Aftershock Forecasts Research Articles

Long-term communication of aftershock forecasts: the Canterbury Earthquake Sequence in Aotearoa, New Zealand

On 14 February 2016, a magnitude (M)5.7 earthquake struck in Christchurch, Aotearoa, New Zealand. The shaking caused damage to historic facades, power outages, cliff collapses, rock falls, liquefaction but no reported injuries or fatalities. This Valentine’s Day earthquake was an aftershock in the Canterbury earthquake sequence (CES), which began on 4 September 2010 with the M7.1 Darfield Earthquake and included the destructive and fatal M6.2 Christchurch aftershock on 22 February 2011. This study eight months after the Valentine’s Day earthquake and six years after the initiation of the CES is the first to explore long-term aftershock forecast information and communication needs. The exploratory study also aimed to gather feedback on aftershock scenarios, an alternate form of communication of the forecast.The qualitative study involved workshops with emergency managers, public health officials, and members of the public in Christchurch. Key findings for long-term communication throughout an earthquake sequence include: 1. divergent earthquake experiences affect aftershock communication response and information needs; 2. understanding aftershock sequence behavior is foundational to sense-making when large aftershocks occur; 3. strategic earthquake sequence updates from the trusted science agency and local agencies could serve as important reminders for earthquake preparedness; 4. communication of aftershock forecast uncertainty could aid with both the credibility of the information and living with uncertainty, and 5. inclusion of impact information and preparedness advice into aftershock forecast scenarios could provide links to actionable information. The paper derives implications for research and practice of long-term communications during an aftershock sequence.

Assessing the Predictive Power of GPS-Based Ground Deformation Data for Aftershock Forecasting

Abstract We present a machine learning approach for aftershock forecasting of the Japanese earthquakes catalog. Our method takes as sole input the ground surface deformation as measured by Global Positioning System (GPS) stations on the day of the mainshock to predict aftershock location. The quality of data heavily relies on the density of GPS stations: the predictive power is lost when the mainshocks occur far from measurement stations, as in offshore regions. Despite this fact and the small number of samples and the large number of parameters, we are able to limit overfitting, which shows that this new approach is very promising.

Spatio-temporal characterization of earthquake sequence parameters and forecasting of strong aftershocks in Xinjiang based on the ETAS model.

In this paper, the Integrated Nested Laplace Algorithm (INLA) is applied to the Epidemic Type Aftershock Sequence (ETAS) model, and the parameters of the ETAS model are obtained for the earthquake sequences active in different regions of Xinjiang. By analyzing the characteristics of the model parameters over time, the changes in each earthquake sequence are studied in more detail. The estimated values of the ETAS model parameters are used as inputs to forecast strong aftershocks in the next period. We find that there are significant differences in the aftershock triggering capacity and aftershock attenuation capacity of earthquake sequences in different seismic regions of Xinjiang. With different cutoff dates set, we observe the characteristics of the earthquake sequence parameters changing with time after the mainshock occurs, and the model parameters of the Ms7.3 earthquake sequence in Hotan region change significantly with time within 15 days after the earthquake. Compared with the MCMC algorithm, the ETAS model fitted with the INLA algorithm can forecast the number of earthquakes in the early period after the occurrence of strong aftershocks more effectively and can forecast the sudden occurrence time of earthquakes more accurately.

Investigating the sensitivity of losses to time-dependent components of seismic risk modeling

Conventional earthquake risk modeling involves several notable simplifications, which neglect: (1) the effects on seismicity of interactions between adjacent faults and the long-term elastic rebound behavior of faults; (2) short-term hazard increases associated with aftershocks; and (3) the accumulation of damage in assets due to the occurrence of multiple earthquakes in a short time window, without repairs. Several recent earthquake events (e.g. 2010–2011 Canterbury earthquakes, New Zealand; 2019 Ridgecrest earthquakes, USA; and 2023 Turkey–Syria earthquakes) have emphasized the need for risk models to account for the aforementioned short- and long-term time-dependent characteristics of earthquake risk. This study specifically investigates the sensitivity of monetary loss (i.e. a possible earthquake-risk-model output) to these time dependencies, for a case-study portfolio in Central Italy. The investigation is intended to provide important insights for the catastrophe risk insurance and reinsurance industry. In addition to salient catastrophe risk insurance features, the end-to-end approach for time-dependent earthquake risk modeling used in this study incorporates recent updates in long-term time-dependent fault modeling, aftershock forecasting, and vulnerability modeling that accounts for damage accumulation. The sensitivity analysis approach presented may provide valuable guidance on the importance and appropriate treatment of time dependencies in regional (i.e. portfolio) earthquake risk models. We find that the long-term fault and aftershock occurrence models are the most crucial features of a time-dependent seismic risk model to constrain, at least for the monetary loss metrics examined in this study. Accounting for damage accumulation is also found to be important, if there is a high insurance deductible associated with portfolio assets.

Aftershock forecasts based on incomplete earthquake catalogues: ETASI model application to the 2023 SE Türkiye earthquake sequence

SUMMARY The epidemic-type aftershock sequence (ETAS) model is the state-of-the-art approach for modelling short-term earthquake clustering and is preferable for short-term aftershock forecasting. However, due to the large variability of different earthquake sequences, the model parameters must be adjusted to the local seismicity for accurate forecasting. Such an adjustment based on the first aftershocks is hampered by the incompleteness of earthquake catalogues after a mainshock, which can be explained by a blind period of the seismic networks after each earthquake, during which smaller events with lower magnitudes cannot be detected. Assuming a constant blind time, direct relationships based only on this additional parameter can be established between the actual seismicity rate and magnitude distributions and those that can be detected. The ETAS-incomplete (ETASI) model uses these relationships to estimate the true ETAS parameters and the catalogue incompleteness jointly. In this study, we apply the ETASI model to the SE Türkiye earthquake sequence, consisting of a doublet of M 7.7 and M 7.6 earthquakes that occurred within less than half a day of each other on 6 February 2023. We show that the ETASI model can explain the catalogue incompleteness and fits the observed earthquake numbers and magnitudes well. A pseudo-prospective forecasting experiment shows that the daily number of detectable m ≥ 2 can be well predicted based on minimal and incomplete information from early aftershocks. However, the maximum magnitude (Mmax ) of the next day’s aftershocks would have been overestimated due to the highly variable b value within the sequence. Instead, using the regional b value estimated for 2000–2022 would have well predicted the observed Mmax values.

Aftershock Forecasting

Aftershocks can compound the impacts of a major earthquake, disrupting recovery efforts and potentially further damaging weakened buildings and infrastructure. Forecasts of the probability of aftershocks can therefore aid decision-making during earthquake response and recovery. Several countries issue authoritative aftershock forecasts. Most aftershock forecasts are based on simple statistical models that were first developed in the 1980s and remain the best available models. We review these statistical models and the wide-ranging research to advance aftershock forecasting through better statistical, physical, and machine-learning methods. Physics-based forecasts based on mainshock stress changes can sometimes match the statistical models in testing but do not yet outperform them. Physical models are also hampered by unsolved problems such as the mechanics of dynamic triggering and the influence of background conditions. Initial work on machine-learning forecasts shows promise, and new machine-learning earthquake catalogs provide an opportunity to advance all types of aftershock forecasts. ▪Several countries issue real-time aftershock forecasts following significant earthquakes, providing information to aid response and recovery.▪Statistical models based on past aftershocks are used to compute aftershock probability as a function of space, time, and magnitude.▪Aftershock forecasting is advancing through better statistical models, constraints on physical triggering mechanisms, and machine learning.▪Large high-resolution earthquake catalogs provide an opportunity to advance physical, statistical, and machine-learning aftershock models.

Towards improving the spatial testability of aftershock forecast models

Abstract. Aftershock forecast models are usually provided on a uniform spatial grid, and the receiver operating characteristic (ROC) curve is often employed for evaluation, drawing a binary comparison of earthquake occurrences or non-occurrence for each grid cell. However, synthetic tests show flaws in using the ROC for aftershock forecast ranking. We suggest a twofold improvement in the testing strategy. First, we propose to replace ROC with the Matthews correlation coefficient (MCC) and the F1 curve. We also suggest using a multi-resolution test grid adapted to the earthquake density. We conduct a synthetic experiment where we analyse aftershock distributions stemming from a Coulomb failure (ΔCFS) model, including stress activation and shadow regions. Using these aftershock distributions, we test the true ΔCFS model as well as a simple distance-based forecast (R), only predicting activation. The standard test cannot clearly distinguish between both forecasts, particularly in the case of some outliers. However, using both MCC-F1 instead of ROC curves and a simple radial multi-resolution grid improves the test capabilities significantly. The novel findings of this study suggest that we should have at least 8 % and 5 % cells with observed earthquakes to differentiate between a near-perfect forecast model and an informationless forecast using ROC and MCC-F1, respectively. While we cannot change the observed data, we can adjust the spatial grid using a data-driven approach to reduce the disparity between the number of earthquakes and the total number of cells. Using the recently introduced Quadtree approach to generate multi-resolution grids, we test real aftershock forecast models for Chi-Chi and Landers aftershocks following the suggested guideline. Despite the improved tests, we find that the simple R model still outperforms the ΔCFS model in both cases, indicating that the latter should not be applied without further model adjustments.

Testing the forecasting skills of aftershock models using a Bayesian framework

The Epidemic Type Aftershock Sequence (ETAS) model and the modified Omori law (MOL) are two aftershock rate models that are used for operational earthquake/aftershock forecasting. Previous studies have investigated the relative performance of the two models for specific case studies. However, a rigorous comparative evaluation of the forecasting performance of the basic aftershock rate models for several different earthquake sequences has not been done before. In this study, forecasts of five prominent aftershock sequences from multiple catalogs are computed using the Bayesian predictive distribution, which fully accounts for the uncertainties in the model parameters. This is done by the Markov Chain Monte Carlo (MCMC) sampling of the model parameters and forward simulation of the ETAS or MOL models to compute the aftershock forecasts. The forecasting results are evaluated using five different statistical tests, including two comparison tests. The forecasting skill tests indicate that the ETAS model tends to perform consistently well on the first three tests. The MOL fails the same tests for certain forecasting time intervals. However, in the comparison tests, it is not definite whether the ETAS model is the better performing model. This work demonstrates the use of forecast testing for different catalogs, which is also applicable to catalogs with a higher magnitude of completeness.

Modern Products for a Vintage Event: An Update on the 1933 Long Beach, California, Earthquake

Abstract When a notable earthquake occurs in the United States, a range of familiar real- and near-real-time products are produced by the U.S. Geological Survey (USGS) Advanced National Seismic System (ANSS), and made available via the ANSS Comprehensive Earthquake Catalog. For historical and early instrumental earthquakes, similar results and products are developed depending on data availability and event significance, drawing from published later studies. The year 2023 marked the ninetieth anniversary of the 11 March 1933 Long Beach, California, earthquake. This anniversary provided the impetus to update ANSS products, drawing on archived and published data. Here, we describe the updated ShakeMap, shaking recordings and intensities, and retrospective aftershock forecast for the Long Beach, California, earthquake. In effect we have developed standard, modern ANSS products for an earthquake that occurred 90 yr ago. Our results show that the distributions of both the ground motions, anchored by three strong-motion recordings, and aftershock magnitudes are consistent with expectations for an M 6.4 mainshock in Southern California. We show that, notwithstanding possible limitations, instrumentally recorded accelerations from the closest station are consistent with predicted shaking and directly estimated macroseismic intensities. Updated data products have been added to the USGS event page, where they are available for download. Public-facing products were also created for the anniversary and are freely available on the USGS website.

Implementation and verification of a real-time system for automatic aftershock forecasting in China

In this paper, an automatic aftershock forecasting system for China is presented. Based on a parameter-free historical analogy method, this system can produce short-term aftershock forecast, including seismic sequence types and the magnitude of the largest aftershock, within a few minutes after a major earthquake and can further provide scientists and government agencies with a set of background information for consultation purposes. First, the system construction concept and operation framework are described, and an evaluation of the forecast performance of the system is then conducted considering earthquakes from 2019 to 2021 in mainland China. The results indicate that the sequence type classification precision reaches 83.5%, and the magnitude of more than 90% of the aftershocks is smaller than that of upper range forecast. This system is fast and easy to control, and all the reports and maps can be produced approximately 5 min after earthquake occurrence. Practical use verifies that the application of this system has greatly improved the efficiency of post-earthquake consultation in mainland China.

Strong aftershocks traffic light system: A case study of the 8 January 2022 MS6.9 Menyuan earthquake, Qinghai Province, China

Strong aftershocks, especially the disaster-causing M≥5.0 kind, are a key concern for mitigation of seismic risks because they often lead to superimposed earthquake damage. However, the real-time forecasting results of the traditional probability prediction models based on statistics are usually far from accurate and therefore unsatisfactory. Borrowing an idea from the foreshock traffic light system (FTLS), which is based on observations of decreasing b-values or increasing differential stress just before a strong aftershock, we constructed a strong aftershock traffic light system (SATLS) that uses data-driven technology to improve the reliability of time sequence b-value calculations, and analyzed the b-value variations of strong aftershocks in the China continent. We applied this system to the MS6.9 Menyuan earthquake occurred on 8 January 2022. The earthquake occurrence rates before the largest aftershock (MS5.2) forecast by the Omi-R-J model were too low, although the model could accurately forecast aftershock rates for each magnitude interval in most time-periods. However, reliable b-values can be calculated using the time-sequence b-value data-driven (TbDD) method, and the results showed that the b-values continued declining from 1.3 days before the MS5.2 aftershock and gradually recovered afterward. This would suggest that the stress evolution in the focal area can provide data for deciding when to post risk alerts of strong aftershocks. In the process of building the SATLS, we studied thirty-four M≥6.0 intraplate earthquake sequences in the China continent and concluded that the differences between the b-values of the aftershock sequences and of the background events, △b = bafter - bbg = ±0.1, could be used as thresholds to determine whether M≥5.0 aftershocks would occur. The △b value obtained using the events before the MS5.2 aftershock of the MS6.9 Menyuan sequence was about -0.04, which would have caused the SATLS to declare a yellow alert, but there would have been some gap expected before a red alert was triggered by the b-value difference derived from the events associated with this strong aftershock. To accurately forecast a strong aftershock of M≥5.0, a deeper understanding of the true b-value and a detailed description of the stress evolution state in the source area is necessary.

An Interactive Viewer to Improve Operational Aftershock Forecasts

Abstract The U.S. Geological Survey (USGS) issues forecasts for aftershocks about 20 minutes after most earthquakes above M 5 in the United States and its territories, and updates these forecasts 75 times during the first year. Most of the forecasts are issued automatically, but some forecasts require manual intervention to maintain accuracy. It is important to identify the sequences whose forecasts will benefit from a modified approach so the USGS can provide accurate information to the public. The oaftools R package (Paris and Michael, 2022) includes functions that analyze and plot earthquake sequences and their forecasts to identify which sequences require such intervention. The package includes the Operational Aftershock Forecast (OAF) Viewer, which incorporates the functions into an interactive web environment that can be used to explore aftershock sequences. The OAF Viewer starts with a global map and table of mainshocks. After a mainshock has been selected, the map and a new table show its aftershocks and the OAF Viewer generates five analytical plots: (1) magnitude–time, which is used to look for patterns in the data; (2) cumulative number, to see how the productivity of the sequence compares to a Reasenberg and Jones (1989) aftershock model over time; (3) magnitude–frequency, to compare the ratio of large to small magnitudes and extrapolate to higher magnitudes with sparse data and lower magnitudes with incomplete data; (4) forecast success, to compare the forecasts with observations for a sequence; and (5) parameter–time, which examines the temporal evolution of the forecast model parameters. The user can interact with the functions provided by the oaftools package through the OAF Viewer or by incorporating the functions into their own analysis methods. The OAF Viewer will help seismologists understand complexities in the data, communicate with the public and emergency managers, and improve the OAF system by maintaining operational awareness.

Effects of large aftershocks on spatial aftershock forecasts during the 2017–2019 western Iran sequence

SUMMARY On 12 November 2017, an earthquake with a moment magnitude of 7.3 struck the west of Iran near the Iraq border. This event was followed about 9 and 12 months later by two large aftershocks of magnitude 5.9 and 6.3, which together triggered intensive seismic activity known as the 2017–2019 Kermanshah sequence. In this study, we analyse this sequence regarding the potential to forecast the spatial aftershock distribution based on information about the main shock and its largest aftershocks. Recent studies showed that classical Coulomb failure stress (CFS) maps are outperformed by alternative scalar stress quantities, as well as a distance-slip probabilistic model (R) and deep neural networks (DNN). In particular, the R-model performed best. However, these test results were based on the receiver operating characteristic (ROC) metric, which is not well suited for imbalanced data sets such as aftershock distributions. Furthermore, the previous analyses also ignored the potential impact of large secondary earthquakes. For the complex Kermanshah sequence, we applied the same forecast models but used the more appropriate MCC-F1 metric for testing. Similar to previous studies, we also observe that the receiver independent stress scalars yield better forecasts than the classical CFS values relying on the specification of receiver mechanisms. However, detailed analysis based on the MCC-F1 metric revealed that the performance depends on the grid size, magnitude cut-off and test period. Increasing the magnitude cut-off and decreasing the grid size and period reduce the performance of all methods. Finally, we found that the performance of the best methods improves when the source information of large aftershocks is additionally considered, with stress-based models outperforming the R model. Our results highlight the importance of accounting for secondary stress changes in improving earthquake forecasts.

Effective uncertainty visualization for aftershock forecast maps

Abstract. Earthquake models can produce aftershock forecasts, which have recently been released to lay audiences. While visualization literature suggests that displaying forecast uncertainty can improve how forecast maps are used, research on uncertainty visualization is missing from earthquake science. We designed a pre-registered online experiment to test the effectiveness of three visualization techniques for displaying aftershock forecast maps and their uncertainty. These maps showed the forecasted number of aftershocks at each location for a week following a hypothetical mainshock, along with the uncertainty around each location's forecast. Three different uncertainty visualizations were produced: (1) forecast and uncertainty maps adjacent to one another; (2) the forecast map depicted in a color scheme, with the uncertainty shown by the transparency of the color; and (3) two maps that showed the lower and upper bounds of the forecast distribution at each location. We compared the three uncertainty visualizations using tasks that were specifically designed to address broadly applicable and user-generated communication goals. We compared task responses between participants using uncertainty visualizations and using the forecast map shown without its uncertainty (the current practice). Participants completed two map-reading tasks that targeted several dimensions of the readability of uncertainty visualizations. Participants then performed a Comparative Judgment task, which demonstrated whether a visualization was successful in reaching two key communication goals: indicating where many aftershocks and no aftershocks are likely (sure bets) and where the forecast is low but the uncertainty is high enough to imply potential risk (surprises). All visualizations performed equally well in the goal of communicating sure bet situations. But the visualization with lower and upper bounds was substantially better than the other designs at communicating surprises. These results have implications for the visual communication of forecast uncertainty both within and beyond earthquake science.

Revisiting Operational Aftershock Forecasting in the Eastern Intermountain West

Abstract We present a magnitude-dependent aftershock model for the eastern Intermountain West (IMW) that can be used to inform and improve reliability of initial U.S. Geological Survey (USGS) operational aftershock forecasts (OAFs). The model is derived after considering recent M ≥ 5.0 aftershock sequences in the eastern IMW, and careful identification of M < 5.0 aftershock sequences from regional earthquake catalogs (western Montana, Utah). We propose a workflow for M < 5.0 earthquake sequences that consists of: (1) spatiotemporal analysis of magnitude of completeness, (2) application of multiple declustering–clustering algorithms, (3) discrimination between mainshock–aftershock sequences (MS-AS) and earthquake swarms, and (4) synthesizing the results to determine the final list of MS-AS. In addition, we examined separately aftershock sequences with 5.0 ≤ M < 6.0 and M ≥ 6.0 and find a notable difference in earthquake productivity. Our results show that the proposed model for IMW differs from the one used in the USGS OAF, and highlights the need for region-specific aftershock modeling. This study validates the hypothesis that earthquake sequences following large surface-faulting earthquakes in the eastern IMW, and perhaps generally in the Basin and Range Province, behave fundamentally differently than those following moderate-size earthquakes.

Prospective and Retrospective Evaluation of the U.S. Geological Survey Public Aftershock Forecast for the 2019–2021 Southwest Puerto Rico Earthquake and Aftershocks

Abstract The Mw 6.4 Southwest Puerto Rico Earthquake of 7 January 2020 was accompanied by a robust fore- and aftershock sequence. The U.S. Geological Survey (USGS) has issued regular aftershock forecasts for more than a year since the mainshock, available on a public webpage. Forecasts were accompanied by interpretive and informational material, published in English and Spanish. Informational products included narrative “scenarios” for how the aftershock sequence could play out, infographics, and a report on the potential duration of the aftershock sequence through the next decade. Forecasts are based on the epidemic-type aftershock sequence (ETAS) model and generated using the USGS AftershockForecaster software—an interactive graphical user interface built on the OpenSHA platform (Field et al., 2003). The initial forecast is based on past sequences in similar tectonic environments; subsequent forecasts are tuned to the ongoing sequence via Bayesian model updating. Probabilistic aftershock forecasts for the next day, week, month, and year were publicly released and archived at a daily to monthly tempo, allowing for a truly prospective test of the forecast. Here, we evaluate the forecast over the first year of the recorded aftershocks. The ETAS-based forecast performed well overall, successfully capturing both the chance of having at least one earthquake of a given magnitude in a forecast interval as well as the non-Poissonian distribution of the total number of aftershocks within an interval. A retrospective analysis shows that the ETAS model is a substantial improvement over the existing Reasenberg and Jones (1989) forecast model. The exercise also reveals some limitations of the current model, in particular, with respect to nonstationarities in the aftershock magnitude distribution and model parameters throughout the evolving sequence.

Spatiotemporal Evolution of Pore Pressure Changes and Coulomb Failure Stress in a Poroelastic Medium for Different Faulting Regimes

AbstractSpatial distribution of aftershocks has been explained by the co‐seismic static Coulomb Failure Stress changes (ΔCFS) to some extent. In practice, the earth's crust is porous medium saturated with fluid filling pores, cavities, cracks, and faults. The pore fluid flow plays an important role in the evolution of the stress field in porous medium of the earth. The ΔCFS can be influenced by the co‐seismic pore pressure changes and the post‐seismic pore fluid flow. In order to understand such impacts, we built 3D fault‐slip finite element models to study the evolution of the pore pressure changes and the ΔCFS based on the fully coupled poroelastic theory. The strike‐slip, reverse, and normal faulting models were investigated separately. The patterns of co‐seismic ΔCFS were similar to the elastic stress changes on a homogeneous half‐space, but there were considerable differences in details especially in the near‐fault area when considering co‐seismic pore pressure change. Due to post‐seismic pore fluid flow, the near‐field stress shadow narrowed gradually in the strike‐slip model. In the reverse faulting model, both the near‐field stress shadow and enhanced areas expanded. In the normal faulting model, the stress shadow near the epicenter reduced. The pore pressure changes decayed sharply at the beginning and then gradually approached to zero because the system evolves to drained conditions. These results can help us to understand the effect of pore fluid flow on Coulomb Failure Stress evolution and might improve the understanding of earthquake triggering and aftershock forecasting.

The June 12, 2017 M6.3 Karaburun-Lesvos earthquake of the Northern Aegean Sea: Aftershock forecasting and stress transfer

The June 12, 2017 Karaburun-Lesvos (North Aegean Sea) earthquake occurred along the NW-SE trending Lesvos fault, along the southern strand of the North Anatolian Fault Zone. In the present study seismotectonic aspects of the 2017 Karaburun-Lesvos earthquake and its aftershock sequence are studied. A rupture model based on finite source analysis of the teleseismic waveforms has shown that the earthquake was associated with a failure of single asperity. About 5 days after the mainshock a temporary seismic network of 8 broadband stations (Real-time Aftershock Forecasting in Turkey, RAFT) had been deployed along the Turkish Aegean coast to enhance the existing regional seismic monitoring and the acquired data have been used to relocate the aftershocks. The temporary deployment significantly improved the aftershock detection capacity and resulted in more precise locations. Prior to the monitoring enhancement a single widespread aftershock cluster was observed; however, the relocated aftershocks, augmented by the RAFT stations, identified two distinct spatially isolated clusters. The first day of the aftershock sequence has been used for retrospective real-time aftershock forecasting up to 7 days following the mainshock. Our results indicate that with a method developed by Omi et al. (2013) can be used forecasting aftershocks over a week period successfully employing incompletely detected aftershocks occurred in the first day following the mainshock. Stress tensor analysis of the 33 aftershock source mechanisms has shown local dominance of the extensional tectonics with azimuth and plunge pairs for the three principal stress axes as σ1, σ2 and σ3 are (255°; 76°), (131°; 8°) and (39°; 11°), respectively. Coulomb stress changes imparted by the mainshock onto the nodal planes of the aftershocks show that ~67% of the 33 aftershocks have been exposed to positive stress change at least on one of the nodal planes.

The Productivity of Cascadia Aftershock Sequences

ABSTRACTThis study addresses questions about the productivity of Cascadia mainshock–aftershock sequences using earthquake catalogs produced by the Geological Survey of Canada and the Pacific Northwest Seismic Network. Questions concern the likelihood that future moderate to large intermediate depth intraslab earthquakes in Cascadia would have as few detectable aftershocks as those documented since 1949. More broadly, for Cascadia, we consider if aftershock productivities vary spatially, if they are outliers among global subduction zones, and if they are consistent with a physical model in which aftershocks are clock-advanced versions of tectonically driven background seismicity. A practical motivation for this study is to assess the likely accuracy of aftershock forecasts based on productivities derived from global data that are now being issued routinely by the U.S. Geological Survey. For this reason, we estimated productivity following the identical procedures used in those forecasts and described in Page et al. (2016). Results indicate that in Cascadia we can say that the next intermediate depth intraslab earthquake will likely have just a few detectable aftershocks and that aftershock productivity appears to be an outlier among global subduction zones, with rates that on average are lower by more than half, except for mainshocks in the upper plate. Our results are consistent with a clock-advance model; productivities may be related to the proximity of mainshocks to a population of seismogenic fault patches and correlate with background seismicity rates. The latter and a clear correlation between productivities with mainshock depth indicate that both factors may have predictive value for aftershock forecasting.

The Productivity of Cascadia Aftershock Sequences

ABSTRACT This study addresses questions about the productivity of Cascadia mainshock–aftershock sequences using earthquake catalogs produced by the Geological Survey of Canada and the Pacific Northwest Seismic Network. Questions concern the likelihood that future moderate to large intermediate depth intraslab earthquakes in Cascadia would have as few detectable aftershocks as those documented since 1949. More broadly, for Cascadia, we consider if aftershock productivities vary spatially, if they are outliers among global subduction zones, and if they are consistent with a physical model in which aftershocks are clock-advanced versions of tectonically driven background seismicity. A practical motivation for this study is to assess the likely accuracy of aftershock forecasts based on productivities derived from global data that are now being issued routinely by the U.S. Geological Survey. For this reason, we estimated productivity following the identical procedures used in those forecasts and described in Page et al. (2016). Results indicate that in Cascadia we can say that the next intermediate depth intraslab earthquake will likely have just a few detectable aftershocks and that aftershock productivity appears to be an outlier among global subduction zones, with rates that on average are lower by more than half, except for mainshocks in the upper plate. Our results are consistent with a clock-advance model; productivities may be related to the proximity of mainshocks to a population of seismogenic fault patches and correlate with background seismicity rates. The latter and a clear correlation between productivities with mainshock depth indicate that both factors may have predictive value for aftershock forecasting.