A cross-correlation objective function for least-squares migration and visco-acoustic imaging

SEG Technical Program Expanded Abstracts 2016

2016

We use elastic least-squares reverse time migration (LSRTM) to invert for the reflectivity images of P-and S-wave impedances. Elastic LSRTM solves the linearized elastic-wave equations for forward modeling and the adjoint equations for backpropagating the residual wavefield at each iteration. Numerical tests on synthetic data and field data reveal the advantages of elastic LSRTM over elastic reverse time migration (RTM) and acoustic LSRTM. For our examples, the elastic LSRTM images have better resolution and amplitude balancing, fewer artifacts, and less crosstalk compared with the elastic RTM images. The images are also better focused and have better reflector continuity for steeply dipping events compared to the acoustic LSRTM images. Similar to conventional leastsquares migration, elastic LSRTM also requires an accurate estimation of the P-and S-wave migration velocity models. However, the problem remains that, when there are moderate errors in the velocity model and strong multiples, LSRTM will produce migration noise stronger than that seen in the RTM images.

Section: Seg/eage Salt Modelmentioning

confidence: 99%

Elastic least-squares reverse time migration

Feng

SEG Technical Program Expanded Abstracts 2016

2016

“…The goal is to find the reflectivity model which maximizes the normalized dot product of the observed and predicted crosscorrelograms. This can be written as (Routh et al (2011);Zhang et al (2013); Dutta et al (2014)),…”

Section: Theorymentioning

confidence: 99%

Mitigation of defocusing by statics and near-surface velocity errors by interferometric least-squares migration

Sinha

SEG Technical Program Expanded Abstracts 2015

2015

Self Cite

CitationMrinal Sinha* and Gerard T. SUMMARYWe propose an interferometric least-squares migration method that can significantly reduce migration artifacts due to statics and errors in the near-surface velocity model. We first choose a reference reflector whose topography is well known from the, e.g., well logs. Reflections from this reference layer are correlated with the traces associated with reflections from deeper interfaces to get crosscorrelograms. These crosscorrelograms are then migrated using interferometric least-squares migration (ILSM). In this way statics and velocity errors at the near surface are largely eliminated for the examples in our paper.

“…The goal is to find the reflectivity model that maximizes the normalized dot product of the observed and predicted crosscorrelograms. This can be written as (Routh et al, 2011;Zhang et al, 2013;Dutta et al, 2014) ϵ…”

Section: Theory Of Ilsmmentioning

confidence: 99%

Mitigation of defocusing by statics and near-surface velocity errors by interferometric least-squares migration with a reference datum

Sinha

2016

GEOPHYSICS

Self Cite

Imaging seismic data with an erroneous migration velocity can lead to defocused migration images. To mitigate this problem, we first choose a reference reflector whose topography is well-known from the well logs, for example. Reflections from this reference layer are correlated with the traces associated with reflections from deeper interfaces to get crosscorrelograms. Interferometric least-squares migration (ILSM) is then used to get the migration image that maximizes the crosscorrelation between the observed and the predicted crosscorrelograms. Deeper reference reflectors are used to image deeper parts of the subsurface with a greater accuracy. Results on synthetic and field data show that defocusing caused by velocity errors is largely suppressed by ILSM. We have also determined that ILSM can be used for 4D surveys in which environmental conditions and acquisition parameters are significantly different from one survey to the next. The limitations of ILSM are that it requires prior knowledge of a reference reflector in the subsurface and the velocity model below the reference reflector should be accurate.