A Bayesian split of Torrey Pines wave records assigns 28% of infragravity energy to edge waves
A Bayesian inversion of 60 days of pressure and velocity records from Torrey Pines State Beach separates the infragravity band and puts roughly 28% of its energy in edge waves running along the shore.
The Scientist · Science desk
What happened
- Researchers collected data from a network of pressure and velocity sensors at Torrey Pines State Beach in California over a period of 60 days, covering both water level and flow.
- They analysed the records with a Bayesian maximum a posteriori technique, a method effective for separating the individual components that make up one measured signal.
- The analysis put edge waves at roughly 28% of the infragravity wave energy measured at the site.
- Infragravity waves both affect and are affected by the shape of coastlines, which links them to erosion, sediment deposition and the degradation of coastal ice.
- Eos presented the approach as a way to extract how infragravity waves take part in shore-wave interactions, with possible use in studying sneaker waves and shoreline decay under sea level rise.
Why it matters
- What's now possible capability The edge wave contribution to run-up can now be entered into a nearshore calculation as a quantity measured at one site, where before it was assumed.
- What it limits constraint The figure comes from a single beach and a single deployment, so carrying it to another coast means assuming the alongshore share travels along with the method.
- Who has to decide decision Teams planning nearshore field work face a hardware choice, because a separation like this takes a spatial array of sensors, and one instrument cannot do it.
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Edge waves travel parallel to the shoreline [4]. Direction is what marks them out, and frequency does not, so a spectrum from one instrument will not reveal it; the separation has to come from how the motion is organised across space. Infragravity waves themselves arise from the interactions of ocean waves in shallow water, where waves grow higher and eventually break [6][12].
The reported share, roughly 28% of infragravity energy [5], leaves about 72% spread among the other components of the band [14]. Neither published account gives an uncertainty range on that figure [13].
Eos says the result has important implications for nearshore wave processes [16]. Reading the 28% as a term missing from run-up and erosion models needs two further steps: that those models treat the infragravity band as a single quantity, and that splitting it changes what they predict.
Eos also describes infragravity waves as producing wave interference and contributing to the dangerous sneaker waves that can kill or injure beachgoers around the world [8]. One hazardous run-up is an event of minutes. Attributing a specific one would need the edge wave share resolved through the record, and what this study reports is a single percentage over a two-month deployment.
The two write-ups descend from one text. Phys.org republished the Eos research spotlight, which is hosted by the American Geophysical Union [10] and was written by the science writer Matthew R. Francis [11]. The study behind it is by Cassandra S. Henderson and co-authors, "Bayesian Analysis of Infragravity Edge Waves," in the Journal of Geophysical Research: Oceans [9].
What to watch
- Whether the edge wave share holds steady across the 60 days or is carried by a few swell events.
- Whether the same MAP separation, applied to arrays on steeper, rockier or ice-fronted coasts, returns anything near 28%.
- Whether a run-up parameterisation with an explicit edge wave term outperforms one without it on the same Torrey Pines records.
Clarity's read
What the record supports and how the coverage leans. The claims behind it follow.
Reality
- Evidence40
- Adoption
- Not scored
- Hype gap+20
- Incentives
- Not scored
- Confidence45
Claim ledger
Ranked by verification strength, evidence, and original report placement.
- [1]
Henderson and team used Bayesian probability methods to assess the contributions of different types of infragravity waves to wave run-up.
- [2]
The researchers collected data from a network of pressure and velocity sensors at Torrey Pines State Beach in California over 60 days.
- [3]
The Bayesian method used, known as the maximum a posteriori (MAP) technique, is effective for separating components of a measured signal.
- [4]
Edge waves run parallel to the shoreline.
- [5]
The researchers found that edge waves account for roughly 28% of infragravity wave energy.
- [6]
When ocean waves reach shallow water, their complex interactions can produce lower-frequency infragravity waves.
- [7]
Infragravity waves strongly affect and are affected by the shape of coastlines, linking them to erosion, sediment deposition and the degradation of coastal ice.
- [8]
When infragravity waves are added in, they can lead to wave interference and phenomena like the dangerous "sneaker waves" that can kill or injure beachgoers around the world.
- [9]
The paper is Cassandra S. Henderson et al, "Bayesian Analysis of Infragravity Edge Waves," Journal of Geophysical Research: Oceans (2026), DOI 10.1029/2026jc024479.
- [10]
The phys.org story is republished courtesy of Eos, which is hosted by the American Geophysical Union.
- [11]
The Eos research spotlight was written by Matthew R. Francis, science writer.
- [12]
When waves reach shallow water, they grow higher and eventually break.
- [13]
Neither the Eos research spotlight nor the phys.org republication reports an uncertainty range for the 28% figure, a breakdown of the remaining infragravity energy, or a comparison of run-up predictions calculated with and without an edge wave term.
- [14]
If edge waves hold roughly 28% of infragravity wave energy, about 72% of the band's energy sits in its other components.
- [15]
Eos says the study demonstrates the ability to take oceanographic wave data and extract exactly how infragravity waves are involved in shore-wave interactions, which could prove helpful in researching phenomena such as sneaker waves and shoreline decay in the face of sea level rise.
- [16]
Eos describes the 28% finding as a result that has important implications for nearshore wave processes.
Sources
2 independent publishers whose own reporting we read for this story.
- eos.orgMaking Sense of Shallow-Water Waves with Advanced Statistics
1 article · September 23, 2026
- phys.orgMaking sense of shallow-water waves with advanced statistics
1 article · September 23, 2026
Topics and entities
Topics
- Bayesian statistics
- Infragravity waves
- Nearshore oceanography
Entities
- Eos
- Torrey Pines State Beach
- American Geophysical Union
- Phys.org
- Journal of Geophysical Research: Oceans
- Matthew R. Francis
- Cassandra S. Henderson