Sparse distribution of depth soundings in the ocean make it necessary to infer depth in the gaps using alternate information such as satellite-derived gravity and a mapping from gravity to depth. We design and train a neural network on a collection of 50 million depth soundings to predict bathymetry globally using gravity anomalies. We find the best result is achieved by pre-filtering depth and gravity in accordance with isostatic admittance theory described in previous predicted depth studies. When training the model, if the training and testing split is a random partition at the same resolution as the data, the training and testing sets will not be independent, and model misfit results will be too optimistic. We solve this problem by partitioning the training and testing set with geographic bins. Our final predicted depth model improves on old predicted depth model rms by 16%, from 165 m to 138 m. Among constrained grid cells, 80% of the predicted values are within 128 m of the true value. Improvements to this model will continue with additional depth measurements, but higher resolution predictions, being limited by upward continuation of gravity, shouldn’t be attempted with this method.

We assess the accuracy and spatial resolution of the Surface Water and Ocean Topography (SWOT) swath altimeter for measuring marine gravity anomalies. The analysis is performed at the Foundation Seamounts in the South Pacific where we developed a highly accurate gravity field by combining the long-wavelength (> 40 km) gravity field derived from previous nadir altimeters with the shorter wavelength gravity field from the seafloor topography as constrained by the ship gravity. In this region, the slope of the ocean variability is 50-100 times smaller than the gravity/slope signal of the seamounts so can be ignored in the analysis. Each SWOT cycle can deliver gravity anomaly/SSS with an accuracy of 2.6 mGal/μrad and a spatial resolution of 14 km, with accuracy diminishing when significant wave height (SWH) exceeds ~6 meters. Averaging repeated SWOT measurements improves the accuracy and resolution. For example, we expect that averaging just 10 repeats (7 months) results in accuracy/resolution that matches the best marine gravity maps based on 230 months of nadir altimetry. With a mission lasting over a year, SWOT promises a substantial leap in marine gravity accuracy and resolution, uncovering previously uncharted details of the seafloor, including thousands of uncharted seamounts.

In this paper, we compute a new hybrid mean sea surface (MSS) model by merging three recent models, CNES_CLS22, SCRIPPS_CLS22 and DTU21, and taking advantage of their respective features. The errors associated with these models were assessed using sea level anomalies for wavelengths ranging from 15 to 100km from Sentinel-3A (S3A), SWOT KaRIn during its calibration phase and ICESat-2 in the Arctic ice-covered regions. The absolute error associated with this new Hybrid23 MSS is estimated at 0.15 ± 0.04 cm² with S3A. The greatest improvements observed on S3A sea level anomalies are mainly located in coastal regions and along geodetic structures: on average, the error is reduced by 23% within 200km along the coast and by 35% in the Indonesian region compared with SCRIPPS_CLS22. Despite these improvements, the MSS error still impacts significantly sea level anomalies computed from altimetry: it explains 15% and 18% of the S3A and SWOT KaRIn respective global variance. It becomes predominant (> 30%) if we consider the shorter wavelengths ([15, 30km]). CNES_CLS15, older, explains up to 88% of the variance of SWOT KaRIn at these wavelengths. MSS errors have become a major limiting factor to the accuracy of sea level anomalies, and hybridization even adds sub-mesoscale errors. SCRIPPS_CLS22 and DTU21 also remain better in certain regions of the North Atlantic above 60°N and in Arctic coastal areas. Finally, many efforts are still required to develop the MSS to a new level of precision, which we could soon achieve with SWOT KaRIn during the scientific phase.

A document by Hugh Harper. Click on the document to view its contents.

To date, approximately 20% of the ocean floor has been surveyed by ships at a spatial resolution of 400 m or better. The remaining 80% has depth predicted from satellite altimeter-derived gravity measurements at a relatively low resolution. There are many remote ocean areas in the southern hemisphere that will not be completely mapped at 400 m resolution during this decade. This study is focused on the development of synthetic bathymetry to fill the gaps. There are two types of seafloor features that are not typically well resolved by satellite gravity: abyssal hills and small seamounts (< 2.5 km tall). We generate synthetic realizations of abyssal hills by combining the measured statistical properties of mapped abyssal hills with regional geology including fossil spreading rate/orientation, rms height from satellite gravity, and sediment thickness. With recent improvements in accuracy and resolution, It is now possible to detect all seamounts taller than about 800 m in satellite-derived gravity and their location can be determined to an accuracy of better than 1 km. However, the width of the gravity anomaly is much greater than the actual width of the seamount so the seamount predicted from gravity will underestimate the true seamount height and overestimate its base dimension. In this study we use the amplitude of the vertical gravity gradient (VGG) to estimate the mass of the seamount and then use their characteristic shape, based on well surveyed seamounts, to replace the smooth predicted seamount with a seamount having a more realistic shape.

The Geodetic Mission (GM) of Jason-2 was planned to provide ground-tracks with a systematic spacing of 4 km after 2 years and 2 km after 4 years to increase the spatial resolution of global altimetric gravity fields. Jason-2 ceased operation after 2 years of GM but provided a fantastic dataset. We highlight and evaluate the improvement to the gravity field which has been derived from the GM. The ageing Jason-2 suffered from several safe-holds and instrument outages. Here, we try to quantify the effect of safe-holds on marine gravity and discuss suitable approaches advising future GM like Jason-3. We evaluate the importance of attempting to “rewind” the mission to recover missing tracks as well as the possibility to continue an existing GM by using the same orbital plane. The latter idea would allow bisecting the already 2-years Jason-2 GM creating a 2 km grid after 2 years of Jason-3 GM.

Seamounts are isolated elevations in the seafloor with circular or elliptical plan, comparatively steep slopes, and relatively small summit area (Menard, 1964). The vertical gravity gradient (VGG), which is the curvature of the ocean surface topography derived from satellite altimeter measurements, has been used to map the global distribution of seamounts (Kim & Wessel, 2011). We used the latest grid of VGG to update and refine the global seamount catalog; we identified 10,796 new seamounts, expanding the catalog by 1/3. 739 well-surveyed seamounts, having heights ranging from 421 m to 2500 m, were then used to estimate the typical radially-symmetric seamount morphology. First, an Empirical Orthogonal Function (EOF) analysis was used to demonstrate that these small seamounts have a basal radius that is linearly related to their height – their shapes are scale invariant. Two methods were then used to compute this characteristic base to height ratio: an average Gaussian fit to the stack of all profiles and an individual Gaussian fit for each seamount in the sample. The first method combined the radial normalized height data from all 739 seamounts to form median and median-absolute deviation. These data were fit by a 3-parameter Gaussian model that explained 99.82% of the variance. The second method used the Gaussian function to individually model each seamount in the sample and further establish the Gaussian model. Using this characteristic Gaussian shape we show that VGG can be used to estimate the height of small seamounts to an accuracy of ~270 m.

Measuring crustal strain and seismic moment accumulation, is crucial for understanding the growth and distribution of seismic hazards along major fault systems. Here we develop a methodology to integrate 4.5 years (2015 - 2019.5) of Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR) and continuous Global Navigation Satellite System (GNSS) time series to achieve 6 to 12-day sampling of surface displacements at ~500 m spatial resolution over the entire San Andreas fault system (SAFS). We decompose the line-of-sight InSAR displacements into three dimensions by combining the deformation azimuth from a GNSS-derived interseismic fault model. We then construct strain rate maps using a smoothing interpolator with constraints from elasticity. The resulting deformation field reveals a wide array of crustal deformation processes including: on- and off-fault secular and transient tectonic deformation; creep rates on all the major faults; and vertical signals associated with hydrological processes. The strain rate maps show significant off-fault components that were not captured by GNSS-only models. These results are important in assessing the seismic hazard in the region.