Lind S. Gee

THE CAPE MENDOCINO, CALIFORNIA, EARTHQUAKES OF APRIL 1992 : SUBDUCTION AT THE TRIPLE JUNCTION

The 25 April 1992 magnitude 7.1 Cape Mendocino thrust earthquake demonstrated that the North America—Gorda plate boundary is seismogenic and illustrated hazards that could result from much larger earthquakes forecast for the Cascadia region. The shock occurred just north of the Mendocino Triple Junction and caused strong ground motion and moderate damage in the immediate area. Rupture initiated onshore at a depth of 10.5 kilometers and propagated up-dip and seaward. Slip on steep faults in the Gorda plate generated two magnitude 6.6 aftershocks on 26 April. The main shock did not produce surface rupture on land but caused coastal uplift and a tsunami. The emerging picture of seismicity and faulting at the triple junction suggests that the region is likely to continue experiencing significant seismicity.

The Dependence of PGA and PGV on Distance and Magnitude Inferred from Northern California ShakeMap Data

We analyze peak ground velocity (PGV) and peak ground acceleration (PGA) data from 95 moderate (3.5 ≤ M r > 100 km, the peak motions attenuate more rapidly than a simple power law (that is, r -γ ) can fit. Instead, we use an attenuation function that combines a fixed power law ( r -0.7 ) with a fitted exponential dependence on distance, which is estimated as exp(-0.0063 r ) and exp(-0.0073 r ) for PGV and PGA, respectively, for moderate earthquakes. We regress log(PGV) and log(PGA) as functions of distance and magnitude. We assume that the scaling of log(PGV) and log(PGA) with magnitude can differ for moderate and large earthquakes, but must be continuous. Because the frequencies that carry PGV and PGA can vary with earthquake size for large earthquakes, the regression for large earthquakes incorporates a magnitude dependence in the exponential attenuation function. We fix the scaling break between moderate and large earthquakes at M 5.5; log(PGV) and log(PGA) scale as 1.06M and 1.00M, respectively, for moderate earthquakes and 0.58M and 0.31M for large earthquakes.

Frequency-Dependent Seismic Attenuation in the Eastern United States as Observed from the 2011 Central Virginia Earthquake and Aftershock Sequence

Abstract Ground shaking due to earthquakes in the eastern United States (EUS) is felt at significantly greater distances than in the western United States (WUS) and for some earthquakes it has been shown to display a strong preferential direction. Shaking intensity variation can be due to propagation path effects, source directivity, and/or site amplification. In this paper, we use S and Lg waves recorded from the 2011 central Virginia earthquake and aftershock sequence, in the Central Virginia Seismic Zone, to quantify attenuation as frequency‐dependent Q ( f ). In support of observations based on shaking intensity, we observe high Q values in the EUS relative to previous studies in the WUS with especially efficient propagation along the structural trend of the Appalachian mountains. Our analysis of Q ( f ) quantifies the path effects of the northeast‐trending felt distribution previously inferred from the U.S. Geological Survey (USGS) “Did You Feel It” data, historic intensity data, and the asymmetrical distribution of rockfalls and landslides.

77 - The Rapid Earthquake Data Integration Project

This chapter focuses on the rapid earthquake data integration project. Interest in rapid access to earthquake information has grown enormously in the last few years. In addition to satisfying inquiries from the public and the media, rapid notification programs provide valuable information for earthquake disaster response. Recognizing the importance of this information for seismic hazard mitigation, efforts to design and implement systems to provide earthquake parameters in a timely manner have expanded over the last 10 years at both the regional and the national level. Similar to most automated earthquake processing systems, rapid earthquake data integration (REDI) operations can be divided into two major elements: event identification and event processing. The event identification element includes operations such as phase picking, event association, and event selection. The event processing element is separated into several stages, with each earthquake assessed for a particular type of processing based on its location and size. The Rapid Earthquake Data Integration System has been developed for the automated estimation of earthquake parameters using data from a sparse, broadband network. The system is designed on the basis of a staged hierarchy of processing, with the goal of providing control of the type and number of processes running at any time. The current processing capabilities include the determination of local and energy magnitude, peak ground motions, and the seismic moment tensor.

Seismometer Self-Noise and Measuring Methods

Seismometer self-noise is usually not considered when selecting and using seismic waveform data in scientific research as it is typically assumed that the self-noise is negligibly small compared to seismic signals. However, instrumental noise is part of the noise in any seismic record, and in particular, at frequencies below a few mHz, the instrumental noise has a frequency-dependent character and may dominate the noise. When seismic noise itself is considered as a carrier of information, as in seismic interferometry (e.g., Chaput et al. 2012), it becomes extremely important to estimate the contribution of instrumental noise to the recordings. Noise in seismic recordings, commonly called seismic background noise or ambient Earth noise, usually refers to the sum of the individual noise sources in a seismic recording in the absence of any earthquake signal. Site noise (e.g., cultural sources, nearby tilt signals, etc.) and noise introduced by the sensitivity of an instrument to non-seismic signals (e.g., temperature and pressure variations, magnetic field changes, etc.) both contribute to the ambient seismic noise levels. The background noise ultimately defines a lower limit for the ability to detect and characterize various seismic signals of interest. Background noise levels have also been found to introduce a systematic bias in arrival times because the amplitude of the seismic phase must rise above the station’s noise levels (Rӧhm et al. 1999). The upper limit of useful signals is governed by the clip level of the recording system (the point at which a recording system’s output is no longer a linearly time-invariant representation of the input). Site noise can be reduced by careful site selection (e.g., hard rock far from strong noise sources) and by emplacing instruments in good vaults or boreholes. It is also possible to reduce sensitivity to non-seismic signals by thermal insulation and appropriate shielding such as pressure chambers (Hanka 2000). At quiet sites with well-installed instrumentation, instrument noise may be the dominant noise source (Berger et al. 2004); this is especially true for long-period seismic data (>100 s period) on very broadband instruments (e.g., Streckeisen STS-1 seismometer). The interpretation of such data only makes sense if the instrumental noise level is known. Also, research on noise levels in seismic recordings, the effect of noise reduction by the installation technique, and the nature and contribution of different noise sources to the recordings require knowledge of instrumental self-noise to rule out bias from the instrumentation self-noise.

Obtaining Changes in Calibration‐Coil to Seismometer Output Constants Using Sine Waves

The midband sensitivity of a broadband seismometer is one of the most commonly used parameters from station metadata. Thus, it is critical for station operators to robustly estimate this quantity with a high degree of accuracy. We develop an in situ method for estimating changes in sensitivity using sine‐wave calibrations, assuming the calibration coil and its drive are stable over time and temperature. This approach has been used in the past for passive instruments (e.g., geophones) but has not been applied, to our knowledge, to derive sensitivities of modern force‐feedback broadband seismometers. We are able to detect changes in sensitivity to well within 1%, and our method is capable of detecting these sensitivity changes using any frequency of sine calibration within the passband of the instrument.