Machine learning in microseismic monitoring

The confluence of our ability to handle big data, significant increases in instrumentation density and quality, and rapid advances in machine learning (ML) algorithms have placed Earth Sciences at the threshold of dramatic progress. ML techniques have been attracting increased attention within the seismic community, and, in particular, in...

Focal deblending: Marine data processing experiences

In contrast to conventional acquisition practices, simultaneous source acquisition allows for overlapping wavefields to be recorded. Relaxing the shot schedule in this manner has certain advantages, such as allowing for faster acquisition and/or denser shot sampling. This flexibility usually comes at the cost of an extra step in the...

An effective scheme of joint migration inversion in the pseudo-time domain

Traditional full-waveform inversion is a non-linear and ill-posed inversion problem. To reduce the non-linearity of it, joint migration inversion (joint migration inversion) was proposed as an alternative. Joint migration inversion tries to minimize the mismatch between measured and modelled reflection data. One key feature of joint migration...

Seismic inversion with deep learning: A proposal for litho-type classification

This article investigates bypassing the inversion steps involved in a standard litho-type classification pipeline and performing the litho-type classification directly from imaged seismic data. We consider a set of deep learning methods that map the seismic data directly into litho-type classes, trained on two variants of synthetic seismic data:...

Automated seismic acquisition geometry design for optimized illumination at the target: a linearized approach

In seismic exploration methods, imperfect spatial sampling at the surface causes a lack of illumination at the target in the subsurface. The hampered image quality at the target area of interest causes uncertainties in reservoir monitoring and production, which can have a substantial economic impact. Especially in the case of a complex...

Seismic inversion of soil damping and stiffness using multichannel analysis of surface wave measurements in the marine environment

Determination of soil material damping is known to be difficult and uncertain, especially in the offshore environment. Using an advanced inversion methodology based on multichannel spectral analysis, Scholte and Love wave measurements are used to characterize subsea soil from a North Sea site. After normalization, a determinant-based objective...

Imaging blended vertical seismic profiling data using full-wavefield migration in the common-receiver domain

For vertical-seismic-profiling (VSP) measurements, the use of blended acquisition, with time-overlapping shot records, can greatly reduce the downtime and, thereby, provide large cost savings. For directly imaging blended VSP measurements, we have used full-wavefield migration (FWM). FWM is an inversion-based imaging scheme that enables us to...

Estimation of primaries and near-offset reconstruction by sparse inversion: Marine data applications

ost wave-equation-based multiple removal algorithms are based on prediction and subtraction of multiples. Especially for shallow water, the prediction strongly relies on a correct interpolation of the missing near offsets. The subtraction of predicted multiples from the data can easily lead to the distortion of primaries if primaries and...

The concept of double blending: Combining incoherent shooting with incoherent sensing

Seismic surveys are designed so that the time interval between shots is sufficiently large to avoid temporal overlap between records. To economize on survey time, the current compromise is to keep the number of shots to an acceptable minimum. The result is a poorly sampled source domain. We propose to abandon the condition of nonoverlapping shot...

Assessing estimated velocity-depth models: Finding error bars in tomographic inversion

In conventional migration velocity analysis methods, a velocity model is estimated that results in flattened events in common-image gathers. However, after this process, no information is available on the accuracy of this velocity model. A statistical analysis of velocity-model parameters is very difficult because of the integrated nature of the...

Imaging of multiple reflections

Current multiple-removal algorithms in seismic processing use either differential moveout or predictability. If the differential moveout between primaries and multiples is small, prediction is the only option available. In the last decade, multidimensional prediction-error filtering by weighted convolution, such as surface-related multiple...