Optimizing Sensor Configurations for the Detection of Slow‐Slip Earthquakes in Seafloor Pressure Records, Using the Cascadia Subduction Zone as a Case Study

Abstract

We present seafloor pressure records from the Cascadia Subduction Zone, alongside oceanographic and geophysical models, to evaluate the spatial uniformity of bottom pressure and optimize the geometry of sensor networks for resolving offshore slow‐slip transients. Seafloor pressure records from 2011 to 2015 show that signal amplitudes are depth‐dependent, with tidally filtered and detrended root‐mean‐squares of <2 cm on the abyssal plain and >6 cm on the continental shelf. This is consistent with bottom pressure predictions from circulation models and comparable to deformation amplitudes from offshore slow slip observed in other subduction zones. We show that the oceanographic component of seafloor pressure can be reduced to ≤1‐cm root‐mean‐square by differencing against a reference record from a similar depth, under restrictions that vary with depth. Instruments at 100–250 m require depths matched within 10 m at separations of <100 km, while locations deeper than 1,400 m are broadly comparable over separations of at least 300 km. Despite the significant noise reduction from this method, no slow slip was identified in the dataset, possibly due to poor spatiotemporal instrument coverage, nonideal deployment geometry, and limited depth‐matched instruments. We use forward predictions of deformation from elastic half‐space models and hindcast pressure from circulation models to generate synthetic slow‐slip observational records and show that a range of slip scenarios produce resolvable signals under depth‐matched differencing. For future detection of offshore slow slip in Cascadia, we recommend a geometry in which instruments are deployed along isobaths to optimize corrections for oceanographic signals.

Water Depth Dependence of Long‐Range Correlation in Nontidal Variations in Seafloor Pressure

“…In an OBP network with a higher density of sites, such as the HOBITSS array (which has a maximum inter-site distance of ∼75 km), we clearly demonstrate that the observed nontidal components have a similarly strong relative water depth dependence, even for the case of a much smaller site spacing such as ours. This reinforces the idea proposed by Fredrickson et al (2019) that the most effective way of utilizing reference sites is to have these sites in similar water depths as the other sites in the areas of interest which are above the zones of deformation. For example, for a network which targets SSEs in a particular area such as HOBITSS, reference sites along-strike of the SSE region, but in similar water depths to the sites above the SSE area, are expected to be the most effective for the removal of oceanographic noise.…”

“…Residuals from OBP data in Cascadia were estimated to be less than 1 hPa RMS (e.g., <1 cm) when taking the difference between sites at similar depths within a range which vary with depth (e.g., within 10 m for sites on the shallower locations (100–250 m) and within 1,000 m for those on the abyssal plain (>1,400 m)) even for sites spaced far apart (<326 km) (Fredrickson et al., 2019). Those results are comparable to what we observe at the Hikurangi subduction zone.…”

“…At the northern Hikurangi subduction zone offshore the North Island of New Zealand, Wallace et al (2016) observed 1-5 cm of vertical crustal deformation during a September/October 2014 SSE using OBP data (which was also observed by onshore GNSS stations), and concluded that the slip may have reached the vicinity of the trench axis. Fredrickson et al (2019) recently demonstrated coherence in bottom pressure changes observed between sites in similar water depths offshore Cascadia, leading them to propose a new method of placing reference sites at common isobaths to achieve larger reductions in oceanographic noise in OBP time series. Here, we further evaluate the efficiency of considering differences between bottom pressure pairs at a range of water depths using the OBP data acquired during a 2014-2015 experiment offshore New Zealand.…”

Detecting Slow Slip Events From Seafloor Pressure Data Using Machine Learning

“…Peninsular areas in these regions have allowed precise on‐land geodetic measurements to detect these events, with limitations. However, shallow SSEs may be present in many other subductions such as Cascadia but remain undetected (Fredrickson et al, 2019). Our method provides a novel approach to detect SSEs using seafloor pressure data in these regions where the trench is far from land‐based geodetic networks.…”

Estuarine Circulation, Mixing, and Residence Times in the Salish Sea

“…[references from the supplement: (Babson et al, 2006) (Brasseale et al, 2019) (Codiga, 2011) ( Davis et al, 2014) (Egbert & Erofeeva, 2002) (Emery & Thomson, 1998) (Feely et al, 2016) (Finlayson, 2005) (Foreman et al, 1995) (Fredrickson et al, 2019) (Giddings & MacCready, 2017) (Giddings et al, 2014) (Haidvogel et al, 2000) (Khangaonkar et al, 2017) (Lavelle et al, 1988) (Liu et al, 2009) (Mass et al, 2003 Each panel represents three segments (i-iii) divided by sections 0 and 1. Each segment has two layers in the vertical, a deep saltier one and a shallow fresher one.…”