Two-dimensional seismic data full-layer tracking method based on semi-supervised classification
A seismic data and horizon tracking technology, which is applied in the fields of electrical digital data processing, special data processing applications, instruments, etc., can solve the problems of difficult identification of clusters, a large number of manual intervention marks, and a large number of manual identifications.
Inactive Publication Date: 2012-12-12
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Moreover, the acquisition of training samples requires a lot of manual intervention
The problems of the model method of M.Aurnhammer and F.Admasu: one is the degree of approximation to complex geological conditions; the other is the solution accuracy and suboptimal solution of the solution method
The problem with the supervised classification algorithm is that it needs a large number of manually marked training samples. The problem with the clustering algorithm is that you need to manually mark the clusters to determine which layer they belong to.
[0009] 2. Unsupervised classification
However, one of the biggest drawbacks of clustering algorithms is that it is very difficult to identify clusters.
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[0018] A method for tracking full horizons of two-dimensional seismic data based on semi-supervised classification, comprising the following steps:
[0019] Step 1. Find the extreme point for waveform fitting, and set the seed point:
[0020] 1) Extremum search:
[0021] We use S={S(x,t)} to represent the seismic profile, where x is the CDP number or line number, t is the round-trip travel time or depth, S(x 0 ,t) represents a single seismic trace. Since horizon lines are mainly located at places such as maximum values, minimum values or zero crossings, the first step in our horizon tracking needs to find these maximum values, minimum values or zero crossings. The maxima and minima of earthquakes are called seismic extremums, and we mainly use seismic extremums as the basis for automatic horizon tracking. Seismic extremes can be defined as:
[0022] e ( x ) = { t : ...
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The invention discloses a two-dimensional seismic data full-layer tracking method based on semi-supervised classification. The method includes: step one, searching extreme points for waveform fitting, and setting seed points; step two, obtaining optimized characteristic parameters according to a characteristic selection algorithm based on a semi-supervised classification algorithm, and obtaining corresponding clustering effects simultaneously; and step three, using the seed points marked at the step one to automatically mark layers that clusters belong to. The two-dimensional seismic data full-layer tracking method based on semi-supervised classification has the advantages that by means of introduction of semi-supervised classification into full-layer tracking, tracking precision is improved while efficiency is guaranteed; and the optimized characteristic parameters are obtained by screening redundant characteristics according to the FSSCEM (feature subset selection and CEM clusters) algorithm. The two-dimensional seismic data full-layer tracking method based on semi-supervised classification is adaptive to complex geological environments with no need of manual intervention, and does not need to mark a great quantity of training samples which are required by supervised classification, and classification precision and automation level, which are higher than those of the clustering algorithm, can be obtained by presetting the seed points in small quantity.
Description
technical field [0001] The invention relates to a method for tracking full layers of two-dimensional seismic data based on semi-supervised classification. Background technique [0002] Seismic data interpretation is a very important part of geological exploration and the only way to understand geological structures, and horizon tracking is one of the core parts of seismic data interpretation. For a long time, the tracking and picking of seismic horizons has been done manually, which consumes a lot of manpower, and is a key issue affecting the efficiency of seismic data interpretation; at the same time, manual tracking and picking can only interpret a small number of horizons due to efficiency problems, and cannot It is usually difficult to provide basic data for detailed seismic data analysis (such as seismic stratigraphic interpretation) to track all horizons. Based on the similarity of seismic horizons, automatic horizon picking by computer is a very challenging task. In ...
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IPC IPC(8): G06F19/00
Inventor 钱峰涂先见姚兴苗胡光岷
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA