Nils Maercklin

Seismic reflections reveal a massive melt layer feeding Campi Flegrei caldera

[1]xa0Campi Flegrei is an active, resurgent caldera that is located a few kilometres west of the city of Naples, a densely populated urban settlement in southern Italy. Identifying, locating at depth and better defining the geometry of the magma feeding system of the caldera is highly relevant for assessing and monitoring its volcanic hazard. Based on a high resolution seismic reflection data set, we investigated the deep structure of the volcano. Here we show that seismic wave amplitude variations with distance from the radiating source provide clear evidence for large amplitude seismic reflections from the top of an extended supercritical fluid-bearing rock formation at about 3,000 m and of an about 7,500 m deep, 1,000 m thick, low velocity layer, which is associated with a mid-crust, partial melting zone beneath the caldera. The modeling of magma properties based on measured seismic velocities indicates a relatively high melt percentage (in the range 80–90%). These new data suggest that a large magmatic sill is present well within the basement formations, which is possibly linked to the surface through a system of deep fractures bordering the caldera. The lateral extension and similar depth of the melt zone observed beneath the nearby Mt.Vesuvius support the hypothesis of a single continuous magma reservoir feeding both of these volcanoes.

Predicting ground motion from induced earthquakes in geothermal areas

Induced seismicity from anthropogenic sources can be a significant nuisance to a local population and in extreme cases lead to damage to vulnerable structures. One type of induced seismicity of particular recent concern, which, in some cases, can limit development of a potentially important clean energy source, is that associated with geothermal power production. A key requirement for the accurate assessment of seismic hazard (and risk) is a ground-motion prediction equation (GMPE) that predicts the level of earthquake shaking (in terms of, for example, peak ground acceleration) of an earthquake of a certain magnitude at a particular distance. Few such models currently exist in regard to geothermal-related seismicity, and consequently the evaluation of seismic hazard in the vicinity of geothermal power plants is associated with high uncertainty. Various ground-motion datasets of induced and natural seismicity (from Basel, Geysers, Hengill, Roswinkel, Soultz, and Voerendaal) were compiled and processed, and moment magnitudes for all events were recomputed homogeneously. These data are used to show that ground motions from induced and natural earthquakes cannot be statistically distinguished. Empirical GMPEs are derived from these data; and, although they have similar characteristics to recent GMPEs for natural and miningrelated seismicity, the standard deviations are higher. To account for epistemic uncertainties, stochastic models subsequently are developed based on a single corner frequency and with parameters constrained by the available data. Predicted ground motions from these models are fitted with functional forms to obtain easy-to-use GMPEs. These are associated with standard deviations derived from the empirical data to characterize aleatory variability. As an example, we demonstrate the potential use of these models using data from Campi Flegrei. Online Material: Sets of coefficients and standard deviations for various groundmotion models.

Twin ruptures grew to build up the giant 2011 Tohoku, Japan, earthquake.

The 2011 Tohoku megathrust earthquake had an unexpected size for the region. To image the earthquake rupture in detail, we applied a novel backprojection technique to waveforms from local accelerometer networks. The earthquake began as a small-size twin rupture, slowly propagating mainly updip and triggering the break of a larger-size asperity at shallower depths, resulting in up to 50u2005m slip and causing high-amplitude tsunami waves. For a long time the rupture remained in a 100–150u2005km wide slab segment delimited by oceanic fractures, before propagating further to the southwest. The occurrence of large slip at shallow depths likely favored the propagation across contiguous slab segments and contributed to build up a giant earthquake. The lateral variations in the slab geometry may act as geometrical or mechanical barriers finally controlling the earthquake rupture nucleation, evolution and arrest.

From Induced Seismicity to Direct Time-Dependent Seismic Hazard

The growing installation of industrial facilities for subsurface exploration worldwide requires continuous refinements in understanding both the mechanisms by which seismicity nis induced by field operations and the related seismic hazard. Particularly in proximity of ndensely populated areas, induced low-to-moderate magnitude seismicity characterized by high-frequency content can be clearly felt by the surrounding inhabitants and, in some cases, may nproduce damage. In this respect we propose a technique for time-dependent probabilistic nseismic-hazard analysis to be used in geothermal fields as a monitoring tool for the effects nof on-going field operations. The technique integrates the observed features of the seismic- nity induced by fluid injection and extraction with a local ground-motion-prediction equation. nThe result of the analysis is the time-evolving probability of exceedance of peak ground acceleration, which can be compared with selected critical values to manage field operations. nTo evaluate the reliability of the proposed technique, we applied it to data collected in The nGeysers geothermal field in Northern California between 1 September 2007 and 15 November n2010. We show that in the period considered the seismic hazard at The Geysers was variable nin time and space, which is a consequence of the field operations and the variation of both nseismicity rate and b-value. We conclude that, for the exposure period taken into account n(i.e., 2 months), as a conservative limit, peak ground acceleration values corresponding to the nlowest probability of exceedance (e.g., 30%) must not be exceeded to ensure safe field operations. We suggest testing the proposed technique at other geothermal areas or in regions where nseismicity is induced, for example, by hydrocarbon exploitation or carbon-dioxide storage.

The Effectiveness of a Distant Accelerometer Array to Compute Seismic Source Parameters: The April 2009 L'Aquila Earthquake Case History

The 6 April 2009 Mw 6.3 LAquila earthquake, central Italy, has been recorded by the Irpinia Seismic Network (ISNet) about 250 km southeast of the epicenter. Up to 19 three-component accelerometer stations could be used to infer the main source parameters with different seismological methods. We obtained an approximate location of the event from arrival times and array-based back-azimuth measurements and estimated the local magnitude (6.1) from an at- tenuation relation for southern Italy. Assuming an omega-square spectral model, we inverted S-wave displacement spectra for moment magnitude (6.3), corner frequency (0.33 Hz), stress drop (2.5 MPa), and apparent stress (1.6 MPa). Waveform modeling using a point source and an extended-source model provided consistent moment tensors with a centroid depth around 6 km and a prevalently normal fault plane solution with a dominant directivity toward the southeast. The relatively high corner frequency and an overestimated moment magnitude of 6.4 from moment tensor inversions are attributed to the rupture directivity effect. To image the rupture geometry, we implemented a beamforming technique that back-projects the recorded direct P-wave amplitudes into the earthquake source region. A northwest-southeast striking rupture of 17 km length is imaged, propagat- ing with an average velocity up to 3 km=s. This value is significantly higher than our estimate of 2:2 km=s from S-wave spectra. Our case study demonstrates that the use of array techniques and a dense accelerometer network can provide quick and robust estimates of source parameters of moderate-sized earthquakes located outside the network.

S‐Wave Identification by Polarization Filtering and Waveform Coherence Analyses

High-resolution imaging with microseismic events requires the use of large and consistent data sets of seismic phase arrival times. In particular the S phase is important to derive physical parameters of the subsurface. Typically this phase is identified on one of the horizontal seismogram components by a change of signal amplitude and frequency as compared to the previous P phase. However, reliable S-phase identification can be difficult for local events because of a signal overlap with the P coda, the presence of converted phases, and possible S-wave splitting due to anisotropy. In this study we propose a new data processing technique aiming at un- iquely identifying the S-phase arrival using all available records from a seismic net- work. The technique combines polarization analysis of single three-component recordings of an event with analysis of lateral waveform coherence across the net- work. This makes it possible to construct seismic sections in which the first arrival is the S phase. This graphical representation can support an operator in both the analysis of single events and in semiautomatic analyses of large datasets. In addition, an automated stacking velocity analysis provides S-wave velocities from these sec- tions. We demonstrate the applicability of this technique using synthetic seismograms, and we evaluate the efficacy on a dataset of three-component velocimeter records from local earthquakes of the Campania-Lucania Apennines (southern Italy) recorded by the Irpinia Seismic Network (ISNet).

Ground-Motion Prediction Equations for The Geysers Geothermal Area based on Induced Seismicity Records

Abstract The Geysers geothermal field in Northern California, which has been actively exploited since the 1960s, is the world’s largest geothermal field. The continuous injection of fluids and the consequent stress perturbations induce seismicity that is clearly felt in the surrounding communities. In order to evaluate seismic hazard due to induced seismicity and the effects of seismicity rate level on the population and buildings in the area, reliable ground‐motion prediction equations (GMPEs) must be developed. This paper introduces the first GMPEs specific for The Geysers area in terms of peak ground velocity (PGV), peak ground acceleration (PGA), and 5% damped spectral acceleration SA( T ) at T =0.2u2009u2009s, 0.5xa0s, and 1.0xa0s. The adopted non‐linear mixed‐effect regression technique to derive the GMPE includes both fixed and random effects, and it permits to account for both inter‐event and intra‐event dependencies in the data. Site‐specific effects are also estimated from the data and are corrected in the final ground‐motion model. We used data from earthquakes recorded at 29 stations of the Berkeley‐Geysers network during the period September 2007 through November 2010. The magnitude range is 1.3≤ M w ≤3.3, whereas the hypocentral distances range between 0.5xa0km and 20xa0km. The comparison of our new GMPE for The Geysers with a standard model derived in a different tectonic context shows that our model is more robust when predictions have to be made for induced earthquakes in this geothermal area.

Three-dimensional model for the crust and upper mantle in the Barents Sea region

The Barents Sea and its surroundings is an epicontinental region which previously has been difficult to access, partly because of its remote Arctic location (Figure 1) and partly because the region has been politically sensitive. Now, however, this region, and in particular its western parts, has been very well surveyed with a variety of geophysical studies, motivated in part by exploration for hydrocarbon resources. Since this region is interesting geophysically as well as for seismic verification, a major study [Bungum et al., 2004] was initiated in 2003 to develop a three-dimensional (3-D) seismic velocity model for the crust and upper mantle, using a grid density of 50 km. n nThis study, in cooperation between NORSAR, the University of Oslo (UiO),and the U.S.Geological Survey (USGS), has led to the construction of a higher-resolution, regional lithospheric model based on a comprehensive compilation of available seismological and geophysical data. Following the methodology employed in making the global crustal model CRUST5.1 [Mooney et al., 1998], the new model consists of five crustal layers: soft and hard sediments, and crystalline upper, middle, and lower crust. Both P- and S-wave velocities and densities are specified in each layer. In addition, the density and seismic velocity structure of the uppermost mantle, essential for Pn and Sn travel time modeling, are included.