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303-4 Spatiotemporal Seismicity Patterns and Strain Release in the Tanganyika-Rukwa Rift, East Africa, Resolved with a Machine-Learning-Enhanced Earthquake Catalog

Session: Honoring the Late Professor Mohamed Abdelsalam: Outstanding Researcher, Generous Colleague, Legendary Mentor, and Ambassador for the Geosciences In Africa (Posters)

Poster Booth No.: 179

Presenting Author:

Meritxell Colet

Authors:

Colet, Meritxell¹, Kolawole, Folarin², Ajala, Rasheed³, Waldhauser, Felix⁴, Wang, Kaiwen⁵

(1) Department of Earth & Environmental Sciences, Columbia University, Palisades, NY, USA, (2) Department of Earth & Environmental Sciences, Columbia University, Palisades, NY, USA, (3) Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY, USA, (4) Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY, USA, (5) Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY, USA; Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China,

Abstract:

We address long-standing knowledge gaps on modes of strain release in active magma-poor continental rift systems, where faulting and seismicity persist in the absence of voluminous volcanism. We explore the Tanganyika-Rukwa Rift Zone in East Africa, where two en-echelon magma-poor rifts define the axes of active plate divergence and previous geophysical imaging have delineated the presence of blind lower crustal melts. We used data from the TANGA 14 seismic array, which comprised 13 stations deployed from June 2014 to September 2015. First, we analyzed the continuous waveform data to construct a preliminary earthquake catalog of about 2200 earthquakes, and perform declustering analysis to identify first-order seismicity space-time patterns, and their geometrical and geomechanical association with active faults. We identify 15 clusters of spatially isolated seismicity that can be categorized into distinct temporal scales and geometries (pipe and patch) indicating fluid-induced swarms and fault creep events, which manifest different modes of strain release in the extending lithosphere. Second, to further resolve the fine-scale characteristics of these seismicity clusters, a high-precision earthquake catalog with improved magnitude completeness is required. To this end, we use machine-learning-based earthquake event detection to build an expanded catalog from the continuous waveform data, after which we apply relative relocation using HypoDD with cross-correlation delay time measurements. We then perform declustering on the machine-enhanced earthquake catalog to examine the refined clusters. The approach allows us to better resolve space-time seismicity patterns that inform the driving mechanisms of strain release in the extending lithosphere of the active magma-poor rift zones.

Geological Society of America Abstracts with Programs. Vol. 57, No. 6, 2025

doi: 10.1130/abs/2025AM-7860

Spatiotemporal Seismicity Patterns and Strain Release in the Tanganyika-Rukwa Rift, East Africa, Resolved with a Machine-Learning-Enhanced Earthquake Catalog

Description

Session Format: Poster

Presentation Date: 10/22/2025

Presentation Room: HBGCC, Hall 1

Poster Booth No.: 179

Author Availability: 3:30–5:30 p.m.

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