Extracting body‐wave arrivals from ambient seismic recordings remains a challenging task, largely because ambient records are usually dominated by surface‐wave energy. Most ambient seismic data‐processing strategies aimed at enhancing body‐wave energy focus on a cross‐correlation plus stack methodology. While this approach is useful for interferometric investigations, it effectively squares the magnitude of unwanted coherent noise events (e.g. surface waves, burst‐like or strong monochromatic energy) that commonly overpower ambient body‐wave signal. Accordingly, coherent noise events are usually treated with a binary accept‐or‐reject decision of individual data windows based on root‐mean‐squared energy considerations. Applying a data‐processing workflow to uncorrelated ambient seismic data represents an alternate strategy for mitigating coherent noise. However, this pre‐stack methodology requires significant computational effort due to the often terabyte‐sized data volumes. To make this approach feasible, we outline an automated processing workflow to automatically identify and mitigate coherent noise events that otherwise does not severely degrade the remaining waveforms. After each processing step, we apply a number of quality control measures to monitor the convergence rate of cross‐correlation plus stack waveforms and for evidence of emerging body‐wave reflection events. We apply the processing flow to an ambient seismic data set acquired on a large‐N array at a mine site near Lalor Lake, Manitoba, Canada. Our quality control analyses suggest that automated preprocessing of uncorrelated ambient seismic recordings successfully mitigates unwanted impulsive and monochromatic coherent noise events. Accordingly, we assert that applying an automated data‐processing approach would be beneficial for body‐wave and other imaging and inversion analyses applied to ambient seismic recordings.

Time-lapse seismic data is used to monitor subsurface changes occurring between consecutive surveys. One way to look at the differences is to create images from each survey and simply subtract them to obtain a difference. It is possible to model image differences directly from the slowness change between two surveys: this leads to a forward modeling operator that essentially performs differential migration. We further exploit the concept of image-domain time-lapse inversion by introducing extended-image time-lapse differences. Extended images are generalizations of the conventional imaging condition, and the resulting images are an extension of subsurface image gathers. We discuss objective functions for image-domain time-lapse inversion from both conventional and extended images. These depart from the more conventional approaches to time-lapse inversion whose objective functions are parameterized in the recorded data domain. By analyzing the forward operators for conventional and extended image differences, we show that the use of image extensions yields a larger data space thus offering additional constraints for time-lapse inversion. We illustrate these concepts numerically by comparing sensitivity kernels as well as inversion results from conventional versus extended time-lapse images.

Long-time marine seismic recordings are becoming more common with the increased use of ocean-bottom nodes (OBNs), which can measure ambient seismic energy at frequencies lower than the typical minimum values in active-source compressed air-gun surveys. Interferometric processing on long-time ambient multicomponent data allows for the extraction of low-frequency (sub-2.0 Hz) responses in virtual source gathers (VSGs). Using 40 days of continuous OBN recordings acquired on a large dense array during a field experiment in the Gulf of Mexico, we find that sub-2.0 Hz surface-wave energy in the computed VSGs is strongly coherent and exhibits an identifiable spatially varying character. In particular, after rotating the data components from a Cartesian geographic into a polar wave-vector reference frame, we find that radial VSGs (i.e., oriented along the vector connecting the virtual source and receiver) clearly indicate that surface-wave propagation is influenced by salt bodies as identified in a colocated active-source survey situated at a minimum of 0.7 km depth below the seafloor, an observation consistent with calculated 0.25–0.50 Hz surface-wave sensitivity kernels. This suggests that low-frequency ambient OBN surface-wave seismology could be important for estimating the long-wavelength elastic material properties (particularly S-wave velocity) and identifying the lateral boundaries of salt bodies without any prior knowledge of subsurface geology.

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Over the past decade, computing power has increased, new sensing technologies have been developed, and our understanding of how we interact with the earth has evolved, leading to new opportunities and priorities in geophysical research. These changes have been more rapid in some areas than others, and new topics have emerged as well. It is challenging for geophysicists, including junior staff and undergraduates starting their geophysics journeys, to stay abreast of scientific and industrial trends. Thus, the Early Career Subcommittee of the SEG Research Committee (RC) thought it imperative to survey members of the RC in 2022. To that end, a survey was conducted, and responses were collected from 43 RC members.