Charlotte Rowe

Accurate regional seismic travel-time (RSTT) predictions rely on regional phases (e.g., Pg, Lg, Pn, Sn) to account for 3D effects in the crust and upper mantle that are not captured by 1D models traditionally used for real-time location. The RSTT prediction model accounts for regional-scale crust and upper mantle structure globally by incorporating regional seismic phases into its travel-time calculations. Previous versions of the RSTT model have used a constant grid cell size of 1°. To improve the tomographic accuracy of recovering velocity structure at regional scales, we perform data-driven grid refinement on the RSTT model down to a 0.125° grid (∼14 km) in pursuit of two main goals: (1) to test the limits of RSTT capability and accuracy of determined velocity structure through variable grid refinement and (2) to image smaller structures in Israel and the Middle East and illuminate upper mantle dynamics operating in this complex tectonic area. We investigate the effects of model ...

The Joint Task Force, Science Monitoring And Reliable Telecommunications (JTF SMART) Subsea Cables, is working to integrate environmental sensors for ocean bottom temperature, pressure, and seismic acceleration into submarine telecommunications cables. The purpose of SMART Cables is to support climate and ocean observation, sea level monitoring, observations of Earth structure, and tsunami and earthquake early warning and disaster risk reduction, including hazard quantification. Recent advances include regional SMART pilot systems that are the first steps to trans-ocean and global implementation. Examples of pilots include: InSEA wet demonstration project off Sicily at the European Multidisciplinary Seafloor and water column Observatory Western Ionian Facility; New Caledonia and Vanuatu; French Polynesia Natitua South system connecting Tahiti to Tubaui to the south; Indonesia starting with short pilot systems working toward systems for the Sumatra-Java megathrust zone; and the CAM-2...

Using the waveform data for Mount St. Helens from October 2004 through April, 2005 available from the IRIS DMC, as well as a special data set including the accelerometer that recorded eleven days of activity on the whaleback dome of St. Helens during February, 2005, we have modified a waveform cross-correlation algorithm previously applied for event clustering and repicking into a correlation scanning detector. This tool is being developed for implementation during routine volcano monitoring, as a means of identifying, characterizing and locating repeating swarm events and quantifying their seismic energy release. Application of the scanning detector to St. Helens data reveals stable swarm-type activity over periods with cross-correlation values exceeding 0.8 for 25 days, within which the repeating events slowly evolve over time. Waveforms show high correlation when as much as 60 s of coda is included in the correlation, suggesting very stable source and path characteristics. We pre...

We explore the success rates of detection and classification algorithms as applied to seismic signals from active volcanoes. The subspace detection method has shown some success in identifying repeating (but not identical) signals from seismic swarm sources, as well as pulling out nonvolcanic long period events within subduction zone tremor. We continue the exploration of this technique as applied to both discrete events and variations within volcanic tremor to determine optimal situations for its use. We will demonstrate both three-dimensional and subband applications both on raw waveforms and derived features such as skewness and kurtosis. The application can be used in both a supervised (select templates and compare) as well as unsupervised (cross-compare all samples and apply clustering to the matrix of comparisons). We compare the method to that of the KKAnalysis tool, which uses a self-organizing map approach to unsupervised clustering for feature vectors derived from the seis...

Seismicity in central New Mexico, southwestern United States, is dominated by earthquakes occurring above the mid-crustal Socorro Magma Body (SMB). The SMB is a sill-like feature >= 3400 km2 in area, with a top surface at 19-km depth spanning the inner Rio Grande rift half-graben system. Inflation of the magma body at rates of several mm/year, perhaps coupled with shallow

ABSTRACT We analyze event archives and continuous waveform data recorded by the Cooperative New Madrid Seismic Network from 1995 to 2008 in conjunction with waveform cross-correlation techniques to investigate the spatiotemporal distribution of small-magnitude (M-D < 2.4) earthquakes in the New Madrid Seismic Zone (NMSZ). The resulting clusters are divided into two major groups based on the interevent time period: (1) swarm clusters, in which the number of highly similar events recorded in a day is more than the seismic zone maximum daily rate (similar to 3 events/day) and (2) repeating earthquakes clusters, which consist of highly similar events separated by longer time periods. Most swarm clusters occur near Ridgely, Tennessee, and this 4-km x 2-km x 2-km elongated source zone produces swarms every 1-3 years that contain large numbers of strikingly similar events. Other swarms and repeating earthquake clusters occur at proposed fault intersections in the crystalline basement or along strong velocity contrasts. Focal mechanism solutions for NMSZ clusters are consistent with previously reported solutions for each major fault. We suggest that anomalously high pore-fluid pressure, inferred from artesian wells, porous intrusions, and faulted, fractured crustal rocks, is the most likely cause of swarm activity. Repeating earthquake ruptures are interpreted as reactivation of small asperities.

ABSTRACT To test the hypothesis that high quality 3D Earth models will produce seismic event locations that are more accurate and more precise than currently used 1D and 2/2.5D models, we are developing a global 3D P wave velocity model of the Earth's crust and mantle using seismic tomography. In this paper, we present the most recent version of our model, SALSA3D (SAndia LoS Alamos 3D) version 1.7, and demonstrate its ability to reduce mislocations for a large set of realizations derived from a carefully chosen set of globally-distributed ground truth (GT) events, compared to existing models and/or systems. Our model is derived from the latest version of the GT catalog of P and Pn travel time picks assembled by Los Alamos National Laboratory. To prevent over-weighting due to ray path redundancy and to reduce the computational burden, we cluster rays to produce representative rays. Reduction in the total number of ray paths is ~50%. The model is represented using the triangular tessellation system described by Ballard et al. (2009), which incorporates variable resolution in both the geographic and radial dimensions. For our starting model, we use a simplified layer crustal model derived from the NNSA Unified model in Eurasia and Crust 2.0 model elsewhere, over a uniform ak135 mantle. Sufficient damping is used to reduce velocity adjustments so that ray path changes between iterations are small. We obtain proper model smoothness by using progressive grid refinement, refining the grid only in areas where the data warrant it. In previous versions of SALSA3D, we based this refinement on velocity changes from previous model iterations. For version 1.7, we utilize the diagonal of the model resolution matrix to control where grid refinement occurs, resulting in more consistent and continuous areas of refinement than before. Our approach produces a smooth, multi-resolution model with node density appropriate to both ray coverage and the velocity gradients required by the data. We compare the travel-time prediction and location capabilities of SALSA3D to standard 1D and 2/2.5D models via location tests on a global event set with GT of 5 km or better. These events generally possess hundreds of Pn and P picks from which we generate different realizations of station distributions, yielding a range of azimuthal coverage and ratios of teleseismic to regional arrivals, with which we test the robustness and quality of relocation. The SALSA3D model reduces mislocation over the standard 1D ak135 model regardless of Pn to P ratio, with improvement most pronounced at higher azimuthal gaps. We currently are testing the use of the full model covariance matrix to produce realistic path-dependent travel time uncertainty during location tests, replacing the standard, distance-dependent, path-independent uncertainty typically used in location algorithms.

Abstract: This project develops a model and methods for routine computation of regional travel times for crustal events anywhere on the globe. To improve on existing methods, the travel time calculations must capture the effect of the three-dimensional (3D) earth, yet the ...

The availability of seismic bulletin sources presents a problem when preparing data sets for studies. With so many choices, which catalog should be used? We have developed a method of merging data from all available seismic bulletins into a single database of non-redundant phases for each event. With this new database, additional ground truth (GT) events are readily identified due to the merging of all possible arrivals for each event. The compilation of over 8500 GT25 or better events in Asia allows the generation of large-scale travel time correction surfaces. We have created Pg, Pn, P, Sg/Lg, Sn, and S surfaces for the 1382 current and historic stations that detected a GT event. The availability of correction surfaces for any and all stations in a large region permit relocations that result in greater accuracy and increased event clustering for entire seismic catalogs. We have adapted a retroactive cross-correlation pick adjustment algorithm to function as a cross-correlation sca...

An eruption of the Soufrière Hills Volcano (SHV) on the eastern Caribbean island of Montserrat began on 18 July 1995 and continued until February 2010. Within nine days of the eruption onset, an existing four-station analog seismic network (ASN) was expanded to 10 sites. Telemetered data from this network were recorded, processed, and archived locally using a system developed by scientists from the U.S. Geological Survey (USGS) Volcano Disaster Assistance Program (VDAP). In October 1996, a digital seismic network (DSN) was deployed with the ability to capture larger amplitude signals across a broader frequency range. These two networks operated in parallel until December 2004, with separate telemetry and acquisition systems (analysis systems were merged in March 2001). Although the DSN provided better quality data for research, the ASN featured superior real-time monitoring tools and captured valuable data including the only seismic data from the first 15 months of the eruption. The...

The problem of signal detection and recognition is an ongoing research topic for seismic monitoring for a range of sources, from anthropogenic signals and volcanic eruption swarms to aftershock sequences and subduction zone tremor. Waveform cross-correlation has proven successful in identifying mine blasts, volcanic dome growth events, stationary seismic swarms. The subspace detection method has been applied successfully to microearthquake

Automatic detection and classification methods are increasingly important in observatory operations, as the volume and rate of incoming data exceed the capacity of human analysis staff to process the data in near-real-time. We explore the success of scanning detection for similar event identification in a variety of seismic waveform catalogs. Several waveform pre-processing methods are applied to previously recorded events which are scanned through triggered and continuous waveform catalogs to determine the success and false alarm rate for detections of repeating signals. Pre-processing approaches include adaptive, cross-coherency filtering, adaptive, auto-associative neural network filtering, discrete wavelet package decomposition and linear predictive coding as well as suites of standard bandpass filters. Classification / detection methods for the various pre-processed signals are applied to investigate the robustness of the individual and combined approaches. The classifiers as a...