Neutronic Design for Heat Pipe Reactor With Annular and Accident Tolerant Fuels

Several core designs of heat pipe reactors with megawatt power were proposed for extreme environments, such as the deep space, the deep sea, and the earthquake locations. However, the existing designs have either the difficulty of manufacture or potential issues of transport. In the present work, a heat pipe design is proposed with an annular fuel element to replace the cylindrical and hexagon fuel elements. In addition, candidate accident tolerant fuels, such as the UN and U3Si2 fuels, are implemented. The neutronic properties of the new reactor design are systematically investigated by the OpenMC Monte Carlo code simulations. It is found that BeO presents a better effect of reducing the axial power deviation than Al2O3. The criticality of the proposed design is verified by two configurations of control drums. The depletion calculations show that each design can operate for decades of years.

Spatiotemporal evolution of deep seismicity beneath the central Himalayas

<p>The Himalayan orogen, formed by the continental collision between the Indian and Eurasian plates, is a unique geological structure that has been extensively studied over the past few decades. These previous studies highlighted the occurrence of earthquakes in the orogen's roots beneath the central Himalayas. However, the characterization of these deep earthquakes remains limited. Here, we compiled a detailed, long-duration catalog, which we use to investigate the spatiotemporal characteristics of seismicity beneath the Himalayan orogen. </p><p>To create this catalog, we collected all available continuous seismic data acquired during the last two decades in the central Himalayas region (i.e., 2001-2005). We applied a systematic, semi-automatic processing routine to obtain absolute earthquake locations using a 1-D velocity model. Using high-quality picks, ~8,000 preliminary earthquake locations have been determined, at least 1,000 of which have hypocentral depths >50 km. We plan to refine the preliminary locations and calculate local magnitudes for the intermediate-depth lithospheric earthquakes. Using this refined catalog, we will analyze the spatiotemporal evolution pattern and properties of the Himalayan deep seismicity. This analysis is expected to provide us with insights into the processes and mechanisms that control seismogenesis beneath the orogen. For example, is seismicity driven by earthquake stress transfer (mainshock-aftershock sequences), or is it caused by external processes like fluids or aseismic slip, or both?</p>

Velocity models for routine earthquake monitoring at volcanoes: from deterministic to Bayesian

<p>Earthquake location is a primary function of volcano observatories worldwide and the resulting catalogs of seismicity are integral to interpretations and forecasts of volcanic activity.  Ensuring earthquake location accuracy is therefore of critical importance.  However, accurate earthquake locations require accurate velocity models, which are not always available.  In addition, difficulties involved in applying traditional velocity modeling methods often mean that earthquake locations are computed at volcanoes using velocity models not specific to the local volcano.   </p><p>Traditional linearized methods that jointly invert for earthquake locations, velocity structure, and station corrections depend critically on having reasonable starting values for the unknown parameters, which are then iteratively updated to minimize the data misfit.  However, these deterministic methods are susceptible to local minima and divergence, issues exacerbated by sparse seismic networks and/or poor data quality common at volcanoes.  In cases where independent prior constraints on local velocity structure are not available, these methods may result in systematic errors in velocity models and hypocenters, especially if the full range of possible starting values is not explored.  Furthermore, such solutions depend on subjective choices for model regularization and parameterization.</p><p>In contrast, Bayesian methods promise to avoid all these pitfalls.  Although these methods traditionally have been difficult to implement due to additional computational burdens, the increasing use and availability of High-Performance Computing resources mean widespread application of these methods is no longer prohibitively expensive.  In this presentation, we apply a Bayesian, hierarchical, trans-dimensional Markov chain Monte Carlo method to jointly solve for hypocentral parameters, 1D velocity structure, and station corrections using data from monitoring networks of varying quality at several volcanoes in the U.S. and South America.  We compare the results with those from a more traditional deterministic approach and show that the resulting velocity models produce more accurate earthquake locations.  Finally, we chart a path forward for more widespread adoption of the Bayesian approach, which may improve catalogs of volcanic seismicity at observatories worldwide. </p>

Monitoring the 2020 Magna, Utah, Earthquake Sequence with Nodal Seismometers and Machine Learning

Immediately following the 18 March 2020 Mww 5.7 Magna, Utah, earthquake, work began on installing a network of three-component, 5 Hz geophones throughout the Salt Lake Valley. After six days, 180 geophones had been sited within 35 km of the epicenter. Each geophone recorded 250 samples per second data onsite for ∼40 days. Here, we integrate the geophone data with data from the permanent regional seismic network operated by the University of Utah Seismograph Stations (UUSS). We use machine learning (ML) methods to create a new catalog of arrival time picks, earthquake locations, and P-wave polarities for 18 March 2020–30 April 2020. We train two deep-learning U-Net models to detect P waves and S waves, assigning arrival times to maximal posterior probabilities, followed by a two-step association process that combines deep learning with a grid-based interferometric approach. Our automated workflow results in 142,000 P picks, 188,000 S picks, and over 5000 earthquake locations. We recovered 95% of the events in the UUSS authoritative catalog and more than doubled the total number of events (5000 vs. 2300). The P and S arrival times generated by our ML models have near-zero biases and standard deviations of 0.05 s and 0.09 s, respectively, relative to corresponding analyst times picked at backbone stations. We also use a deep-learning architecture to automatically determine 70,000 P-wave first motions, which agree with 93% of 5876 hand-picked up or down first motions from both the backbone and nodal stations. Overall, the use of ML led to large increases in the number of arrival times, especially S times, that will be useful for future tomographic studies, as well as the discovery of thousands more earthquakes than exist in the UUSS catalog.

Using Aftershocks to Help Locate Historical Earthquakes

For historical earthquakes, the spatial distributions of macroseismic intensity reports are commonly used to estimate the event locations. The methods to locate historical earthquakes assume that the highest seismic intensity shows the best estimate of the location of the earthquake. Uncertainties in the locations estimated from macroseismic data can be due to an uneven geographic distribution of sites with intensity reports, variations in intensities due to local soil conditions, ambiguous historical reports, and earthquake directivity effects. Additional constraint on the location of a historical earthquake can come from places where most aftershocks were felt, because these localities may have been closest to the fault on which the mainshock took place. Examples of estimated earthquake locations based on aftershocks are those of the 1727 MLg 5.6 earthquake in northeastern Massachusetts, the MLg 5.7 earthquake in Maine, and the 1755 MLg 6.2 earthquake offshore of Cape Ann, Massachusetts. In all of these cases, the earthquake locations based on the aftershock data are somewhat different from previous locations derived from the macroseismic intensities alone. Uncertainties with this method include identifying aftershocks in historical accounts and the possibility that smaller events that are reported following a strong earthquake are not on or near the mainshock rupture. Even so, evidence of possible aftershock activity may help constrain the location of that mainshock. Because aftershocks of strong earthquakes (M≥7) can last months to years, archival research for aftershocks must be carried out with a somewhat different mindset than that for a mainshock.

Probabilistic earthquake locations of induced seismicity in the Groningen region, the Netherlands

SUMMARY The Groningen gas reservoir, situated in the northeast of the Netherlands, is western Europe’s largest producing gas field and has been in production since 1963. The gas production has induced both subsidence and seismicity. Seismicity is detected and located using the Koninklijk Nederlands Meteorologisch Instituut shallow-borehole array for the period 2015–2017, incorporating the back projection techniques of QuakeMigrate and the nonlinear location procedure to constrain earthquake locations and depths. The uncertainties on the estimated depths are estimated taking into account velocity model, changes in station array geometry and uncertainties in the measurement of arrival times of the P and S waves. We show that the depth distribution of seismicity is consistent with nucleation within the reservoir (28 per cent) or in the overburden (60 per cent) within ∼500 m from the top of the reservoir. Earthquakes with hypocentres in the overburden likely originate from overlying Zechstein anhydrite caprock. Based on their depth distribution, it seems like the earthquakes are primarily driven by the elastic strain in the reservoir and overburden, induced by the reservoir compaction. We estimate the probability of earthquakes nucleating beneath the reservoir in the underlying Carboniferous limestone and basement, to be no more than 12 per cent.

Improved earthquake location in the gold mining regions of South Africa using new velocity models

Reliable local earthquake locations depend on many factors of which a major one is the velocity model. Currently the Council for Geoscience (CGS) has been using the global IASP91 velocity model for earthquake locations in the cluster networks. To continue improving the earthquake locations it is necessary that new velocity models are determined for each cluster region (Central and East Rand - CERAND, the Klerksdorp – Orkney – Stilfontein – Hartebeesfontein – KOSH and the Far West and West Rand - WRAND). The availability of good quality data recorded by the cluster networks since their inception in 2010 provides an opportunity to conduct this work. Thus data from the cluster networks database were selected according to set quality criteria to obtain parametric data for 130 earthquakes in the CERAND region, 404 in the KOSH region and 1024 in the WRAND region. These data were used to determine a minimum 1-D velocity model with associated station corrections for each of the regions using the VELEST software package. Comparison of epicentres obtained using the new velocity models to epicentres previously published by the CGS, showed improvement in the quality of the new locations. Thus, the new models will be implemented in the day-to-day analysis of data recorded in the three study regions by the cluster network of stations.