Understanding the amplitudes of noise correlation measurements

Cross correlation of ambient seismic noise is known to result in time series from which station-station travel-time measurements can be made. Part of the reason that these cross-correlation travel-time measurements are reliable is that there exists a theoretical framework that quantifies how these travel times depend on the features of the ambient noise. However, corresponding theoretical results do not currently exist to describe how the amplitudes of the cross correlation depend on such features. For example, currently it is not possible to take a given distribution of noise sources and calculate the cross correlation amplitudes one would expect from such a distribution. Here, we provide a ray-theoretical framework for calculating cross correlations. This framework differs from previous work in that it explicitly accounts for attenuation as well as the spatial distribution of sources and therefore can address the issue of quantifying amplitudes in noise correlation measurements. After introducing the general framework, we apply it to two specific problems. First, we show that we can quantify the amplitudes of coherency measurements, and find that the decay of coherency with station-station spacing depends crucially on the distribution of noise sources. We suggest that researchers interested in performing attenuation measurements from noise coherency should first determine how the dominant sources of noise are distributed. Second, we show that we can quantify the signal-to-noise ratio of noise correlations more precisely than previous work, and that these signal-to-noise ratios can be estimated for given situations prior to the deployment of seismometers. It is expected that there are applications of the theoretical framework beyond the two specific cases considered, but these applications await future work

Quantifying the influence of sea ice on ocean microseism using observations from the Bering Sea, Alaska

Microseism is potentially affected by all processes that alter ocean wave heights. Because strong sea ice prevents large ocean waves from forming, sea ice can therefore significantly affect microseism amplitudes. Here we show that this link between sea ice and microseism is not only a robust one but can be quantified. In particular, we show that 75–90% of the variability in microseism power in the Bering Sea can be predicted using a fairly crude model of microseism damping by sea ice. The success of this simple parameterization suggests that an even stronger link can be established between the mechanical strength of sea ice and microseism power, and that microseism can eventually be used to monitor the strength of sea ice, a quantity that is not as easily observed through other means

A model for seasonal changes in GPS positions and seismic wave speeds due to thermoelastic and hydrologic variations

It is known that GPS time series contain a seasonal variation that is not due to tectonic motions, and it has recently been shown that crustal seismic velocities may also vary seasonally. In order to explain these changes, a number of hypotheses have been given, among which thermoelastic and hydrology-induced stresses and strains are leading candidates. Unfortunately, though, since a general framework does not exist for understanding such seasonal variations, it is currently not possible to quickly evaluate the plausibility of these hypotheses. To fill this gap in the literature, I generalize a two-dimensional thermoelastic strain model to provide an analytic solution for the displacements and wave speed changes due to either thermoelastic stresses or hydrologic loading, which consists of poroelastic stresses and purely elastic stresses. The thermoelastic model assumes a periodic surface temperature, and the hydrologic models similarly assume a periodic near-surface water load. Since all three models are two-dimensional and periodic, they are expected to only approximate any realistic scenario; but the models nonetheless provide a quantitative framework for estimating the effects of thermoelastic and hydrologic variations. Quantitative comparison between the models and observations is further complicated by the large uncertainty in some of the relevant parameters. Despite this uncertainty, though, I find that maximum realistic thermoelastic effects are unlikely to explain a large fraction of the observed annual variation in a typical GPS displacement time series or of the observed annual variations in seismic wave speeds in southern California. Hydrologic loading, on the other hand, may be able to explain a larger fraction of both the annual variations in displacements and seismic wave speeds. Neither model is likely to explain all of the seismic wave speed variations inferred from observations. However, more definitive conclusions cannot be made until the model parameters are better constrained

On establishing the accuracy of noise tomography travel-time measurements in a realistic medium

It has previously been shown that the Green's function between two receivers can be retrieved by cross-correlating time series of noise recorded at the two receivers. This property has been derived assuming that the energy in normal modes is uncorrelated and perfectly equipartitioned, or that the distribution of noise sources is uniform in space and the waves measured satisfy a high frequency approximation. Although a number of authors have successfully extracted travel-time information from seismic surface-wave noise, the reason for this success of noise tomography remains unclear since the assumptions inherent in previous derivations do not hold for dispersive surface waves on the Earth. Here, we present a simple ray-theory derivation that facilitates an understanding of how cross correlations of seismic noise can be used to make direct travel-time measurements, even if the conditions assumed by previous derivations do not hold. Our new framework allows us to verify that cross-correlation measurements of isotropic surface-wave noise give results in accord with ray-theory expectations, but that if noise sources have an anisotropic distribution or if the velocity structure is non-uniform then significant differences can sometimes exist. We quantify the degree to which the sensitivity kernel is different from the geometric ray and find, for example, that the kernel width is period-dependent and that the kernel generally has non-zero sensitivity away from the geometric ray, even within our ray theoretical framework. These differences lead to usually small (but sometimes large) biases in models of seismic-wave speed and we show how our theoretical framework can be used to calculate the appropriate corrections. Even when these corrections are small, calculating the errors within a theoretical framework would alleviate fears traditional seismologists may have regarding the robustness of seismic noise tomography

The relationship between noise correlation and the Green's function in the presence of degeneracy and the absence of equipartition

Recent derivations have shown that when noise in a physical system has its energy equipartitioned into the modes of the system, there is a convenient relationship between the cross correlation of time-series recorded at two points and the Green's function of the system. Here, we show that even when energy is not fully equipartitioned and modes are allowed to be degenerate, a similar (though less general) property holds for equations with wave equation structure. This property can be used to understand why certain seismic noise correlation measurements are successful despite known degeneracy and lack of equipartition on the Earth

Joint inversion of Rayleigh wave phase velocity and ellipticity using USArray: Constraining velocity and density structure in the upper crust

Rayleigh wave ellipticity, or H/V ratio, observed on the surface is particularly sensitive to shallow earth structure. In this study, we jointly invert measurements of Rayleigh wave H/V ratio and phase velocity between 24–100 and 8–100 sec period, respectively, for crust and upper mantle structure beneath more than 1000 USArray stations covering the western United States. Upper crustal structure, in particular, is better constrained by the joint inversion compared to inversions based on phase velocities alone. In addition to imaging Vs structure, we show that the joint inversion can be used to constrain Vp/Vs and density in the upper crust. New images of uppermost crustal structure (<3 km depth) are in excellent agreement with known surface features, with pronounced low Vs, low density, and high Vp/Vs anomalies imaged in the locations of several major sedimentary basins including the Williston, Powder River, Green River, Denver, and San Juan basins. These results demonstrate not only the consistency of broadband H/V ratios and phase velocity measurements, but also that their complementary sensitivities have the potential to resolve density and Vp/Vs variations

Constraints on the long-period moment-dip tradeoff for the Tohoku earthquake

Since the work of Kanamori and Given (1981), it has been recognized that shallow, pure dip-slip earthquakes excite long-period surface waves such that it is difficult to independently constrain the moment (M_0) and the dip (δ) of the source mechanism, with only the product M_0 sin(2δ) being well constrained. Because of this, it is often assumed that the primary discrepancies between the moments of shallow, thrust earthquakes are due to this moment-dip tradeoff. In this work, we quantify how severe this moment-dip tradeoff is depending on the depth of the earthquake, the station distribution, the closeness of the mechanism to pure dip-slip, and the quality of the data. We find that both long-period Rayleigh and Love wave modes have moment-dip resolving power even for shallow events, especially when stations are close to certain azimuths with respect to mechanism strike and when source depth is well determined. We apply these results to USGS W phase inversions of the recent M9.0 Tohoku, Japan earthquake and estimate the likely uncertainties in dip and moment associated with the moment- dip tradeoff. After discussing some of the important sources of moment and dip error, we suggest two methods for potentially improving this uncertainty