Earth's long period background seismic wavefield is dominated by two distinct processes that couple ocean wave energy to a global microseism wavefield. We assess global microseism intensity in the secondary (4-10 s) and primary (14-20 s) bands, and across eight 2 s-wide period bands between 4 and 20 s. Robustly estimated primary and secondary secular amplitude trends are estimated at 73 globally distributed seismic station sites with continuous recording spanning at least 20 years, from as early as the late 1980s through October 2025. These trends are positive at significance for 61 (84%) and 46 (63%) stations with global average rates for vertical-component acceleration of 0.17 0.04 and 0.11 0.05%/yr, for the primary and secondary bands, respectively, with corresponding rates of energy increase of 0.27 0.08 and 0.15 0.09%/yr. Secular intensification is also observed within all 2 s period bands between 4 and 20 s. Amplitude histories for the longest primary microseism periods (18-20 s) correlate to near-antipodal distances, reflecting long-range teleconnections attributed to large-fetch storm systems, long-range swell and Rayleigh wave propagation, and geographically correlated El Ni & ntilde;o Southern Oscillation and other geographically extensive atmospheric influences on storms and waves. The lower average rates of intensification for the secondary microseism suggest that crossing wave systems in remote regions are either under-observed or are intensifying more slowly than the primary microseism, possibly due to increasing swell unidirectionality. Secular intensification is greatest at the longest primary microseism periods. This is consistent with a broadening of the global ocean wave spectrum by approximately 0.01%/yr which may reflect an increasing occurrence of large storm systems.

Seismology has been used as a tool for understanding the current physical properties of the interior of the Earth and its dynamic evolution with remarkable success over the last century. Much of this progress is due to the ever-expanding set of high-quality quantitative observations of teleseismic waveforms recorded at seismographic stations worldwide. In this work, we revisit historical seismological studies that helped first identify a core distinct in physical properties from the overlying mantle, followed by the detection of an inner core that was eventually verified to be solid based on normal-mode eigenperiods. Along with a brief overview of past studies of the Earth's inner core, we examine the reproducibility of these results and discuss how historical data compare against modern observations. After accounting for past normal-mode misidentifications, we confirm that introducing a solid inner core is required to afford significant improvements in fits to both radial modes and core-sensitive spheroidal overtones. Strong shear dissipation in the inner core of the radial reference Earth model, REM1D (Q mu= 89:54), fits the reference datasets of both normal-mode eigenperiods and quality factors accounting for physical dispersion. Because a liquid region would only have bulk dissipation, a narrow range of low Q mu values that are preferred by the reference datasets affords additional evidence of a solid inner core. In addition, we find that there is little systematic bias in the timing accuracy of historical data, although large variances exist. Investigations into the temperature, composition, and evolution of the inner core, as well as the reproducibility of past studies, can benefit from the reconciliation of historical and modern seismological datasets.

Abstract Earth’s microseism wavefield dominates seismic background levels at periods between approximately 4 and 20 s, and reflects periodic and secular variations in ocean swell energy. Ocean wave energy couples to the seismic wavefield via distinct primary microseism and secondary microseism (PM and SM) source mechanisms, which are excited by basal swell tractions and seafloor pressure variations due to crossing seas, respectively. This study examines annual amplitude variations for the globally dominant PM (14–20 s) and SM (4–10 s) period bands. Annual harmonic variations are represented by four-term Fourier series fits to vertical-component seasonally smoothed acceleration time series from 73 stations in the Global Seismographic and GEOSCOPE networks with over 20 yr of recording and at least 75% data completeness. These annual periodic functions fit between 14%–95% (PM) and 22%–97% (SM) of signal variance. Station annual peak-to-peak variations range between 1.2–14.3 dB (PM) and 1.5–20.6 dB (SM). An asymmetry in microseism features exists between the Northern (NH) and Southern (SH) Hemispheres. High-latitude NH stations show highly correlated PM and SM annual amplitude variations. This character dominates the wider extratropical NH but diminishes at tropical latitudes, and widespread relative PM–SM decorrelation is observed in the SH. These hemispheric characteristics reflect systematic differences in both extratropical storm activity and ocean wave state. Greater annual variation and seasonal predictability in the NH reflect the influence of the large continental landmasses that enhance both storm intensity seasonality and SM-generating coastal wave reflection. Notable clusters of low PM–SM correlation stations are also observed in continental Antarctica due to seasonal sea ice influences, and in East Asia, reflecting unusual PM and SM source responses to South Asian monsoonal and tropical cyclone ocean wave influences.

We report the discovery of an unprecedented, monochromatic low-frequency seismic source arising from the fjords of North-East Greenland. Following a landslide and tsunami event in Dickson fjord on 16 September 2023, the seismic waves were detected by broad-band seismometers worldwide. Here we focus on a detailed analysis of the long-period seismic signal, while a reconstruction of the dynamics of the landslide is presented by Svennevig et al. in session NH3.5. Both frequency and phase velocity of the waves are consistent with fundamental mode Rayleigh- and Love-waves. However, the decay rate of these waves is much slower than predicted for freely propagating surface waves so that we infer a long-lasting and slowly decaying source process. Although the 16 September 2023 event was by far the largest, analysis of historical seismic data has revealed five other previously undetected events, all with a fundamental frequency between 10.85 and 11.02 mHz. The signal of the largest two events initially decayed with a quality factor, Q close to Q=500, which increased to Q=3000 within the first 10 hours and could thus be detected for up to nine days. The smaller four events had a slow decay-rate (Q>1000) for their entire duration. In comparison, the global average attenuation of Rayleigh waves at these frequencies is Q=117 for PREM, thus precluding a single, impulsive source for these signals.Gleaning archives of optical and SAR satellite images reveals that at least four out of six events could be associated with landslides in Dickson fjord, the two others remain unresolved. However, such rapid transient events cannot explain the long duration of the radiated seismic waves. Our modelling of the largest event shows that a transversal seiche in Dickson fjord, excited by a landslide induced tsunami, can account for both the monochromatic low frequency signal as well as its seismic signal amplitude and radiation pattern. However, the seiche modelling results in Q values lower than 250 and hence the seiche needs to be continuously driven for the entire duration of the observed seismic signal. Thus, a full understanding of the source process that produces the monochromatic signal remains enigmatic.

The amplitude and frequency content of background seismic noise is highly variable with geographic location. Understanding the characteristics and behavior of background seismic noise as a function of location can inform approaches to improve network performance and in turn increase earthquake detection capabilities. Here, we calculate power spectral density estimates in one-hour windows for over 15 yr of vertical-component data from the nine-station Caribbean network (CU) and look at background noise within the 0.05 -300 s period range. We describe the most visually apparent features observed at the CU stations. One of the most prominent features occurs in the 0.75 -3 s band for which power levels are systematically elevated and decay as a function of proximity to the coastline. Further examination of this band on 1679 contiguous USArray Transportable Array stations reveals the same relationship. Such a relationship with coastal distance is not observed in the 4 -8 s range more typical of globally observed secondary microseisms. A simple surface-wave amplitude decay model fits the observed decay well with geometric spreading as the most important factor for stations near the coast ( <- 50 km). The model indicates that power levels are strongly influenced by proximity to coastline at 0.75 -3 s. This may be because power from nearshore wave action at 0.75 -3 s overwhelms more distant and spatially distributed secondary microseism generation. Application of this basic model indicates that a power reduction of- 25 dB can be achieved by simply installing the seismometer 25 km away from the coastline. This finding may help to inform future site locations and array design thereby improving network performance and data quality, and subsequently earthquake detection capabilities.

The U.S. Geological Survey (USGS) Global Seismographic Network (GSN) Program operates two thirds of the GSN, a network of state-of-the-art, digital seismological and geophysical sensors with digital telecommunications. This network serves as a multiuse scientific facility and a valuable resource for research, education, and monitoring. The other one third of the GSN is funded by the National Science Foundation (NSF), and the operations of this component are overseen by EarthScope. This collaboration between the USGS, EarthScope, and NSF has allowed for the development and operations of the GSN to be a truly multiuse network that provides near real-time open access data, facilitating fundamental discoveries by the Earth science community, supporting the earthquake hazards mission of the USGS, benefitting tsunami monitoring by the National Oceanic and Atmospheric Administration, and contributing to nuclear test monitoring and treaty verification. In this article, we describe the installation and evolution of the seismic networks operated by the USGS that ultimately led to the USGS portion of the GSN (100 stations under network codes IU, IC, and CU) as they are today and envision technological advances and opportunities to further improve the utility of the network in the future. This article focuses on the USGS-operated component of the GSN; a companion article on the GSN stations funded by the NSF and operated by the Cecil and Ida Green Institute of Geophysics and Planetary Physics, Scripps Institution of Oceanography, University of California at San Diego by Davis et al. (2023) appears in this volume.

Climate change is increasingly predisposing polar regions to large landslides. Tsunamigenic landslides have occurred recently in Greenland ( Kalaallit Nunaat ), but none have been reported from the eastern fjords. In September 2023, we detected the start of a 9-day-long, global 10.88-millihertz (92-second) monochromatic very-long-period (VLP) seismic signal, originating from East Greenland. In this study, we demonstrate how this event started with a glacial thinning–induced rock-ice avalanche of 25 × 10 6 cubic meters plunging into Dickson Fjord, triggering a 200-meter-high tsunami. Simulations show that the tsunami stabilized into a 7-meter-high long-duration seiche with a frequency (11.45 millihertz) and slow amplitude decay that were nearly identical to the seismic signal. An oscillating, fjord-transverse single force with a maximum amplitude of 5 × 10 11 newtons reproduced the seismic amplitudes and their radiation pattern relative to the fjord, demonstrating how a seiche directly caused the 9-day-long seismic signal. Our findings highlight how climate change is causing cascading, hazardous feedbacks between the cryosphere, hydrosphere, and lithosphere.

Modern seismic data are collected, distributed, and analyzed using digital formats, and this has become a standard for the field. Although most modern seismometers still make use of analog electronic circuits, their data are converted from an analog voltage output to time-tagged counts by way of digitization. Although much of the digitization process is not complicated to conceptualize, there is a fair bit of jargon in digitizer specifications, and a few pitfalls that can arise in the processes of recording and analyzing ground-motion data. In this article, we review some of the fundamental physical properties of data acquisition systems and the basic steps in digitizing data from an analog instrument (specifically a seismometer). We then briefly discuss the digitization process and some of the key properties needed to make these data useful for seismological applications. Finally, we discuss some of the filtering processes that naturally arise from digitization and how it can affect the processing workflow. The end goal is to provide a user guide that will enable seismologists to have a working knowledge of the digitization process. We focus on aspects central to seismological applications and have tried to avoid getting bogged down in signal processing formalism.

Abstract Ocean waves excite continuous globally observable seismic signals. We use data from 52 globally distributed seismographs to analyze the vertical component primary microseism wavefield at 14–20 s period between the late 1980s and August 2022. This signal is principally composed of Rayleigh waves generated by ocean wave seafloor tractions at less than several hundred meters depth, and is thus a proxy for near-coastal swell activity. Here we show that increasing seismic amplitudes at 3σ significance occur at 41 (79%) and negative trends occur at 3σ significance at eight (15%) sites. The greatest absolute increase occurs for the Antarctic Peninsula with respective acceleration amplitude and energy trends ( ± 3σ) of 0.037 ± 0.008 nm s−2y−1 (0.36 ± 0.08% y−1) and 4.16 ± 1.07 nm2 s−2y−1 (0.58 ± 0.15% y−1), where percentage trends are relative to historical medians. The inferred global mean near-coastal ocean wave energy increase rate is 0.27 ± 0.03% y−1 for all data and is 0.35 ± 0.04% y−1 since 1 January 2000. Strongly correlated seismic amplitude station histories occur to beyond 50∘ of separation and show regional-to-global associations with El Niño and La Niña events.

ABSTRACT An increase in seismic stations also having microbarographs has led to increased interest in the field of seismoacoustics. A review of the recent advances in this field can be found in Dannemann Dugick et al. (2023). The goal of this note is to draw the attention of the readers of Dannemann Dugick et al. (2023) to several additional interactions between the solid Earth and atmosphere that have not been classically considered in the field of seismoacoustics. The 15 January 2022 Hunga Tonga–Hunga Ha‘api eruption produced acoustic gravity waves that were recorded globally. For example, the Lamb wave from this eruption produced early-arriving and long-lasting tsunami waves. This eruption also provided globally recorded coupling of atmospheric modes with solid Earth modes, providing another example of the complex interactions that can occur at the boundary between the atmosphere and the solid Earth. Even in the absence of large atmospheric signals, collocated pressure sensors at seismic stations can be a useful tool for estimating the local substructure, such at VS30, the average shear velocity of the upper 30 m. Finally, at low frequencies, it is possible to use pressure records to correct out atmospheric disturbances recorded on seismometers. We briefly review the aforementioned, nontraditional seismoacoustic topics that we feel are important to consider as part of the full suite of interactions occurring between the solid Earth and atmosphere.

Abstract The authors have requested that this preprint be removed from Research Square.

One of the most prominent challenges related to legacy seismic data is determining how these data can be appropriately used in modern research applications. The wide variety of instrumentation used in the analog era, the format of recording on paper wrapped around a helicorder drum, and limited metadata information introduces ambiguities that are not typical of modern digital data. Therefore, techniques must be developed to help characterize uncertainties in legacy data. This article presents an analysis that compares corecorded signals from two instruments-a Trillium Compact or PressEwing (PE) seismometer for sensing ground motion and two recording systems: a modern Q330 digitizer or heated-stylus system. Analyses of the recordings in both time and frequency domains indicate time uncertainty on the order of one second, identify a flat response in a 10-60 s band for the PE and drum recorder, and highlight how specific features of scans and paper seismograms (e.g., repeated portions of scans and line thickness) can cause timing jumps or reduced trace amplitude.

Records of pressure variations on seismographs were historically considered unwanted noise; however, increased deployments of collocated seismic and acoustic instrumentation have driven recent efforts to use this effect induced by both wind and anthropogenic explosions to invert for near-surface Earth structure. These studies have been limited to shallow structure because the pressure signals have relatively short wavelengths (<∼300 m). However, the 2022 eruption of Hunga Tonga–Hunga Ha’apai (also called “Hunga”) volcano in Tonga generated rare, globally observed, high-amplitude infrasound signals with acoustic wavelengths of tens of kilometers. In this study, we examine the acoustic-to-seismic coupling generated by the Hunga eruption across 82 Global Seismographic Network (GSN) stations and show that ground motion amplitudes are related to upper (0 to ∼5 km) crust material properties. We find high (>0.8) correlations between pressure and vertical component ground motion at 83% of the stations, but only 30% of stations show this on the radial component, likely due to complex tilt effects. We use average elastic properties in the upper 5.2 km from the CRUST1.0 model to estimate vertical seismic/acoustic coupling coefficients (SV/A) across the GSN network and compare these to recorded observations. We exclude many island stations from these comparisons because the 1° resolution of the CRUST1.0 model places a water layer below these stations. Our simple modeling can predict observed SV/A within a factor of 2 for 94% of the 51 non-island GSN stations with high correlations between pressure and ground motion. These results indicate that analysis of acoustic-to-seismic coupling from the eruption could be used to place additional constraints on crustal structure models at stations with collocated seismic and pressure sensors. Ultimately, this could improve tomographic imaging models, which rely on methods that are sensitive to local structure.

SUMMARY Acoustic energy originating from explosions, sonic booms, bolides and thunderclaps have been recorded on seismometers since the 1950s. Direct pressure loading from the passing acoustic wave has been modelled and consistently observed to produce ground deformations of the near surface that have retrograde elliptical particle motions. In the past decade, increased deployments of colocated seismometers and infrasound sensors have driven efforts to use the transfer function between direct acoustic-to-seismic coupling to infer near-surface material properties including seismic velocity structure and elastic moduli. In this study, we use a small aperture (≈600 m) array of broadband seismometers installed in different manners and depths in both granite and sedimentary overburden to understand the fundamental nature and repeatability of seismic excitation from 1 to 15 Hz using horizontally propagating acoustic waves generated by 97 local (2–10 km) explosions. In agreement with modelling, we find that the ground motions induced by acoustic-to-seismic coupling attenuate rapidly with depth. We confirm the modelled relation between acoustic and ground motion amplitudes, but show that within one acoustic wavelength, the uncertainty in the transfer coefficient between seismic and acoustic energy at a given seismic station increases linearly with separation distance between the seismic and acoustic sensor. We attribute this observation to the rapid decorrelation of the infrasonic wavefield across small spatial scales and recommend colocating seismic and infrasound sensors for use in studies seeking to invert for near-surface material properties. Additionally, contrary to acoustic-to-seismic coupling theory and prior observations, we find that seismometers emplaced in granite do not record retrograde elliptical particle motions in response to direct pressure loading. We rule out seismometer tilt effects as a likely source of this observations and suggest that existing models of acoustic-to-seismic excitation may be too simplistic for seismometers placed in high rigidity materials.

The 15 January 2022 climactic eruption of Hunga volcano, Tonga, produced an explosion in the atmosphere of a size that has not been documented in the modern geophysical record. The event generated a broad range of atmospheric waves observed globally by various ground-based and spaceborne instrumentation networks. Most prominent was the surface-guided Lamb wave (≲0.01 hertz), which we observed propagating for four (plus three antipodal) passages around Earth over 6 days. As measured by the Lamb wave amplitudes, the climactic Hunga explosion was comparable in size to that of the 1883 Krakatau eruption. The Hunga eruption produced remarkable globally detected infrasound (0.01 to 20 hertz), long-range (~10,000 kilometers) audible sound, and ionospheric perturbations. Seismometers worldwide recorded pure seismic and air-to-ground coupled waves. Air-to-sea coupling likely contributed to fast-arriving tsunamis. Here, we highlight exceptional observations of the atmospheric waves.

ABSTRACT Since 2004, the most complete estimate of background noise levels across the continental United States was attained using 61 broadband seismic stations to calculate power spectral density (PSD) probability density functions. To improve seismic noise estimates across the United States, we examine vertical component seismic data from the EarthScope USArray Transportable Array seismic network that rolled across the United States and southeastern Canada between 2004 and 2015 and form a large (10 TB) PSD database from 1679 stations that contains no smoothing or binning of the spectral estimates. Including station outages, our database has a mean of 98.9% data completeness, and we present maps showing the spatial and temporal variability of seismic noise in six bands of interest between 0.2 and 75 s period. At 0.2 s period, seismic noise across the eastern United States is predominantly anthropogenically generated and may be subsequently amplified more than 20 decibels in the sandy and water-saturated sediments of the southeastern U.S. Coastal Plain and Mississippi Embayment. In these sediments, 1 s noise shows similar amplification and is generated through a variety of mechanisms across the United States including cultural activity throughout Kentucky and the southeastern Appalachian Mountains, lake waves around the Great Lakes, and ocean waves throughout New England, the Pacific Northwest, and Florida. Both 0.2 and 1 s noise levels are the lowest in the Intermountain West portion of the United States. We attribute this to a combination of installations on crystalline rocks and reduced population density. Finally, we find that sensors emplaced in sandy, water-saturated sediments observe median, diurnal variations in vertical component power at 18–75 s period, which we infer arise through local deformation driven by pressure variations. Ultimately, our results underscore that for shallow (<5 m depth) sensor installation, bedrock provides superior broadband noise performance compared to unconsolidated sediments.

The U.S. Geological Survey (USGS) maintains an archive of 189,180 digitized scans of analog seismic records from the World-Wide Standardized Seismograph Network (WWSSN). Although these scans have been made public, the archive is too large to manually review, and few researchers have utilized large numbers of these records. To facilitate further research using this historical dataset, we develop a simple convolutional neural network (CNN) that rapidly ( similar to 4.75 s/film chip) classifies scanned film chip images (called "chips," because they are individually cut segments of 70 mm film) into four categories of "interestingness" to earthquake seismologists based on the presence of earthquakes and other seismic signals in the record: "no interest," "little interest," "interest," and "high interest." The CNN, dubbed "Seismic Analog Record Network" (SARNet), can identify four types of seismic traces ("no events," "minor events," "major events," and "errors") in 200 x 200 pixel subcrops with an accuracy of 92% using a confidence threshold of 85%. SARNet then converts 100 random subcrops from each film chip into the overall classification of interestingness. In this task, SARNet performed as well as expert human classifiers in determining the film chip's overall interest grade. Applying SARNet to 34,000 film chips in the WWSSN archive found that 21% of the images were of "high interest" and had an "indeterminate" rate of only 4%. Thus, the need for the manual review of images was reduced by 79%. Sorting of film chips derived from SARNet will expedite further exploration of the archive of digitized analog seismic records stored at the USGS.

Estimating the detection threshold of a seismic network (the minimum magnitude earthquake that can be reliably located) is a critical part of network design and can drive network maintenance efforts. The ability of a station to detect an earthquake is often estimated by assuming the spectral amplitude for an earthquake of a given size, assuming an attenuation relationship, and comparing the predicted amplitude with the average station background noise level. This approach has significant uncertainty because of unknown regional attenuation and complications in computing small event power spectra, and it fails to account for the specific capabilities of the automatic seismic phase picker used in monitoring. We develop a data-driven approach to determine network detection thresholds using a multiband phase picking algorithm that is currently in use at the U.S. Geological Survey National Earthquake Information Center. We apply this picking algorithm to cataloged earthquakes to determine an empirical relationship of the observability of earthquakes as a function of magnitude and distance. Using this relationship, we produce maps of detection threshold using station spatial configuration and station noise levels. We show that quiet, well-sited stations significantly increase the detection capabilities of a network compared with a network composed of many noisy stations. Because our method is data driven, it has two distinct advantages: (1) it is less dependent on theoretical assumptions of source spectra and models of regional attenuation, and (2) it can easily be applied to any seismic network. This tool allows for an objective approach to the management of stations in regional seismic networks.