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

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

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

Organization of Physical Interactomes as Uncovered by Network Schemas

Large-scale protein-protein interaction networks provide new opportunities for understanding cellular organization and functioning. We introduce network schemas to elucidate shared mechanisms within interactomes. Network schemas specify descriptions of proteins and the topology of interactions among them. We develop algorithms for systematically uncovering recurring, over-represented schemas in physical interaction networks. We apply our methods to the S. cerevisiae interactome, focusing on schemas consisting of proteins described via sequence motifs and molecular function annotations and interacting with one another in one of four basic network topologies. We identify hundreds of recurring and over-represented network schemas of various complexity, and demonstrate via graph-theoretic representations how more complex schemas are organized in terms of their lower-order constituents. The uncovered schemas span a wide range of cellular activities, with many signaling and transport related higher-order schemas. We establish the functional importance of the schemas by showing that they correspond to functionally cohesive sets of proteins, are enriched in the frequency with which they have instances in the H. sapiens interactome, and are useful for predicting protein function. Our findings suggest that network schemas are a powerful paradigm for organizing, interrogating, and annotating cellular networks

Hypocentre estimation of induced earthquakes in Groningen

Induced earthquakes due to gas production have taken place in the province of Groningen in the northeast of The Netherlands since 1986. In the first years of seismicity, a sparse seismological network with large station distances from the seismogenic area in Groningen was used. The location of induced earthquakes was limited by the few and wide spread stations. Recently, the station network has been extended significantly and the location of induced earthquakes in Groningen has become routine work. Except for the depth estimation of the events. In the hypocentre method used for source location by the Royal Netherlands Meteorological Institute (KNMI), the depth of the induced earthquakes is by default set to 3 km which is the average depth of the gas-reservoir. Alternatively, a differential traveltime for P-waves approach for source location is applied on recorded data from the extended network. The epicentre and depth of 87 induced earthquakes from 2014 to July 2016 have been estimated. The newly estimated epicentres are close to the induced earthquake locations from the current method applied by the KNMI. It is observed that most induced earthquakes take place at reservoir level. Several events in the same magnitude order are found near a brittle anhydrite layer in the overburden of mainly rock salt evaporites.Applied Geophysics and Petrophysic

Development of seismicity and probabilistic hazard assessment for the Groningen gas field

The increase in number and strength of shallow induced seismicity connected to the Groningen gas field since 2003 and the occurrence of a M L 3.6 event in 2012 started the development of a full probabilistic seismic hazard assessment (PSHA) for Groningen, required by the regulator. Densification of the monitoring network resulted in a decrease of the location threshold and magnitude of completeness down to ∼ M L = 0.5. Combined with a detailed local velocity model, epicentre accuracy could be reduced from 0.5–1 km to 0.1–0.3 km and a vertical resolution ∼0.3 km. Time-dependent seismic activity is observed and taken into account into PSHA calculations. Development of the Ground Motion Model for Groningen resulted in a significant reduction of the hazard. Comparison of different implementations of the PSHA, using different source models, based on either a compaction model and production scenarios or on extrapolation of past seismicity, and methods of calculation, shows similar result