Charles W. Holland

Bio: Charles W. Holland is an academic researcher from Portland State University. The author has contributed to research in topics: Seabed & Scattering. The author has an hindex of 25, co-authored 125 publications receiving 1729 citations. Previous affiliations of Charles W. Holland include Pennsylvania State University.

Trans-dimensional geoacoustic inversion

Abstract: This paper develops a general trans-dimensional Bayesian methodology for geoacoustic inversion. Trans-dimensional inverse problems are a generalization of fixed-dimensional inversion that includes the number and type of model parameters as unknowns in the problem. By extending the inversion state space to multiple subspaces of different dimensions, the posterior probability density quantifies the state of knowledge regarding inversion parameters, including effects due to limited knowledge about appropriate parametrization of the environment and error processes. The inversion is implemented here using a reversible-jump Markov chain Monte Carlo algorithm and the seabed is parametrized using a partition model. Unknown data errors are addressed by including a data-error model. Jumps between dimensions are implemented with a birth–death methodology that allows transitions between dimensions by adding or removing interfaces while maintaining detailed balance in the Markov chain. Trans-dimensional inversion results in an inherently parsimonious solution while partition modeling provides a naturally self-regularizing algorithm based on data information content, not on subjective regularization functions. Together, this results in environmental estimates that quantify appropriate seabed structure as supported by the data, allowing sharp discontinuities while approximating smooth transitions where needed. This approach applies generally to geoacoustic inversion and is illustrated here with seabed reflection-coefficient data.

High-resolution geoacoustic inversion in shallow water: a joint time- and frequency-domain technique

Abstract: High-resolution geoacoustic data are required for accurate predictions of acoustic propagation and scattering in shallow water Since direct measurement of geoacoustic data is difficult, time-consuming, and expensive, inversion of acoustic data is a promising alternative However, the main problem encountered in geoacoustic inversion is the problem of uniqueness, ie, many diverse geoacoustic models can be made to fit the same data set A key, and perhaps unique, aspect of this approach is the combination of data analysis in both the space–time and the space–frequency domains This combination attempts to ameliorate the uniqueness problem by exploiting as much independent data as possible In order to meet the stringent requirements of high spatial resolution and uniqueness, an entire method has been developed including a new measurement technique, processing/analysis technique, and inversion strategy These techniques are described and then illustrated with a shallow-water data set Sound-speed gradients in the upper few meters of the sub-bottom appear to be much higher (one order of magnitude) than generally assumed And, although often ignored, a large density gradient was observed in the top layer that played an acoustically significant role

Parallel tempering for strongly nonlinear geoacoustic inversion

Abstract: This paper applies parallel tempering within a Bayesian formulation for strongly nonlinear geoacoustic inverse problems. Bayesian geoacoustic inversion consists of sampling the posterior probability density (PPD) of seabed parameters to estimate integral properties, such as marginal probability distributions, based on ocean acoustic data and prior information. This sampling is usually carried out using the Markov-chain Monte Carlo method of Metropolis-Hastings sampling. However, standard sampling methods can be very inefficient for strongly nonlinear problems involving multi-modal PPDs with the potential to miss important regions of the parameter space and to significantly underestimate parameter uncertainties. Parallel tempering achieves efficient/effective sampling of challenging parameter spaces with the ability to transition freely between multiple PPD modes by running parallel Markov chains at a series of increasing sampling temperatures with probabilistic interchanges between chains. The approach is illustrated for inversion of (simulated) acoustic reverberation data for which the PPD is highly multi-modal. While Metropolis-Hastings sampling gives poor results even with very large sample sizes, parallel tempering provides efficient, convergent sampling of the PPD. Methods to enhance the efficiency of parallel tempering are also considered.

Efficient trans-dimensional Bayesian inversion for geoacoustic profile estimation

Abstract: This paper considers the efficiency of trans-dimensional (trans-D) Bayesian inversion based on reversible-jump Markov-chain Monte Carlo (rjMCMC) sampling, with application to geophysical inverse problems for a depth-dependent earth or seabed model of an unknown number of layers (seabed acoustic reflectivity inversion is the specific example). Trans-D inversion is applied to sample the posterior probability density over geoacoustic/geophysical parameters for a variable number of layers, providing profile estimates with uncertainties that include the uncertainty in the model parameterization. However, the approach is computationally intensive. The efficiency of rjMCMC sampling is largely determined by the proposal schemes which are applied to generate perturbed values for existing parameters and new values for parameters assigned to layers added to the model. Several proposal schemes are considered here, some of which appear new for trans-D geophysical inversion. Perturbations of existing parameters are considered in a principal-component space based on an eigen-decomposition of the unit-lag parameter covariance matrix (computed from successive models along the Markov chain, a diminishing adaptation). The relative efficiency of proposing new parameters from the prior versus a Gaussian distribution focused near existing values is examined. Parallel tempering, which employs a sequence of interacting Markov chains in which the likelihood function is successively relaxed, is also considered as a means to increase the acceptance rate of new layers. The relative efficiency of various proposal schemes is compared through repeated inversions with a pragmatic convergence criterion.

Using ADV backscatter strength for measuring suspended cohesive sediment concentration

Abstract: Laboratory experiments were conducted at two institutes to reveal the relationship between acoustic backscatter strength and suspended sediment concentration (SSC). In total, three acoustic Doppler velocimeters (ADVs) with different frequencies (5, 10 and 16 MHz) were tested. Two different commercial clays and one natural sediment from Clay Bank site in the York River were checked for acoustic responses. The SSCs of selected sediments were artificially changed between a selected low and a high value in tap or de-ion water. Each ADV showed quite different backscatter responses depending on the sediment type and SSC. Not all devices had a good linear relationship between backscatter strength and SSC. Within a limited range of SSC, however, the backscatter strength can be well correlated with the SSC. Compared with optical backscattering sensor (OBS), the fluctuation of ADV backscatter signals was too noisy to be directly converted to the instantaneous changes of SSC due to high amplification ratio and small sampling volume. For the more accurate signal conversion for finding the fluctuation of SSC, the ensemble average should be applied to increase the signal-to-noise ratio. There are unexpected responses for the averaged backscatter wave strength: (1) high signals from small particles but low signals from large particles; and (2) two linear segments in calibration slope. These phenomena would be most likely caused by the different gain setting built in ADVs. The different acoustic responses to flocculation might also contribute somewhat if flocs are tightly packed. This study suggests that an ADV could be a useful instrument to estimate suspended cohesive sediment concentration and its fluctuation if the above concerns are clarified.

Transdimensional inversion of receiver functions and surface wave dispersion

Abstract: We present a novel method for joint inversion of receiver functions and surface wave dispersion data, using a transdimensional Bayesian formulation. This class of algorithm treats the number of model parameters (e.g. number of layers) as an unknown in the problem. The dimension of the model space is variable and a Markov chain Monte Carlo (McMC) scheme is used to provide a parsimonious solution that fully quantifies the degree of knowledge one has about seismic structure (i.e constraints on the model, resolution, and trade-offs). The level of data noise (i.e. the covariance matrix of data errors) effectively controls the information recoverable from the data and here it naturally determines the complexity of the model (i.e. the number of model parameters). However, it is often difficult to quantify the data noise appropriately, particularly in the case of seismic waveform inversion where data errors are correlated. Here we address the issue of noise estimation using an extended Hierarchical Bayesian formulation, which allows both the variance and covariance of data noise to be treated as unknowns in the inversion. In this way it is possible to let the data infer the appropriate level of data fit. In the context of joint inversions, assessment of uncertainty for different data types becomes crucial in the evaluation of the misfit function. We show that the Hierarchical Bayes procedure is a powerful tool in this situation, because it is able to evaluate the level of information brought by different data types in the misfit, thus removing the arbitrary choice of weighting factors. After illustrating the method with synthetic tests, a real data application is shown where teleseismic receiver functions and ambient noise surface wave dispersion measurements from the WOMBAT array (South-East Australia) are jointly inverted to provide a probabilistic 1D model of shear-wave velocity beneath a given station.

A fourth inventory of Antarctic subglacial lakes

Abstract: Antarctic subglacial lakes are studied for three main scientific reasons. First, they form an important component of the basal hydrological system which is known to affect the dynamics of the ice sheet. Second, they are amongst the most extreme viable habitats on Earth and third, if sediments exist on their floors, they may contain high-resolution records of ice sheet history. Here we present a new inventory of locations, dimensions and data sources for 379 subglacial lakes. Several major advances are responsible for the rise in the total number of lakes from the 145 known at the time of the last inventory in 2005. New radar datasets have been collected in previously unexplored regions of the ice sheet while digital data collection and processing techniques have allowed improvements to lake identification methods. Satellite measurements of ice surface elevation change caused by the movement of subglacial water have also been found to be widespread in Antarctica, often in places where radar data are absent. These advances have changed our appreciation of the Antarctic subglacial environment and have expanded our understanding of the behaviour of subglacial lakes.

A Parallel Tempering algorithm for probabilistic sampling and multimodal optimization

Abstract: S U M M A R Y Non-linear inverse problems in the geosciences often involve probabilistic sampling of multimodal density functions or global optimization and sometimes both. Efficient algorithmic tools for carrying out sampling or optimization in challenging cases are of major interest. Here results are presented of some numerical experiments with a technique, known as Parallel Tempering, which originated in the field of computational statistics but is finding increasing numbers of applications in fields ranging from Chemical Physics to Astronomy. To date, experience in use of Parallel Tempering within earth sciences problems is very limited. In this paper, we describe Parallel Tempering and compare it to related methods of Simulated Annealing and Simulated Tempering for optimization and sampling, respectively. A key feature of Parallel Tempering is that it satisfies the detailed balance condition required for convergence of Markov chain Monte Carlo (McMC) algorithms while improving the efficiency of probabilistic sampling. Numerical results are presented on use of Parallel Tempering for trans-dimensional inversion of synthetic seismic receiver functions and also the simultaneous fitting of multiple receiver functions using global optimization. These suggest that its use can significantly accelerate sampling algorithms and improve exploration of parameter space in optimization. Parallel Tempering is a meta-algorithm which may be used together with many existing McMC sampling and direct search optimization techniques. It’s generality and demonstrated performance suggests that there is significant potential for applications to both sampling and optimization problems in the geosciences.

Novel physical interpretations of K-distributed reverberation

Abstract: Interest in describing and modeling envelope distributions of sea-floor backscatter has increased recently, particularly with regard to high-resolution active sonar systems. Sea-floor scattering that results in heavy-tailed-matched-filter-envelope probability distribution functions (i.e., non-Rayleigh distributions exemplified by the K, Weibull, Rayleigh mixture, or log-normal distributions) is often the limiting factor in the performance of these types of sonar systems and in this context is referred to as reverberation or acoustic clutter analogous to radar clutter. Modeling of reverberation has traditionally entailed fitting various candidate distributions to time samples of the envelope of the scattered sonar (or radar) returns. This type of descriptive analysis and the asymptotic (infinite number of scatterers) analysis defining the K-distribution yield little insight into the environmental mechanisms responsible for heavy-tailed distributions (e.g., distributions and, clustering of discrete scatterers, patchiness in geo-acoustic properties, scattering strength of scatterers, etc.) and do not allow evaluation of the effect of changing sonar system parameters such as bandwidth and beamwidth. In contrast, we derive the envelope distribution for the scattered returns starting from simple physical descriptions of the environment with a finite number of scatterers. It is shown that plausible descriptions of the environment can lead to K-distributed reverberation. This result explains, at least partially, the success of the K-distribution in the modeling of radar clutter and sonar reverberation at a variety of frequencies and scales. The finite-number-of-scatterers model is then used to predict how the shape parameter of the K-distribution will change as the beamwidth of a towed-array receiver is varied. Analysis of reverberation data from a low-frequency (450-700 Hz) active sonar system illustrates that, within our ability to estimate it, the shape parameter of the K-distribution is proportional to the beamwidth of the towed-array receiver, a result important for sonar simulation and performance prediction models. These results should prove useful in developing methods for modeling, predicting and mitigating reverberation on high-resolution sonar systems.

Fish Population and Behavior Revealed by Instantaneous Continental Shelf-Scale Imaging

Abstract: Until now, continental shelf environments have been monitored with highly localized line-transect methods from slow-moving research vessels. These methods significantly undersample fish populations in time and space, leaving an incomplete and ambiguous record of abundance and behavior. We show that fish populations in continental shelf environments can be instantaneously imaged over thousands of square kilometers and continuously monitored by a remote sensing technique in which the ocean acts as an acoustic waveguide. The technique has revealed the instantaneous horizontal structural characteristics and volatile short-term behavior of very large fish shoals, containing tens of millions of fish and stretching for many kilometers.