Academic literature on the topic 'Data migraton'

The accuracy of time migrations done with finite‐difference schemes deteriorates with increasing reflector dip. Some properties of migration in general, and of finite‐difference approaches in particular, suggest a way of improving the accuracy of finite‐difference schemes for migrating steep dips. First, although data will be undermigrated when too low a velocity is used in migration, a correctly migrated result can be obtained by migrating again, this time with the previously undermigrated result as input. In fact, a sequence of undermigrations will yield the correct result as long as the sum of the squares of the migration velocities used in the different migration stages equals the square of the correct migration velocity. A second property is that the apparent spatial dip of a reflector perceived by the migration process is a function of not only the time dip of the unmigrated reflection, but also the velocity used in the migration. In a sequence of low‐velocity migrations, the apparent spatial dip perceived at each migration stage can be considerably less than the true dip. Thus, because finite‐difference migration is accurate for small spatial dips, the cascaded migrations yield a more accurate result than that of single‐stage migration. Also, because each migration stage is done with low velocity, the depth step can be large; hence, the computational effort need not be. The accuracy of the method is not compromised (in fact, it improves) in media in which velocity increases with depth. Moreover, the cascaded approach suffers no more than other methods of time migration where velocity varies mildly in the lateral direction. In applications of the method to stacked data from the Gulf of Mexico, reflections from near‐vertical flanks of salt domes were migrated with accuracy comparable to that achieved by frequency‐wavenumber domain migration.

In practice, migration of seismic data requires decision making with regard to: (1) Different migration strategies ? 2-D/3-D, post-stack/prestack, and time/depth migrations; (2) different migration algorithms for a given strategy ? integral, finite-difference and frequency-wavenumber methods; (3) different parameters for a given algorithm ? aperture width, depth-step size, stretch factor; (4) the input data ? profile length, noise content, spatial aliasing and boundary effects; (5) and finally, migration velocities ? the weak link between the seismic method and the subsurface geology that the former tries to image.The seismic interpreter, whose main role is to infer subsurface geology from the migrated data, normally should not be burdened with the decisions concerning the above factors. Fortunately, migration results often are self-evident; a feature considered geologically implausible on a migrated section can be associated with one or more of the above factors. Based on large number of field data cases, I will discuss each of these factors and provide some generally applicable guidelines for migration that an interpreter can invoke in practice.

Ongoing changes to global weather patterns and human modifications of the environment have altered the breeding and non-breeding ranges of migratory species, the timing of their migrations, and even whether they continue to migrate at all. Animal movements are arguably one of the most difficult behaviours to study, particularly in smaller birds that migrate tens to thousands of kilometres seasonally, often moving hundreds of kilometres each day. The recent miniaturization of tracking and logging devices has led to a radical transformation in our understanding of avian migratory behaviour and migratory connectivity. While advances in technology have altered the way researchers study migratory behaviour in the field, advances in techniques related to the study of physiological and genetic mechanisms underlying migratory behaviour have rarely been integrated into field studies of tracking. To predict the capacity of migrants to adjust to a changing planet, it is essential that we combine avian migration data with physiological and genetic measurements taken at key time points prior to, during and after migration.

The derivation of ground-penetrating radar (GPR) images in which the amplitudes of reflections and diffractions are consistent with subsurface electromagnetic property contrasts requires migration algorithms that correctly account for the antenna radiation patterns and the vectorial character of electromagnetic wavefields. Most existing vector-migration techniques are based on far-field approximations of Green’s functions, which are inappropriate for the majority of GPR applications. We have recently developed a method for rapidly computing practically exact-field Green’s functions in a multicomponent vector migration scheme. A significant disadvantage of this scheme is the extra effort needed to record, process, and migrate at least two components of GPR data. By making straightforward modifications to the multi-component algorithm, we derive an equivalent exact-field sin-gle-component vector-migration scheme that can be applied to most standard GPR data acquired using a single copolarized antenna pair. Our new formulation is valid for polarization-independent features (e.g., most point scatterers, spheres, and planar objects), but not for polarization-dependent ones (e.g., most underground public utilities). A variety of tests demonstrates the stability of the exact-field single-component operators. Applications of the new scheme to synthetic and field-recorded GPR data containing dipping planar reflections produce images that are virtually identical to the corresponding multicomponent vector-migrated images and are almost invariant to the relative orientations of antennas and reflectors. The new single-component vector-migration scheme is appropriate for migrating the majority of GPR data acquired by researchers and practitioners.