We evaluate the efficacy of crustal scale receiver function imaging in the presence of crustal scattering. First, we show that the resolution of an image is not appreciably affected by geologically realistic crustal scattering. Rather, we show that the image contrast is reduced: the image of actual targets get 'buried' in the noise created by arrivals arising from crustal scattering. Then, we construct three classes of models in which we simulate a flat Moho, a Moho with topography, and a subduction zone. For all the models, we simulate the Moho as a finite-thickness velocity transition zone and the crust with a three-layer heterogeneous zone that contains binary von-Karman statistics with varying degrees of rms density and velocity contrasts. We use finite-difference simulations to generate a suite of varying-slowness seismograms, which we then image with pre-stack 2-D Kirchhoff migration and CCP stacking. We find that for a small degree of rms density/velocity contrasts, both imaging methodologies faithfully image the models, but for rms density/velocity variations greater than approximately 5 per cent per cent, we observed distortion and/or artefacts to the images. For migration, the distortion takes the form of amplitude variations for horizontal model features, whereas for the CCP imaging, the main affect is the enhancement of any horizontal feature in the model.
Wave gradiometry relates the spatial gradients of a wavefield to its velocity and radiation patterns through two spatial coefficients for any dimension. One coefficient gives the slowness of the wave in any given dimension, and the other coefficient gives the change in amplitude as a function of position and direction along the wavefront. In this paper, we develop the mathematical foundations for scalar wave gradiometry in three dimensions, building on previous work in 1D and 2D wave gradiometry. We validate our method by synthetic tests and find that our method can accurately estimate wave direction and speed. Estimating spatial amplitude changes is not as robust, however. Numerical tests indicate that the wave gradiometry method is highly sensitive to uncorrelated noise in the data as well as the presence of interfering waves.
ABSTRACT We present a new method to discriminate between earthquakes and buried explosions using observed seismic data. The method is different from previous seismic discrimination algorithms in two main ways. First, we use seismic spatial gradients, as well as the wave attributes estimated from them (referred to as gradiometric attributes), rather than the conventional three-component seismograms recorded on a distributed array. The primary advantage of this is that a gradiometer is only a fraction of a wavelength in aperture compared with a conventional seismic array or network. Second, we use the gradiometric attributes as input data into a machine learning algorithm. The resulting discrimination algorithm uses the norms of truncated principal components obtained from the gradiometric data to distinguish the two classes of seismic events. Using high-fidelity synthetic data, we show that the data and gradiometric attributes recorded by a single seismic gradiometer performs as well as a conventional distributed array at the event type discrimination task.
Resolving the time dependent terms in the seismic moment tensor provides important informa- tion that can be used to interpret the source process of an explosion, including the separation of isotropic explosion terms from shear forces and potentially isolated force couples. In this report, we detail our method of inverting three component seismic data for the seismic moment tensor. We review possible seismic source models from the simplest isotropic explosion type source to those incorporating the six independent moment tensor terms. The inversion we describe is formulated in the frequency domain, and results in estimates of time dependent moment tensor components. The inversion relies on an accurate estimate of the Green's functions of the Earth. However, given the complexity of the Earth, we explore the effects of inaccuracies in the presumed Earth model used to estimate the Green's functions needed for the inversion. Specifically, we explore the effects of stochastic variations in the Earth models on the inversion results. These tests are syn- thetic throughout, and show that adding stochastic density/velocity heterogeneity in the presumed Earth model results in reduced amplitude seismic moment tensor estimates, as well as degrading the data misfit. We suggest two mitigation strategies. First, produce a suite of Green's functions using different realizations of the stochastic field within the Earth Model. Secondly, perform the in- version in the power spectral domain, eliminating all phase information. Finally, we analyze actual seismic data collected in winter 2017/2018. The seismic data was collected at in active geothermal well site outside of Winnimucca, NV, and was produced during well stimulation operations. In general, the inversion results were poor, with a high degree of data misfit. We hypothesize that the poor results are a function of a poorly constrained Earth model as well as noisy, high-frequency data being used in the inversion.
ABSTRACT As a part of the series of Source Physics Experiments (SPE) conducted on the Nevada National Security Site in southern Nevada, we have developed a local-to-regional scale seismic velocity model of the site and surrounding area. Accurate earth models are critical for modeling sources like the SPE to investigate the role of earth structure on the propagation and scattering of seismic waves. We combine seismic body waves, surface waves, and gravity data in a joint inversion procedure to solve for the optimal 3D seismic compressional and shear-wave velocity structures and earthquake locations subject to model smoothness constraints. Earthquakes, which are relocated as part of the inversion, provide P- and S-body-wave absolute and differential travel times. Active source experiments in the region augment this dataset with P-body-wave absolute times and surface-wave dispersion data. Dense ground-based gravity observations and surface-wave dispersion derived from ambient noise in the region fill in many areas where body-wave data are sparse. In general, the top 1–2 km of the surface is relatively poorly sampled by the body waves alone. However, the addition of gravity and surface waves to the body-wave dataset greatly enhances structural resolvability in the near surface. We discuss the methodology we developed for simultaneous inversion of these disparate data types and briefly describe results of the inversion in the context of previous work in the region.
