A 3D Full Stress Tensor Model for Oklahoma

Chengping Chai, Andrew A. Delorey, Monica Maceira, Will Levandowski, Robert A. Guyer, Haijiang Zhang, David Coblentz, Paul A. Johnson

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

The stress tensor is an important property for upper crustal studies such as those that involve pore fluids and earthquake hazards. At tectonic plate scale, plate boundary forces and mantle convection are the primary drivers of the stress field. In many local settings (10–100 s of km and <10 km depth) in tectonic plate interiors, we can simplify by assuming a constant background stress field that is perturbed by local heterogeneity in density and elasticity. Local stress orientation and sometimes magnitude can be estimated from earthquake and borehole-based observations when available. Modeling of the local stress field often involves interpolating sparse observations. We present a new method to estimate the 3D stress field in the upper crust and demonstrate it for Oklahoma. We created a 3D material model by inverting multiple types of geophysical observations simultaneously. Integrating surface-wave dispersion, local travel times and gravity observations produces a model of P-wave velocity, S-wave velocity, and density. The stress field can then be modeled using finite element simulations. The simulations are performed using our simplified view of the local stress field as the sum of a constant background stress field that is perturbed by local density and elasticity heterogeneity and gravitational body forces. An orientation of N82°E, for the maximum compressive tectonic force, best agrees with previously observed stress orientations and faulting types in Oklahoma. The gravitational contribution of the horizontal stress field has a magnitude comparable to the tectonic contribution for the upper 5 km of the subsurface.

Original languageEnglish
Article numbere2020JB021113
JournalJournal of Geophysical Research: Solid Earth
Volume126
Issue number4
DOIs
StatePublished - Apr 2021

Funding

Portions of this work were supported by the U.S. Department of Energy, Office of Fossil Energy, Carbon Storage Program through the Science-informed Machine Learning for Accelerating Real-Time Decisions in Subsurface Applications (SMART) Initiative. This work was made possible by support from the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy (EERE), Office of Technology Development, Geothermal Technologies Office under contract DE-AC05-00OR22725 with Oak Ridge National Laboratory (CC, MM) and at Los Alamos National Laboratory under the U.S. Department of Energy’s Geothermal Technologies Office project 3.1.8.7 ML (AD, PAJ, RAG). The authors also acknowledge initial support from the U.S. Department of Energy under contract DE-AC52-06NA25396. The authors acknowledge comments from Singanallur Venkatakrishnan, Philip Bingham, and Jens-Erik Lund Snee. The authors thank the Editors Martha Savage and Rachel Abercrombie and three anonymous reviewers for constructive comments. We thank developers of the Generic Mapping Tools (GMT, Wessel et al., 2013), Obspy (Beyreuther et al., 2010), Numpy, Scikit-learn (Pedregosa et al., 2011), Matplotlib and Bokeh (http://bokeh.pydata.org, last accessed August 2019) for making their packages available. This manuscript has been authored in part by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). Portions of this work were supported by the U.S. Department of Energy, Office of Fossil Energy, Carbon Storage Program through the Science‐informed Machine Learning for Accelerating Real‐Time Decisions in Subsurface Applications (SMART) Initiative. This work was made possible by support from the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy (EERE), Office of Technology Development, Geothermal Technologies Office under contract DE‐AC05‐00OR22725 with Oak Ridge National Laboratory (CC, MM) and at Los Alamos National Laboratory under the U.S. Department of Energy’s Geothermal Technologies Office project 3.1.8.7 ML (AD, PAJ, RAG). The authors also acknowledge initial support from the U.S. Department of Energy under contract DE‐AC52‐06NA25396. The authors acknowledge comments from Singanallur Venkatakrishnan, Philip Bingham, and Jens‐Erik Lund Snee. The authors thank the Editors Martha Savage and Rachel Abercrombie and three anonymous reviewers for constructive comments. We thank developers of the Generic Mapping Tools (GMT, Wessel et al., 2013 ), Obspy (Beyreuther et al., 2010 ), Numpy, Scikit‐learn (Pedregosa et al., 2011 ), Matplotlib and Bokeh ( http://bokeh.pydata.org , last accessed August 2019) for making their packages available. This manuscript has been authored in part by UT‐Battelle, LLC, under contract DE‐AC05‐00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid‐up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan ).

FundersFunder number
DOE Public Access Plan
Matplotlib and Bokeh
U.S. Department of Energy
Office of Fossil Energy
Office of Energy Efficiency and Renewable Energy
Office of Technology Development
Oak Ridge National Laboratory
Los Alamos National LaboratoryDE‐AC52‐06NA25396
Geothermal Technologies OfficeDE‐AC05‐00OR22725

    Keywords

    • Oklahoma
    • gravity
    • joint inversion
    • stress model
    • surface-wave dispersion
    • travel times

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