Comparative analysis of matching pursuit algorithms for Kirchhoff migration on compressed data

Currently, the amount of recorded data in a seismic survey is in the order of hundreds of Terabytes. The processing of such amount of data implies significant computational challenges. One of them is the I/O bottleneck between the main memory and the node memory. This bottleneck results from the fact that the disk memory access speed is thousands-fold slower than the processing speed of the co-processors (eg. GPUs). We propose a special Kirchhoff migration that develops the migration process over compressed data. The seismic data is compressed by using three well-known Matching Pursuit algorithms. Our approach seeks to reduce the number of memory accesses to the disk required by the Kirchhoff operator and to add more mathematical operations to the traditional Kirchhoff migration. Thus, we change slow operations (memory access) for fast operations (math operations). Experimental results show that the proposed method preserves, to a large extent, the seismic attributes of the image for a compression ratio up to 20:1.

3-D Multi-Component Reverse Time Migration Method for Tunnel Seismic Data

Migration imaging is a key step in tunnel seismic data processing. Due to the limitation of tunnel space, tunnel seismic data are small-quantity, multi-component, and have a small offset. Kirchhoff migration based on the ray theory is limited to the migration aperture and has low migration imaging accuracy. Kirchhoff migration can no longer meet the requirements of high-precision migration imaging. The reverse time migration (RTM) method is used to realize cross-correlation imaging by reverse-time recursion principle of the wave equation. The 3-D RTM method cannot only overcome the effect of small offset, but also realize multi-component data imaging, which is the most accurate migration method for tunnel seismic data. In this paper, we will study the 3-D RTM method for multi-component tunnel seismic data. Combined with the modeled data and the measured data, the imaging accuracy of the 3-D Kirchhoff migration and 3-D RTM is analyzed in detail. By comparing single-component and multi-component Kirchhoff migration and RTM profile, the advantages of the multi-component RTM method are summarized. Compared with the Kirchhoff migration method, the 3-D RTM method has the following advantages: (1) it can overcome the effect of small offset and expand the range of migration imaging; (2) multi-component data can be realized to improve the energy of anomalous interface; (3) it can make full use of multiple waves to realize migration imaging and improve the resolution of the anomalous interface. The modeled data and the measured data prove the advantages of the 3-D multi-component RTM method.

SUBSTANTIATION OF PARAMETERS OF THE 3D CDP FIELD OBSERVATION SYSTEM

The substantiation of parameters of the 3D observation system is considered from the perspective of the Kirchhoff migration. At the first step of this transformation, on the basis of diffraction transformation on a gather of CSP, the problem of wavelet extraction reflected from specified points of the medium (image points) is solved. The characteristic of the directivity of this transformation is determined by parameters of the arrangement of devices. At the second step, summation is performed by gathers of the common image point (СIP). The distribution density of the observation system sources determines the stacking fold by CIP. In the process of selecting survey parameters, the comparative analysis of equivalent observation systems with the same data properties for the migration task, but with different parameters of the observation system, is of great important. The relationship between the step of common midpoints of the observation system and the step of traces of resulting images of the medium is discussed. The Gaussian beam migration algorithm is considered as a method for solving the problem of constructing an image of the medium that correctly takes into account the irregularity of the initial data.

Research Committee Update

In the last Research Committee Update (TLE, September 2020, 681–682), I explained that the title of this column comes from a book by Charles Murray, The Curmudgeon’s Guide to Getting Ahead. With the curmudgeon still in mind, I have a few more comments to make about our lives as researchers, pushing the leading edge of technology in our industries. In small companies, it helps to be a contrarian, to develop novel algorithms in areas overlooked by large research groups or the academic groups funded by them. One such technology that our group started working on a few years ago, nudged by the research group at Saudi Aramco, is diffraction imaging (DI). Aramco was looking for a company with a good quality commercial Kirchhoff migration, since this particular DI implementation involved modifying a Kirchhoff kernel.

Enhance Seismic Quality With A Fast, Robust and Economic Solution Using Broadband Technology

Often many companies doing seismic reprocessing project with the latest advanced seismic processing technology to handle their seismic problems. This kind of solution might work for some companies but certainly will not for many others. Innovative solutions are required to overcome seismic problem or issue considering time, budget and technical robustness. The objective of this paper is to demonstrate a seismic processing project that was run in a relatively low oil price condition, to handle a low seismic resolution problem in thinly bedded reservoirs. In addition to budget and technical constraints, timing and schedule is also an issue since the updated interpretation is required to be used for updating a static model in less than a 3 months time window. A broadband processing method on 100 km2 post-stack 3D seismic data was applied to enhance frequency content which leads to enhanced seismic resolution to resolve objective reservoirs. This method is considered fast, robust and economical. The procedure would be enhancing the spectrum by designing a unique filter into the dataset after inverse Kirchhoff migration application. After some iterations, forward Kirchhoff migration and multi spatial time-variant filter was applied to generate the desired output. Overall processing time was completed within budget in a one-month period only. As a result, forty percent (40%) increase in dominant frequency was achieved as final deliverables from 25Hz to 35Hz. The original dataset with 25Hz dominant frequency can only resolve reservoirs with thickness greater than 20m. The thickness of individual objective sandstone reservoirs in the study area was ranging from 1.5m to 20m with average thickness around 6-10m. However, stacked reservoir thickness within the same flooding surface interval of the same unit is relatively thicker than 10m. The 35Hz dominant frequency data from broadband processing can resolve reservoir with thickness greater than 14m and better resolve stacked reservoirs even though very thin reservoir below resolution remains unresolved. Broadband seismic technology on post-stack dataset offers a fast, robust and economical solution focused on target which can be duplicated in other area/data to provide successful interpretation projects.

Traveltime-table compression using artificial neural networks for Kirchhoff-migration processing of microseismic data

Massive computation of seismic traveltimes is widely used in seismic processing, for example, for the Kirchhoff migration of seismic and microseismic data. Implementation of the Kirchhoff migration operators uses large precomputed traveltime tables (for all sources, receivers, and densely sampled imaging points). We have tested the idea of using artificial neural networks for approximating these traveltime tables. The neural network has to be trained for each velocity model, but then the whole traveltime table can be compressed by several orders of magnitude (up to six orders) to the size of less than 1 MB. This makes it convenient to store, share, and use such approximations for processing large data volumes. We evaluate some aspects of choosing neural-network architecture, training procedure, and optimal hyperparameters. On synthetic tests, we find a reasonably accurate approximation of traveltimes by neural networks for various velocity models. A final synthetic test shows that using the neural-network traveltime approximation results in good accuracy of microseismic event localization (within the grid step) in the 3D case.