Brian R. Calder

The Kauai Experiment

The Kauai Experiment was conducted from June 24 to July 9, 2003 to provide a comprehensive study of acoustic propagation in the 8–50 kHz band for diverse applications. Particular sub‐projects were incorporated in the overall experiment 1) to study the basic propagation physics of forward‐scattered high‐frequency (HF) signals including time/angle variability, 2) to relate environmental conditions to underwater acoustic modem performance including a variety of modulation schemes such as MFSK, DSSS, QAM, passive‐phase conjugation, 3) to demonstrate HF acoustic tomography using Pacific Missile Range Facility assets and show the value of assimilating tomographic data in an ocean circulation model, and 4) to examine the possibility of improving multibeam accuracy using tomographic data. To achieve these goals, extensive environmental and acoustic measurements were made yielding over 2 terabytes of data showing both the short scale (seconds) and long scale (diurnal) variations. Interestingly, the area turned out...

Assessment of Waveform Features for Lidar Uncertainty Modeling in a Coastal Salt Marsh Environment

There is currently great interest in lidar surveys of salt marshes to support coastal management and decision making. However, vertical uncertainty of lidar elevations is generally higher in salt marshes than in upland areas, and it can be difficult to empirically quantify due to the challenges of obtaining ground control in marshes. Assuming that most of the component uncertainties in the lidar geolocation equation will remain essentially constant over a relatively small location, it is posited that vertical uncertainty in a marsh will vary mostly as a function of surface and cover characteristics. These, in turn, should affect lidar waveforms recorded during the survey, and therefore, analysis of the waveform shapes may allow for prediction of vertical uncertainty variation. Waveforms at three test sites were used to compute 16 computationally efficient features that describe the shapes; and simple, multilinear, and principal component regressions were used to evaluate their ability to predict elevation differences between lidar and Global Positioning System ground control. The results show that a simple estimate of waveform width can explain over 50% of the total variability in elevation differences but that multilinear regression does not significantly improve the performance. Somewhat surprisingly, skewness of the waveform does not appear to be a good predictor of elevation differences in these cases.

Maximum a posteriori resampling of noisy, spatially correlated data

In any geologic application, noisy data are sources of consternation for researchers, inhibiting interpretability and marring images with unsightly and unrealistic artifacts. Filtering is the typical solution to dealing with noisy data. However, filtering commonly suffers from ad hoc (i.e., uncalibrated, ungoverned) application. We present here an alternative to filtering: a newly developed method for correcting noise in data by finding the “best” value given available information. The motivating rationale is that data points that are close to each other in space cannot differ by “too much,” where “too much” is governed by the field covariance. Data with large uncertainties will frequently violate this condition and therefore ought to be corrected, or “resampled.” Our solution for resampling is determined by the maximum of the a posteriori density function defined by the intersection of (1) the data error probability density function (pdf) and (2) the conditional pdf, determined by the geostatistical kriging algorithm applied to proximal data values. A maximum a posteriori solution can be computed sequentially going through all the data, but the solution depends on the order in which the data are examined. We approximate the global a posteriori solution by randomizing this order and taking the average. A test with a synthetic data set sampled from a known field demonstrates quantitatively and qualitatively the improvement provided by the maximum a posteriori resampling algorithm. The method is also applied to three marine geology/geophysics data examples, demonstrating the viability of the method for diverse applications: (1) three generations of bathymetric data on the New Jersey shelf with disparate data uncertainties; (2) mean grain size data from the Adriatic Sea, which is a combination of both analytic (low uncertainty) and word-based (higher uncertainty) sources; and (3) side-scan backscatter data from the Marthas Vineyard Coastal Observatory which are, as is typical for such data, affected by speckle noise. Compared to filtering, maximum a posteriori resampling provides an objective and optimal method for reducing noise, and better preservation of the statistical properties of the sampled field. The primary disadvantage is that maximum a posteriori resampling is a computationally expensive procedure.

Ultraprecise Absolute Time Synchronization for Distributed Acquisition Systems

In this paper, we describe an algorithm built on top of a precision time protocol (PTP) implementation that allows for synchronization, syntonization and absolute time referencing to coordinated universal time (UTC), including the estimation of timestamp uncertainty. We call this the software grandmaster (SWGM) algorithm, since it provides similar services to a PTP grandmaster clock. We show that SWGM allows timestamps to be coordinated between multiple participants in a distributed measurement system with typical performance of plusmn 86 ns [root mean square (rms)] over commodity switched Ethernet connections using hardware PTP and hardware-derived timestamps. We further show that when software-derived timestamps are used the uncertainty in the timestamps is primarily driven by the latency of the system calls to read the PTP hardware, and may be on the order of 15-25 s (rms) depending on process priority, hardware bus speed, and host processor clock rate. We also show that SWGM is robust against dropped network packets up to approximately 60% loss of packets.

So, How Deep Is the Mariana Trench?

HMS Challenger made the first sounding of Challenger Deep in 1875 of 8184 m. Many have since claimed depths deeper than Challengers 8184 m, but few have provided details of how the determination was made. In 2010, the Mariana Trench was mapped with a Kongsberg Maritime EM122 multibeam echosounder and recorded the deepest sounding of 10,984 ± 25 m (95%) at 11.329903°N/142.199305°E. The depth was determined with an update of the HGM uncertainty model combined with the Lomb-Scargle periodogram technique and a modal estimate of depth. Position uncertainty was determined from multiple DGPS receivers and a POS/MV motion sensor.

Experimental and Numerical Studies of Sound Propagation Over a Submarine Canyon Northeast of Taiwan

A study of sound propagation over a submarine canyon northeast of Taiwan was made using mobile acoustic sources during a joint ocean acoustic and physical oceanographic experiment in 2009. The acoustic signal levels (equivalently, transmission losses) are reported here, and numerical models of 3-D sound propagation are employed to explain the underlying physics. The data show a significant decrease in sound intensity as the source crossed over the canyon, and the numerical model provides a physical insight into this effect. In addition, the model also suggests that reflection from the canyon seabed causes 3-D sound focusing when the direction of propagation is along the canyon axis, which remains to be validated in a future experiment. Environmental uncertainties of water sound speed, bottom geoacoustic properties, and bathymetry are addressed, and the implications for sound propagation prediction in a complex submarine canyon environment are also discussed.

Focused sound from three-dimensional sound propagation effects over a submarine canyon

Ship noise data reveal an intensification of the near-surface sound field over a submarine canyon. Numerical modeling of sound propagation is used to study the effect. The noise data were collected during an ocean acoustic and physical oceanography experiment northeast of Taiwan in 2009. In situ measurements of water sound-speed profiles and a database of high-resolution bathymetry are used in the modeling study. The model results suggest that the intensification is caused by three-dimensional sound focusing by the concave canyon seafloor. Uncertainties in the model results from unsampled aspects of the environment are discussed.

Acoustics and oceanographic observations collected during the QPE Experiment by Research Vessels OR1, OR2 and OR3 in the East China Sea in the Summer of 2009

Abstract : This document describes data, sensors, and other useful information pertaining to the ONR sponsored QPE field program to quantify, predict and exploit uncertainty in observations and prediction of sound propagation. This experiment was a joint operation between Taiwanese and U.S. researchers to measure and assess uncertainty of predictions of acoustic transmission loss and ambient noise, and to observe the physical oceanography and geology that are necessary to improve their predictability. This work was performed over the continental shelf and slope northeast of Taiwan at two sites: one that was a relatively flat, homogeneous shelf region and a more complex geological site just shoreward of the shelfbreak that was influenced by the proximity of the Kuroshio Current. Environmental moorings and ADCP moorings were deployed and a shipboard SeaSoar vehicle was used to measure environmental spatial structure. In addition, multiple bottom moored receivers and a horizontal hy drophone array were deployed to sample transmission loss from a mobile source and ambient noise. The acoustic sensors, environmental sensors, shipboard resources, and experiment design, and their data, are presented and described in this technical report.

Modeling Uncertainty in Photogrammetry-Derived National Shoreline

Tidally-referenced shoreline data serve a multitude of purposes, ranging from nautical charting, to coastal change analysis, wetland migration studies, coastal planning, resource management and emergency management. To assess the suitability of the shoreline for a particular application, end users require reliable estimates of the uncertainty in the shoreline position. Previous studies on modeling uncertainty in shoreline mapping from remote sensing data have focused on airborne light detection and ranging; to date, these methods have not been extended to aerial imagery and photogrammetric shoreline mapping, which remains the primary shoreline mapping method used by the National Geodetic Survey. The aim of this article is to develop and test a rigorous total propagated uncertainty model for shoreline compiled from both tide-coordinated and non-tide-coordinated aerial imagery using photogrammetric methods. The uncertainty model is developed using data from a study site in northeast Maine. For the study area, the standard uncertainty was found to be ∼3.2–3.3 m, depending on whether the imagery was tide coordinated or not. The uncertainty model developed in this paper can easily be extended from the study area to other areas and may facilitate estimation of uncertainty in inundation models and marsh migration models.

Experiments for Multibeam Backscatter Adjustments on the NOAA Ship Fairweather

A series of experiments were conducted to adjust and normalize the acoustic backscatter acquired by Reson 8111 and 8160 systems. The dependency of the backscatter on the receiver gain, transmit power, pulse width and acquisition mode was analyzed. Empirical beam patterns are calculated as the difference between the backscatter measured by the sonars and the expected backscatter. Expected acoustic backscatter is estimated based on a mathematical model