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1. Follow the Trace: Becoming a Seismo-Detective with a Campus-Based Raspberry Shake Seismometer

   https://doi.org/10.1785/0220230365  

   Löberich, Eric; Long, Maureen D (May 2024, Seismological Research Letters)

   Abstract Seismic signals, whether caused by earthquakes, other natural phenomena, or artificial noise sources, have specific characteristics in the time and frequency domains that contain crucial information reflecting their source. The analysis of seismic time series is an essential part of every seismology-oriented study program. Enabling students to work with data collected from their own campus, including signals from both anthropogenic and natural seismic sources, can provide vivid, practical examples to make abstract concepts communicated in classes more concrete and relevant. Data from research-grade broadband seismometers enable us to record time series of vibrations at a broad range of frequencies; however, these sensors are costly and are often deployed in remote places. Participation in the Raspberry Shake citizen science network enables seismology educators to record seismic signals on our own campuses and use these recordings in our classrooms and for public outreach. Yale University installed a Raspberry Shake three-component, low-cost seismometer in the Earth and Planetary Sciences department building in Summer 2022, enabling the detection of local, regional, and teleseismic earthquakes, microseismic noise, and anthropogenic noise sources from building construction, an explosive event in a steam tunnel, and general building use. Here, we discuss and illustrate the use of data from our Raspberry Shake in outreach and education activities at Yale. In particular, we highlight a series of ObsPy-based exercises that will be used in courses taught in our department, including our upper-level Introduction to Seismology course and our undergraduate classes on Natural Disasters and Forensic Geoscience. 

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2. Coregistration of heading to visual cues in retrosplenial cortex

   https://doi.org/10.1038/s41467-023-37704-5  

   Sit, Kevin K.; Goard, Michael J. (April 2023, Nature Communications)

   Abstract Spatial cognition depends on an accurate representation of orientation within an environment. Head direction cells in distributed brain regions receive a range of sensory inputs, but visual input is particularly important for aligning their responses to environmental landmarks. To investigate how population-level heading responses are aligned to visual input, we recorded from retrosplenial cortex (RSC) of head-fixed mice in a moving environment using two-photon calcium imaging. We show that RSC neurons are tuned to the animal’s relative orientation in the environment, even in the absence of head movement. Next, we found that RSC receives functionally distinct projections from visual and thalamic areas and contains several functional classes of neurons. While some functional classes mirror RSC inputs, a newly discovered class coregisters visual and thalamic signals. Finally, decoding analyses reveal unique contributions to heading from each class. Our results suggest an RSC circuit for anchoring heading representations to environmental visual landmarks. 

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3. Unsupervised Deep Clustering of Seismic Data: Monitoring the Ross Ice Shelf, Antarctica

   https://doi.org/10.1029/2021JB021716  

   Jenkins, II, William F.; Gerstoft, Peter; Bianco, Michael J.; Bromirski, Peter D. (September 2021, Journal of Geophysical Research: Solid Earth)

   Abstract Advances in machine learning (ML) techniques and computational capacity have yielded state‐of‐the‐art methodologies for processing, sorting, and analyzing large seismic data sets. In this study, we consider an application of ML for automatically identifying dominant types of impulsive seismicity contained in observations from a 34‐station broadband seismic array deployed on the Ross Ice Shelf (RIS), Antarctica from 2014 to 2017. The RIS seismic data contain signals and noise generated by many glaciological processes that are useful for monitoring the integrity and dynamics of ice shelves. Deep clustering was employed to efficiently investigate these signals. Deep clustering automatically groups signals into hypothetical classes without the need for manual labeling, allowing for the comparison of their signal characteristics and spatial and temporal distribution with potential source mechanisms. The method uses spectrograms as input and encodes their salient features into a lower‐dimensional latent representation using an autoencoder, a type of deep neural network. For comparison, two clustering methods are applied to the latent data: a Gaussian mixture model (GMM) and deep embedded clustering (DEC). Eight classes of dominant seismic signals were identified and compared with environmental data such as temperature, wind speed, tides, and sea ice concentration. The greatest seismicity levels occurred at the RIS front during the 2016 El Niño summer, and near grounding zones near the front throughout the deployment. We demonstrate the spatial and temporal association of certain classes of seismicity with seasonal changes at the RIS front, and with tidally driven seismicity at Roosevelt Island. 

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4. Identifying Different Classes of Seismic Noise Signals Using Unsupervised Learning

   https://doi.org/10.1029/2020GL088353  

   Johnson, Christopher W.; Ben‐Zion, Yehuda; Meng, Haoran; Vernon, Frank (August 2020, Geophysical Research Letters)

   Abstract Proper classification of nontectonic seismic signals is critical for detecting microearthquakes and developing an improved understanding of ongoing weak ground motions. We use unsupervised machine learning to label five classes of nonstationary seismic noise common in continuous waveforms. Temporal and spectral features describing the data are clustered to identify separable types of emergent and impulsive waveforms. The trained clustering model is used to classify every 1 s of continuous seismic records from a dense seismic array with 10–30 m station spacing. We show that dominate noise signals can be highly localized and vary on length scales of hundreds of meters. The methodology demonstrates the complexity of weak ground motions and improves the standard of analyzing seismic waveforms with a low signal‐to‐noise ratio. Application of this technique will improve the ability to detect genuine microseismic events in noisy environments where seismic sensors record earthquake‐like signals originating from nontectonic sources. 

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5. Microscopic defect dynamics during a brittle-to-ductile transition

   https://doi.org/10.1073/pnas.2305667120  

   O’Ghaffari, Hoagy; Peč, Matěj; Mittal, Tushar; Mok, Ulrich; Chang, Hilary; Evans, Brian (October 2023, Proceedings of the National Academy of Sciences)

   Deformation of all materials necessitates the collective propagation of various microscopic defects. On Earth, fracturing gives way to crystal-plastic deformation with increasing depth resulting in a “brittle-to-ductile” transition (BDT) region that is key for estimating the integrated strength of tectonic plates, constraining the earthquake cycle, and utilizing deep geothermal resources. Here, we show that the crossing of a BDT in marble during deformation experiments in the laboratory is accompanied by systematic increase in the frequency of acoustic emissions suggesting a profound change in the mean size and propagation velocity of the active defects. We further identify dominant classes of emitted waveforms using unsupervised learning methods and show that their relative activity systematically changes as the rocks cross the brittle–ductile transition. As pressure increases, long-period signals are suppressed and short-period signals become dominant. At higher pressures, signals frequently come in avalanche-like patterns. We propose that these classes of waveforms correlate with individual dominant defect types. Complex mixed-mode events indicate that interactions between the defects are common over the whole pressure range, in agreement with postmortem microstructural observations. Our measurements provide unique, real-time data of microscale dynamics over a broad range of pressures (10 to 200 MPa) and can inform micromechanical models for semi-brittle deformation. 

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