Characteristics of Ground Motion Generated by Wind Interaction With Trees, Structures, and Other Surface Obstacles

Identifying Different Classes of Seismic Noise Signals Using Unsupervised Learning

Characteristics and spatial variability of wind noise on near-surface broadband seismometers

PrefaceCh. 1Historical Introduction3Ch. 2Continuum Mechanics25Ch. 3Equations of Motion56Ch. 4Normal Modes109Ch. 5Seismic Source Representation146Ch. 6Anelasticity and Attenuation193Ch. 7Rayleigh-Ritz Method237Ch. 8Spheroidal and Toroidal Oscillations257Ch. 9Elastic and Anelastic Perturbations327Ch. 10Synthetic Seismograms363Ch. 11Love and Rayleigh Waves405Ch. 12Mode-Ray Duality451Ch. 13Perturbation Theory537Ch. 14Mode Splitting and Coupling596Ch. 15Body-Wave Ray Theory669Ch. 16Surface-Wave JWKB Theory737App. AVectors and Tensors811App. BSpherical Harmonics838App. CGeneralized Spherical Harmonics877App. DWhole Earth Catalogue932Bibliography967Index999

Theoretical global seismology

Global seismographic networks (GSNs) emerged during the late nineteenth and early twentieth centuries, facilitated by seminal international developments in theory, technology, instrumentation, and data exchange. The mid‐ to late‐twentieth century saw the creation of the World‐Wide Standardized Seismographic Network (1961) and International Deployment of Accelerometers (1976), which advanced global geographic coverage as seismometer bandwidth increased greatly allowing for the recording of the Earth's principal seismic spectrum. The modern era of global observations and rapid data access began during the 1980s, and notably included the inception of the GEOSCOPE initiative (1982) and GSN (1988). Through continual improvements, GEOSCOPE and the GSN have realized near‐real time recording of ground motion with state‐of‐art data quality, dynamic range, and timing precision to encompass 180 seismic stations, many in very remote locations. Data from GSNs are increasingly integrated with other geophysical data (e.g., space geodesy, infrasound and Interferometric Synthetic Aperture Radar). Globally distributed seismic data are critical to resolving crust, mantle, and core structure; illuminating features of the plate tectonic and mantle convection system; rapid characterization of earthquakes; identification of potential tsunamis; global nuclear test verification; and provide sensitive proxies for environmental changes. As the global geosciences community continues to advance our understanding of Earth structure and processes controlling elastic wave propagation, GSN infrastructure offers a springboard to realize increasingly multi‐instrument geophysical observatories. Here, we review the historical, scientific, and monitoring heritage of GSNs, summarize key discoveries, and discuss future associated opportunities for Earth Science.

Achievements and Prospects of Global Broadband Seismographic Networks After 30 Years of Continuous Geophysical Observations

/pdf/low-frequency-seismic-noise-characteristics-from-the-iqpj3hzjos.pdf

Low-Frequency Seismic Noise Characteristics From the Analysis of Co-Located Seismic and Pressure Data

\n We propose a single-station approach to estimate near-surface elastic structure using collocated pressure and seismic instruments. Our main result in this study is near-surface rigidity (shear modulus) structure at 784 EarthScope Transportable Array (TA) stations in operation from mid-2011 to the end of 2018 using coherent horizontal seismic and pressure signals at 0.02 Hz. We isolate time periods for which surface pressure change is the dominant excitation source for seismic signals by searching for data windows with large pressure variations and high-seismic-pressure coherence. We emphasize the importance of using horizontal seismic components for two reasons: first, horizontal seismic signals are significantly higher than vertical signals at 0.02 Hz due to ground tilt, and second, we can analytically compute the predicted horizontal signals without an assumption of atmospheric pressure wavespeed (which is required for predicting the vertical excitation). Sensitivity kernels from 0.01 to 0.05 Hz show that this pressure–seismic coupling is mostly dependent on rigidity shallower than 50 or 100 m. Our estimates of shallow elastic structure show good spatial agreement with large-scale surface geological features. For instance, stations in the Appalachian Mountains mostly have high rigidity, whereas low-rigidity sites dominate the Mississippi Alluvial Plain. Because of the lack of measured velocity profiles, we quantitatively validate our approach by comparing with VS30 models that are based on proxies such as topographic slopes and large-scale surface geology. We estimate near-surface rigidity at 784 TA stations, where these locations have no prior structure information. Our method provides independent information for seismic hazard studies.

Estimating Near‐Surface Rigidity from Low‐Frequency Noise Using Collocated Pressure and Horizontal Seismic Data

Author(s): Tanimoto, Toshiro; Wang, Jiong | Abstract: SUMMARY An algorithm developed in Tanimoto a Wang for deriving shallow elasticity structure from colocated pressure and seismic instruments is applied to data at nine stations in the Pinon Flat Observatory (PFO) in Southern California. Depth resolution kernels indicate that this approach can recover near-surface shear-modulus structure in the upper 50–100 m of the Earth. Our estimate for Vs30 at the closest station (BPH01) agrees with an independent result by Yong et al. which was based on an active source survey with a geophone array. Vs30 derived from the borehole S-wave speed model by Fletcher et al. also agrees with estimates at nearby locations, considering the size of uncertainties. Derived structures are much smoother than their models, however. This is because our method analyses slow quasi-static deformation of the solid Earth in the frequency range 0.005–0.05 Hz that inherently limits depth resolution. Lack of detailed variations in structures does not seem to be the problem for estimating Vs30 as Vs30 is an averaged quantity from the upper 30 m of the Earth. Also our estimates are not affected by different choices of frequency range in the inversion. However, our method may have some difficulty at stiff, hard-rock sites because ground deformation caused by surface pressure can become small and the key observables in our method, the ratios between seismic amplitude and surface pressure change, become difficult to measure accurately.

/pdf/shallow-elasticity-structure-from-colocated-pressure-and-asj3phdc9c.pdf

Shallow elasticity structure from colocated pressure and seismic stations in the Piñon Flat Observatory and estimation of Vs30

Incorporating Wind Information in the Inversion of Co-Located Pressure and Seismic Data for Shallow Elastic Structure

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2018JB017132

Theory for Deriving Shallow Elasticity Structure From Colocated Seismic and Pressure Data

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