Matched Field Processing Approach Research Articles

Localizing a quiet moving source with range-coherent matched field processing

We introduce range-coherent matched field processing (MFP) to lower the signal-to-noise ratio (SNR) threshold required to successfully localize a narrowband source in a well-characterized shallow water environment. To accomplish this, range-coherent MFP coherently processes snapshots formed from a moving source. This approach differs from existing MFP approaches that treat each snapshot as having a random phase. Range-coherent MFP requires determination of the source phase acquired between snapshots. With that information, MFP can be applied to the cross-spectrum of the ''supervector'' of concatenated snapshots acquired at different times. Viewed another way, range-coherent MFP is simply MFP applied to a passive synthetic aperture formed from a moving source. The synthetic aperture geometry depends on source velocity, which is included in the MFP search space. Range-coherent MFP produces robust velocity estimates at low SNR, which permits the use of a longer FFT in pre-processing. The synthetic aperture array gain plus the increased input SNR afforded by the enhanced pre-processing significantly lower the signal level required for successful localization. We present the application of range-coherent MFP to data from the SWellEx-96 experiment to localize a source that is too quiet for conventional methods to localize.

Range-coherent matched field processing for low signal-to-noise ratio localization.

Range-coherent matched field processing (MFP) coherently combines snapshots to localize a moving, narrowband source. This approach differs from existing MFP approaches that treat each snapshot as having a random phase due to both unknown motion through the medium and imprecise knowledge of the source frequency. Range-coherent MFP requires determination of the source phase acquired between snapshots. With that information, MFP can be applied to the cross-spectrum of snapshots acquired at different times, since relative phase between snapshots is determined by the medium properties, source location, and source velocity. Viewed another way, range-coherent MFP is simply MFP applied to a passive synthetic aperture formed from a moving source. The synthetic aperture geometry depends on source velocity, which is included in the MFP search space. Range-coherent MFP produces robust velocity estimates at low signal-to-noise ratio (SNR), which permits the use of a longer fast Fourier transform in pre-processing. The synthetic aperture array gain plus the increased input SNR afforded by the enhanced pre-processing significantly lowers the required signal level for successful localization. In data from the SWellEx-96 experiment, range-coherent MFP successfully localizes a source that is too quiet for conventional methods to localize.

Factorized wave propagation model in tree-type pipe networks and its application to leak localization

In a leaky tree-structured pipe network, transient head and discharge at a measurement location arise from a superposition of the waves along the set of pipes linking the measurement location to each boundary node. Moreover, the solution can be split into a leak-free part and a term that accounts for the scattering from the leak, the latter varies linearly with leak size and nonlinearly with leak location. It is then shown that if the head adjacent to each boundary node is measured, any reasonably-sized leak can be uniquely and efficiently identified by the matched-field processing approach. The efficiency of the leak identification scheme stems from the linear dependence of wave scattering on leak size. The proposed method is successfully applied both numerically and to pilot data from a tree-structured system of viscoelastic pipes.

High-sensitivity microseismic monitoring: Automatic detection and localization of subsurface noise sources using matched-field processing and dense patch arrays

Recent advancements in seismic data acquisition and computational power have enhanced the deployment of dense seismic monitoring networks. The growing volume of recorded data requires the development of automated techniques to monitor and image zones of seismicity. We have developed an automatic detection and localization method that demands minimal a priori information for retrieval of the spatial distribution of subsurface noise sources (including, but not limited to, microseismic activity), in a reservoir and in the near vicinity during a hydraulic fracturing treatment. This method is based on matched-field processing (MFP), which takes advantage of the phase coherence that is recorded at dense arrays of sensors to localize noise sources. MFP is applied with a distributed set of patch arrays in the context of geophysics exploration. The MFP approach is applied to ambient noise recordings, and it provides results that are consistent with the classic localization methods applied to high-amplitude microseismic signals (in particular, using the relative template-based method). Furthermore, MFP provides enhanced sensitivity of detection and spatially extended information about structural heterogeneities. MFP opens a route to continuous, automatic, statistics-based, and high-sensitivity reservoir monitoring and imaging for geophysics exploration. Potential applications can also be envisaged for seismic monitoring of volcanic and geyser activities, and for other types of hydrothermal activity.

Short Timescale Analysis of Microseisms and Application to Array Calibration

AbstractPrimary and secondary microseisms are analyzed in this study using a novel matched field processing approach that allows for analysis of features with temporal scales of the order of seconds. The majority of previous microseism research employs time averaging; hence, very little is currently known about the properties of the wavefield on such short timescales. We aim to better understand the nature of the microseismic wavefield through applying our novel matched field processing approach to example data from two seismic arrays (USArray and Rio Grande Rift‐Flex Array) in the United States. We find that surface and body wave microseisms on short timescales are observed as pulses of coherent energy, which may be separated in time, embedded in the continuous signal. The pulses display a much larger coherence value in comparison to the commonly employed time averaging approaches, given that they can be separated in time. This allows us to study the spatial correlation of the wavefield and gives an insight into the source and path propagation effects of surface and body waves. We find that the correlation of the short timescale surface wavefield between two stations is dependent on the distance between them and is strongly dependent on their geometric position with respect to the source. Correlations on the PKP wavefield show a decrease with increasing source‐station distance and a mild decrease for azimuthally distributed stations at the same source‐station distance. Finally, we demonstrate how the pulse wavefield can be used for array calibration purposes.

Matched Field Processing of Three‐Component Seismic Array Data Applied to Rayleigh and Love Microseisms

AbstractWe extend three‐component plane wave beamforming to a more general form and devise a framework, which incorporates velocity heterogeneities of the seismic propagation medium and allows us to estimate accurately sources that do not follow the simple plane wave assumption. This is achieved by utilizing fast marching to track seismic wave fronts for given surface wave phase velocity maps. The resulting matched field processing approach is used to study the surface wave locations of Rayleigh and Love waves at 8 and 16 s based on data from four seismic arrays in the western United States. By accurately accounting for the path propagation effects, we are able to map microseism surface wave source locations more accurately than conventional plane wave beamforming. In the primary microseisms frequency range, Love waves are dominant over Rayleigh waves and display a directional radiation pattern. In the secondary microseisms range, we find the general source regions for both wave types to be similar, but on smaller scales differences are observed. Love waves are found to originate from a larger area than Rayleigh waves and their energy is equal or slightly weaker than Rayleigh waves. The energy ratios are additionally found to be source location dependent. Potential excitation mechanisms are discussed which favor scattering from Rayleigh‐to‐Love waves.

Shallow-water sparsity-cognizant source-location mapping.

Using passive sonar for underwater acoustic source localization in a shallow-water environment is challenging due to the complexities of underwater acoustic propagation. Matched-field processing (MFP) exploits both measured and model-predicted acoustic pressures to localize acoustic sources. However, the ambiguity surface obtained through MFP contains artifacts that limit its ability to reveal the location of the acoustic sources. This work introduces a robust scheme for shallow-water source localization that exploits the inherent sparse structure of the localization problem and the use of a model characterizing the acoustic propagation environment. To this end, the underwater acoustic source-localization problem is cast as a sparsity-inducing stochastic optimization problem that is robust to model mismatch. The resulting source-location map (SLM) yields reduced ambiguities and improved resolution, even at low signal-to-noise ratios, when compared to those obtained via classical MFP approaches. An iterative solver based on block-coordinate descent is developed whose computational complexity per iteration is linear with respect to the number of locations considered for the SLM. Numerical tests illustrate the performance of the algorithm.

Locating hydrothermal acoustic sources at Old Faithful Geyser using Matched Field Processing

In 1992, a large and dense array of geophones was placed around the geyser vent of Old Faithful, in the Yellowstone National Park, to determine the origin of the seismic hydrothermal noise recorded at the surface of the geyser and to understand its dynamics. Old Faithful Geyser (OFG) is a small-scale hydrothermal system where a two-phase flow mixture erupts every 40 to 100 min in a high continuous vertical jet. Using Matched Field Processing (MFP) techniques on 10-min-long signal, we localize the source of the seismic pulses recorded at the surface of the geyser. Several MFP approaches are compared in this study, the frequency-incoherent and frequency-coherent approach, as well as the linear Bartlett processing and the non-linear Minimum Variance Distorsionless Response (MVDR) processing. The different MFP techniques used give the same source position with better focalization in the case of the MVDR processing. The retrieved source position corresponds to the geyser conduit at a depth of 12 m and the localization is in good agreement with in situ measurements made at Old Faithful in past studies.

Compressing the computational burden of matched‐field processing for sound source localization.

Sound source localization in shallow water environment is done using matched field processing (MFP), also referred to as time‐reversal imaging or backprojection method. Standard MFP is usually implemented by matching (using a cross‐correlation operation) the received acoustic data from the sought source with modeled data (or replicas) for point source located at multiple test locations over the a priori search area. Consequently, a direct implementation of MFP (i.e., brute force search) over a large search area can be highly computationally demanding especially when attempting to locate repeatedly several stationary or moving unknown sources in a complex environment. We formulated instead a compressive MFP approach allowing for significant computation time savings with a computational cost that scales with the logarithm of the number of independent‐or uncorrelated‐ test locations. This approach leverages the key concepts behind group testing and compressed sensing by computing instead the expected value of multiple ambiguity surface realizations obtained by backprojection of orthogonal random signals from the receiver locations. Thus compressive MFP allows to estimate the actual ambiguity function using few replica computations by evaluating hypothetical random groups of test locations instead of individual test locations (as done in conventional MFP).

Compressive matched field processing.

Sound source localization in shallow water environment is commonly done using matched field processing (MFP). MFP is usually implemented by systematically placing a test point source at each point of a search grid, computing the acoustic field (replicas) at all the elements of the array and then correlating this modeled field with the data from the real point source whose localization is unknown to determine the best-fit location. However, a direct implementation of MFP (i.e., brute force search) over a large grid space—or search area—is computationally demanding especially in the presence of complex propagation environments. We formulated instead the localization problem of a few acoustic sources as the sparse approximation of the measured signals in a specific dictionary of atoms obtained from discretization of this sparse search space. Spatial random projections are performed to efficiently span the search space. This compressive MFP approach allows for significant computation time savings with a computational cost increasing with the number of random projections instead of the number of search grid points. The performance of this approach will be illustrated using numerical and experimental data in a shallow water waveguide.

THE MATCHED FIELD PROCESSING OF BINAURAL DOLPHIN-LIKE SIGNALS FOR THE DETECTION AND IDENTIFICATION OF BURIED TARGETS

The Matched Field Processing (MFP) approach to be discussed here is intended to extract subtle differences between apparently similar signals. The technique is applied coherently to an array of data, i.e. to two receivers. One of the main advantages to this work is that even though we use MFP, there is no modeling involved. Since the available binaural data are quite limited and show very strong, obviously different returns from all the targets (not the subtle differences realistically expected), we found it necessary to manipulate the data to bring them more into line with expectations. In particular, scattered returns from a drum were reduced, i.e. multiplied by a small constant factor, then added to the scattered returns from bottom-only data using various time shifts. The shifts simulated a family of returns from a low signal-to-noise (S/N) 55 gallon drum target. This family with shifted bottom scattering mimics returns from multiple placements of the targets on the bottom. These new target "data" (comprised of manipulated real data) seem at first glance to be nearly identical to the original bottom-only returns. Thus, the new target data display subtle differences from the bottom-only data. The MFP approach (based on the linear, a.k.a., Bartlett, processor) was then applied to these new "data". They were processed and yielded a target "template" of scattered returns varying as a function of time and frequency characterizing the returns scattered from the drum. Additionally, a similar template was computed for the buried manta-like target data and is seen to be quite different from the drum template. This new type of template can easily be used to detect scattering from particular target types in low S/N situations. It is not proposed that dolphins are using these templates, but, rather, that the templates display scattering characteristics which the dolphins may be using. More data would be extremely useful in determining the templates under a variety of conditions, e.g. for lower S/N levels, different bottom types, targets types, source ranges, depths, and scattering angles, etc.

Matched field processing of existing binaural dolphinlike signals for MCM

A new matched field processing (MFP) approach has been applied to existing (but limited) dolphinlike binaural data. The frequencies range from 25 to 200 kHz with the received quasi-monostatic echo lasting less than 4.0 ms. The data discussed consist of backscatter echoes from a mud bottom, a drum buried in the bottom, and a Manta-like object also buried in the bottom. The MFP approach (using NO modeling of any kind) was designed to extract differences between apparently similar signals. Since the available data show overly strong and different returns for all the targets, we first manipulate the data to bring them more into line with expectations. The new data were then processed to yield a target ‘‘template’’ of scattered returns as a function of time and frequency characterizing the target returns. These templates can easily be used to detect and identify scattering from targets on or in the bottom in low S/N situations. [Work supported by ONR.]

Performance of acoustic tomography via matched-field processing

Results will be presented showing the deep ocean performance of the matched-field processing (MFP) approach to acoustic tomography as discussed in Tolstoy etal. [J. Acoust. Soc. Am. 89, 1119 (1991)] and Tolstoy [J. Acoust. Soc. Am. 91, 781 (1992)]. These results are generated for the simulated environment provided by Chiu etal., which was selected and designed for comparison between presently available tomography techniques. The MFP approach requires the presence of at least three vertical arrays plus low-frequency sources, e.g., air-deployed shots, distributed around the region perimeter. The resolution of the final inverse solution is dependent on the number and placement of the sources and arrays, on the quality and availability of additional environmental information, e.g., sound-speed profile data at the arrays, and on our ability to mode filter or time window the acoustic data to eliminate bottom interacting energy.

REVIEW OF MATCHED FIELD PROCESSING FOR ENVIRONMENTAL INVERSE PROBLEMS

Matched Field Processing (MFP) is a signal processing technique that has enjoyed much recent success in the Underwater Acoustics community, mainly as a consequence of the high accuracy currently achievable in predictions of non-isotropic, non-planar ocean acoustic fields. MFP is applied to acoustic fields measured on arrays of hydrophones and has been used primarily to solve the inverse source problem, i.e., to determine the unknown range, depth, and bearing of acoustic sources in a known ocean environment. However, the MFP approach has also been applied to the environmental inverse problem, i.e., to determine the characteristics of an unknown ocean environment. This paper will review the work done in this latter area.

Linearization of the matched field processing approach to acoustic tomography

This paper continues the approach presented in Tolstoy et al. [J. Acoust. Soc. Am. 89, 1119–1127 (1991)] but offers a much improved inversion technique, i.e., a linearization of the problem which reformats the computations in terms of a simple nonsquare matrix inversion for an overdetermined system. This work was motivated by the desire to improve upon the speed and accuracy of the original algorithm that had a tendency to stall unpredictably [Tolstoy and Frazer, in Computational Acoustics, edited by D. Lee, A. Cakmak, and R. Vichnevetsky (North-Holland, Amsterdam, 1991)]. This work has the additional bonus that sound-speed accuracies are now an order of magnitude better than the earlier technique. In addition, calculations confirm that for simulations with white, Gaussian, uncorrelated noise the linear/Bartlett processor results are identical to those of the minimum-variance/Capon processor. Finally, optimal source–receiver configurations have been determined by exhaustively computing the condition numbers for the associated matrices in the new linear formulation. Simulation results with the new linearization plus matrix inversion scheme show that three arrays located at optimal coordinates in a 250×250-km ocean region with shot sources distributed around the perimeter can result in 3-D sound-speed profiles determined to accuracies better than 0.07 m/s and better than 0.03 m/s for four arrays located at optimal coordinates (assuming perfect knowledge of sound-speed profiles at source and array coordinates, no array deformations, perfect elimination of bottom interacting energy). Such accuracies are not to be expected under experimental conditions.

Matched catastrophe deconvolution with application to the inversion of marine seismic refraction data

The problem of extracting accurate estimates of the travel times of unresolved arrivals (e.g., the second and third arrivals within a triplication) from a set of noisy bandlimited measurements of a time‐dependent acoustic wavefield is addressed. The method of solution presented is based on the assumption that the underlying caustic structure (or, equivalently, the travel time curve structure) of the wavefield is known. Because generic caustics associated with causal wavefields take on only certain forms, this is a weak assumption. Additionally, it is assumed that the medium structure varies as a function of depth only. This further restricts the class of observable caustic sections. These constraints are incorporated into a matched field processing approach to the deconvolution problem. The technique is demonstrated using a measured marine seismic refraction data set. A Herglotz‐Wiechert inversion of the resulting kinematic data T(R) agrees well with previously published waveform inversions of the same data set. Advantages of the new approach over linearized waveform inversions are discussed.