A Consistent Geological-Seismological Model For Earthquake Occurrence in New Zealand

<p>For the development of earthquake occurrence models, historical earthquake catalogues and compilations of mapped, active faults are often used. The goal of this study is to develop new methodologies for the generation of an earthquake occurrence model for New Zealand that is consistent with both data sets. For the construction of a seismological earthquake occurrence model based on the historical earthquake record, 'adaptive kernel estimation' has been used in this study. Based on this method a technique has been introduced to filter temporal sequences (e.g. aftershocks). Finally, a test has been developed for comparing different earthquake occurrence models. It has been found that the adaptive kernel estimation with temporal sequence filtering gives the best joint fit between the earthquake catalogue and the earthquake occurrence model, and between two earthquake occurrence models obtained from data from two independent time intervals. For the development of a geological earthquake occurrence model based on fault information, earthquake source relationships (i.e. rupture length versus rupture width scaling) have been revised. It has been found that large dip-slip and strike-slip earthquakes scale differently. Using these source relationships a dynamic stochastic fault model has been introduced. Whereas earthquake hazard studies often do not allow individual fault segments to produce compound ruptures, this model allows the linking of fault segments by chance. The moment release of simulated fault ruptures has been compared with the theoretical deformation along the plate boundary. When comparing the seismological and the geological earthquake occurrence model, it has been found that a 'good' occurrence model for large dip-slip earthquakes is given by the seismological occurrence model using the Gutenberg-Richter magnitude frequency distribution. In contrast, regions dominated by long strike-slip faults produce large earthquakes but not many small earthquakes and the occurrence of earthquakes on such faults should be inferred from the dynamic fault model.</p>

A Consistent Geological-Seismological Model For Earthquake Occurrence in New Zealand

<p>For the development of earthquake occurrence models, historical earthquake catalogues and compilations of mapped, active faults are often used. The goal of this study is to develop new methodologies for the generation of an earthquake occurrence model for New Zealand that is consistent with both data sets. For the construction of a seismological earthquake occurrence model based on the historical earthquake record, 'adaptive kernel estimation' has been used in this study. Based on this method a technique has been introduced to filter temporal sequences (e.g. aftershocks). Finally, a test has been developed for comparing different earthquake occurrence models. It has been found that the adaptive kernel estimation with temporal sequence filtering gives the best joint fit between the earthquake catalogue and the earthquake occurrence model, and between two earthquake occurrence models obtained from data from two independent time intervals. For the development of a geological earthquake occurrence model based on fault information, earthquake source relationships (i.e. rupture length versus rupture width scaling) have been revised. It has been found that large dip-slip and strike-slip earthquakes scale differently. Using these source relationships a dynamic stochastic fault model has been introduced. Whereas earthquake hazard studies often do not allow individual fault segments to produce compound ruptures, this model allows the linking of fault segments by chance. The moment release of simulated fault ruptures has been compared with the theoretical deformation along the plate boundary. When comparing the seismological and the geological earthquake occurrence model, it has been found that a 'good' occurrence model for large dip-slip earthquakes is given by the seismological occurrence model using the Gutenberg-Richter magnitude frequency distribution. In contrast, regions dominated by long strike-slip faults produce large earthquakes but not many small earthquakes and the occurrence of earthquakes on such faults should be inferred from the dynamic fault model.</p>

Novel Intelligent Spatiotemporal Grid Earthquake Early-Warning Model

The integration analysis of multi-type geospatial information poses challenges to existing spatiotemporal data organization models and analysis models based on deep learning. For earthquake early warning, this study proposes a novel intelligent spatiotemporal grid model based on GeoSOT (SGMG-EEW) for feature fusion of multi-type geospatial data. This model includes a seismic grid sample model (SGSM) and a spatiotemporal grid model based on a three-dimensional group convolution neural network (3DGCNN-SGM). The SGSM solves the problem concerning that the layers of different data types cannot form an ensemble with a consistent data structure and transforms the grid representation of data into grid samples for deep learning. The 3DGCNN-SGM is the first application of group convolution in the deep learning of multi-source geographic information data. It avoids direct superposition calculation of data between different layers, which may negatively affect the deep learning analysis model results. In this study, taking the atmospheric temperature anomaly and historical earthquake precursory data from Japan as an example, an earthquake early warning verification experiment was conducted based on the proposed SGMG-EEW. Five groups of control experiments were designed, namely with the use of atmospheric temperature anomaly data only, use of historical earthquake data only, a non-group convolution control group, a support vector machine control group, and a seismic statistical analysis control group. The results showed that the proposed SGSM is not only compatible with the expression of a single type of spatiotemporal data but can also support multiple types of spatiotemporal data, forming a deep-learning-oriented data structure. Compared with the traditional deep learning model, the proposed 3DGCNN-SGM is more suitable for the integration analysis of multiple types of spatiotemporal data.

Bayesian Estimation of the ETAS Model for Earthquake Occurrences

ABSTRACT The epidemic-type aftershock sequence (ETAS) model is widely used in seismic forecasting. However, most studies of ETAS use point estimates for the model parameters, which ignores the inherent uncertainty that arises from estimating these from historical earthquake catalogs, resulting in misleadingly optimistic forecasts. In contrast, Bayesian statistics allows parameter uncertainty to be explicitly represented and fed into the forecast distribution. Despite its growing popularity in seismology, the application of Bayesian statistics to the ETAS model has been limited by the complex nature of the resulting posterior distribution, which makes it infeasible to apply to catalogs containing more than a few hundred earthquakes. To combat this, we develop a new framework for estimating the ETAS model in a fully Bayesian manner, which can be efficiently scaled up to large catalogs containing thousands of earthquakes. We also provide easy-to-use software that implements our method.

Greece and Turkey Shaken by African tectonic retreat

Earthquakes are a consequence of the motions of the planet’s tectonic plates, yet predicting when and where they may occur, and how to prepare remain some of the shortcomings of using scientific knowledge to protect human life. A devastating Mw 7.0 earthquake on October 30, 2020, offshore Samos Island, Greece was a consequence of the Aegean and Anatolian upper crust being pulled apart by north–south extensional stresses resulting from slab rollback, where the African plate is subducting northwards beneath Eurasia, while the slab is sinking by gravitational forces, causing it to retreat southwards. Since the retreating African slab is coupled with the overriding plate, it tears the upper plate apart as it retreats, breaking it into numerous small plates with frequent earthquakes along their boundaries. Historical earthquake swarms and deformation of the upper plate in the Aegean have been associated with massive volcanism and cataclysmic devastation, such as the Mw 7.7 Amorgos earthquake in July 1956 between the islands of Naxos and Santorini (Thera). Even more notable was the eruption of Santorini 3650 years ago, which contributed to the fall of the Minoan civilization. The Samos earthquake highlights the long historical lack of appreciation of links between deep tectonic processes and upper crustal deformation and geological hazards, and is a harbinger of future earthquakes and volcanic eruptions, establishing a basis for studies to institute better protection of infrastructure and upper plate cultures in the region.

Real-time determination of earthquake focal mechanism via deep learning

An immediate report of the source focal mechanism with full automation after a destructive earthquake is crucial for timely characterizing the faulting geometry, evaluating the stress perturbation, and assessing the aftershock patterns. Advanced technologies such as Artificial Intelligence (AI) has been introduced to solve various problems in real-time seismology, but the real-time source focal mechanism is still a challenge. Here we propose a novel deep learning method namely Focal Mechanism Network (FMNet) to address this problem. The FMNet trained with 787,320 synthetic samples successfully estimates the focal mechanisms of four 2019 Ridgecrest earthquakes with magnitude larger than Mw 5.4. The network learns the global waveform characteristics from theoretical data, thereby allowing the extensive applications of the proposed method to regions of potential seismic hazards with or without historical earthquake data. After receiving data, the network takes less than two hundred milliseconds for predicting the source focal mechanism reliably on a single CPU.

Paleo-earthquake Evidence and Earthquake Recurrence for Düzce Fault, Turkey

Paleoseismological trenching was performed along the Düzce fault providing some preliminary insight about its seismogenic behavior. Dating was based on radiocarbon analysis of peat samples collected from the trenches and suggested seven earthquakes have occurred since 1740 BC. Integrating date constraints of events exposed in the trenches suggests a periodical earthquake recurrence model. According to a linear sequential event serial that has minimum misfit determined by considering the probability curve limits of the sample dates, the earthquake recurrence interval is between 384 and 460 years (or possibly between AD 394 and 400). A probability curve was also calculated for the date of the last earthquake (1999 Düzce earthquake) considering the probability distributions of sample dates based on the same event serial. This probability-distribution-based method, similarly, predicted that the 1999 Düzce earthquake occurred between 1933–2005 (± 36 years) with a 68 % probability. After this verification. Using this method, it was estimated that the next earthquake along the Düzce fault has a 68 % probability of occurring between 2328–2392. According to this calculation, the earthquake recurrence interval is about 391 ± 34 years with a 68 % probability and the AD 967 historical earthquake likely ruptured the Düzce fault. Assuming an average slip of 350 cm (the average slip of the 1999 earthquake), the slip rate was estimated to be between 8.7–11.2 mm/a.

Using Google Earth Images to Extract Dense Landslides Induced by Historical Earthquakes at the Southwest of Ordos, China

As historical earthquake records are simple, determining the source parameters of historical strong earthquakes over an extended period is difficult. There are numerous uncertainties in the study of historical earthquakes based on limited literature records. Co-seismic landslide interpretation combined with historical documents can yield the possibility of reducing these uncertainties. The dense co-seismic landslides can be preserved for hundreds to thousands of years in Loess Plateau, North China; furthermore, there are notable attribute differences between earthquake landslides and rainfall-triggered landslides. Along the southwestern margin of the Ordos Block, only one severe earthquake has been recorded in the past 3,000 years. The records of “Sanchuan exhaustion and Qishan collapse” provide clues for an investigation of the 780 BC Qishan earthquake. In this study, combined with historical documents, current high-resolution Google Earth images were used to extract historical landslides along the southwestern of the Ordos Block. There were 6,876 landslides with a total area of 643 km2. The landslide-intensive areas were mainly distributed along the Longxian–Qishan–Mazhao Fault in the loess valley area on the northeastern side of the fault. Loess tableland and river terraces occur on the southwest side of the fault; dense landslides have not been examined due to the topographical conditions in this area. By analyzing the spatial distribution of historical earthquake damage in this region, comparing the characteristics of rainfall-triggered landslides, and combining existing dating results for bedrock collapse and loess landslides, the interpretation of dense historical landslides can be linked to the Qishan Earthquake. The interpretation results are associated with historical records. Analyses of current earthquake cases show that the distribution of dense landslides triggered by strong earthquakes can indicate the episeismic area of an earthquake. In addition, the non-integrated landslide catalog without small- and medium-scale coseismic landslides can be used to effectively determine the source parameters of historical strong earthquakes and perform quantitative evaluations. This study evaluates the focal parameters of the 780 BC Qishan earthquake based on interpretations of the spatial distribution range of historical landslides as representations of the range of the extreme earthquake zone.