By Kou-Yuan Huang
Using development reputation has develop into progressively more vital in seismic oil exploration. reading a wide quantity of seismic info is a difficult challenge. Seismic mirrored image info within the one-shot seismogram and stacked seismogram may possibly comprise a few structural info from the reaction of the subsurface. Syntactic/structural trend attractiveness concepts can realize the structural seismic styles and enhance seismic interpretations. The syntactic research equipment comprise: (1) the error-correcting finite-state parsing; (2) the converted error-correcting Earley's parsing; (3) the parsing utilizing the fit primitive degree; (4) the Levenshtein distance computation; (5) the possibility ratio try; (6) the error-correcting tree automata; and (7) a hierarchical method. Syntactic seismic trend popularity will be one of many milestones of a geophysical clever interpretation process. The syntactic equipment during this ebook may be utilized to different components, akin to the scientific prognosis procedure. The paintings can be of curiosity to geophysicists, laptop scientists and electric engineers.
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Extra info for Syntactic Pattern Recognition for Seismic Oil Exploration
The recognition results of Ricker wavelets are quite encouraging. 2. INTRODUCTION Since seismic Ricker wavelets have structural information, it is natural to adopt a syntactic (structural) approach in seismic pattern analysis [58, 64, 65]. Each Ricker wavelet can be represented by a string of symbols (terminals, primitives). Each of these strings can then be recognized by the finite-state automaton allowing each Ricker wavelet to be classified. A block diagram of the classification system is shown in Fig.
Two parsing methods for attributed strings are proposed. One is the modified minimum distance error-correcting Earley's parsing algorithm a n d t h e other is the parsing algorithm using the m a t c h primitive measure ( M P M ) . 1 is the system that two parsing methods can be used for t h e recognition of seismic wavelets . Input signals Location of wavelets Attributed string representation Error-correcting Earley's parsing or parsing using match primitive measure Classification results Recognition Training Training wavelets Attributed ♦(string representation Inference of attributed grammar Fig.
This inferred grammar will generate excessive strings if we apply syn tactic rules only. However, we can use semantic rules (inherited attributes) to restrict the grammar so that no excessive strings are generated. 6. MINIMUM-DISTANCE ERROR-CORRECTING EARLEY'S PARSING FOR A T T R I B U T E D STRING A modified Earley's parsing algorithm is here developed for attributed context-free languages. Here, errors of insertion, deletion, and substitu tion transformation are all considered in the derivation of Earley's item lists.