SCEC Award Number 22145 View PDF
Proposal Category Individual Proposal (Integration and Theory)
Proposal Title Do ultra-shallow nanoseisms exist, and are they observable? Probing near-surface structure and mechanics using nanoseisms analyzed by dynamic wavefield migration of dense data from the Sage Brush Flats Nodal Experiment
Investigator(s)
Name Organization
Nori Nakata Massachusetts Institute of Technology Zhendong Zhang Massachusetts Institute of Technology Malcolm White Massachusetts Institute of Technology
Other Participants
SCEC Priorities 3g, 3b, 3e SCEC Groups Seismology, FARM
Report Due Date 03/15/2023 Date Report Submitted 05/10/2023
Project Abstract
 Very small microearthquakes are hypothesized to pervade the very shallow subsurface (top 1-2 km of Earth's crust) near active fault zones; however, such phenomena are yet to be confidently observed. Part of the problem is that, assuming such events do occur, the seismic waves they emanate are likely to be very weak and thus difficult to differentiate from background noise. In this work we use the Geometric-mean Reverse Time Migration (GmRTM) imaging condition to search for these events beneath a dense array of 1108 single-component geophones deployed in the San Jacinto Fault Zone. GmRTM is well suited to localizing such minute sources of energy because it dynamically backprojects entire waveforms into the subsurface (rather than kinematically backprojecting waveforms as in beamforming methods) and stacks waveform energy multiplicatively rather than additively. We scan continuous waveform data and identify one candidate event located roughly 500 m below the array. We suggest that this candidate event can be used as a template to search for additional similar events in future work.
Intellectual Merit This project contributes to the following SCEC research objectives, strategies, and priorities: 1. Enhance and continue to develop earthquake catalogs that include smaller events. This project contributes to the search for a previously unobserved class of events that, if identified, will enable significant enhancement of existing catalogs with small events. 2. Advance practical strategies for densification of seismic instrumentation in Southern California, including borehole instrumentation, and develop innovative algorithms to utilize data from these networks. This project implements a novel imaging algorithm that leverages data from dense seismic arrays. Associated code is publicly available. 3. Investigate near-fault crustal properties, evaluate fault structural complexity, and develop constraints on crustal structures and state of stress. This project contributes to the search for a previously unobserved class of signals that, if identified, will help improve our understanding of shallow, near-fault crustal properties and constrain the associated stress state. 4. Small earthquakes. Reducing the magnitude of completeness of detected earthquakes can increase the amount of available information for many studies. We welcome proposals on improved detection of small earthquakes and separation of small events from other sources of weak ground motion. This project implements a novel algorithm that helps differentiate small earthquakes from other non-seismic events by identifying coherent energy and localizing the source. 5. Develop computational tools for advanced signal processing algorithms, such as those used in ambient noise seismology and tomography as well as InSAR and other forms of geodesy. This project implemented a new computational tool for conducting GmRTM imaging with dense array data. This project also contributed to the development of HDF5eis, a new data schema for managing big multi-dimensional time series data, which is publicly available.
Broader Impacts We contribute two computational tools to the community for future research. The first is software for GmRTM imaging using dense array data. The second is the HDF5eis data schema for managing big data sets associated with such arrays.
Exemplary Figure Figure 3.

Caption: GmRTM image for the candidate event showing (top left panel) map view, (top right panel) vertical N/S transect corresponding to the vertical crosshair in the top left panel, and (bottom left panel) vertical E/W panel corresponding to the horizontal crosshair in the top left panel. The crosshairs indicate the pixel with maximum image intensity. Black circles in top left panel represent sensor locations.

Credits: Malcolm C. A. White