ALBERT

Description: Microseisms in the period of 2–10 s are generated in deep oceans and near coastal regions. It is common for microseisms from multiple sources to arrive at the same time at a given seismometer. It is therefore desirable to be able to measure multiple slowness vectors accurately. Popular ways to estimate the direction of arrival of ocean induced microseisms are the conventional (fk) or adaptive (Capon) beamformer. These techniques give robust estimates, but are limited in their resolution capabilities and hence do not always detect all arrivals. One of the limiting factors in determining direction of arrival with seismic arrays is the array response, which can strongly influence the estimation of weaker sources. In this work, we aim to improve the resolution for weaker sources and evaluate the performance of two deconvolution algorithms, Richardson–Lucy deconvolution and a new implementation of CLEAN-PSF. The algorithms are tested with three arrays of different aperture (ASAR, WRA and NORSAR) using 1 month of real data each and compared with the conventional approaches. We find an improvement over conventional methods from both algorithms and the best performance with CLEAN-PSF. We then extend the CLEAN-PSF framework to three components (3C) and evaluate 1 yr of data from the Pilbara Seismic Array in northwest Australia. The 3C CLEAN-PSF analysis is capable in resolving a previously undetected Sn phase.

Description: Recently developed methods for inferring abrupt changes in data series enable such change points in time or space to be identified, and also allow us to estimate noise levels of the observed data. The inferred probability distributions of these parameters provide insights into the capacity of the observed data to constrain the geophysical analysis and hence the magnitudes, and likely sources, of uncertainty. We carry out a change-point analysis of sections of four borehole geophysical logs (density, neutron absorption, sonic interval time, and electrical resistivity) using transdimensional Bayesian Markov chain Monte Carlo to sample a model parameter space. The output is an ensemble of values which approximate the posterior distribution of model parameters. We compare the modeled change points, borehole log parameters, and the variance of the noise distribution of each log with the observed lithology classes down the borehole to make an appraisal of the uncertainty characteristics inherent in the data. Our two examples, one with well-defined lithology changes and one with more subtle contrasts, show quantitatively the nature of the lithology contrasts for which the geophysical borehole log data will produce a detectable response in terms of inferred change points. We highlight the different components of variation in the observed data: due to the geologic process (dominant lithology changes) that we hope to be able to infer, geologic noise due to variability within each lithology, and analytical noise due to the measurement process. This inference process will be a practical addition to the analytical tool box for borehole and other geophysical data series. It reveals the level of uncertainties in the relationships between the data and the observed lithologies and would be of great use in planning and interpreting the results of subsequent routine processing.

Description: Inductive machine learning algorithms attempt to recognize patterns in, and generalize from empirical data. They provide a practical means of predicting lithology, or other spatially varying physical features, from multidimensional geophysical data sets. It is for this reason machine learning approaches are increasing in popularity for geophysical data inference. A key motivation for their use is the ease with which uncertainty measures can be estimated for nonprobabilistic algorithms. We have compared and evaluated the abilities of two nonprobabilistic machine learning algorithms, Random Forests (RF) and Support Vector Machines (SVM), to recognize ambiguous supervised classification predictions using uncertainty calculated from estimates of class membership probabilities. We formulated a method to establish optimal uncertainty threshold values to identify and isolate the maximum number of incorrect predictions while preserving most of the correct classifications. This is illustrated using a case example of the supervised classification of surface lithologies in a folded, structurally complex, metamorphic terrain. We found that (1) the use of optimal uncertainty thresholds significantly improves overall classification accuracy of RF predictions, but not those of SVM, by eliminating the maximum number of incorrectly classified samples while preserving the maximum number of correctly classified samples; (2) RF, unlike SVM, was able to exploit dependencies and structures contained within spatially varying input data; and (3) high RF prediction uncertainty is spatially coincident with transitions in lithology and associated contact zones, and regions of intense deformation. Uncertainty has its upside in the identification of areas of key geologic interest and has wide application across the geosciences, where transition zones are important classes in their own right. The techniques used in this study are of practical value in prioritizing subsequent geologic field activities, which, with the aid of this analysis, may be focused on key lithology contacts and problematic localities.

Description: The frequency–wavenumber (fk) and Capon methods are widely used in seismic array studies of background or ambient noise to infer the backazimuth and slowness of microseismic sources. We present an implementation of these techniques for the analysis of microseisms (0.05–2 Hz) which draws on array signal processing literature from a range of disciplines. The presented techniques avoid frequency mixing in the cross-power spectral density and therefore yield an accurate slowness vector estimation of the incoming seismic waves. Using synthetic data, we show explicitly how the frequency averaged broad-band approach can result in a slowness-shifted spectrum. The presented implementation performs the slowness estimations individually for each frequency bin and sums the resulting slowness spectra over a specific frequency range. This may be termed an incoherently averaged signal, or IAS, approach. We further modify the method through diagonal loading to ensure a robust solution. The synthetic data show good agreement between the analytically derived and inferred error in slowness. Results for real (observed) data are compared between the approximate and IAS methods for two different seismic arrays. The IAS method results in the improved resolution of features, particularly for the Capon spectrum, and enables, for instance, Rg and Lg arrivals from similar backazimuths to be separated in the case of real data.

Description: Debate is ongoing as to which tectonic model is most consistent with the known geology of southeast Australia, formerly part of the eastern margin of Gondwana. In particular, numerous tectonic models have been proposed to explain the enigmatic geological relationship between Tasmania and the mainland, which is separated by Bass Strait. One of the primary reasons for the lack of certainty is the limited exposure of basement rocks, which are masked by the sea and thick Mesozoic–Cenozoic sedimentary and volcanic cover sequences. We use ambient noise tomography recorded across Bass Strait to generate a new shear wave velocity model in order to investigate crustal structure. Fundamental mode Rayleigh wave phase velocity dispersion data extracted from long-term cross-correlation of ambient noise data are inverted using a transdimensional, hierarchical, Bayesian inversion scheme to produce phase velocity maps in the period range 2–30 s. Subsequent inversion for depth-dependent shear wave velocity structure across a dense grid of points allows a composite 3-D shear wave velocity model to be produced. Benefits of the transdimensional scheme include a data-driven parametrization that allows the number and distribution of velocity unknowns to vary, and the data noise to also be treated as an unknown in the inversion. The new shear wave velocity model clearly reveals the primary sedimentary basins in Bass Strait as slow shear velocity zones which extend down to 14 km in depth. These failed rift basins, which formed during the early stages of Australia–Antarctica break-up, appear to be overlying thinned crust, where high velocities of 3.8–4.0 km s−1 occur at depths greater than 20 km. Along the northern margin of Bass Strait, our new model is consistent with major tectonic boundaries mapped at the surface. In particular, we identify an east dipping velocity transition zone in the vicinity of the Moyston Fault, a major tectonic boundary between the Lachlan and Delamerian orogens, which are part of the Phanerozoic accretionary terrane that makes up eastern Australia. A pronounced lineament of high shear wave velocities (∼3.7–3.8 km s−1) in the lower crust of our new model may represent the signature of relict intrusive magmatism from failed rifting in the early stages of Australia–Antarctica break-up along a crustal scale discontinuity in the Selwyn Block microcontinent which joins Tasmania and Victoria.

Description: Primary and secondary microseisms are analyzed in this study using a novel matched field processing approach that allows for analysis of features with temporal scales of the order of seconds. The majority of previous microseism research employs time averaging; hence, very little is currently known about the properties of the wavefield on such short timescales. We aim to better understand the nature of the microseismic wavefield through applying our novel matched field processing approach to example data from two seismic arrays (USArray and Rio Grande Rift-Flex Array) in the United States. We find that surface and body wave microseisms on short timescales are observed as pulses of coherent energy, which may be separated in time, embedded in the continuous signal. The pulses display a much larger coherence value in comparison to the commonly employed time averaging approaches, given that they can be separated in time. This allows us to study the spatial correlation of the wavefield and gives an insight into the source and path propagation effects of surface and body waves. We find that the correlation of the short timescale surface wavefield between two stations is dependent on the distance between them and is strongly dependent on their geometric position with respect to the source. Correlations on the PKP wavefield show a decrease with increasing source-station distance and a mild decrease for azimuthally distributed stations at the same source-station distance. Finally, we demonstrate how the pulse wavefield can be used for array calibration purposes.

Description: We extend three-component plane wave beamforming to a more general form and devise a framework, which incorporates velocity heterogeneities of the seismic propagation medium and allows us to estimate accurately sources that do not follow the simple plane wave assumption. This is achieved by utilizing fast marching to track seismic wave fronts for given surface wave phase velocity maps. The resulting matched field processing approach is used to study the surface wave locations of Rayleigh and Love waves at 8 and 16 s based on data from four seismic arrays in the western United States. By accurately accounting for the path propagation effects, we are able to map microseism surface wave source locations more accurately than conventional plane wave beamforming. In the primary microseisms frequency range, Love waves are dominant over Rayleigh waves and display a directional radiation pattern. In the secondary microseisms range, we find the general source regions for both wave types to be similar, but on smaller scales differences are observed. Love waves are found to originate from a larger area than Rayleigh waves and their energy is equal or slightly weaker than Rayleigh waves. The energy ratios are additionally found to be source location dependent. Potential excitation mechanisms are discussed which favor scattering from Rayleigh-to-Love waves. ©2018. American Geophysical Union. All Rights Reserved.