about 5 km across. the depth of the chamber is roughly at the level of buoyant equilibrium for basaltic melt in the crust. Owing to poor vertical resolution at shallow depths in the undershooting geometry the top of this shallow magma chamber is not well resolved. On the other hand, the bottom of the chamber is well resolved. the chamber is underlain by rocks of average or high velocity for that depth. the magma chamber is a persistent feature, big enough (10km3) to supply magma for large eruptions and to supply heat to permit remelting of hydrated basaltic crust to produce silicic magmas at shallow levels. the chamber is fed by magma fracturing from below. the model agrees with phenomenological models of magma chambers in Iceland based on geological observations and provides a quantification of those models in terms of depth and size. On the other hand, it is fundamentally different from recent models of magma chambers at mid-ocean ridges which may be more akin to the pervasive region of partial melt at depth beneath Iceland. This underlines the important effect of the Icelandic hotspot on tectonics and volcanism in Iceland and implies a substantially different crustal and thermal structure in Iceland from that of ‘normal’ mid-ocean ridges.
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  • 3
    Publication Date: 2016-04-16
    Description: We introduce a method for mapping the noise-source distribution of microseisms which uses information from the full length of covariograms (cross-correlations). We derive a forward calculation based on the plane-wave assumption in 2-D, to formulate an iterative, linearized inversion of covariogram envelopes in the time domain. The forward calculation involves bandpass filtering of the covariograms. The inversion exploits the well-known feature of noise cross-correlation, that is, an anomaly in the noise field that is oblique to the interstation direction appears as cross-correlation amplitude at a smaller time lag than the in-line, surface wave arrival. Therefore, the inversion extracts more information from the covariograms than that contained at the expected surface wave arrival, and this allows us to work with few stations to find the propagation directions of incoming energy. The inversion is naturally applied to data that retain physical units that are not amplitude normalized in any way. By dividing a network into groups of stations, we can constrain the source location by triangulation. We demonstrate results of the method with synthetic data and one year (2012) of data from the Swedish National Seismic Network and also look at the seasonal variation of source distribution around Scandinavia. After preprocessing and cross-correlation, the stations are divided into five groups of 9–12 stations. We invert the envelopes of each group in eight period ranges between 2 and 25 s. Results show that the noise sources at short periods (less than 12 s) lie predominantly in the North Atlantic Ocean and the Barents Sea, and at longer periods the energy appears to have a broader distribution. The strongly anisotropic source distribution in this area is estimated to cause significant biases of velocity measurements compared to the level of heterogeneity in the region. The amplitude of the primary microseisms varies little over the year, but secondary microseisms are much weaker in summer than in winter. Furthermore, the peak period of the secondary microseisms shifts from 5–6 s in winter to 4–5 s during the summer.
    Keywords: Seismology
    Print ISSN: 0956-540X
    Electronic ISSN: 1365-246X
    Topics: Geosciences
    Published by Oxford University Press on behalf of The Deutsche Geophysikalische Gesellschaft (DGG) and the Royal Astronomical Society (RAS).
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  • 4
    Publication Date: 2016-10-09
    Description: Relative location methods are commonly used to precisely locate earthquake clusters consisting of similar waveforms. Repeating waveforms are often recorded at volcanoes, where, however, the crust structure is expected to contain strong heterogeneities and therefore the 1-D velocity model assumption that is made in most location strategies is not likely to describe reality. A peculiar cluster of repeating low-frequency seismic events was recorded on the south flank of Katla volcano (Iceland) from 2011. As the hypocentres are located at the rim of the glacier, the seismicity may be due to volcanic or glacial processes. Information on the size and shape of the cluster may help constraining the source process. The extreme similarity of waveforms points to a very small spatial distribution of hypocentres. In order to extract meaningful information about size and shape of the cluster, we minimize uncertainty by optimizing the cross-correlation measurements and relative-location process. With a synthetic test we determine the best parameters for differential-time measurements and estimate their uncertainties, specifically for each waveform. We design a location strategy to work without a pre-defined velocity model, by formulating and inverting the problem to seek changes in both location and slowness, thus accounting for azimuth, take-off angles and velocity deviations from a 1-D model. We solve the inversion explicitly in order to propagate data errors through the calculation. With this approach we are able to resolve a source volume few tens of metres wide in horizontal directions and around 100 metres in depth. There is no suggestion that the hypocentres lie on a single fault plane and the depth distribution indicates that their source is unlikely to be related to glacial processes as the ice thickness is not expected to exceed few tens of metres in the source area. Our method is designed for a very small source region, allowing us to assume a constant slowness for the whole cluster and to include the effects of 3-D heterogeneity such as refraction. Similar circumstances may arise in other volcanic regions with a high level of heterogeneity and where densely clustered earthquakes are often recorded.
    Keywords: Seismology
    Print ISSN: 0956-540X
    Electronic ISSN: 1365-246X
    Topics: Geosciences
    Published by Oxford University Press on behalf of The Deutsche Geophysikalische Gesellschaft (DGG) and the Royal Astronomical Society (RAS).
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  • 5
    Publication Date: 2017-01-07
    Description: A double-correlation method is introduced to locate tremor sources based on stacks of complex, doubly-correlated tremor records of multiple triplets of seismographs back projected to hypothetical source locations in a geographic grid. Peaks in the resulting stack of moduli are inferred source locations. The stack of the moduli is a robust measure of energy radiated from a point source or point sources even when the velocity information is imprecise. Application to real data shows how double correlation focuses the source mapping compared to the common single correlation approach. Synthetic tests demonstrate the robustness of the method and its resolution limitations which are controlled by the station geometry, the finite frequency of the signal, the quality of the used velocity information and noise level. Both random noise and signal or noise correlated at time shifts that are inconsistent with the assumed velocity structure can be effectively suppressed. Assuming a surface wave velocity, we can constrain the source location even if the surface wave component does not dominate. The method can also in principle be used with body waves in 3-D, although this requires more data and seismographs placed near the source for depth resolution.
    Keywords: Seismology
    Print ISSN: 0956-540X
    Electronic ISSN: 1365-246X
    Topics: Geosciences
    Published by Oxford University Press on behalf of The Deutsche Geophysikalische Gesellschaft (DGG) and the Royal Astronomical Society (RAS).
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  • 6
    Publication Date: 2018-06-23
    Print ISSN: 0956-540X
    Electronic ISSN: 1365-246X
    Topics: Geosciences
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  • 7
    Publication Date: 2017-01-01
    Print ISSN: 0956-540X
    Electronic ISSN: 1365-246X
    Topics: Geosciences
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  • 8
    Publication Date: 1994-10-01
    Print ISSN: 0031-9201
    Electronic ISSN: 1872-7395
    Topics: Geosciences , Physics
    Published by Elsevier
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  • 9
    Publication Date: 1994-07-01
    Print ISSN: 0031-9201
    Electronic ISSN: 1872-7395
    Topics: Geosciences , Physics
    Published by Elsevier
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  • 10
    Publication Date: 1994-01-01
    Print ISSN: 0148-0227
    Electronic ISSN: 2156-2202
    Topics: Geosciences
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