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Spectral induced polarization (SIP), describing the measurement of the frequency domain electrical impedance magnitude and phase of porous materials, has been widely used to characterize subsurface hydrological/biogeochemical properties and processes. SIP data collected at frequencies higher than 100 Hz are expected to describe the polarization of small particles providing insights into the physicochemical properties of clays, nanoparticles and microorganisms. However, the phase measurements at these high frequencies are often contaminated by errors due to the parasitic capacitive coupling of the SIP instrument, especially for lower conductivity samples. We developed a model showing the measured phase is the sum of the true sample phase and an error term ωC in Z x, where ω is the angular frequency; C in is the instrument input capacitance and Z x is a measurable impedance function related to the sample holder properties and the reference resistor. Based on this model, a new phase correction method is proposed that results in highly accurate SIP data up to 20 kHz as well as the determination of C in. We tested the correction method using electric circuits, NaCl fluids and three unconsolidated samples (sand, sand-clay and sand-pyrite mixtures). The corrected phase for the circuit and NaCl fluid experiments showed excellent agreement with the theoretical phase response across the studied frequency range (errors <1 mrad). For unconsolidated samples, removal of errors results in phase spectra more consistent with expected polarization mechanisms, as based on phase peaks recorded for small pyrite and clay particles at high frequencies. These phase peaks could not be identified in the uncorrected data. Our approach can substantially enhance the value of the SIP method for the characterization of fine-grained sediments and rocks. [ABSTRACT FROM AUTHOR]

We introduce a new approach for locating earthquakes using arrival times derived from waveforms. The most costly computational step of the algorithm scales as the number of stations in the active seismographic network. In this approach, a variation on existing grid search methods, a series of full waveform simulations are conducted for all receiver locations, with sources positioned successively at each station. The traveltime field over the region of interest is calculated by applying a phase picking algorithm to the numerical wavefields produced from each simulation. An event is located by subtracting the stored traveltime field from the arrival time at each station. This provides a shifted and time-reversed traveltime field for each station. The shifted and time-reversed fields all approach the origin time of the event at the source location. The mean or median value at the source location thus approximates the event origin time. Measures of dispersion about this mean or median time at each grid point, such as the sample standard error and the average deviation, are minimized at the correct source position. Uncertainty in the event position is provided by the contours of standard error defined over the grid. An application of this technique to a synthetic data set indicates that the approach provides stable locations even when the traveltimes are contaminated by additive random noise containing a significant number of outliers and velocity model errors. It is found that the waveform-based method out-performs one based upon the eikonal equation for a velocity model with rapid spatial variations in properties due to layering. A comparison with conventional location algorithms in both a laboratory and field setting demonstrates that the technique performs at least as well as existing techniques. [ABSTRACT FROM AUTHOR]

We present a new approach to reduce a sparse, linear system of equations associated with tomographic inverse problems. We begin by making a modification to the commonly used compressed sparse-row format, whereby our format is tailored to the sparse structure of finite-frequency (volume) sensitivity kernels in seismic tomography. Next, we cluster the sparse matrix rows to divide a large matrix into smaller subsets representing ray paths that are geographically close. Singular value decomposition of each subset allows us to project the data onto a subspace associated with the largest eigenvalues of the subset. After projection we reject those data that have a signal-to-noise ratio (SNR) below a chosen threshold. Clustering in this way assures that the sparse nature of the system is minimally affected by the projection. Moreover, our approach allows for a precise estimation of the noise affecting the data while also giving us the ability to identify outliers. We illustrate the method by reducing large matrices computed for global tomographic systems with cross-correlation body wave delays, as well as with surface wave phase velocity anomalies. For a massive matrix computed for 3.7 million Rayleigh wave phase velocity measurements, imposing a threshold of 1 for the SNR, we condensed the matrix size from 1103 to 63 Gbyte. For a global data set of multiple-frequency P wave delays from 60 well-distributed deep earthquakes we obtain a reduction to 5.9 per cent. This type of reduction allows one to avoid loss of information due to underparametrizing models. Alternatively, if data have to be rejected to fit the system into computer memory, it assures that the most important data are preserved. [ABSTRACT FROM PUBLISHER]