Search for: All records

Lithospheric weakening mechanisms in non-volcanic segments of active continental rifts remain poorly understood, raising important questions about the geodynamic processes that drive magma-poor rifting. Here, we investigate the crustal and uppermost mantle structure beneath the non-volcanic Albertine-Rhino Graben (ARG) and the adjoining volcanic Edward-George Rift (EGR), East Africa. The ARG exhibits anomalous focusing of intra-rift faulting typically associated with magma-rich, early-stage rifts. Through field observations of rift structures, combined with 3D inversions and 2D forward modeling of gravity data, we investigate the potential controls on intra-rift tectonic strain in a setting with little to no magmatism. Field ground-truthing in the southern ARG reveals prominent rift-axial basement-rooted faulting that post-dates the establishment of border faults. Gravity inversion results show low-density anomalies extending from the surface to about 50 km depth beneath both the EGR and southern ARG, with the strongest anomalies under the ARG at around 15 km. 2D gravity modeling suggests that the lower crust and uppermost mantle are both thinned and less dense beneath these rift segments. In the EGR, crustal thinning and low-density anomalies align with low P-wave velocity zones, suggesting the presence of melt. Given the similar degree of crustal thinning and de-densification in the southern ARG, we infer that trapped lower-crustal melts may also exist beneath the rift, potentially contributing to the early focusing of intra-rift strain. We propose that in non-volcanic rifts, deep, unexposed (‘blind’) melts may play a key role in mechanical weakening of the lithosphere, enabling continued tectonic extension even in the absence of significant surface volcanism. 

This is the TDEFNODE block inversion software used in the study "Constraining the Kinematics of the Victoria Microplate and the Northern Western Branch of the East African Rift System; The software is installed and run on a Linux terminal. All the files needed to run TDEFNODE for this study, along with their outputs, are included in TDEFNODE_block_inversion_modeling_NWB_Kwagalakwe_2025_v2.tar.gz, which is a revised version of TDEFNODE_block_inversion_modeling_NWB_Kwagalakwe_2025.tar.gz (Version v1). 

Natron Rift 2024 dataset of campaign GNSS observations 

GPS Dataset for campaign observations at HYDP 

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.