When large atmospheric pressure variations occur at the Earth’s surface, the solid Earth deforms elastically in response. Low-frequency seismic data record such deformations. This thesis provides a better understanding of this near-surface deformation and its applications with data from the USArray Transportable Array (TA) stations after 2012.

The two main sets of data we use are: low-frequency seismic signals, recorded on the stations’ broadband seismometers, and atmospheric pressures on collocated pressure sensors. We observe high coherence between seismic noise signals and pressure variations in their power spectral densities (PSDs) in the frequency band between 0.01 and 0.05 Hz. When atmospheric pressure variations are sufficiently large, recorded seismic signals varies alongside pressure data. The ratios between seismic and pressure data can be formulated to solve for near-surface elastic structure, especially the shear-modulus. Knowledge of the near-surface elastic structure is important for its application on seismic hazard and ground motion prediction studies.

We analyze data from over nine hundred TA stations from 2012 to 2019, spanning across the Central and Eastern US (CEUS) and Alaska. The two interconnected methods we develop and use for this dataset are the half-space method and the layered inversion method. Both methods, founded with similar fundamental backgrounds, regard the excitation source as a propagating plane pressure wave on the surface that generates ground deformation. The half-space model assumes a homogeneous medium under the surface, whereas the layered model assumes a vertically heterogeneous medium. In essence, the half-space model is a unique case of the layered model. The former permits straightforward estimations of near-surface shear-modulus at fixed frequencies, but it lacks depth resolution to constrain important parameters of seismic hazards such as Vs30—the averaged shear-wave velocity down to 30 meters deep. In contrast, the layered inversion allows to estimate Vs30 by utilizing data at multiple frequencies from 0.01 to 0.05 Hz and resulting layered velocity models.

Our results for the TA stations indicate that the two methods can resolve unique geological regions such as the Appalachian Mountains, and the Mississippi Alluvium Plain. For the Appalachian Mountains, where the bedrock is likely to be near the surface, our results indicate fast Vs30 at stations located in the area. For the Mississippi Alluvium Plain, known for thick alluvial sediments near the surface, our results demonstrate slow Vs30 at stations there. Large-scale geology maps and Vs30 models also corroborate our results. Our estimated Vs30s agree with mapped Quaternary sediment depths: fast Vs30 stations are often associated with areas categorized as thin sediment, while slow Vs30 stations are observable at locations with thick underlying sediment.

Seismic noise is the continuous vibration of the ground due to various non-earthquake causes and is generally regarded as an unwanted component of the signal recorded by a seismograph. Its primary sources include human activity, ocean waves, wind, and atmospheric phenomena. It has long been discarded in seismic analysis but it contains valuable information about its excitation sources and Earth structure. This thesis aims at exploring two main components of seismic noise: anthropogenic noise (human activity) and microseisms (ocean waves). It attempts to clarify noise source characteristics, excitation mechanisms, and propagation processes. By using seismic records from seismometers and Distributed Acoustic Sensing (DAS), and applying several processing techniques, we show discoveries on patterns of human behavior, SH-wave microseisms excitation mechanisms, and precise microseism source locations.First, in Chapter 2, in regards to the anthropogenic noise, we find seismic noise is positively correlated with human activity and economic development over 20 years. We choose an iconic event: the COVID-19 pandemic to study human response recorded in seismic noise records, on the ground that cities in mainland China and Italy imposed restrictions on travel and daily activities in response to COVID-19. It gives us an unprecedented opportunity to study the relationship between human behavior and seismic noise. In this study, we are primarily concerned with seismic noise with frequencies above 1 Hz, known as "cultural noise", mainly generated by local transportation systems. We demonstrate that seismic noise can provide an absolute real-time, anonymous characterization of human activity. In Chapter 3, with respect to the microseisms in the frequency band 0.05-0.5 Hz, we present body-wave microseisms caused by two remote low-pressure systems off the coast of southeastern Australia and southeastern Greenland, detected by a large, dense array (~350 stations) in China. We then use two years of data to study the noise sources of body wave microseisms around the globe. The study points out that SH-wave microseisms, which theoretically should not be excited by the wave-to-wave interaction of ocean waves, can be clearly observed. We also demonstrate that SH waves can only be observed when the source region is close to an area of thick ocean-bottom sediments. Finally, in Chapter 4, we locate the precise sources of high-frequency microseisms in the frequency band 0.5-2 Hz using the new seismic measurement technique DAS. Although microseisms have been observed for more than 100 years, precise locations of their excitation sources in the oceans are still elusive. DAS data offer opportunities for deciphering the locations of excitation sources near the coast that were not possible at all by regular seismographs, including ocean-bottom seismographs. Using DAS data off the coast of Valencia, Spain, and applying a cross-correlation approach, we show that the sources of high-frequency microseisms (0.5-2 Hz) are confined between 7 and 27 km offshore, where the water depth varies from 25 to 100 m. Over time, we observe that these sources move quickly along narrow areas, often confined within an area of a few kilometers. Our method with DAS data allows us to characterize microseisms with high spatiotemporal resolutions, opening a new chapter in understanding the global and complex seismic phenomena that occur in the oceans.