Changes in frequency–size relationship from small to large earthquakes

Pacheco, J. F.; Scholz, Christopher H.; Sykes, Lynn R.

doi:10.1038/355071a0

Cited by 381 publications

(210 citation statements)

References 14 publications

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“…The lithosphere consists of two layers: the seismogenic upper layer, or schizosphere, characterized by elastobrittle deformation, and the aseismic lower layer, or plastosphere (Scholz, 1982(Scholz, , 1990, in which ductile deformation is predominant. The seismogenic layer along the San Andreas Fault Zone in California, for example, features an average thickness of about 15 km (Scholz, 1990;Pacheco et al, 1992). The results obtained in this study indicate that the seismogenic layer in central-western China reaches, on average, a depth of about 20 km.…”

Section: Discussionsupporting

confidence: 53%

Double-difference relocation of earthquakes in central-western China, 1992–1999

et al. 2005

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The double-difference earthquake location algorithm was applied to the relocation of 10,057 earthquakes that occurred in central-western China (21 • N to 36 • N, 98 • E to 111 • E) during the period from 1992 to 1999. In total, 79,706 readings for P waves and 72,169 readings for S waves were used in the relocation. The relocated seismicity (6,496 earthquakes) images fault structures at seismogenic depths that are in close correlation with the tectonic structure of major fault systems expressed at the surface. The new focal depths confirm that most earthquakes (91%) in this region occur at depths less than 20 km.

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Section: Discussionsupporting

confidence: 53%

Double-difference relocation of earthquakes in central-western China, 1992–1999

et al. 2005

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“…For example, the maximum earthquake magnitude that any given fault can generate may be subject to different interpretations of, for example, fault length, seismogenic depth, and slip rate. More generally, different frequency-magnitude curves can be envisioned to conform to either a modified G-R relationship in which the b-value is greater for larger earthquakes (Pacheco et al, 1992;Sornette and Virieux, 1992;Romanowicz and Rundle, 1993;Kagan, 1999;Pisarenko and Sornette, 2004) or a characteristic model for the largest earthquake (Wesnousky, 1994;Kagan, 2002b). If hazard analysis is based on the seismic moment (M 0 =l LWD) of potential earthquakes, then values and associated uncertainties for first-order parameters such as rupture length (L), width (W), shear modulus (l), and average slip (D) must be provided.…”

Section: Uncertaintiesmentioning

confidence: 99%

Probabilistic Analysis of Tsunami Hazards*

2006

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Abstract. Determining the likelihood of a disaster is a key component of any comprehensive hazard assessment. This is particularly true for tsunamis, even though most tsunami hazard assessments have in the past relied on scenario or deterministic type models. We discuss probabilistic tsunami hazard analysis (PTHA) from the standpoint of integrating computational methods with empirical analysis of past tsunami runup. PTHA is derived from probabilistic seismic hazard analysis (PSHA), with the main difference being that PTHA must account for far-field sources. The computational methods rely on numerical tsunami propagation models rather than empirical attenuation relationships as in PSHA in determining ground motions. Because a number of source parameters affect local tsunami runup height, PTHA can become complex and computationally intensive. Empirical analysis can function in one of two ways, depending on the length and completeness of the tsunami catalog. For sitespecific studies where there is sufficient tsunami runup data available, hazard curves can primarily be derived from empirical analysis, with computational methods used to highlight deficiencies in the tsunami catalog. For region-wide analyses and sites where there are little to no tsunami data, a computationally based method such as Monte Carlo simulation is the primary method to establish tsunami hazards. Two case studies that describe how computational and empirical methods can be integrated are presented for Acapulco, Mexico (site-specific) and the U.S. Pacific Northwest coastline (region-wide analysis).

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“…The recent data compilation of Romanowicz [1992] shows that the scaling relation log(P) --(3/2) log(A) + const breaks down also for large strike-slip earthquakes. While the break in the scaling log(P) --(3/2) log(A) + const for large strike-slip earthquakes (rela.ted to the change from b --1 for small events to b = 1.5 for large ones [Rundle, 1989;Pacheco et al, 1992]) is readily understood as a consequence of a predominantly 1-D rupture propagation for long and narrow faults, we cannot offer an explanation for the scaling log(P) = log(A) + const of our simulated small events.…”

Section: 129mentioning

confidence: 99%