SUPERSTACK—AN ITERATIVE STACKING ALGORITHM*

Normal moveout (NMO) correction is one of the common steps of seismic data processing. Conventional NMO correction is generally based on a velocity function, but velocity analysis is considered time consuming and labor intensive. Moreover, the field seismic data observed in complex areas can have a low signal-to-noise ratio, resulting in inaccurate picked velocity parameters and poor NMO correction. Conventional NMO correction has stretching issues, most significantly in shallow and large-offset areas. To address these problems, a new multiscale dynamic time-warping (MSD) algorithm for velocity-independent stretch-free NMO correction is investigated. This algorithm has higher accuracy and stronger noise resistance, as our comparisons with other applications of dynamic time warping show. In our algorithm, the original seismic data are decomposed into multiple morphological scales (using mathematical morphology theory), and alignment errors are calculated separately at each scale, followed by further adaptive weighted summation. Accumulation and backtracking operations based on alignment errors produce time-shift sequences for automatic flattening of the gathers. The effectiveness of the presented approach is tested on synthetic and field data. The results determine that compared with traditional velocity-based NMO correction methods, our method can accurately flatten seismic gathers without the NMO stretch and is robust to strong noise.

Seismic data processing is essential in hydrocarbon exploration, with normal moveout (NMO) correction being a pivotal step in enhancing seismic signal quality. However, conventional NMO correction often suffers from the stretch effect, which distorts seismic reflections and degrades data quality, especially in long-offset data. This study addresses the issue by analyzing synthetic models and proposing a nonhyperbolic stretch-free NMO correction technique. The proposed method significantly improves seismic data quality by preserving up to 90% of the original amplitude, maintaining frequency content stability at 30 Hz, and achieving a high reduction of stretch-related distortions. Compared to conventional NMO, our technique results in clearer seismic gathers, enhanced temporal resolution, and more accurate velocity models. These improvements have substantial implications for high-resolution subsurface imaging and precise reservoir characterization.This work offers a robust and computationally efficient solution to a longstanding limitation in seismic processing, advancing the reliability of exploration in geologically complex environments.

Two-way traveltime (TWT) is one of the most important seismic attributes for reservoir characterization. Erroneous analysis of TWT can lead to incorrect estimates of velocity models resulting in improper structural interpretation of the subsurface. TWT analysis starts with the most fundamental step of seismic data processing, namely, normal moveout (NMO) correction. NMO correction is generally performed in the offset-time (X-t) domain, by fitting a hyperbolic curve to the observed traveltimes corresponding to each reflection event. The commonly used NMO equation is valid for small offsets and does not consider anisotropy — an effect commonly observed in fractured media. The performance of NMO correction depends on the quality of data in the prestack domain and the underlying geology. In the plane wave (τ-p) domain, the estimation of layer parameters can be done using an exact equation for delay-time free from the approximation errors present in the X-t domain. In this domain, a layer stripping approach can also be used to account for the presence of multiple anisotropic and isotropic layers.

In correlation‐based signal separation algorithms, the received mixed signals are fed to a de‐coupling system designed to minimize the output cross‐correlation functions. If minimizaion is perfect, each of the system's outputs carries only one signal independent of the others. In these algorithms, the computation burden of the output cross‐correlation functions normally slows down the separation algorithm. This paper, describes a computationally efficient method for off‐line pre‐computation of the needed cross‐correlation functions. Explicit formulas have been derived for the output cross‐correlation functions in terms of the received input signals and the de‐coupling system parameters. Then, it is shown that signal separation amounts to the least‐squares solution of a system of linear equations describing these output cross‐correlation functions, evaluated over a batch of lags. Next, a fast RLS‐type adaptive algorithm is devised for on‐line signal separation. In this respect, an algorithm is derived for updating the de‐coupling parameters as data comes in. This update is achieved recursively, along the negative of the steepest descent directions of an objective cost function describing the output cross‐correlation functions over a batch of lags, subject to equal output power constraints. Illustrative examples are given to demonstrate the effectiveness of the proposed algorithms. Copyright © 2002 John Wiley & Sons, Ltd.

An efficient algorithm for blind separation of binary symmetrical signals is proposed, which don’t depend on the statistical characteristics of source signals. The mixture matrix is estimated accurately by using the relations of sensor signals in case of no noise and it is also proved in this paper, and the estimated mixture matrix is a primary column transformation of the original mixture matrix by the algorithm, through which the source signals are recovered by permutations and sign changes of their rows. In practice, they can be corrected by introducing headers in the bit-streams and differently encoding them. The algorithm is shown simple and efficient in last simulations.

The algorithm for separation of signals from two different wireless standards (Bluetooth and IEEE 802.11b standard), operating within the same frequency band is proposed in this paper. The components separation is performed using the time-frequency representation and the concept of Compressive Sensing. Knowing the signals nature, it is possible to select just a small set of time-frequency points that entirely belongs to the IEEE 802.11b signal. These points are extracted from the original time-frequency representation and are used to recover the full signal by using Compressive Sensing method. Once when the components of IEEE 802.11b signal are known, it is possible to reconstruct the remaining components in the band, belonging to the Bluetooth signal. Unlike the conventional separation methods, such as windowing or filtering, this approach works well even in the case of overlapping signals as well. The theory is proved with experimental results.

An attempt is made in this study to use two distinct algorithms to inspect blind source separation (BSS). In this paper, we have used multiuser Kurtosis (MUK) algorithm for BSS and an information maximization algorithm for separation and deconvolution of voice signals. Among the various criteria available for evaluating the objective function for BSS, we have considered information theory principles and Kurtosis as a measure for statistical independence. The MUK algorithm uses a combination of Gram–Schmidt orthogonalization and a stochastic gradient update to achieve non-Gaussian behavior. A correlation coefficient is used as an evaluation criterion to analyze the performance of both the algorithms. Simulations results are presented at the end of the study along with the performance tabulation for discussion and analysis.

The problem of blind separation of signals in a post-nonlinear mixture is addressed in this paper. A post-nonlinear mixture is formed by a component-wise nonlinear distortion of a linear mixture. Hence, a nonlinear adjusting part, placed in front of the linear separation structure, is needed to compensate for the distortion in separating such signals. The learning rules for the post-nonlinear separation structure are derived by a maximum likelihood approach. An algorithm for the blind separation of post-nonlinearly mixed suband super-Gaussian signals is proposed, based on some previous works. Multilayer perceptrons are used in this algorithm to model the nonlinear part of the separation structure. The algorithm switches between sub- and super-Gaussian probability models during learning according to a stability condition, and it operates in a block-adaptive manner. The effectiveness of the algorithm is verified by experiments on artificial and natural signals.

Aiming at a kind of specific situation encountered in practice, the paper proposes a weak-signal separation algorithm based on extended alternating projection neural networks by combining the time-domain features of the signal with the frequency-domain features of the signal and taking advantage of conclusions on EAPNN. Simulation results demonstrate that the algorithm is effective and that the EAPNN-based signal separation algorithm is better than the RLS-based signal separation algorithm. Although the EAPNN-based algorithm is designed for the specific situation, it is also applicable to the other situations and a basic frame of the EAPNN-based signal separation is presented. Owing to adopting neural network structure, the EAPNN-based algorithm is prone to parallel computation and VLSI design, consequently can satisfy real-time processing needs.

An efficient method for parameter estimation and separation of multi-component LFM signals

In this work, the authors describe an algorithm for the real-time separation of mixed non-stationary signals. The algorithm is based on entropy maximization of the system output, and the final recursive updating equation is simple and easy to implement.

Most of the existing algorithms for blind sources separation have a limitation that sources are statistically independent. However, in many practical applications, the source signals are nonnegative and mutual statistically dependent signals. When the observations are nonnegative linear combinations of nonnegative sources, the correlation coefficients of the observations are larger than these of source signals. In this letter, a novel Nonnegative Matrix Factorization (NMF) algorithm with least correlated component constraints to blind separation of convolutive mixed sources is proposed. The algorithm relaxes the source independence assumption and has low-complexity algebraic computations. Simulation results on blind source separation including real face image data indicate that the sources can be successfully recovered with the algorithm.

There are a large variety of applications that require considering sources that usually behave light or strong dependence and this is not the case that common blind signal separation (BSS) algorithms can do. The purpose of this paper is to develop non-parametric BSS algorithm for linear dependent source signals, which is proposed under the framework of contrast method. The contrast function is derived from the Schweizer-Wolff measure of pairwise dependence between the variables. Simulation results show that the proposed algorithm is able to separate the dependent signals and yield ideal performance.

Recently, the use of unmanned aerial vehicles for both reconnaissance and fire strikes has been increasing. The control of such vehicles and the transmission of telemetry information are carried out using a combined command and telemetry radio link and frequency hopping spread spectrum technology. In such conditions, the task of developing automatic algorithms for detecting and separating radio links for portable radio monitoring devices becomes especially relevant. The aim of this work is to extend the capabilities of portable radio monitoring devices to detect unmanned aerial vehicles’ radio signals in sophisticated electronic environment by automating signal processing. Formally, the research task is to detect and separate several frequency hopping spread spectrum signals in same frequency band. To solve this task, it is first necessary to detect the frequency channels and estimate frequency and, if necessary, temporal and modulation parameters of the signals in these channels. Estimates of signal parameters values are recorded in associative arrays. The paper proposes a scheme of the algorithm for automatic detection and separation of signals with frequency hopping spread spectrum, as well as the structure of the array with the results of frequency band analysis. In the study of developed algorithm, we considered the practically important option of separating radio links with frequency hopping spread spectrum by the spectral width of the frequency element and the value of the signal-to-noise ratio in the frequency channel, since these features can be quite easily distinguished in the frequency domain. The radio links separation was performed by cluster analysis of scatter plots of parameter pairs estimates using a Gaussian mixture model. The developed algorithm can be implemented in existing and prospective portable radio monitoring devices to automatically detect and determine the number of unmanned aerial vehicles by their radio links signals, in particular, under conditions of a priori uncertainty about the values of signal parameters.

Two previously undescribed structural basins are outlined by offshore geophysical data across the Shumagin continental shelf in the western Gulf of Alaska. The first basin, herein named Shumagin basin, is between the Semidi and Shumagin Islands and is roughly2equidimensional, with an area of about 3,800 km2. It contains an upper section of presumed Cenozoic sediment as much as 2.5 km thick, characterized by reflection horizons that show divergences in dip, local wedge-outs, and broad folds against the flanks of the basin. This section is in turn underlain by what appear to be complexly deformed sedimentary beds of probable late Cretaceous to early Tertiary age. Shumagin basin is therefore a structural depression in older deformed sedimentary rocks that has been filled by a relatively young deformed sequence The second basin, herein named Sanak basin, is an elongate fault-bounded basin northeast of Sanak Island with an area of approximately 1,200 km2. The basin contains sedimentary-deposits of presumed Cenozoic age that may be more than 7 km thick and that are broken by probable growth faults indicating deposition contemporaneous with basin subsidence. The northeast and southwest flanks of Sanak basin appear to be underlain by Cretaceous turbidites and early Tertiary intrusive rocks exposed on the nearby outer Shumagin Islands. Anticlinal folding and stratigraphic truncation occurred during basin subsidence. Available data are insufficient to determine if additional basins are present on the Shumagin shelf or to evaluate with certainty the number and size of potential hydrocarbon bearing structures within Shumagin and Sanak basins. However, the data suggest that prospective stratigraphic and structural features are present. INTRODUCTION The Shumagin shelf extends for 500 km along the Alaska Peninsula from the Semidi Islands southwest to Unimak Island at the south end of the peninsula (Fig. 1). As part of the investigations to evaluate the resource potential of the outer continental shelves of the United States, Geophysical Service, Inc., under contract to the U.S. Geological Survey, collected approximately 500 km of gravity, magnetic, and 24- fold common depth point (CDP) seismic reflection data on the Shumagin shelf in 1975. The CDP data were digitally recorded at 24-fold with a 6 second record length using an air gun array as a sound source. Navigati9n was controlled by an integrated satellite doppler sonar system, supplemented by Loran-C. @ Processing was done by Petty-Ray Geophysical Company in Houston, Texas. Gravity data were acquired with a LaCoste-Romberg stable platform gravity meter and magnetic data were collected with a Varian proton magnetometer. The seismic records are available in an open-file report by Bruns and Bayer (1977). These data outline two structural basins (Fig. 1), herein named Shumagin and Sanak basins, which could contain potential oil and gas resources. Available information is presently insufficient to show if other basins or thick sediment accumulations are present beneath the Shumagin shelf.