Abstract: A process for tomographic development of seismic section data having known source and receiver positions. The process employs forward modeling to estimate travel times of the rays through the earth medium. The medium is assigned a cellular constitution and ray tracking proceeds through individual cell analysis within a two dimensional neighborhood to join a series of individual cell velocity values, e.g. the velocity values at the cell corners. Ray tracing proceeds utilizing derived expressions for position, orientation, and travel time of the ray in the cell as a function of cell dimensions, initial ray position, ray orientation, and the neighborhood velocity values. A travel time equation is then derived and used to construct a model parameter estimation and finalized estimated data output.

Abstract: A method for classifying seismic data which uses a pattern recognition approach to comparing and equating data characteristics. Data is processed for a selected training area of such as a seismic section wherein a stochastic model of the data establishes data characteristics of either non-causal or causal type for selective comparison to similar data characteristics for other test areas of the data thereby to establish or classify similarities and/or distinctions between given training and test areas.

Abstract: A method for continuous velocity estimation of seismic data uses non-linear signal matching. Common depth point trace data is examined at selected offsets and non-linear signal matching is performed to derive moveout information from which the RMS and interval velocities may be further estimated. Non-linear signal matching for selected offsets of each CDP trace gather functions to derive characteristic match curve data at selected depths or travel time intervals. The match curve data inherently contains moveout information that relates to energy velocities for the particular trace position. Derived velocity data for selected time depths may then be output across a line of survey.

Abstract: A process for non-linear matching of time analog signals, particularly seismic signals, that have generally similar response characteristics but different time bases is disclosed. The process models the signals to be matched as a stretched version of each signal with additive noise, and a match curve is estimated adhering to a global criterion of maximization of the likelihood function. The global criterion is further optimized in accordance with the principle of path optimality and other a priori information may also be considered. The match curve may then be used to identify such as correlated signal events, conparison of signal sections, and numerous seismic data processing functions using multidata or multisensor time analog data input.

Abstract: A machine process for identification and extraction of magnitude and direction of edges and lines in noisy multidimensional imagery data. A digital picture function is viewed as a sampling of the underlying reflectance function of the objects in the scene or pattern with noise added to the true function values. The edges or lines relate to those places in the image where there are jumps in the values of the reflectance function or its derivatives. By expressing the function in some parametric form in the local neighborhood of the pixel under consideration, the edges or lines and the types of edges (left to right or right to left) or lines (peak to trough) may be inferred from the values of the parameters. Assuming the noise is Gaussian, significant edges or lines are detected by performing the statistical hypothesis tests on the parameters of the function. Recursive relations are used for efficiently estimating the parameters of the function.

Abstract: A computer implemented method for representation and recognition of waveforms uses information derived from several groups of such waveforms of general similarity. Each signal is considered as an additive combination of the common signal of the respective group and a noise term. The common signals of the groups of waveforms are extracted using a set of data-derived orthonormal basis waveforms, and a criterion is developed to evaluate the combination of individual basis waveforms used for the common signal extraction. The estimated common signals then represent a template pattern of the waveform or signal groups that can be used in waveform recognition.

Abstract: A machine process for identification and extraction of magnitude and direction of edges and lines in noisey multidimensional imagery data. A digital picture function is viewed as a sampling of the underlying reflectance function of the objects in the scene or pattern with noise added to the true function values. The edges or lines relate to those places in the image where there are jumps in the values of the function or its derivatives. By expressing the function in some parametric form, the edges or line may by inferred from the values of the parameters.