Integration of feature extraction, attribute combination and image segmentation for object delineation on seismic images

Automatic geological interpretation, specifically modeling salt dome and fault detection, is controversial task on seismic images from complex geological media. In advanced techniques of seismic interpretation and modeling, various strategies are utilized for combination and integration different information layers to obtain an image adequate for automatic extraction of the object from seismic data. Efficiency of the selected feature extraction, data integration and image segmentation methods are the most important parameters that affect accuracy of the final model. Moreover, quality of the seismic data also affects confidence of the selected seismic attributes for integration. The present study proposed a new strategy for efficient delineation and modeling of geological objects on the seismic image. The proposed method consists of extraction specific features by the histogram of oriented gradients (HOG) method, statistical analysis of the HOG features, integration of features through hybrid attribute analysis and image classification or segmentation. The final result is a binary model of the target under investigation. The HOG method here modified accordingly for extraction of the related features for delineation of salt dome and fault zones from seismic data. The extracted HOG parameter then is statically analyzed to define the best state of information integration. The integrated image, which is the hybrid attribute, then is used for image classification, or image segmentation by the image segmentation method. The seismic image labeling procedure performs on the related seismic attributes, evaluated by the extracted HOG feature. Number of HOG feature and the analyzing parameters are also accordingly optimized. The final image classification then is performed on an image which contains all the embedded information on all the related textural conventional and statistical attributes and features. The proposed methods here apply on four seismic data examples, synthetic model of salt dome and faults and two real data that contain salt dome and fault. Results have shown that the proposed method can more accurately model the targets under investigation, compared to advanced extracted attributes and manual interpretations

Expand Dimensional of Seismic Data and Random Noise Attenuation Using Low-Rank Estimation

Random noise attenuation in seismic data requires employing leading-edge methods to attain reliable denoised data. Efficient noise removal, effective signal preservation and recovery, reasonable processing time with a minimum signal distortion and seismic event deterioration are properties of a desired noise suppression algorithm. There are various noise attenuation methods available that more or less have these properties. We aim to obtain more effective denoised seismic data by assuming 3-D seismic data as a tensor in order three and increasing its dimension to 4-D seismic data by employing continuous wavelet transform (CWT). First, we map 3-D block seismic data to smaller blocks to estimate the low-rank component. The CWT of the tensor is calculated along the third dimension to extract the singular values and their related left/right vectors in the wavelet domain. Afterward, the effective low-rank component is extracted using optimized coefficients for each singular value. Thresholding is applied in the wavelet domain along the third dimension to calculate effective coefficients. Two synthetic and field data examples are considered for performance evaluation of the proposed method, and the results were compared with the competitive random noise suppression methods, such as the tensor optimum shrinkage singular value decomposition, the iterative block tensor singular value thresholding, and the block matching 4-D algorithms. Qualitative and quantitative comparison of the proposed method with other methods indicates that the proposed method efficiently eliminates random noise from seismic data

An Improvement in Temporal Resolution of Seismic Data Using Logarithmic Time-frequency Transform Method

The improvement in the temporal resolution of seismic data is a critical issue in hydrocarbon exploration. It is important for obtaining more detailed structural and stratigraphic information. Many methods have been introduced to improve the vertical resolution of reflection seismic data. Each method has advantages and disadvantages which are due to the assumptions and theories governing their issues. In this paper, we improve the temporal resolution of reflection seismic data using the logarithmic time-frequency transform method. This method has minimum user-defined parameters. The algorithm uses valuable properties of both the time-frequency transform and the cepstrum to extend the frequency band at each translation of the spectral decomposing window. In this method, the displacement of amplitude spectrum by its logarithm is the basic idea of the algorithm. We tested the mentioned algorithm on both synthetic and real data. The results of the both tests show that the introduced method can increase the temporal resolution of seismic data

Interpretation of gravity data using 2-D continuous wavelet transformation and 3-D inverse modeling

Recently the continuous wavelet transform has been proposed for interpretation of potential field anomalies. In this paper, we introduced a 2D wavelet based method that uses a new mother wavelet for determination of the location and the depth to the top and base of gravity anomaly. The new wavelet is the first horizontal derivatives of gravity anomaly of a buried cube with unit dimensions. The effectiveness of the proposed method is compared with Li and Oldenburg inversion algorithm and is demonstrated with synthetics and real gravity data. The real gravity data is taken over the Mobrun massive sulfide ore body in Noranda, Quebec, Canada. The obtained results of the 2D wavelet based algorithm and Li and Oldenburg inversion on the Mobrun ore body had desired similarities to the drill-hole depth information. In all of the inversion algorithms the model non-uniqueness is the challenging problem. Proposed method is based on a simple theory and there is no model non-uniqueness on it.9 page(s

Random noise attenuation in seismic data using an adaptive thresholding and the second-order variant time-reassigned synchrosqueezing transform

Seismic data analysis often faces the challenge of random noise contamination from various sources. To overcome this, innovative noise attenuation methods utilizing seismic signal properties are needed. This study focuses on efficiently suppressing random noise in the domain of time and frequency by accurately estimating instantaneous frequency using the single-valued group delay characteristic of seismic signals. The time-reassigned synchrosqueezing transform (TSST) and its second-order variant (TSST2) offer high-resolution time-frequency representations (TFRs) for noise suppression. Expanding on these advancements, we propose an efficient noise suppression method that integrates the adaptive thresholding model into the TSST2 framework and employs sparse representation of the TFR through low-rank estimation. This method effectively attenuates noise while preserving essential signal information. The proposed approach operates trace by trace on recorded data, initially transforming it into a sparse subspace using TSST2. The adaptive thresholding model then decomposes the resulting TFR into sparse and semi-low-rank components, achieving a high-resolution and sparse TFR for efficient separation of noise and signal. After noise suppression, the seismic data can be fully reconstructed by inversely transforming the semi-low-rank component data into the time domain. This method addresses previous limitations in noise attenuation techniques and provides a practical solution for enhancing seismic data quality

Identification of reservoir fractures on FMI image logs using Canny and Sobel edge detection algorithms

Because of the significant impact of fractures on production in hydrocarbon reservoirs, identification of these phenomena is a very important issue. Image logs are one of the best tools for revealing and studying fractures in reservoir and researcher can get lots of information about geological features in wells, by studying and analyzing these logs. In this research, two approaches have been used to determine the fractures in two wells A and B located in one of the oil fields in southwest of Iran. In the first approach, using Geolog software (version-7), after processing and correction of raw image log data, the number, position, dip, extension, layering, density and expansion of fractures have been identified. In the second approach, considering that the fractures in FMI images have edges, the Canny and Sobel filters as edge detection operators in image processing have been used to detect fractures in these images