Researchers Measured Wave Patterns at Torrey Pines

A 2026 study identified the energy contribution of edge waves to shoreline infragravity oscillations.

Updated on Sept. 23, 2026 in Geography

Waves washing over a sandy Pacific beach with rocky bluffs in the distance under morning light. — Researchers at Torrey Pines used Bayesian probability to measure energy patterns in coastal infragravity waves, aiding in predicting shoreline erosion. AI Illustration. Upload story photo >

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Researchers successfully used Bayesian probability methods to isolate complex wave signal components at Torrey Pines State Beach. The 2026 study provides a quantitative breakdown of energy sources within coastal infragravity waves.

Why it matters

Understanding the energy distribution of infragravity waves is essential for modeling shoreline decay and predicting hazardous phenomena like sneaker waves. This research provides a new analytical framework for parsing complex wave interactions at the coastline.

The team utilized maximum a posteriori (MAP) estimation to isolate signal components from 60 days of sensor data. This statistical approach successfully partitioned energy sources, revealing that edge waves account for exactly 28% of the total infragravity wave energy.

The players

Torrey Pines State Beach

A coastal reserve in San Diego utilized as the primary field site for oceanographic sensor deployments.

The details

Researchers utilized pressure and velocity sensors to capture raw wave data, which was then analyzed using Bayesian probability methods—a statistical approach that updates the probability of a hypothesis as more evidence becomes available. Specifically, they applied a maximum a posteriori technique, a method that identifies the most probable value for a parameter given the observed data, to disentangle the overlapping signals of complex infragravity waves, or waves with periods longer than the incident wind waves that can significantly impact beach morphology.

The research study was published in 2026.
Data collection at Torrey Pines State Beach spanned 60 days.

The Tech Race

This study follows a pattern set by the Journal of Geophysical Research by refining coastal hydrodynamic modeling capabilities. It advances the field beyond legacy spectral analysis by applying Bayesian statistical techniques to isolate discrete energy contributors in nearshore zones.

This methodology allows oceanographers to build more accurate predictive models for shoreline decay and hazardous sneaker waves in San Diego. Local authorities and coastal planners can leverage these insights to improve long-term shoreline management strategies.

The takeaway

The study demonstrates that Bayesian statistical tools can successfully deconstruct complex coastal wave energy into actionable components. Future research will likely focus on applying this methodology to broader coastal regions to test the universal applicability of the 28% edge wave energy finding.