A multi-decadal view of seismic methods for detecting precursors of magma movement and eruption

We present the chain of time-reverse modeling, image space wavefield decomposition and several imaging conditions as a migration-like algorithm called time-reverse imaging. The algorithm locates subsurface sources in passive seismic data and diffractors in active data. We use elastic propagators to capitalize on the full waveforms available in multicomponent data, although an acoustic example is presented as well. For the elastic case, we perform wavefield decomposition in the image domain with spatial derivatives to calculate P and S potentials. To locate sources, the time axis is collapsed by extracting the zero-lag of auto and cross-correlations to return images in physical space. The impulse response of the algorithm is very dependent on acquisition geometry and needs to be evaluated with point sources before processing field data. Band-limited data processed with these techniques image the radiation pattern of the source rather than just the location. We present several imaging conditions but we imagine others could be designed to investigate specific hypotheses concerning the nature of the source mechanism. We illustrate the flexible technique with synthetic 2D passive data examples and surface acquisition geometry specifically designed to investigate tremor type signals that are not easily identified or interpreted in the time domain.

Source location using time‐reverse imaging

Space and time spectra of stationary stochastic waves, with special reference to microtremors

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Marchenko imagingMarchenko imaging

In the 1990s, the method of time-reversed acoustics was developed. This method exploits the fact that the acoustic wave equation for a lossless medium is invariant for time reversal. When ultrasonic responses recorded by piezoelectric transducers are reversed in time and fed simultaneously as source signals to the transducers, they focus at the position of the original source, even when the medium is very complex. In seismic interferometry the time-reversed responses are not physically sent into the earth, but they are convolved with other measured responses. The effect is essentially the same: The time-reversed signals focus and create a virtual source which radiates waves into the medium that are subsequently recorded by receivers. A mathematical derivation, based on reciprocity theory, formalizes this principle: The crosscorrelation of responses at two receivers, integrated over different sources, gives the Green’s function emitted by a virtual source at the position of one of the receivers and observed by the other receiver. This Green’s function representation for seismic interferometry is based on the assumption that the medium is lossless and nonmoving. Recent developments, circumventing these assumptions, include interferometric representations for attenuating and/or moving media, as well as unified representations for waves and diffusion phenomena, bending waves, quantum mechanical scattering, potential fields, elastodynamic, electromagnetic, poroelastic, and electroseismic waves. Significant improvements in the quality of the retrieved Green’s functions have been obtained with interferometry by deconvolution. A trace-by-trace deconvolution process compensates for complex source functions and the attenuation of the medium. Interferometry by multidimensional deconvolution also compensates for the effects of one-sided and/or irregular illumination.

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Tutorial on seismic interferometry: Part 2 — Underlying theory and new advances

Interferometry allows for synthesis of data recorded at any two receivers into waves that propagate between these receivers as if one of them behaves as a source. This is accomplished typically by crosscorrelations. Based on perturbation theory and representation theorems, we show that interferometry also can be done by deconvolutions for arbitrary media and multidimensional experiments. This is important for interferometry applications in which (1) excitation is a complicated source-time function and/or (2) when wavefield separation methods are used along with interferometry to retrieve specific arrivals. Unlike using crosscorrelations, this method yields only causal scattered waves that propagate between the receivers. We offer a physical interpretation of deconvolution interferometry based on scattering theory. Here we show that deconvolution interferometry in acoustic media imposes an extra boundary condition, which we refer to as the free-point or clamped-point boundary condition, depending on the measured field quantity. This boundary condition generates so-called free-point scattering interactions, which are described in detail. The extra boundary condition and its associated artifacts can be circumvented by separating the reference waves from scattered wavefields prior to interferometry. Three wavefield-separation methods that can be used in interferometry are direct-wave interferometry, dual-field interferometry, and shot-domain separation. Each has different objectives and requirements.

Interferometry by deconvolution: Part 1 — Theory for acoustic waves and numerical examples

Wavefield-based migration velocity analysis using the semblance principle requires computation of images in an extended space in which we can evaluate the imaging consistency as a function of overlapping experiments. Usual industry practice is to assemble those seismic images in common-image gathers that represent reflectivity as a function of depth and extensions, e.g., reflection angles. We introduce extended common-image point (CIP) gathers constructed only as a function of the spaceand time-lag extensions at sparse and irregularly distributed points in the image. Semblance analysis using CIP’s constructed by this procedure is advantageous because we do not need to compute gathers at regular surface locations and we do not need to compute extensions at all depth levels. The CIP’s also give us the flexibility to distribute them in the image at irregular locations aligned with the geologic structure. Furthermore, the CIP’s remove the depth bias of common-image gathers constructed as a function of the depth axis. An interpretation of the CIP’s using the scattering theory shows that they are scattered wavefields associated with sources and receivers inside the subsurface. Thus, when the surface wavefields are correctly reconstructed, the extended CIP’s are characterized by focused energy at the origin of the spaceand time-lag axes. Otherwise, the energy defocuses from the origin of the lag axes proportionally with the cumulative velocity error in the overburden. This information can be used for wavefield-based tomographic updates of the velocity model, and if the velocity used for imaging is correct, the coordinate-independent CIP’s can be a decomposed as a function of the angles of incidence.

http://newton.mines.edu/paul/journals/2010_gpreic.pdf

Extended imaging conditions for wave‐equation migration

Deconvolution interferometry successfully recovers the impulse response between two receivers without the need for an independent estimate of the source function. Here we extend the method of interferometry by deconvolution to multicomponent data in elastic media. As in the acoustic case, elastic deconvolution interferometry retrieves only causal scattered waves that propagate between two receivers as if one acts as a pseudosource of the point-force type. Interferometry by deconvolution in elastic media also generates artifacts because of a clamped-point boundary condition imposed by the deconvolution process. In seismic-while-drilling (SWD) practice, the goal is to determine the subsurface impulse response from drill-bit noise records. Most SWD technologies rely on pilot sensors and/or models to predict the drill-bit source function, whose imprint is then removed from the data. Interferometry by deconvolution is of most use to SWD applications in which pilot records are absent or provide unreliable estimates of bit excitation. With a numerical SWD subsalt example, we show that deconvolution interferometry provides an image of the subsurface that cannot be obtained by correlations without an estimate of the source autocorrelation. Finally, we test the use of deconvolution interferometry in processing SWD field data acquired at the San Andreas Fault Observatory at Depth (SAFOD). Because no pilot records were available for these data, deconvolution outperforms correlation in obtaining an interferometric image of the San Andreas fault zone at depth.

Interferometry by deconvolution: Part 2 — Theory for elastic waves and application to drill-bit seismic imaging

Iterative substitution of the coupled Marchenko equations is a novel methodology to retrieve the Green's functions from a source or receiver array at an acquisition surface to an arbitrary location in an acoustic medium. The methodology requires as input the single-sided reflection response at the acquisition surface and an initial focusing function, being the time-reversed direct wavefield from the acquisition surface to a specified location in the subsurface. We express the iterative scheme that is applied by this methodology explicitly as the successive actions of various linear operators, acting on an initial focusing function. These operators involve multidimensional crosscorrelations with the reflection data and truncations in time. We offer physical interpretations of the multidimensional crosscorrelations by subtracting traveltimes along common ray paths at the stationary points of the underlying integrals. This provides a clear understanding of how individual events are retrieved by the scheme. Our interpretation also exposes some of the scheme's limitations in terms of what can be retrieved in case of a finite recording aperture. Green's function retrieval is only successful if the relevant stationary points are sampled. As a consequence, internal multiples can only be retrieved at a subsurface location with a particular ray parameter if this location is illuminated by the direct wavefield with this specific ray parameter. Several assumptions are required to solve the Marchenko equations. We show that these assumptions are not always satisfied in arbitrary heterogeneous media, which can result in incomplete Green's function retrieval and the emergence of artefacts. Despite these limitations, accurate Green's functions can often be retrieved by the iterative scheme, which is highly relevant for seismic imaging and inversion of internal multiple reflections.

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On Green’s function retrieval by iterative substitution of the coupled Marchenko equations

S U M M A R Y Seismic imaging provides much of our information about the Earth’s crustal structure. The principal source of imaging errors derives from simplicistically modeled predictions of the complex, scattered wavefields that interact with each subsurface point to be imaged. A new method of wavefield extrapolation based on inverse scattering theory produces accurate estimates of these subsurface scattered wavefields, while still using relatively little information about the Earth’s properties. We use it for the first time to create real target-oriented seismic images of a North Sea field. We synthesize underside illumination from surface reflection data, and use it to reveal subsurface features that are not present in an image from conventional migration of surface data. To reconstruct underside reflections, we rely on the so-called downgoing focusing function, whose coda consists entirely of transmission-bornmultiple scattering. As such, we provide the first field data example of reconstructing underside reflections with contributions from transmitted multiples, without the need to first locate or image any reflectors in order to reconstruct multiple scattering effects.

Ivan Vasconcelos

Papers

Interferometry by deconvolution: Part 1 — Theory for acoustic waves and numerical examples

Extended imaging conditions for wave‐equation migration

Interferometry by deconvolution: Part 2 — Theory for elastic waves and application to drill-bit seismic imaging

On Green’s function retrieval by iterative substitution of the coupled Marchenko equations

Target-oriented Marchenko imaging of a North Sea field