Accurate estimation of seismic source parameters of induced seismicity by a combined approach of generalized inversion and genetic algorithm: Application to The Geysers geothermal area, California

Picozzi, Matteo; Oth, Adrien; Parolai, Stefano; Bindi, Dino; Landro, Grazia De; Amoroso, Ortensia

doi:10.1002/2016jb013690

Cited by 35 publications

(33 citation statements)

References 76 publications

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“…Pacheco et al 1992;Scholz 1997;Main 2000;Burroughs & Tebbens 2002). This might be the case of the TG area, where the geothermal reservoir, which hosts the whole seismicity, is bounded at about 4 km of depth by Pleistocene felsite intrusion (Picozzi et al 2017a). On the other hand, we highlight that the scaling of σ with M 0 would imply that in the case of M w the characterization of the seismicity CFM using a power-law functional form may be questionable.…”

Section: U M U L At I V E F R E Q U E N C Y -M a G N I T U D E D I mentioning

confidence: 90%

“…Furthermore, the theoretical study on the reliability of estimates of source parameters of small earthquakes recently presented by Kwiatek & Ben-Zion (2016) indicates that, considering the sensor characteristics, the hypocentral distances, the magnitude range and the level of seismic noise in the TG data, reliable earthquake source parameters (i.e. M 0 and f c ), and hence also E r , can be estimated for the range of magnitude analysed by Picozzi et al (2017a).…”

Section: The Geysers (Tg)mentioning

confidence: 99%

“…Recordings are freely available via the Northern California Earthquake Data Center (NCEDC). As for the Central Italy data set, source parameters (M 0 , σ and f c ) are obtained by applying a GIT approach (Picozzi et al 2017a) and the Izutani & Kanamori (2001) approach to obtain E r . It is worth noting that obtaining robust E r estimates for small magnitude earthquakes, as those of the TG data set, is still a topic of current research.…”

Section: The Geysers (Tg)mentioning

confidence: 99%

“…To accomplish such a goal, we use four different data sets whose source parameters (i.e. seismic moment, M 0 , corner frequency, f c , or directly the seismic radiated energy, E r ) are available from previous studies: a global data set including earthquakes with M w > 5.5 (Di Giacomo et al 2010); a regional data set including earthquakes in the magnitude range 2.7-7.2 that occurred in Japan (Oth 2013); a local data set including earthquakes in the magnitude range 2.5-6.5 that occurred in Central Italy (Bindi et al 2018); and finally, a data set for induced events within The Geyser geothermal area (Picozzi et al 2017a) in the magnitude range 1.5-4.…”

Section: Introductionmentioning

confidence: 99%

“…γ ; whereas the parameter γ controlling the high-frequency spectral fall-off is equal to 2 for the Italian data, corresponding to the Brune's source model (Brune 1970) and variable between 2 and 3 for The Geysers, Picozzi et al 2017a). In the case of the Japanese data set, M(f) was obtained by the frequency band-limited empirical source spectra derived from GIT combined with the Brune's source model (Brune 1970) at frequencies lower and higher than those limiting the empirical one.…”

Section: Introductionmentioning

confidence: 99%

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Moment and energy magnitudes: diversity of views on earthquake shaking potential and earthquake statistics

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The size of an earthquake can be defined either from the seismic moment (M 0) or in terms of radiated seismic energy (E r). These two parameters look at the source complexity from different perspectives: M 0 is a static measure of the earthquake size, whereas E r is related to the rupture kinematics and dynamics. For practical applications and for dissemination purposes, the logarithms of M 0 and E r are used to define the moment magnitude M w and the energy magnitude M E , respectively. The introduction of M w and M E partially obscure the complementarity of M 0 and E r. The reason is due to the assumptions needed to define any magnitude scale. For example, in defining M w , the apparent stress (i.e. the ratio between M 0 and E r multiplied by the rigidity) was assumed to be constant, and under this condition, M w and M E values would only differ by an offset which, in turn, depends on the average apparent stress of the analysed data set. In any case, when the apparent stress is variable and, for example, scales with M 0 , the value of M E derived from M w cannot be used to infer E r. In this study, we investigate the similarities and differences between M w and M E in connection with the scaling of the source parameters using a data set of around 4700 earthquakes recorded at both global and regional scales and belonging to four data sets. These cover different geographical areas and extensions and are composed by either natural or induced earthquakes in the magnitude range 1.5 ≤ M w ≤ 9.0. Our results show that M E is better than M w in capturing the high-frequency ground shaking variability whenever the stress drop differs from the reference value adopted to define M w. We show that M E accounts for variations in the rupture processes, introducing systematic event-dependent deviations from the mean regional peak ground motion velocity scaling. Therefore, M E might be a valid alternative to M w for deriving ground motion prediction equations for seismic hazard studies in areas where strong systematic stress drop scaling with M 0 are found, such as observed for induced earthquakes in geothermal regions. Furthermore, we analyse the different data sets in terms of their cumulative frequency-magnitude distribution, considering both M E and M w. We show that the b values from M w (b Mw) and M E (b ME) can be significantly different when the stress drop shows a systematic scaling relationship with M 0. We found that b ME is nearly constant for all data sets, while b Mw shows an inverse linear scaling with apparent stress.

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