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Major southern San Andreas earthquakes modulated by lake-filling events

Nature volume 618, pages 761–766 (2023)Cite this article

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Abstract

Hydrologic loads can stimulate seismicity in the Earth’s crust1. However, evidence for the triggering of large earthquakes remains elusive. The southern San Andreas Fault (SSAF) in Southern California lies next to the Salton Sea2, a remnant of ancient Lake Cahuilla that periodically filled and desiccated over the past millennium3,4,5. Here we use new geologic and palaeoseismic data to demonstrate that the past six major earthquakes on the SSAF probably occurred during highstands of Lake Cahuilla5,6. To investigate possible causal relationships, we computed time-dependent Coulomb stress changes7,8 due to variations in the lake level. Using a fully coupled model of a poroelastic crust9,10,11 overlying a viscoelastic mantle12,13, we find that hydrologic loads increased Coulomb stress on the SSAF by several hundred kilopascals and fault-stressing rates by more than a factor of 2, which is probably sufficient for earthquake triggering7,8. The destabilizing effects of lake inundation are enhanced by a nonvertical fault dip14,15,16,17, the presence of a fault damage zone18,19 and lateral pore-pressure diffusion20,21. Our model may be applicable to other regions in which hydrologic loading, either natural8,22 or anthropogenic1,23, was associated with substantial seismicity.

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Fig. 1: Regional context.
Fig. 2: Lake and earthquake history.
Fig. 3: Finite-element-model results.
Fig. 4: Stress effects of lake loading.

Data availability

The Abaqus data files, Lake Cahuilla and Salton Sea data files and processed model results are available on Zenodo (http://doi.org/10.5281/zenodo.7714217). Source data are provided with this paper.

Code availability

All relevant MATLAB post-processing codes and sample-plotting codes are available on Zenodo (http://doi.org/10.5281/zenodo.7714217).

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Acknowledgements

This work was supported by the Southern California Earthquake Center (grant 21091) to M.W. and NSF (EAR-1841273), NASA (80NSSC22K0506) and USGS (G20AP00051) to Y.F. This research benefited from correspondence with R. Guyer. This project used Quaternary fault data from the USGS. We acknowledge use of the CSRC high-performance computing cluster at San Diego State University.

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Authors and Affiliations

  1. Department of Geological Sciences, San Diego State University, San Diego, CA, USA

    Ryley G. Hill, Matthew Weingarten & Thomas K. Rockwell

  2. Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA

    Ryley G. Hill & Yuri Fialko

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Contributions

R.G.H. constructed the finite-element model, performed analysis of the model results and wrote the manuscript. M.W. managed the study, assisted with building the model, provided access to the modelling software, acquired funding, helped conceive the experiment and commented on the manuscript. T.K.R. carried out the palaeoseismic analysis, conceived the experiment and contributed to the manuscript. Y.F. provided advice on modelling and interpretation of the model results and contributed to the manuscript.

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Correspondence to Ryley G. Hill.

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Extended data figures and tables

Extended Data Fig. 1 Monte Carlo statistical test of the lake and earthquake timings.

Results of Monte Carlo statistical testing (10,000 samples) based on sampling earthquake PDF distributions and lake timings. After sampling the earthquake PDFs, we determine how many fall inside the lake timings when the lake was greater than 70% full. We compare these timings to a uniform random distribution of seven times across the same lake-loading-time range. We find that the mean timings that occur within lakes is >97% of the earthquake timings of a uniform random distribution that occur within lakes.

Extended Data Fig. 2 Numerical domain.

3D finite-element method model domain. The model mesh contains about 2 million tetrahedron elements. The light blue colour represents the extent of ancient Lake Cahuilla. The prescribed vertical load is hydrostatic, to the lake maximum water head (97.2 m). The solid red line is the SSAF fault trace. The fault zone is modelled as a slab dipping to the northeast at 60° (ref. 54), with the assumed thickness of 200 m (refs. 19,46,84).

Extended Data Fig. 3 Pore-pressure effects of lake loading.

Pore pressure (MPa) on the SSAF as a function of time (year CE) at 7 km depth for location 21, a point on the fault near the centre of the lake (see Supplementary Fig. 5). Each model is based on the variable fault permeability, with model 1 as the most permeable and model 5 as the no damage zone (Extended Data Table 2).

Extended Data Fig. 4 Analytic solution for periodic loading of a poroelastic half-space.

1D analytical model of pore pressure for a variety of different depths (blue) with surface lake-level pore pressure (black). The smaller surface profile from 1905 to the present is the Salton Sea2. Finite-element method model 2 at 7.2 km depth (green line) shows the effect of 3D diffusion with a high-permeability fault damage zone embedded in a lower-permeability host rock. The finite-element method model at 7.2 km resembles pore pressure in the 1D analytical case at 1 km, demonstrating how a fault damage zone can transmit pore pressure to depth effectively. γ = 0.1685; kfault = 1e−15 (m2); khost/1Dmodel = 1e−18 (m2).

Extended Data Fig. 5 Stress effects of lake loading.

Similar to Fig. 4a but for a point farther away from the lake centre (point 24 in Supplementary Fig. 5).

Extended Data Fig. 6 Conceptual model of the undrained and drained effects from a lake load.

The instantaneous and transient effects of the undrained and drained effects. At t = 0, the undrained effect is felt almost instantaneously throughout the poroelastic medium beneath the lake. As time progresses, this effect attempts to equilibrate at depth. At t = 0, the drained effect is not felt except for the surface poroelastic medium and the bottom of the lake. As time progresses, this effect increases pore pressure as diffusion drives fluid from the surface down. Furthermore, as the lake load is applied, areas of compression form immediately beneath the lake, whereas areas of extension are formed near the edges.

Extended Data Table 1 Earthquake dates
Full size table
Extended Data Table 2 Material properties
Full size table

Supplementary information

Supplementary Figures

This file contains Supplementary Figs. 1–11.

OxCal Model

Calculation of probable age ranges based on radiocarbon measurements from Rockwell et al.6.

Peer Review File

Supplementary Video 1

Spatiotemporal pore-pressure evolution. This video describes the change in relative pore pressure across the SSAF for each time step in our model of the six lake-loading cycles of ancient Lake Cahuilla that also includes the load of the Salton Sea. The * point on the fault is the location of maximum pore pressure for average seismogenic depth of 7 km. The pore pressure at each step associated with * is plotted on the right. Two other curves are plotted that represent a ratio of what portion of the fault has positive pore pressure. The black line is for pore pressure at 7 km depth and the grey line is for pore pressure across the entire fault.

Supplementary Video 2

Spatiotemporal CFS evolution. This description is the same as for Supplementary Video 1 except for CFS instead of pore pressure.

Supplementary Video 3

Positive/negative CFS evolution. This video describes the binary positive CFS (red) versus negative CFS (blue) across the SSAF for each time step in our model. A black * plotted on the fault plane represents the location at each time step with the maximum CFS for the entire fault. Two lines are plotted on the right, which represent the positive (red) versus negative (blue) ratio for CFS on the SSAF.

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Hill, R.G., Weingarten, M., Rockwell, T.K. et al. Major southern San Andreas earthquakes modulated by lake-filling events. Nature 618, 761–766 (2023). https://doi.org/10.1038/s41586-023-06058-9

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  • DOI: https://doi.org/10.1038/s41586-023-06058-9

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