In this paper we outline a procedure we use for routine moment‐tensor analysis of regional data from broadband seismic stations in northwestern North America and apply it to the moment magnitude 5.5, March, 1993, Scotts Mills, Oregon, earthquake. The results compare favorably with those obtained from teleseismic data. We found that the earthquake occurred at a depth of 13–15 km and had a mechanism with approximately equal amounts of reverse and right‐lateral strike‐slip components. The estimated stress drop of 40 bar is average on a world‐wide basis, supporting the view that the rather large damage was caused primarily by poor construction and not by exceptional properties of the source. The Scotts Mills earthquake is most likely related to the Mt. Angel Fault. This fault is a part of the Gales Creek‐Mt. Angel structural lineament (GCMAL) extending about 150 km across the Willamette Valley. At present data are not sufficient to estimate the likelihood of an earthquake involving the entire GCMAL, but given its length an earthquake of magnitude 7 is conceivable. The results of this study, together with investigations of other earthquakes, suggest that sparse broadband networks can be used efficiently for determining source parameters of earthquakes of magnitude greater than 4.0 in regions with infrequent seismicity.
Subsurface rock properties are manifested in seismic records as variations in traveltimes, amplitudes, and waveforms. It is commonly acknowledged that traveltimes are sensitive to the long wavelength part of the velocity, whereas amplitudes are sensitive to the short wavelength part of the velocity. The inherent sensitivity of seismic velocity at different wavelengths suggests an approach that decomposes the waveform data into traveltime and amplitude components. Therefore we propose a divide‐and‐conquer approach to the elastic waveform inversion problem. We first estimate the smoothly varying background velocity from the traveltime and the rapidly changing perturbations from the amplitude by amplitude variation with offset (AVO) inversion based on linearized reflection coefficient. Then we combine the perturbation with the background to obtain a starting model to be used in the final waveform inversion that models all converted waves and internal multiples assuming a 1-D earth model. For estimating the background velocity, we use the flatness of events as the objective criterion, and simulated annealing as a search tool. Three different model parameterization schemes (constant velocity blocks, splines, and arctangent models) are compared, with the arctangent having the most flexibility and least artifacts. Having obtained the background velocities, we analyze the AVO effects to estimate the perturbations to the background, for which we use a linearized inversion method. The combination of the perturbation and background should be sufficiently close to the true model so that the inverse problem becomes quasi‐linear. A full elastic waveform inversion is used to fine‐tune the combined model to obtain P-wave and S-wave velocity and density, again using either a nonlinear optimization method or an iterative linearized solution. Application of the inversion algorithm to synthetic data from an 84-layer model was able to predict the full reflectivity data and recover the true model parameters. Application to one seismic line in the Carolina Trough area found a thin gas zone which produces strong Bottom Simulating Reflectors (BSRs).
In the fall of 2005, BP commissioned Fairfield Industries to conduct a large, wide-azimuth survey over Atlantis Field in deepwater Gulf of Mexico. The purpose of the survey was wide-azimuth, subsalt imaging using P-waves. The seafloor topography is complex with scoured furrows at the base of the escarpment. A shallow salt body covers a large part of the survey area and rafts the seafloor at the escarpment, leading to very strong velocity contrasts. The water depth in the area varies from approximately 1300 m above the Sigsbee escarpment to 2200 m below it.The survey used 902 four-component (4-C) ocean-bottom nodes, which BP commissioned Fairfield to build. The nodes were placed on the seafloor using a remotely operated vehicle. A dual hydroacoustic-aided inertial navigation (HAIN) system provided a positioning accuracy of approximately 0.5% of water depth (about 11 m in this case). The node spacing was approximately 426 m in both the x and y directions, using a hexagonal grid and covered an area of about 250 km 2 .Once placed on the seafloor the nodes were completely autonomous. The battery life and memory capacity were sufficient for about 28 days of continuous recording at a 2-ms sample rate. The geophones were fixed (nongimballed) and orthogonal, and the nodes were placed as much as possible on horizontal seafloor, such that one component was approximately vertical.The shot coverage was also on a hexagonal grid with approximately 50-m spacing over an area of about 730 km 2 . The survey was acquired in two overlapping patches, with 1628 node placements in total. The time between shots was 11.2 s, with a 12-s trace length, although the nodes actually recorded continuously.The acquisition design and operations are described in much more detail in the accompanying article by Beaudoin and Ross. This article will focus on the processing of the seismic data set for P-wave imaging. The processing of the data to date has all been performed on common receiver gathers. Each gather is well sampled in both the x and y directions and contains about 160 000 traces per component. The processing flow was classical-node positioning, quality control, noise removal, geophone orientation, PZ summation and difference, source-side multiple attenuation, and common receiver wave-equation depth migration.The overall positioning accuracy of the nodes was very good (Figure 1). Using the direct arrival to invert for the node locations, we were able to confirm the positions derived from the HAIN system to within 10 m for 95% of the nodes, and to within 20 m for more than 99%. About a third of the nodes had a bulk time shift due to a clock synchronization error, but this was easily detected and corrected. Furthermore, careful analysis of maps of the residual traveltime misfits (Figure 2) showed that the traveltime inversion was sufficiently sensitive to allow us to detect a small discrepancy in the shot locations that correlated with the direction of travel of the source boat. We think that this was due to a difference in the geometric cent...
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