Standard seismic processing steps such as velocity analysis and reverse time migration (imaging) usually assume that all reflections are primaries: Multiples represent a source of coherent noise and must be suppressed to avoid imaging artifacts. Many suppression methods are relatively ineffective for internal multiples. We show how to predict and remove internal multiples using Marchenko autofocusing and seismic interferometry. We first show how internal multiples can theoretically be reconstructed in convolutional interferometry by combining purely reflected, up- and downgoing Green’s functions from virtual sources in the subsurface. We then generate the relevant up- and downgoing wavefields at virtual sources along discrete subsurface boundaries using autofocusing. Then, we convolve purely scattered components of up- and downgoing Green’s functions to reconstruct only the internal multiple field, which is adaptively subtracted from the measured data. Crucially, this is all possible without detailed modeled information about the earth’s subsurface. The method only requires surface reflection data and estimates of direct (nonreflected) arrivals between subsurface virtual sources and the acquisition surface. The method is demostrated on a stratified synclinal model and shown to be particularly robust against errors in the reference velocity model used.

We present a 1D shear-velocity model for Los Humeros geothermal field (Mexico) obtained from three-component beamforming of ambient seismic noise, imaging for the first time the bottom of the sedimentary basement ∼5  km below the volcanic caldera, as well as the brittle-ductile transition at ∼10  km depth. Rayleigh-wave dispersion curves are extracted from ambient seismic noise measurements and inverted using a Markov chain Monte Carlo scheme. The resulting probability density function provides the shear-velocity distribution down to 15 km depth, hence, much deeper than other techniques applied in the area. In the upper 4 km, our model conforms to a profile from local seismicity analysis and matches geological structure inferred from well logs, which validates the methodology. Complementing information from well logs and outcrops at the near surface, discontinuities in the seismic profile can be linked to geological transitions allowing us to infer structural information of the deeper subsurface. By constraining the extent of rocks with brittle behavior and permeability conditions at greater depths, our results are of paramount importance for the future exploitation of the reservoir and provide a basis for the geological and thermodynamic modeling of active superhot geothermal systems, in general.

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We have evaluated an explicit relationship between the representations of internal multiples by source-receiver interferometry and an inverse-scattering series. This provides a new insight into the interaction of different terms in each of these internal multiple prediction equations and explains why amplitudes of estimated multiples are typically incorrect. A downside of the existing representations is that their computational cost is extremely high, which can be a precluding factor especially in 3D applications. Using our insight from source-receiver interferometry, we have developed an alternative, computationally more efficient way to predict internal multiples. The new formula is based on crosscorrelation and convolution: two operations that are computationally cheap and routinely used in interferometric methods. We have compared the results of the standard and the alternative formulas qualitatively in terms of the constructed wavefields and quantitatively in terms of the computational cost using examples from a synthetic data set.