Catalog Of Events Research Articles

Machine learning-assisted processing workflow for multi-fiber DAS microseismic data

In recent years, Distributed Acoustic Sensing (DAS) deployed in deviated wells has been increasingly used for microseismic monitoring. DAS can provide observations of microseismic wavefields with high spatial resolution and wide aperture, at the cost of unusually large data volumes compared with conventional downhole microseismic monitoring. To tackle this big-data challenge, we have developed key elements of a processing workflow that is assisted by machine learning techniques. We trained a convolutional neural network (CNN) for event detection and a U-Net model for both P- and S-wave arrival time picking. The workflow was applied to two multiwell DAS datasets acquired during hydraulic fracturing completions in western Canada. These datasets also include co-located 3C borehole geophone arrays that enable further comparison between catalogs from both sensor types. Compared with a traditional short-term average/long-term average (STA/LTA) method for event detection, our results indicate that the CNN method has a lower false-trigger rate and increases the event catalog size by a factor of 2.6–5.6. U-Net yields arrival-time picks with relatively small errors, high efficiency, and minimal user intervention, providing hypocenter location and focal depth that is arguably more accurate than the geophone catalog. While the proposed automated workflow requires substantial effort to build high-quality and large training datasets, it enables the use of DAS for real-time seismicity monitoring and risk management after the training stage. Although the DAS system detected fewer events than the geophone catalog and missed smaller magnitude events, our results indicate that fiber-optic sensors provide enough sensitivity to detect and locate sufficient events to characterize the treatment stages. DAS also captured induced events located at a hypocentral distance of >1 km, which are possibly indicative of reactivation of structural features.

Fault-based probabilistic seismic hazard analysis in regions with low strain rates and a thick seismogenic layer: a case study from Malawi

SUMMARY Historical and instrumental earthquake catalogs in low strain rate regions are not necessarily indicative of the long-term spatio-temporal distribution of seismicity. This implies that probabilistic seismic hazard analysis (PSHA) should also consider geologic and geodetic data through fault-based seismogenic sources. However, it is not always clear how on-fault magnitude-frequency distributions (MFDs) should be described and, if the seismogenic layer is especially thick, how fault sources should be extrapolated down-dip. We explore these issues in the context of a new PSHA for Malawi, where regional extensional rates are 0.5–2 mm yr−1, the seismogenic layer is 30–40-km thick, the instrumental catalog is ∼60 yr long and fault-based sources were recently collated in the Malawi Seismogenic Source Model. Furthermore, Malawi is one of several countries along the East African Rift where exposure to seismic hazard is growing, but PSHA does not typically consider fault sources. We use stochastic event catalogs to explore different fault source down-dip extents and MFDs. Our PSHA indicates that hazard levels are highest for a Gutenberg–Richter on-fault MFD, even at low probabilities of exceedance (2 per cent in 50 yr), whilst seismic hazard levels are also sensitive to how relatively short (<50 km) fault sources are extrapolated down-dip. For sites close to fault sources (<40 km), seismic hazard levels are doubled compared to previous instrumental-seismicity based PSHA in Malawi. Cumulatively, these results highlight the need for careful fault source modelling in PSHA of low strain rate regions and the need for new fault-based PSHA elsewhere in the East Africa Rift.

Implementation of the Event Metadata System for physics analysis in the NICA experiments

NICA (Nuclotron-based Ion Collider fAcility) is a new accelerator complex, which is under construction at the Joint Institute for Nuclear Research in Dubna to study properties of dense baryonic matter. The experiments of the NICA projects have already generated and obtained substantial volumes of event data, and it is expected that the overall number of stored events will increase from the current value of hundreds of millions to several billion events per year. The developed Event Metadata System is required to store and index experimental event records obtained at NICA facilities, allowing to quickly search for only necessary events based on various criteria for further processing and physics analyses. The main aim of the information system that is described is to speed up desired physics analyses and data quality checks. In addition, it allows reducing the load of the NICA computing infrastructure. The Event Metadata System, which is presented in the paper, is integrated with other experimental and software systems, in particular, NICA ROOT-based frameworks and implemented Condition Database. An access and management of the event metadata in the Event Catalogue being used to select only events for a particular physics analysis are provided via several interfaces, such as REST API, Web UI, and a dedicated C++/ROOT interface. The detailed architecture of the system and developed services including monitoring are also presented.

Analysis of the 2021 Milford, Utah earthquake swarm: Enhanced earthquake catalog and migration patterns

An earthquake swarm occurred in Spring 2021 in south-central Utah near the town of Milford. The University of Utah Seismograph Stations located 125 earthquakes between March 19 and May 10 with magnitudes ranging from 0.5 to 3.2. We implement a matched-filter technique in order to identify additional earthquakes that went undetected during the routine network location. The 125 network-located earthquakes are used as templates and are cross-correlated with continuous data for the dates Feb 17—June 10. This time period corresponds to approximately 1 month before the earthquake swarm began through 1 month after it ended. For the matched-filter analysis, we rely heavily on station FOR1, which is located within 5 km of most template events. Four other stations within 20–30 km of most template events provide a supplement to the closest station. The matched-filter implementation results in the detection of over 600 earthquakes in addition to the original 125 catalog events. This is one of the largest swarms ever recorded in Utah, and no previous large swarms have been recorded in this location. We use HypoDD to obtain relative double-difference locations of the catalog events. Both routine locations and HypoDD relocations of the catalog events suggest a fault dipping west beneath the mountain range, opposite of typical Basin and Range normal faults that dip beneath alluvial valleys. Moment tensors for the largest five events show normal faulting consistent with the west-dipping fault seen in the seismicity. Hydrothermal features in the area, including a geothermal power plant, suggest that fluids may be a contributing factor to the earthquake swarm triggering. We examine the role of fluids by exploring bounds on diffusion parameters and investigating spatial migration characteristics of the swarm seismicity. We conclude that this swarm is the result of heterogenous stress conditions in a prefractured region.

Eight-year catalog of deep short-term slow slip events at the Nankai trough based on objective detection algorithm using strain and tilt records

In subduction zones, slip deficit monitoring along the plate interface is important for understanding the seismogenesis of megathrust earthquakes. In the last two decades, aseismic slip transients, such as slow slip events (SSEs), which are usually synchronized with tectonic tremors, have been detected in subduction zones worldwide. Frequent SSEs are particularly important for releasing slip deficits during the inter-seismic periods of megathrust earthquakes. In southwest Japan, deep short-term SSEs have been primarily monitored with strain and tilt records because the SSEs in this region are small. However, strain and tilt records are so sensitive that they record not only SSEs, but also rainfall and local groundwater movements, which temporally affect the quality of data making it difficult to apply an automated detection algorithm. Therefore, previously reported short-term SSE catalogs, based on strain and tilt records, were created by visual inspections, although they are not suitable for generating a long-term catalog. In this study, a quantitative detection algorithm was developed to detect short-term SSEs using strain and tilt records. The problem of temporally varying data quality was solved by introducing the prior probability of log-normal distributions in the fitting variance. This method was applied to an 8-year (2013–2020) dataset of strains and tilts from southwest Japan. A total of 96 events were detected, among which, 78 corresponded with SSEs previously reported by the Geological Survey of Japan (GSJ). Although the GSJ catalog contained more events with smaller magnitudes, such events were difficult to distinguish from noise using the developed method. Three of the remaining 18 events were considered SSEs that were not reported in the GSJ catalog. Others could be artifacts because there were no obvious signals in the global navigation satellite system records (with events of magnitude > 6.0). Previous studies have suggested the existence of aseismic transients deeper or shallower than regular short-term SSEs in southwest Japan. However, detection results from this study did not confirm such events.Graphical

A Living Catalog of Parker Solar Probe IS⊙IS Energetic Particle Enhancements

Energetic charged particles are pervasive throughout the heliosphere with contributions from solar energetic particle events, stream and corotating interaction regions, galactic cosmic rays, anomalous cosmic rays, and suprathermal ions. The Integrated Science Investigation of the Sun (IS⊙IS) on board the Parker Solar Probe is a suite of energetic particle detectors covering the energy range ∼20 keV–200 MeV nuc−1. IS⊙IS measures energetic particles closer to the Sun than any instrument suite in history, providing a singular view of the energetic particle population in a previously unexplored region. To enable the global research community to efficiently use IS⊙IS data, we have developed an online living catalog of energetic particle enhancements observed by the IS⊙IS instruments. Event identification methodology, information on accessing the catalog, highlights of several events, and a summary of the overall trends are presented. Also included is a summary Event Catalog showing many of the key event parameters for IS⊙IS events to the time of writing.

Testing time evolution of mass distribution of black hole mergers

The detection of gravitational-wave events revealed that there are numerous populations of black hole (BH) binaries that can merge within the age of the Universe. Although several formation channels of such binaries are known, considerable theoretical uncertainties associated with each channel defeat the robust prediction of how much each channel contributes to the total merger rate density. Given that the time evolution of the merger rate density in some channels is (exactly or nearly) independent of BH masses, clarifying this feature from observational data will shed some light on the nature of BH binaries. On the basis of this motivation, we formulate a methodology to perform a statistical test of whether the mass distribution of BH mergers evolves over time by hypothesis testing. Our statistical test requires neither a priori specification of the mass distribution, which is largely uncertain, nor that of the time dependence of merger rate. We then apply it to mock data for some concrete shapes of the merger rate density and show that the proposed method rejects/(does not reject) the null hypothesis correctly for a large sample size. After this verification, the method is applied to a catalog of the gravitational-wave events obtained during the LIGO-Virgo’s third observing run. We find that the selection bias degrades the effectiveness of our method for the O3 catalog owing to the reduction in the number of and the maximum redshifts of the merger events that we can explore. Within the range where the method can be applied, there is no indication of the time evolution of the mass distribution of merger rate density. This limitation will be eased in future observations containing more events, and our hypothesis testing will help determine whether the merger rate density evolves over time independently of BH masses.

Seismic Response of Nenana Sedimentary Basin, Central Alaska

ABSTRACTNenana basin in central Alaska is a long (90 km), narrow (12 km), and deep (7 km) sedimentary basin aligned with an active fault zone producing Mw≥6 earthquakes. From 2015 to 2019, 13 broadband seismic stations were deployed in the region as part of the Fault Locations and Alaska Tectonics from Seismicity project. These stations recorded a wide range of earthquakes, including Mw 3–4 directly below the basin as well as several regional earthquakes Mw>6. These 43 local and regional earthquakes, in addition to five teleseismic events and continuously recorded ambient noise, provide a data set that we use to quantify the response of Nenana basin to the seismic wavefield. We calculate spectral ratios between each station and a bedrock reference station for 48 earthquakes. We find amplification of 11–14 dB (amplification ratio 3.5–5.0) for low frequencies (0.1–0.5 Hz), and 8–15 dB (amplification ratio 2.5–5.6) for high frequencies (0.5–4.0 Hz) on the vertical component. At low frequencies, amplification of the earthquake wavefield agrees well with amplification of seismic noise, with both data sets exhibiting stronger amplification on the horizontal components, in comparison with the vertical component. Furthermore, stations overlying the deeper part of the basin exhibit stronger amplification, whereas stations at the margin of the basin exhibit minimal low-frequency amplification. At higher frequencies, amplification occurs at both deeper basin stations and also marginal basin stations. Our study establishes a catalog of diverse events for future theoretical and numerical studies that can use Nenana basin to better understand the complex influence of sedimentary basins on the seismic wavefield.

Search for spatial coincidence between IceCube neutrinos and radio pulsars

We search for a spatial association between radio pulsars and ultra-high energy neutrinos using the publicly available IceCube point source neutrino events catalog. For this purpose we use the unbinned maximum likelihood method to search for a statistically significant excess from each of the pulsars in the ATNF catalog. We do not find any pulsars with detection significance much higher than that expected from a Gaussian distribution, Therefore, we conclude that none of the currently known pulsars contribute to the diffuse neutrino flux detected by IceCube.

Hallmarks of cancer: The insulin-like growth factors perspective.

The identification of a series of attributes or hallmarks that are shared by virtually all cancer cells constitutes a true milestone in cancer research. The conceptualization of a catalogue of common genetic, molecular, biochemical and cellular events under a unifying Hallmarks of Cancer idea had a major impact in oncology. Furthermore, the fact that different types of cancer, ranging from pediatric tumors and leukemias to adult epithelial cancers, share a large number of fundamental traits reflects the universal nature of the biological events involved in oncogenesis. The dissection of a complex disease like cancer into a finite directory of hallmarks is of major basic and translational relevance. The role of insulin-like growth factor-1 (IGF1) as a progression/survival factor required for normal cell cycle transition has been firmly established. Similarly well characterized are the biochemical and cellular activities of IGF1 and IGF2 in the chain of events leading from a phenotypically normal cell to a diseased one harboring neoplastic traits, including growth factor independence, loss of cell-cell contact inhibition, chromosomal abnormalities, accumulation of mutations, activation of oncogenes, etc. The purpose of the present review is to provide an in-depth evaluation of the biology of IGF1 at the light of paradigms that emerge from analysis of cancer hallmarks. Given the fact that the IGF1 axis emerged in recent years as a promising therapeutic target, we believe that a careful exploration of this signaling system might be of critical importance on our ability to design and optimize cancer therapies.

A new catalogue of solar flare events from soft X-ray GOES signal in the period 1986–2020

Solar flares, along with other sun-originated events such as Coronal Mass Ejections, fast solar wind streams, and solar energetic particles are among the most relevant events in Space Weather. Moreover, solar flares are the most energetic processes that occur in our solar system. The in-depth study of their occurrence statistics, both over extended periods or during individual solar cycles, allows us to improve and constrain the basic physical models of their origin. Increasing the number of detected events, especially those of lower intensity, and the number of physical parameters that describe the detected flares is, therefore, a mandatory goal.In this paper, we present a computationally efficient algorithm for the detection of solar flares in the soft-X solar flux provided by the GOES (NASA/NOAA) satellite constellation. Our code produces a new flare catalogue increasing the number of events with respect to the official GOES list. In addition to increasing the number of identified events, the catalogue contains information such as: an estimate of the total energy released, start and end time of the event, possible overlap with other events, background level of the GOES X-ray emission close to the revealed event. After a detailed description of the detection algorithm, we carry out a preliminary analysis of the flares reported in our catalogue and compare our results with the official list of GOES for the period from 1998 to 2020.

Deep learning and Bayesian inference of gravitational-wave populations: Hierarchical black-hole mergers

The catalog of gravitational-wave events is growing, and so are our hopes of constraining the underlying astrophysics of stellar-mass black-hole mergers by inferring the distributions of, e.g., masses and spins. While conventional analyses parametrize this population with simple phenomenological models, we propose an emulation-based approach that can compare astrophysical simulations against gravitational-wave data. We combine state-of-the-art deep-learning techniques with hierarchical Bayesian inference and exploit our approach to constrain the properties of repeated black-hole mergers from the gravitational-wave events in the most recent LIGO/Virgo catalog. Deep neural networks allow us to (i) construct a flexible single-channel population model that accurately emulates simple parametrized numerical simulations of hierarchical mergers, (ii) estimate selection effects, and (iii) recover the branching ratios of repeated-merger generations. Among our results, we find the following: The distribution of host-environment escape speeds favors values less than $100~\mathrm{km\,s^{-1}}$ but is relatively flat, with around $37\%$ of first-generation mergers retained in their host environments; first-generation black holes are born with a maximum mass that is compatible with current estimates from pair-instability supernovae; there is multimodal substructure in both the mass and spin distributions, which, in our model, can be explained by repeated mergers; and binaries with a higher-generation component make up at least $14\%$ of the underlying population. Though these results are inferred through emulation of a simplified model, the deep-learning pipeline we present is readily applicable to realistic astrophysical simulations

Forecasting GIC Activity Associated With Solar Wind Shocks for the Australian Region Power Network

AbstractsSpace weather induced Geomagnetically Induced Currents (GICs) that are hazardous to power networks at high latitudes are often the result of rapid changes in near‐Earth space current systems such as the auroral electrojets and field‐aligned currents. GIC activity at low‐middle latitudes may be driven by currents that are more relevant to these regions such as the ring or magnetopause current systems. Solar wind shocks create rapid changes in the magnetopause current that manifest as large step changes in the geomagnetic field at the Earth's surface, often referred to as geomagnetic sudden impulses (SIs), that are effective drivers of GIC activity in power networks at low‐middle latitudes. This paper describes the results of a study into the relationship between the driver of SIs, solar wind shocks, and their potential impact on power systems in the Australian region, as quantified by a GIC‐index. The initial data set for analysis was produced by programmatically scanning solar wind data obtained from the ACE satellite spanning the period 1998–2008 for solar wind shocks. For each identified solar wind shock, geomagnetic field data from the Australian region were analyzed to determine the corresponding SI and GIC‐index. Statistical analyses of GIC‐indices and various solar wind parameters associated with the shocks resulted in an empirical model that is a function of solar wind dynamic pressure, geomagnetic latitude, and change in solar wind speed with good operational predictive capability. This model was further assessed using online catalogs of solar wind shock events.

A Meteor of Apparent Interstellar Origin in the CNEOS Fireball Catalog

The earliest confirmed interstellar object, ‘Oumuamua, was discovered in the solar system by Pan-STARRS in 2017, allowing for a calibration of the abundance of interstellar objects of its size ∼100 m. This was followed by the discovery of Borisov, which allowed for a similar calibration of its size ∼0.4–1 km. One would expect a much higher abundance of significantly smaller interstellar objects, with some of them colliding with Earth frequently enough to be noticeable. Based on the CNEOS catalog of bolide events, we identify the ∼0.45 m meteor detected at 2014 January 8 17:05:34 UTC as originating from an unbound hyperbolic orbit. The U.S. Department of Defense has released an official letter stating that “the velocity estimate reported to NASA is sufficiently accurate to indicate an interstellar trajectory,” which we rely on here as confirmation of the object’s interstellar trajectory. Based on the data provided by CNEOS, we infer that the meteor had an asymptotic speed of v ∞ ∼ 42.1 ± 5.5 km s−1 outside of the solar system. Note that v ∞ here refers to the velocity of the meteor outside the solar system, not the velocity of the meteor outside the atmosphere. Its origin is approximately toward R.A. 49.°4 ± 4.°1 and decl. 11.°2 ± 1.°8, implying that its initial velocity vector was 58 ± 6 km s−1 away from the velocity of the local standard of rest (LSR).

Traveltime-based microseismic event location using artificial neural network

Location of earthquakes is a primary task in seismology and microseismic monitoring, essential for almost any further analysis. Earthquake hypocenters can be determined by the inversion of arrival times of seismic waves observed at seismic stations, which is a non-linear inverse problem. Growing amounts of seismic data and real-time processing requirements imply the use of robust machine learning applications for characterization of seismicity. Convolutional neural networks have been proposed for hypocenter determination assuming training on previously processed seismic event catalogs. We propose an alternative machine learning approach, which does not require any pre-existing observations, except a velocity model. This is particularly important for microseismic monitoring when labeled seismic events are not available due to lack of seismicity before monitoring commenced (e.g., induced seismicity). The proposed algorithm is based on a feed-forward neural network trained on synthetic arrival times. Once trained, the neural network can be deployed for fast location of seismic events using observed P-wave (or S-wave) arrival times. We benchmark the neural network method against the conventional location technique and show that the new approach provides the same or better location accuracy. We study the sensitivity of the proposed method to the training dataset, noise in the arrival times of the detected events, and the size of the monitoring network. Finally, we apply the method to real microseismic monitoring data and show that it is able to deal with missing arrival times in efficient way with the help of fine tuning and early stopping. This is achieved by re-training the neural network for each individual set of picked arrivals. To reduce the training time we used previously determined weights and fine tune them. This allows us to obtain hypocenter locations in near real-time.

On the Mass Ratio Distribution of Black Hole Mergers in Triple Systems

Observations have shown that the majority of massive stars, the progenitors of black holes (BHs), have on average more than one stellar companion. In triple systems, wide inner binaries can be driven to a merger by a third body due to long-term secular interactions, most notably by the eccentric Lidov–Kozai effect. In this study, we explore the properties of BH mergers in triple systems and compare their population properties to those of binaries produced in isolation and assembled in dense star clusters. Using the same stellar physics and identical assumptions for the initial populations of binaries and triples, we show that stellar triples yield a significantly flatter mass ratio distribution from q = 1 down to q ∼ 0.3 than either binary stars or dense stellar clusters, similar to the population properties inferred from the most recent catalog of gravitational-wave events, though we do not claim that all the observed events can be accounted for with triples. While hierarchical mergers in clusters can also produce asymmetric mass ratios, the unique spins of such mergers can be used to distinguish them from those produced from stellar triples. All three channels occupy distinct regions in the total mass–mass ratio space, which may allow them to be disentangled as more BH mergers are detected by LIGO, Virgo, and KAGRA.

LOSS OF AUTORISATION OF THE COMPANY TRADE UNION ORGANIZATION IMPACT ON COMPANY SOURCES OF LABOR LAW SELECTED ISSUES

The amendment of 5 July 2018 introduced some changes to the Trade Unions Act of 1991 in the scope of the rules for the acquisition and loss of legal status of a company trade union by trade unions in the workplace. The legislator extended the catalogue of events resulting in the loss of the status of a company trade union organization and established a juridical procedure for verifying the number of trade union members indicated in the information presented to the employer. These amendments were aimed at eliminating the state of uncertainty resulting fromthe existing regulations. However at the sametime, the legislator did not regulate the legal consequences of losing the status of a company trade union organization in relation to the sphere ofcompany sources of labor law. The author notices the legal gap in this field and related interpretation problems, indicating the need for regulating this issue by the legislator

GTUNE: An Assembled Global Seismic Dataset of Underground Nuclear Test Blasts

AbstractFrom catalogs of available declassified underground nuclear explosions, we compiled a comprehensive seismic waveform and event catalog termed GTUNE (Georgia Tech Underground Nuclear Explosions). Nuclear blast seismic records are sourced from previously prepared published datasets and openly available waveforms from online sources. All seismic traces were assembled into a user-friendly format compatible with most python-based machine learning (ML) packages. The GTUNE dataset includes the raw seismogram time series, event coordinates and origin time, sampling rate, station metadata, channel, epicentral distance, and P-wave arrival time from the origin dataset when available and otherwise identified using a tuned automated picker. This is the first openly available comprehensive global underground nuclear blast seismic dataset and consists of 28,123 vertical-component waveforms from 774 nuclear test blasts between 1961 and 2017 recorded between 0 and 90 epicentral degrees. For stations where data are not directly included due to data-sharing restrictions, the mechanisms to acquire and process these data are included. In this article, we describe various steps involved in data collection and quality control to ensure accurate labels, and present summary properties of the catalog and data set. The catalog was initially developed for applications with ML methods but can be used for a wide range of studies such as source physics, earth structure, and event detection methodological development.

Integrated Geostationary Solar Energetic Particle Events Catalog: GSEP

We present a catalog of solar energetic particle (SEP) events covering solar cycles 22, 23 and 24. We correlate and integrate three existing catalogs based on Geostationary Operational Environmental Satellite integral proton flux data. We visually verified and labeled each event in the catalog to provide a homogenized data set. We have identified a total of 341 SEP events of which 245 cross the Space Weather Prediction Center threshold of a significant proton event. The metadata consists of physical parameters and observables concerning the possible source solar eruptions, namely flares and coronal mass ejections for each event. The sliced time-series data of each event, along with intensity profiles of proton fluxes in several energy bands, have been made publicly available. This data set enables researchers in machine learning and statistical analysis to understand the SEPs and the source eruption characteristics useful for space weather prediction.

Detection of Slow Slip Events Using Wavelet Analysis of GNSS Recordings

ABSTRACT At many places, tectonic tremor is observed in relation to slow slip and can be used as a proxy to study slow slip events of moderate magnitude in which surface deformation is hidden in Global Navigation Satellite System (GNSS) noise. However, in subduction zones in which no clear relationship between tremor and slow slip occurrence is observed, these methods cannot be applied, and we need other methods to be able to better detect and quantify slow slip. Wavelets methods such as the Discrete Wavelet Transform and the Maximal Overlap Discrete Wavelet Transform (MODWT) are mathematical tools for analyzing time series simultaneously in the time and the frequency domains by observing how weighted differences of a time series vary from one period to the next. In this article, we use wavelet methods to analyze GNSS time series and seismic recordings of slow slip events in Cascadia. We use detrended GNSS data, apply the MODWT transform, and stack the wavelet details over several nearby GNSS stations. As an independent check on the timing of slow slip events, we also compute the cumulative number of tremor in the vicinity of the GNSS stations, detrend this signal, and apply the MODWT transform. In both the time series, we can then see simultaneous waveforms where timing corresponds to the timing of slow slip events. We assume that there is a slow slip event whenever there is a positive peak followed by a negative peak in the wavelet signal. We verify that there is a good agreement between slow slip events detected with only GNSS data and slow slip events detected with only tremor data for northern Cascadia. The wavelet-based detection method effectively detects events of magnitude higher than 6, as determined by independent event catalogs (e.g., Michel et al., 2019). As a demonstration of using the wavelet analysis in a region without significant tremor, we also analyze GNSS data from New Zealand and detect slow slip events that are spatially and temporally close to those detected previously by other studies.