The Relationship between M and M-L: A Review and Application to Induced Seismicity in the Groningen Gas Field, The Netherlands

In response to induced earthquakes associated with conventional gas production in the Groningen gas field in the Netherlands, several networks of seismic monitoring instruments have been installed in the region (Dost et al., 2017). The recordings recovered from these networks have been of fundamental importance to the development of ground-motion prediction models that underpin hazard and risk modeling to inform decision making regarding mitigation measures (van Elk et al., 2019). In late 2018, it was discovered that the surface accelerographs of the G-network had been installed with a calibration error such that the majority of the instruments were recording half of the correct ground-motion amplitudes. The error was swiftly corrected via the website of Royal Netherlands Meteorological Institute (KNMI), which operates the networks. The calibration error explains, for example, the relatively low amplitudes observed in some of the KNMI network recordings in figure 3 of Bommer, Dost, et al. (2017). After discovery of the calibration error, work immediately began to assess the impact on the ground-motion models that have been developed as part of the induced seismic hazard and risk modeling effort in Groningen. The early ground-motion model of Bommer et al. (2016) was not affected because it was developed using only recordings from the KNMI B-network. The subsequent ground-motion models for the prediction of peak ground acceleration (PGA), peak ground velocity (PGV), and acceleration response spectra combined recordings from the B- and G-networks but fortuitously did not use the surface accelerographs of the G-network. Rather, from these stations, recordings from the 200 m geophones were used instead, a decision partly motivated by the improved signal-to-noise ratios of the deeper recordings. Another key consideration was the desire to bypass uncertainty in the amplification factors relative to the buried reference rock horizon at ∼800 m depth because the G-network stations had not benefited from the same in situ near-surface shear-wave velocity measurements as were conducted for the B-network accelerographs (Noorlandt et al., 2018). Two elements of the more recent ground-motion models did make use of the surface accelerograph recordings from both the B- and G-networks, but in neither case did the calibration error have any impact at all. The model for predicting ground-motion durations (Bommer, Stafford, et al., 2017) uses the significant duration definition, which is determined as the interval between accumulation of 5% and 75% of the total Arias intensity, a metric that is entirely insensitive to amplitude scaling of the record. The component-to-component variability model (Stafford et al., 2019)—used to transform the geometric mean amplitudes predicted for the hazard into the arbitrary horizontal component used in the risk calculations—was derived from ratios of the two horizontal components of each accelerogram, which are also completely independent of amplitude scaling. The study by Stafford et al. (2019) additionally proposed a model for spatial correlations among response spectral ordinates in the Groningen field that made use of recordings from the G-network. The inclusion of these records will have influenced the results of that study but most likely only by a small amount given that the results were obtained by averaging over multiple datasets and modeling approaches and that some of these analyses were entirely independent of the G-network. The seismic risk calculations for the Groningen field (van Elk et al., 2019) currently approximate spatial correlation through rules for sampling variability within and between site-response zones (Rodriguez-Marek et al., 2017) rather than directly implementing the model of Stafford et al. (2019). Another element of the ground-motion modeling that made use of the surface accelerograph recordings is the relationship between local and moment magnitudes in Groningen, as presented by Dost et al. (2018). This relationship—which in the magnitude range of relevance (ML ≥ 2:5) is one of equivalence between the two scales—is invoked for assigning seismic moments to events as part of the inversions of Fourier amplitude spectra for source, path, and site parameters, as well as in calibrating the upper branches of the ground-motion logic tree to match predictions from ground-motion prediction equations derived for tectonic earthquakes. Because recordings from surface accelerographs of the G-network were included in the calculation of seismic moments, many of the moment magnitude values required correction: the changes in values are illustrated in Figure 1, and a corrected version of the electronic supplement is now presented as E Table S1 (available in the supplemental content to this erratum). As can be appreciated in Figure 1, the impact has mainly affected smaller magnitudes because the larger earthquakes in the database were predominantly recorded by the accelerographs of the B-network. The correction of the data resulted in a small change to the quadratic relationship between the two magnitude scales, as illustrated in Figure 2. The corrected equation is M = 0:0469M2L + 0:6387ML + 0:6375: (1) As would be expected, the corrected relationship predicts slightly larger moment magnitudes for local magnitudes smaller than ML 2.5, but the conclusion of equivalence, on average, at higher magnitudes is unchanged. The quadratic form of equation (1) is only a convenient way to express the relationship in a single formula, and in practice, it is probably appropriate to assume a linear relationship (Mw = ML) for larger magnitudes; consequently, the apparent divergence from this model that would be implied by extrapolation of the cyan curve in Figure 2 to larger magnitudes can be safely ignored. In light of this finding, it may be concluded that the Groningen ground-motion models have been entirely unaffected by the unfortunate calibration error. However, for any application involving smaller-magnitude induced earthquakes in the Groningen field, the updated model presented herein should now be used

A database of damaging small-to-medium magnitude earthquakes

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Developing a model for the prediction of ground motions due to earthquakes in the Groningen gas field

AbstractMajor efforts are being undertaken to quantify seismic hazard and risk due to production-induced earthquakes in the Groningen gas field as the basis for rational decision-making about mitigation measures. An essential element is a model to estimate surface ground motions expected at any location for each earthquake originating within the gas reservoir. Taking advantage of the excellent geological and geophysical characterisation of the field and a growing database of ground-motion recordings, models have been developed for predicting response spectral accelerations, peak ground velocity and ground-motion durations for a wide range of magnitudes. The models reflect the unique source and travel path characteristics of the Groningen earthquakes, and account for the inevitable uncertainty in extrapolating from the small observed magnitudes to potential larger events. The predictions of ground-motion amplitudes include the effects of nonlinear site response of the relatively soft near-surface deposits throughout the field.</jats:p

Ground-motion prediction models for induced earthquakes in the Groningen gas field, the Netherlands

Small-magnitude earthquakes induced by gas production in the Groningen field in the Netherlands have prompted the development of seismic risk models that serve both to estimate the impact of these events and to explore the efficacy of different risk mitigation strategies. A core element of the risk modelling is ground-motion prediction models (GMPM) derived from an extensive database of recordings obtained from a dense network of accelerographs installed in the field. For the verification of damage claims, an empirical GMPM for peak ground velocity (PGV) has been developed, which predicts horizontal PGV as a function of local magnitude, ML; hypocentral distance, Rhyp; and the time-averaged shear-wave velocity over the upper 30 m, VS30. For modelling the risk due to potential induced and triggered earthquakes of larger magnitude, a GMPM for response spectral accelerations has been developed from regressions on the outputs from finite-rupture simulations of motions at a deeply buried rock horizon. The GMPM for rock motions is coupled with a zonation map defining frequency-dependent non-linear amplification factors to obtain estimates of surface motions in the region of thick deposits of soft soils. The GMPM for spectral accelerations is formulated within a logic-tree framework to capture the epistemic uncertainty associated with extrapolation from recordings of events of ML ≤ 3.6 to much larger magnitudes

Characterisation of ground motion recording stations in the Groningen gas field

The seismic hazard and risk analysis for the onshore Groningen gas field requires information about local soil properties, in particular shear-wave velocity (VS). A fieldwork campaign was conducted at 18 surface accelerograph stations of the monitoring network. The subsurface in the region consists of unconsolidated sediments and is heterogeneous in composition and properties. A range of different methods was applied to acquire in situ VS values to a target depth of at least 30 m. The techniques include seismic cone penetration tests (SCPT) with varying source offsets, multichannel analysis of surface waves (MASW) on Rayleigh waves with different processing approaches, microtremor array, cross-hole tomography and suspension P-S logging. The offset SCPT, cross-hole tomography and common midpoint cross-correlation (CMPcc) processing of MASW data all revealed lateral variations on length scales of several to tens of metres in this geological setting. SCPTs resulted in very detailed VS profiles with depth, but represent point measurements in a heterogeneous environment. The MASW results represent VS information on a larger spatial scale and smooth some of the heterogeneity encountered at the sites. The combination of MASW and SCPT proved to be a powerful and cost-effective approach in determining representative VS profiles at the accelerograph station sites. The measured VS profiles correspond well with the modelled profiles and they significantly enhance the ground motion model derivation. The similarity between the theoretical transfer function from the VS profile and the observed amplification from vertical array stations is also excellent

Comparisons among the five ground-motion models developed using RESORCE for the prediction of response spectral accelerations due to earthquakes in Europe and the Middle East

This article presents comparisons among the five ground-motion models described in other articles within this special issue, in terms of data selection criteria, characteristics of the models and predicted peak ground and response spectral accelerations. Comparisons are also made with predictions from the Next Generation Attenuation (NGA) models to which the models presented here have similarities (e.g. a common master database has been used) but also differences (e.g. some models in this issue are nonparametric). As a result of the differing data selection criteria and derivation techniques the predicted median ground motions show considerable differences (up to a factor of two for certain scenarios), particularly for magnitudes and distances close to or beyond the range of the available observations. The predicted influence of style-of-faulting shows much variation among models whereas site amplification factors are more similar, with peak amplification at around 1s. These differences are greater than those among predictions from the NGA models. The models for aleatory variability (sigma), however, are similar and suggest that ground-motion variability from this region is slightly higher than that predicted by the NGA models, based primarily on data from California and Taiwan