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The Seismic Hazard Inferred from Tectonics based on the Global Strain Rate Map (SHIFT_GSRM) earthquake forecast was designed to provide high-resolution estimates of global shallow seismicity to be used in seismic hazard assessment. This model combines geodetic strain rates with global earthquake parameters to characterize long-term rates of seismic moment and earthquake activity. Although SHIFT_GSRM properly computes seismicity rates in seismically active continental regions, it underestimates earthquake rates in subduction zones by an average factor of approximately 3. We present a complementary method to SHIFT_GSRM to more accurately forecast earthquake rates in 37 subduction segments, based on the conservation of moment principle and the use of regional interface seismicity parameters, such as subduction dip angles, corner magnitudes, and coupled seismogenic thicknesses. In seven progressive steps, we find that SHIFT_GSRM earthquake-rate underpredictions are mainly due to the utilization of a global probability function of seismic moment release that poorly captures the great variability among subduction megathrust interfaces. Retrospective test results show that the forecast is consistent with the observations during the 1 January 1977 to 31 December 2014 period. Moreover, successful pseudoprospective evaluations for the 1 January 2015 to 31 December 2018 period demonstrate the power of the regionalized earthquake model to properly estimate subduction-zone seismicity.
Full-waveform-based characterization of acoustic emission activity in a mine-scale experiment
(2020)
Understanding fracturing processes and the hydromechanical relation to induced seismicity is a key question for enhanced geothermal systems (EGS). Commonly massive fluid injection, predominately causing hydroshearing, are used in large-scale EGS but also hydraulic fracturing approaches were discussed. To evaluate the applicability of hydraulic fracturing techniques in EGS, six in situ, multistage hydraulic fracturing experiments with three different injection schemes were performed under controlled conditions in crystalline rock at the Aspo Hard Rock Laboratory (Sweden). During the experiments the near-field ground motion was continuously recorded by 11 piezoelectric borehole sensors with a sampling rate of 1 MHz. The sensor network covered a volume of 30x30x30 m around a horizontal, 28-m-long injection borehole at a depth of 410 m. To extract and characterize massive, induced, high-frequency acoustic emission (AE) activity from continuous recordings, a semi-automated workflow was developed relying on full waveform based detection, classification and location procedures. The approach extended the AE catalogue from 196 triggered events in previous studies to more than 19600 located AEs. The enhanced catalogue, for the first time, allows a detailed analysis of induced seismicity during single hydraulic fracturing experiments, including the individual fracturing stages and the comparison between injection schemes. Beside the detailed study of the spatio-temporal patterns, event clusters and the growth of seismic clouds, we estimate relative magnitudes and b-values of AEs for conventional, cyclic progressive and dynamic pulse injection schemes, the latter two being fatigue hydraulic fracturing techniques. While the conventional fracturing leads to AE patterns clustered in planar regions, indicating the generation of a single main fracture plane, the cyclic progressive injection scheme results in a more diffuse, cloud-like AE distribution, indicating the activation of a more complex fracture network. For a given amount of hydraulic energy (pressure multiplied by injected volume) pumped into the system, the cyclic progressive scheme is characterized by a lower rate of seismicity, lower maximum magnitudes and significantly larger b-values, implying an increased number of small events relative to the large ones. To our knowledge, this is the first direct comparison of high resolution seismicity in a mine-scale experiment induced by different hydraulic fracturing schemes.
Accelerograms are the primary source for characterizing strong ground motion. It is therefore of paramount interest to have high-quality recordings free from any nonphysical contamination. Frequently, accelerograms are affected by baseline jumps and drifts, either related to the instrument and/or a major earthquake. In this work, I propose a correction method for these undesired baseline drifts based on segmented linear least squares. The algorithm operates on the integrated waveforms and combines all three instrument components to estimate a model that modifies the baseline to be at zero continuously. The procedure consists of two steps: first a suite of models with variable numbers of discontinuities is derived for all three instrument components. During this process, the number of discontinuities is reduced in a parsimonious way, for example, two very close discontinuities are merged into a single one. In the second step, the optimal model is selected on the basis of the Bayesian information criterion. I exemplify the application on synthetic waveforms with known discontinuities and on observed waveforms from a unified strong-motion database of the Japan Meteorological Agency (JMA) and the National Research Institute for Earth Science and Disaster Prevention (NIED, Japan) networks for the major events of the 2016 Kumamoto earthquakes. After the baseline jump correction, the waveforms are furthermore corrected for displacement according to Wang et al.(2011). The resulting displacements are comparable to the Interferometric Synthetic Aperture Radar-derived displacement estimates for the Kumamoto earthquake sequence.
We investigate the source characteristics of picoseismicity (M-w < -2) recorded during a hydraulic fracturing in situ experiment performed in the underground Aspo Hard Rock Laboratory, Sweden. The experiment consisted of six stimulations driven by three different water injection schemes and was performed inside a 28-m-long, horizontal borehole located at 410-m depth. The fracturing processes were monitored with a variety of seismic networks including broadband seismometers, geophones, high-frequency accelerometers, and acoustic emission sensors thereby covering a wide frequency band between 0.01 and 100,000Hz. Here we study the high-frequency signals with dominant frequencies exceeding 1000 Hz. The combined seismic network allowed for detection and detailed analysis of 196 small-scale seismic events with moment magnitudes M-W < -3.5 (source sizes of decimeter scale) that occurred solely during the stimulations and shortly after. The double-difference relocated hypocenter catalog as well as source parameters were used to study the physical characteristics of the induced seismicity and then compared to the stimulation parameters. We observe a spatiotemporal migration of the picoseismic events away and toward the injection intervals in direct correlation with changes in the hydraulic energy (product of fluid injection pressure and injection rate). We find that the total radiated seismic energy is extremely low with respect to the product of injected fluid volume and pressure (hydraulic energy). The radiated seismic energy correlates well with the hydraulic energy rate. The obtained fault plane solutions for particularly well-characterized events signify the reactivation of preexisting rock defects under influence of increased pore fluid pressure on fault plane orientations in good correspondence with the local stress field orientation.
In probabilistic seismic hazard analysis, different ground-motion prediction equations (GMPEs) are commonly combined within a logic tree framework. The selection of appropriate GMPEs, however, is a non-trivial task, especially for regions where strong motion data are sparse and where no indigenous GMPE exists because the set of models needs to capture the whole range of ground-motion uncertainty. In this study we investigate the aggregation of GMPEs into a mixture model with the aim to infer a backbone model that is able to represent the center of the ground-motion distribution in a logic tree analysis. This central model can be scaled up and down to obtain the full range of ground-motion uncertainty. The combination of models into a mixture is inferred from observed ground-motion data. We tested the new approach for Northern Chile, a region for which no indigenous GMPE exists. Mixture models were calculated for interface and intraslab type events individually. For each source type we aggregated eight subduction zone GMPEs using mainly new strong-motion data that were recorded within the Plate Boundary Observatory Chile project and that were processed within this study. We can show that the mixture performs better than any of its component GMPEs, and that it performs comparable to a regression model that was derived for the same dataset. The mixture model seems to represent the median ground motions in that region fairly well. It is thus able to serve as a backbone model for the logic tree.
In this paper, an underground experiment at the Aspo Hard Rock Laboratory (HRL) is described. Main goal is optimizing geothermal heat exchange in crystalline rock mass at depth by multistage hydraulic fracturing with minimal impact on the environment, that is, seismic events. For this, three arrays with acoustic emission, microseismicity and electromagnetic sensors are installed mapping hydraulic fracture initiation and growth. Fractures are driven by three different water injection schemes (continuous, progressive and pulse pressurization). After a brief review of hydraulic fracture operations in crystalline rock mass at mine scale, the site geology and the stress conditions at Aspo HRL are described. Then, the continuous, single-flow rate and alternative, multiple-flow rate fracture breakdown tests in a horizontal borehole at depth level 410 m are described together with the monitoring networks and sensitivity. Monitoring results include the primary catalogue of acoustic emission hypocentres obtained from four hydraulic fractures with the in situ trigger and localizing network. The continuous versus alternative water injection schemes are discussed in terms of the fracture breakdown pressure, the fracture pattern from impression packer result and the monitoring at the arrays. An example of multistage hydraulic fracturing with several phases of opening and closing of fracture walls is evaluated using data from acoustic emissions, seismic broad-band recordings and electromagnetic signal response. Based on our limited amount of in situ tests (six) and evaluation of three tests in Avro granodiorite, in the multiple-flow rate test with progressively increasing target pressure, the acoustic emission activity starts at a later stage in the fracturing process compared to the conventional fracturing case with continuous water injection. In tendency, also the total number and magnitude of acoustic events are found to be smaller in the progressive treatment with frequent phases of depressurization.
The selection of earthquake focal mechanisms (FMs) for stress tensor inversion (STI) is commonly done on a spatial basis, that is, hypocentres. However, this selection approach may include data that are undesired, for example, by mixing events that are caused by different stress tensors when for the STI a single stress tensor is assumed. Due to the significant increase of FM data in the past decades, objective data-driven data selection is feasible, allowing more refined FM catalogues that avoid these issues and provide data weights for the STI routines. We present the application of angular classification with expectation-maximization (ACE) as a tool for data selection. ACE identifies clusters of FM without a priori information. The identified clusters can be used for the classification of the style-of-faulting and as weights of the FM data. We demonstrate that ACE effectively selects data that can be associated with a single stress tensor. Two application examples are given for weighted STI from South America. We use the resulting clusters and weights as a priori information for an STI for these regions and show that uncertainties of the stress tensor estimates are reduced significantly.
The mechanisms leading to large earthquakes are poorly understood and documented. Here we characterize the long-term precursory phase of the 1 April 2014 M(w)8.1 North Chile megathrust. We show that a group of coastal GPS stations accelerated westward 8months before the main shock, corresponding to a M(w)6.5 slow slip event on the subduction interface, 80% of which was aseismic. Concurrent interface foreshocks underwent a diminution of their radiation at high frequency, as shown by the temporal evolution of Fourier spectra and residuals with respect to ground motions predicted by recent subduction models. Such ground motions change suggests that in response to the slow sliding of the subduction interface, seismic ruptures are progressively becoming smoother and/or slower. The gradual propagation of seismic ruptures beyond seismic asperities into surrounding metastable areas could explain these observations and might be the precursory mechanism eventually leading to the main shock.
Applying conservation of energy to estimate earthquake frequencies from strain rates and stresses
(2020)
Estimating earthquake occurrence rates from the accumulation rate of seismic moment is an established tool of seismic hazard analysis. We propose an alternative, fault-agnostic approach based on the conservation of energy: the Energy-Conserving Seismicity Framework (ENCOS). Working in energy space has the advantage that the radiated energy is a better predictor of the damage potential of earthquake waves than the seismic moment release. In a region, ENCOS balances the stationary power available to cause earthquakes with the long-term seismic energy release represented by the energy-frequency distribution's first moment. Accumulation and release are connected through the average seismic efficiency, by which we mean the fraction of released energy that is converted into seismic waves. Besides measuring earthquakes in energy, ENCOS differs from moment balance essentially in that the energy accumulation rate depends on the total stress in addition to the strain rate tensor. To validate ENCOS, we exemplarily model the energy-frequency distribution around Southern California. We estimate the energy accumulation rate due to tectonic loading assuming poroelasticity and hydrostasis. Using data from the World Stress Map and assuming the frictional limit to estimate the stress tensor, we obtain a power of 0.8 GW. The uncertainty range, 0.3-2.0GW, originates mainly from the thickness of the seismogenic crust, the friction coefficient on preexisting faults, and models of Global Positioning System (GPS) derived strain rates. Based on a Gutenberg-Richter magnitude-frequency distribution, this power can be distributed over a range of energies consistent with historical earthquake rates and reasonable bounds on the seismic efficiency.
The steady increase of ground-motion data not only allows new possibilities but also comes with new challenges in the development of ground-motion models (GMMs). Data classification techniques (e.g., cluster analysis) do not only produce deterministic classifications but also probabilistic classifications (e.g., probabilities for each datum to belong to a given class or cluster). One challenge is the integration of such continuous classification in regressions for GMM development such as the widely used mixed-effects model. We address this issue by introducing an extension of the mixed-effects model to incorporate data weighting. The parameter estimation of the mixed-effects model, that is, fixed-effects coefficients of the GMMs and the random-effects variances, are based on the weighted likelihood function, which also provides analytic uncertainty estimates. The data weighting permits for earthquake classification beyond the classical, expert-driven, binary classification based, for example, on event depth, distance to trench, style of faulting, and fault dip angle. We apply Angular Classification with Expectation-maximization, an algorithm to identify clusters of nodal planes from focal mechanisms to differentiate between, for example, interface- and intraslab-type events. Classification is continuous, that is, no event belongs completely to one class, which is taken into account in the ground-motion modeling. The theoretical framework described in this article allows for a fully automatic calibration of ground-motion models using large databases with automated classification and processing of earthquake and ground-motion data. As an example, we developed a GMM on the basis of the GMM by Montalva et al. (2017) with data from the strong-motion flat file of Bastias and Montalva (2016) with similar to 2400 records from 319 events in the Chilean subduction zone. Our GMM with the data-driven classification is comparable to the expert-classification-based model. Furthermore, the model shows temporal variations of the between-event residuals before and after large earthquakes in the region.