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Seismic data for studying the near surface have historically been extremely sparse in cities, limiting our ability to understand small-scale processes, locate small-scale geohazards, and develop earthquake hazard microzonation at the scale of buildings. In recent years, distributed acoustic sensing (DAS) technology has enabled the use of existing underground telecommunications fibers as dense seismic arrays, requiring little manual labor or energy to maintain. At the Fiber-Optic foR Environmental SEnsEing array under Pennsylvania State University, we detected weak slow-moving signals in pedestrian-only areas of campus. These signals were clear in the 1 to 5 Hz range. We verified that they were caused by footsteps. As part of a broader scheme to remove and obscure these footsteps in the data, we developed a convolutional neural network to detect them automatically. We created a data set of more than 4000 windows of data labeled with or without footsteps for this development process. We describe improvements to the data input and architecture, leading to approximately 84% accuracy on the test data. Performance of the network was better for individual walkers and worse when there were multiple walkers. We believe the privacy concerns of individual walkers are likely to be highest priority. Community buy-in will be required for these technologies to be deployed at a larger scale. Hence, we should continue to proactively develop the tools to ensure city residents are comfortable with all geophysical data that may be acquired.

We analyze active and passive seismic data recorded by the Stanford distributed acoustic sensing array (SDASA) located in conduits under the Stanford University campus. For the active data we used low-energy sources (betsy gun and sledge hammer) and recorded data using both the DAS array and 98 three-component nodes deployed along a 2D line. The joint analysis of shot profiles extracted from the two data sets shows that some surface waves and refracted events are consistently recorded by the DAS array. In areas where geophone coupling was suboptimal because of surface obstructions, DAS recordings are more coherent. In contrast, surface waves are more reliably recorded by the geophones than the DAS array. Because of the noisy environment and weak sources, neither data set shows clear reflections. We demonstrate the repeatability of DAS recordings of local earthquakes by comparing two weak events (magnitude 0.95 and 1.34) with epicenters 100 m apart that occurred only one minute from each other. Analyzing another local, and slightly stronger, earthquake (magnitude 2.0) we show how the kinematics of both the P-arrival and S-arrival can be measured from the DAS data. Interferometric analysis of passive data shows that reliable virtual-source responses can be extracted from the DAS data. We observe Rayleigh waves when correlating aligned receivers, and Love waves when correlating receivers belonging to segments of the array parallel to each other. Dispersion analysis of the virtual sources shows the expected decrease in surface-wave velocity with increasing frequency.

Are you an early-career geoscientist or engineer? Have you heard about the SEG Research Committee? Either way, we want you to know how the committee plans to work with early-career scientists and professionals to achieve their goals, and we invite you to join us in making changes.

Building on past workshops focused on distributed acoustic sensing (DAS), the SEG Research Committee plans to organize a DAS-focused postconvention workshop in 2020 titled “DAS: Advances in fiber-optic sensing over the last decade.” This year's program will continue to showcase cutting-edge fiber-optic technology, case studies in the energy industry, and CO2 sequestration while expanding on novel uses of machine learning with massive DAS data, the use of new open DAS data for improved reproducibility, and the role of DAS in smart cities.