Seismic Wave Field Model Excited by a Finite Long Cylindrical Charge

The amplitude-frequency characteristic of a seismic wave excited by explosion sources directly affects the accuracy of seismic exploration. To reveal the effect law related to a cylindrical charge, the research proposes a seismic wavefield model excited by a long cylindrical charge. According to the characteristics of the blasting cavity generated by a finite length cylindrical charge, the seismic wavefield characteristics of a cylindrical charge excitation is obtained by superposing the seismic wavefield excited by a series of spherical charges. Numerical simulation results show that the calculation error of the blasting cavity characteristics of the theoretical model is within 10%. The comparison with field experimental results shows that the error of the model is within 9.4%. The velocity field of the excited seismic wave is almost the same as that of the spherical charge when the explosion distance to the cylindrical charge with finite length is 16-21 times longer than the charge length, but the frequency of the seismic wave is 30% higher than for a spherical charge. Moreover, the explosive velocity has a certain influence on the amplitude-frequency characteristic of the seismic wave excited by the cylindrical charge. The established theoretical model can accurately describe the amplitude-frequency characteristics of the seismic wavefield excited by a cylindrical charge with finite length.

Unsupervised deep learning with higher-order total-variation regularization for multidimensional seismic data reconstruction

In 3D marine seismic acquisition, the seismic wavefield is not sampled uniformly in the spatial directions. This leads to a seismic wavefield consisting of irregularly and sparsely populated traces with large gaps between consecutive sail-lines especially in the near-offsets. The problem of reconstructing the complete seismic wavefield from a subsampled and incomplete wavefield, is formulated as an underdetermined inverse problem. We investigate unsupervised deep learning based on a convolutional neural network (CNN) for multidimensional wavefield reconstruction of irregularly populated traces defined on a regular grid. The proposed network is based on an encoder-decoder architecture with an overcomplete latent representation, including appropriate regularization penalties to stabilize the solution. We proposed a combination of penalties, which consists of the L2-norm penalty on the network parameters, and a first- and second-order total-variation (TV) penalty on the model. We demonstrate the performance of the proposed method on broad-band synthetic data, and field data represented by constant-offset gathers from a source-over-cable data set from the Barents Sea. In the field data example we compare the results to a full production flow from a contractor company, which is based on a 5D Fourier interpolation approach. In this example, our approach displays improved reconstruction of the wavefield with less noise in the sparse near-offsets compared to the industry approach, which leads to improved structural definition of the near offsets in the migrated sections.

The seismic wavefield as seen by Distributed Acoustic Sensing (DAS) arrays: local, regional and teleseismic sources

Distributed acoustic sensing (DAS) exploiting fibre optic cables provides a means for high-density sampling of the seismic wavefield. The scattered returns from multiple laser pulses provide local averages of strain rate over a finite gauge length, and the nature of the signal depends on the orientation of the cable with respect to the passing seismic waves. The properties of the wavefield in the slowness-frequency domain help to provide understanding of the nature of DAS recordings. For local events the dominant part of the strain rate can be extracted from the difference of ground velocity resolved along the fibre at the ends of the gauge interval, with an additional contribution just near the source. For more distant events the response at seismic frequencies can be represented as the acceleration along the fibre modulated by the horizontal slowness resolved in the same direction, which means there is a strong dependence on cable orientation. These representations of the wavefield provide insight into the character of the DAS wavefield in a range of situations from a local jump source, through a regional earthquake to teleseismic recording with different cable configurations and geographic locations. The slowness domain representation of the DAS signal allows analysis of the array response of cable configurations indicating the important role of the slowness weighting associated with the effect of gauge length. Unlike seismometer arrays the response is not described by a single generic stacking function. For high frequency waves, direct stacking enhances P, SV waves and Rayleigh waves; an azimuthal weighted stack provides retrieval of SH and Love waves at the cost of enhanced sidelobes in the array response.

Multi-Scale Pseudo-Bending Raytracing for Arbitrary Complex Media

Raytracing is a fast and effective numerical simulation method of the seismic wavefield. It plays an important role in field data acquisition design, wavefield analysis, identification, and tomography. In raytracing, pseudo-bending (PB) is a fast and efficient method, but it is unsuitable for complex media with sudden velocity changes. An improved pseudo-bending raytracing method is presented in this paper, which can be applied to any complex medium. The proposed method first decomposes complex medium into multi-scale velocity components and then applies the pseudo-bending approach to the velocity components of different scales. The numerical simulation of seismic wavefield from models shows that the improved multi-scale pseudo-bending (MSPB) method can be applied to a medium with continuous velocity variation and any complex medium with abrupt velocity change.

Inverting DAS data using energy conservation principles

Distributed acoustic sensing (DAS) technologies are now becoming widespread, in particular in Vertical Seismic Profiling (VSP). Being a spatially densely sampled recording of seismic wavefield, DAS data provides an extended measurement as compared with point geophone VSP. We developed a basic theory that enables intuitive geophysical understanding of DAS data using the concepts of kinetic and potential energy and their fluxes. We start by relating DAS and geophone measurements to potential energy and kinetic energy, correspondingly. We use this relationship and energy balancing along the well to come up with a scheme for inverting DAS and geophone wavefields for density and velocity simultaneously. Then, recognizing that it may be impractical to have both geophones and DAS, we propose a second inversion scheme that eliminates the need for geophones and uses upgoing and downgoing DAS wavefields instead. There is no need for first-break picking windowing the data and full DAS records can be utilized in both inversion schemes. We test the feasibility of these inversion schemes on 2D elastic synthetics.