Detection of artifacts and transients in Earth Science observing systems with machine learning

Our proposal is responsive to AIST objective O2 under the Analytic Collaborative Frameworks (ACF) thrust area, addressing challenges in assimilating, manipulating and visualizing data associated with geodetic observing systems (GNSS and InSAR). We seek to create open-source software to provide a rich, interactive environment where machine learning (ML) models are used as collaborator to direct the attention of the human analyst to non-physical artifacts and real transient events that require interpretation. The proposed system will be realized through two coupled sub-systems: a novel "back-end" ML software called the Transient and Artifact Continuous Learning System (TACLS), and a significant upgrade to our "front end" interactive MGViz user environment, originally designed to view displacement time series and their underlying metadata, to now interact and display layers of spatiotemporal information. Our uniqueness is our archive of thousands of artifacts and transients, and acquired expertise in creating calibrated and validated Earth Science Data Records (ESDRs) from thousands of GNSS stations and 30 years of data; these will be used to train the ML algorithms. ESDRs include crustal deformation and strain rate fields, which reflect steady-state and transient motions due to postseismic processes, episodic tremor and slip - ETS, volcanic inflation, and mostly vertical motions due to other natural (tectonic, geomorphic) and anthropogenic processes (sea level rise, subsidence due to water extraction), and atmospheric precipitable water as a harbinger of extreme weather events. Our interactive environment will also be designed for displacement fields of higher precision and spatial resolution produced through GNSS/InSAR integration. A primary bottleneck to the extraction of scientific insight from geodetic data is the challenge to separate non-physical artifacts and secular trends from scientifically relevant transients, which often requires costly and intensive manual intervention to achieve the most robust and accurate results. This process is often performed redundantly, inefficiently and inconsistently by groups of students and individual researchers, focused on a particular science problem. An example of a transient is slow slip deformation, which plays a crucial role in advancing our understanding of earthquake dynamics and hazards, indicating possible change of stress on the fault interface, triggered earthquake swarms or seismicity and release of accumulated elastic strain. Increasing evidence suggests that slow slip often precedes and possibly leads to the large earthquakes. As another example, GPS-based integrated water vapor estimates enable improved forecasting skill for extreme weather events, improved understanding of long-term water vapor trends, probable maximum precipitation, and retrospective analysis of weather events and watch/warning situational awareness involving extremes in precipitable water. We will demonstrate the MGViz/TACLS system by two representative science test cases. The first will address transient tectonic signals and associated hazards in the subduction zones of the Pacific Rim with the participation of the NOAA/NWS Pacific Tsunami Warning Center (PTWC) in Hawaii. The second will track variations in atmospheric water vapor as precursors to weather events such as monsoons and atmospheric rivers to forecast flash flooding, with the participation of the National Weather Service's (NWS) Weather forecasting Offices (WFOs) in southern California. We propose a third year to our project that will transfer the MGViz/TACLS system to PTWC and the WFOs. All software developed under this proposal will be deposited under the existing, publicly accessible MGViz repository. This software will be freely available to anyone to use with no restrictions other than those stipulated in the license. Our entry level is TRL 4 with an output level of TRL 6 at the end of year 2 and TRL 7 at the end of year 3.

Advance Earth system science knowledge through the Identification, develop, and demonstrate innovative information systems technologies

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