We show that resonances in near‐surface layers (path effects), or a combination of near‐surface resonances and source resonances (source effects), could be responsible for the sharp spectral peaks found in the records of gas‐piston activity and volcanic tremor recorded near Puu Oo crater on Kilauea Volcano, Hawaii. Two types of sources are found to be compatible with observations and can not be distinguished using our data. In one model, impulsive, explosion point sources are embedded in the Puu Oo structure. In the other model, impulsive, point sources of pressure excite the resonances of a fluid‐filled crack embedded in that structure. Both models require shallow sources (z < 100 m) and display a strong dependence of the radiated spectrum on source depth over a depth range comparable to the overall thickness of the surficial layers. Based on these results, it may be possible to track changes in source depth through temporal changes in observed spectra, a potentially useful tool for monitoring volcanic activity at this site. We estimate the depth and spatial extent of volcanic tremor and gas‐piston activity using two dense arrays with respective apertures of 800 and 120 m located near Puu Oo. Measurements of slowness (ray parameter) and azimuth as a function of time clearly indicate that the sources of volcanic tremor and gas‐piston activity are located beneath or in close proximity to the Puu Oo crater at depths shallower than approximately 1 km. Based on slowness and particle motion analyses we find that the records of volcanic tremor and gas‐piston events at Puu Oo consist of a complex combination of body and surface waves.
In this paper we present a method for analyzing seismic signals recorded at an array of seismometers. The method is based on the multiple signal characterization (MUSIC) method. Four important features of this approach are: (1) it has the ability to resolve multiple closely spaced sources, (2) it works with both stationary, and nonstationary signals, (3) it is most sensitive to the strongest sources, and (4) it provides a geometric interpretation of the solution to the direction finding problem. We have developed a numerical algorithm that extends the MUSIC method to nonstationary and correlated signals. We present results using this algorithm to measure directions of arrival and amplitudes of multiple plane waves in a homogeneous medium.
We present an approach for measuring the spatial extent, duration, directions and speeds of rupture propagation during an earthquake using array signal processing techniques. Unlike most previous approaches, no assumptions are made regarding the directions and speeds of propagation. The solution we obtain is unique, and its accuracy, precision and resolution can be estimated. We use preprocessing and coprocessing techniques that improve the accuracy/precision of existing frequency‐wavenumber methods. Subarray spatial averaging modifies frequency‐wavenumber techniques so that they are sensitive to multiple deterministic (transient) as well as stochastic (stationary) signals. This technique is also advantageous because it ensemble averages the cross spectral matrix and reduces the variance of the frequency‐wavenumber spectrum. Seismogram alignment is a preprocessing procedure that accounts for nonplanar wavefronts due to lateral velocity variations beneath an array. This procedure also allows us to study signals of interest using much shorter time windows than has previously been possible. The use of short time windows is important for reducing uncertainty in estimates of arrival times of seismic phases. We tested subarray spatial averaging and seismogram alignment with a variety of frequency‐wavenumber techniques and found that the multiple signal classification (MUSIC) method gave the best resolution of multiple signals. We also note that standard theoretical estimates of uncertainties in peak locations of frequency‐wavenumber spectra are much smaller than those typically observed using seismic arrays and we present a different formula that more accurately describes observed uncertainties. Using synthetic P and S body‐wave seismograms from extended earthquake sources we show that the above array signal processing techniques can be combined with ray theory to obtain accurate estimates of the locations and rupture times of an earthquake's high‐frequency seismic sources. We show how to estimate uncertainties in source locations and rupture times due to limitations of the data, uncertainties in source parameters, and uncertainties in velocity structure. We find that the size of the uncertainties can be very sensitive to a fault's geometry relative to an array and we suggest criteria for optimizing an array's location. We show that high‐frequency source locations and their associated rupture times can be used to estimate an earthquake's spatial extent, duration, directions and speeds of rupture propagation.
We construct and evaluate a new three-dimensional model of crust and upper mantle structure in Western Eurasia and North Africa (WENA) extending to 700 km depth and having 1°p arameterization. The model is compiled in an a priori fashion entirely from existing geophysical literature, specifically, combining two regionalized crustal models with a high-resolution global sediment model and a global upper mantle model. The resulting WENA1.0 model consists of 24 layers: water, three sediment layers, upper, middle, and lower crust, uppermost mantle, and 16 additional upper mantle layers. Each of the layers is specified by its depth, compressional and shear velocity, density, and attenuation (quality factors, Q P and Q S ). The model is tested by comparing the model predictions with geophysical observations including: crustal thickness, surface wave group and phase velocities, upper mantle P n velocities, receiver functions, P-wave travel times, waveform characteristics, regional 1-D velocities, and Bouguer gravity. We find generally good agreement between WENA1.0 model predictions and empirical observations for a wide variety of independent data sets. We believe this model is representative of our current knowledge of crust and upper mantle structure in the WENA region and can successfully be used to model the propagation characteristics of regional seismic waveform data. The WENA1.0 model will continue to evolve as new data are incorporated into future validations and any new deficiencies in the model are identified. Eventually this a priori model will serve as the initial starting model for a multiple data set tomographic inversion for structure of the Eurasian continent.
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