Search for: All records

Natron Rift 2024 dataset of campaign GNSS observations 

These are supplementary data, code, and model files associated with the manuscript "Detecting Transient Deformation at the Active Volcano Ol Doinyo Lengai in Tanzania with the TZVOLCANO Network" in consideration for publication in the Geophysical Research Letters. tzvolcano_code_and_models.zip contains all necessary Targeted Projection Operator (TPO) software, input, and output files for the GNSS inversions presented in our manuscript necessary to reproduce the results. The TPO program is a Unix/Linux code developed by Kang-Hyeun Ji working at the Korea Institute for Geoscience and Mineral Resources, Daejeon, South Korea. The source code is available in the supplementary Zenodo repository. We also include input and output model files for the USGS code dMODELS for reproducibility. Please see the README.txt file for more details. This study was funded by the US National Science Foundation grant number EAR-1943681 to Virginia Tech, internal university funds via Ardhi University, and Ministry of Science and ICT of Korea Basic Research Project GP2021-006 to the Korea Institute of Geosciences and Mineral Resources. We acknowledge and thank the EarthScope Consortium for archiving and making TZVOLCANO GNSS datasets freely available, supported by the National Science Foundation’s Seismological Facility for the Advancement of Geoscience (SAGE) Award under Cooperative Support Agreement EAR-1851048 and Geodetic Facility for the Advancement of Geoscience (GAGE) Award under NSF Cooperative Agreement EAR-1724794. 

{"Abstract":["Supplementary code and model files for the manuscript entitled "Elucidating the Magma Plumbing System of Ol Doinyo Lengai (Natron Rift, Tanzania) Using Satellite Geodesy and Numerical Modeling". OlDoinyoLengai_code_and_models.zip contains all necessary Matlab code, functions, input and output files for the GNSS, InSAR, and joint inversions presented in our manuscript necessary to reproduce the results. dMODELS is an open source code developed by the United States Geological Survey. The originally published program is available here: https://pubs.usgs.gov/tm/13/b1/ and the revised software archived here will also be available through the USGS website code.usgs.gov/vsc/publications/OlDoinyoLengai or by contacting Maurizio Battaglia. With this manuscript we are providing an update to dMODELS that includes improved graphics and joint inversion capabilities for both InSAR and GNSS data. <\/p>"],"Other":["This work was funded by the National Science Foundation (NSF) grant number EAR-1943681, Virginia Tech, Korean Institute of Geosciences and Minerals (KIGAM), and Ardhi University. Funding for this work also came from USAID via the Volcano Disaster Assistance Program and from the U.S. Geological Survey (USGS) Volcano Hazards Program.This material is based on services provided by the GAGE Facility, operated by UNAVCO, Inc., with support from the National Science Foundation, the National Aeronautics and Space Administration, and the U.S. Geological Survey under NSF Cooperative Agreement EAR-1724794. We acknowledge and thank Alaska Satellite Facility for making InSAR data freely available and TZVOLCANO GNSS data sets available through the UNAVCO data archive."]} 

Artificial intelligence applications within the geosciences are becoming increasingly common, yet there are still many challenges involved in adapting established techniques to geoscience data sets. Applications in the realm of volcanic hazards assessment show great promise for addressing such challenges. Here, we describe a Jupyter Notebook we developed that ingests real-time Global Navigation Satellite System (GNSS) data streams from the EarthCube CHORDS (Cloud-Hosted Real-time Data Services for the geosciences) portal TZVOLCANO, applies unsupervised learning algorithms to perform automated data quality control (“noise reduction”), and explores autonomous detection of unusual volcanic activity using a neural network. The TZVOLCANO CHORDS portal streams real-time GNSS positioning data in 1[Formula: see text]s intervals from the TZVOLCANO network, which monitors the active volcano Ol Doinyo Lengai in Tanzania, through UNAVCO’s real-time GNSS data services. UNAVCO’s real-time data services provide near-real-time positions processed by the Trimble Pivot system. The positioning data (latitude, longitude and height) are imported into the Jupyter Notebook presented in this paper in user-defined time spans. The positioning data are then collected in sets by the Jupyter Notebook and processed to extract a useful calculated variable in preparation for the machine learning algorithms, of which we choose the vector magnitude for further processing. Unsupervised K-means and Gaussian Mixture machine learning algorithms are then utilized to locate and remove data points (“filter”) that are likely caused by noise and unrelated to volcanic signals. We find that both the K-means and Gaussian Mixture machine learning algorithms perform well at identifying regions of high noise within tested GNSS data sets. The filtered data are then used to train an artificial intelligence neural network that predicts volcanic deformation. Our Jupyter Notebook has promise to be used for detecting potentially hazardous volcanic activity in the form of rapid vertical or horizontal displacement of the Earth’s surface. 

Abstract Over the last 7 years, geodetic data have detected periods of uplift and subsidence of the active volcano Ol Doinyo Lengai in Tanzania. Although numerous eruptions of the volcano have occurred historically, a systematic investigation of transient deformation using continuous Global Navigation Satellite System (GNSS) data has not been undertaken. We use the Targeted Projection Operator (TPO) to assess 7 years of continuous GNSS data from the TZVOLCANO network for transient signals and find rapid uplift spanning March 2022–December 2022 and then steady‐state uplift through August 2023. We conduct a nonlinear inversion of the GNSS velocities associated with the transient signal using dMODELS and find consistency with an inflating spheroidal source located 2.3 ± 0.6 km beneath the crater. Prior to March 2022, geodetic data indicated quiescence just below Ol Doinyo Lengai, thus detecting transient deformation with TPO allows for tracking changes in the magmatic system over time in the Natron Rift.