Ground-roll noise extraction and suppression using high-resolution linear Radon transform

Key technologies for complex surface seismic acquisition in the Sichuan Basin and their application effect

The ultimate goal in seismic signal processing (pre- processing or main processing) is to enhance the signal to noise ratio (S-N-R). In this study, two high pass (low cut frequency) filters were designed in frequency - wave number (F-K) space and applied to onshore 3D seismic data acquired from a typical Niger Delta field to suppress dispersive ground roll in a bid to enhance S-N-R. The seismic shot records were initially displayed to show the seismic data ensembles in the conventional offset - time (X-T) domain. A frequency - wave number spectrum was then created using a VISTA software algorithm to analyze the spectral patterns of the signals and noise. Two high pass filters were then designed in the F-K space to mute the low frequency ground roll from the data. The filters were then applied to the entire seismic data ensemble for the filtering operation. The results obtained after the two filtering operations were compared to ascertain the optimal filter which was most effective for the suppression of the unwanted ground roll noise.

Three-dimensional (3D) land seismic datasets were acquired from Central Depobelt in the Niger Delta region, Nigeria, with with the aim of attenuating ground roll noise from the dataset. The Omega (Schlumberger) software 2018 version was used along with frequency offset coherent noise suppression (FXCNS) and Anomalous Amplitude Attenuation (AAA) algorithms for ground roll attenuation. From the results obtained, Frequency Offset Coherent Noise Suppression (FXCNS) attenuates ground roll while AAA algorithm attenuates the residual high amplitude noise from the seismic data. Average frequency of the ground roll in the seismic data is 10.50Hz which falls within the actual range of ground roll frequency which is within the range of 3.00 – 18.00Hz. The average velocity of the ground roll in the seismic data is 477.36ms-1 while the velocity of ground roll ranges between 347.44 and 677.37ms-1. The wavelength of ground roll in the seismic data is 50.28m. The amplitude of the ground roll of -6.24dB is maximum at 4.2Hz. Frequency of signal ranges between 10.21 and 25.12Hz with an average of 17.67Hz. Signal amplitude of -8.32dB is maximum at 6.30Hz, while its wavelength is 57.12m. The results of this work can be used in the seismic source-receiver design for application in the area of study. Moreover, with ground roll noise attenuated, a better image of the subsurface geology is obtained hence reducing the risk of obtaining a wild cat drilling.

Estimation of near-surface shear-wave velocities and quality factors using multichannel analysis of surface-wave methods

Distributed acoustic sensing (DAS) is a new seismic monitoring technology. DAS generates a large amount of data, necessitating the development of new technologies to allow for cost-effective processing and handling. The raw seismic data is noisy and must be processed. The curvelet transform is an excellent choice for processing seismic data due to its localized nature, as well as its frequency and dip characteristics. However, its capabilities are limited in case of noise other than white. This paper proposes a denoising method based on a combination of the curvelet transform and a whitening filter, as well as a procedure for estimating noise variance. The whitening filter is included to improve the performance of the curvelet transform in both coherent and incoherent noise cases, as well as to simplify the noise variance estimation method and make it easier to use standard threshold methodology without delving into the curvelet domain. Two data sets are used to validate the suggested technique. Pseudo-synthetic data set created by adding noise to the actual noise-free data collection from the Netherlands offshore F3 block and the on-site data set (with ground roll noise) from east Texas, USA. Experimental results demonstrate that the proposed algorithm achieves the best results under various types of noise.

Multichannel analysis of surface waves (MASW) method is an effective tool for obtaining vertical shear wave profiles from a single non-invasive measurement. One key step of the MASW method is generation of a dispersion image and extraction of a reliable dispersion curve from raw multichannel shot records. Because different Rayleigh-wave modes normally interfere with each other in the time and space domain, it is necessary to perform mode separation and reconstruction to increase the accuracy of phase velocities determined from a dispersion image. In this paper, we demonstrate the effectiveness of high-resolution linear Radon transform (LRT) as a means of separating and reconstructing multimode, dispersive Rayleigh-wave energy. We first introduce high-resolution LRT methods and Rayleigh-wave mode separation using high-resolution LRT. Next, we use synthetic data and a real-world example to demonstrate the effectiveness of Rayleigh-wave mode separation using high-resolution LRT. Our synthetic and real-world results demonstrate that (1) high-resolution LRT successfully separates and reconstructs multimode dispersive Rayleigh-wave energy with high resolution allowing the multimode energy to be more accurately determined. The horizontal resolution of the Rayleigh-wave method can be increased by extraction of dispersion curves from a pair of traces in the mode-separated shot gather and (2) multimode separation and reconstruction expand the usable frequency range of higher mode dispersive energy, which increases the depth of investigation and provides a means for accurately determining cut-off frequencies.

Seismic data processing plays a key role in the field of geophysics. The collected seismic data are inevitably contaminated by various types of noise, which makes the effective signals difficult to be accurately discriminated. A fundamental issue is how to improve the signal-to-noise ratio of seismic data. Due to the complex characteristics of noise and signals, it is a challenge for the denoising model to suppress noise and recover weak signals. To suppress random noise in seismic data, we propose a multi-scale deformable convolution neural network denoising model based on U-Net, named MSDC-Unet. The MSDC-Unet mainly contains modules of deformable convolution and dilated convolution. The deformable convolution can change the shape of the convolution kernel to adjust the shape of seismic signals to fit different features, while the dilated convolution with different dilation rates is used to extract feature information at different scales. Furthermore, we combine Charbonnier loss and structure similarity index measure (SSIM) to better characterize geological structures of seismic data. Several examples of synthetic and field seismic data demonstrate that the proposed method is effective in the comprehensive results in terms of quantitative metrics and visual effect of denoising, compared with two traditional denoising methods and two deep convolutional neural network denoising models.

Ground-roll is a common coherent noise in land seismic records. It has a low frequency, low velocity, and, yet, strong energy, which often conceals important information about reflections. Various approaches were proposed to suppress or remove ground-roll from reflections. The main difficulty of this task is the accurate separation of the ground-roll and reflections without damaging the morphological structure and frequency characteristics of both waves. In this article, we directly aim at the separation in the low-frequency band, where ground-roll and reflections are overlapped, and the recovery of low-frequency reflections deteriorated by ground-roll noise. First, we explore the morphological self-similarity of different frequency bands in seismic data and then utilize this similarity to synthesize pseudolow-band reflections from higher frequency band reflections that contain no ground-roll contamination. After the pseudolow-band reflections are generated, the pattern coding method is then applied to learn patterns from pseudolow-band reflections. The learned pattern dictionary is used to reconstruct the low-band reflections from the mixture of ground-roll and low-band reflections. After the whole scheme, recovered low-band reflections and clean reflections in other frequency bands are combined to form the recovered reflections. Detailed analysis is conducted to illustrate the validity of the scheme, and experiments on both synthetic data and real-land records show promising results.

Seismic wavelets play a critical role in determining the resolution potential of seismic exploration data. In marine seismic exploration, broadband processing techniques, especially plane wave domain deghosting, have emerged as a significant area of research. Accurate estimation of seismic wavelets is essential for broadband processing. Traditionally, the direct wave recorded by towed streamer has been regarded as an interference signal and has not been effectively utilized. Existing methods for extracting source wavelets from direct wave data are often complex and impractical for industrial applications. This study investigates the characteristics of direct wave data in marine seismic surveys, revealing that the direct wave is a superposition of the source direct wave and the source ghost wave, characterized by a high signal-to-noise ratio. By applying deghosting technology (typically employed in reflection seismic data processing) to direct wave data, we can effectively isolate the source direct wave from the source ghost wave. The extracted source direct wave effectively functions as the desired source wavelet. This approach overcomes the limitations of current source wavelet extraction methods, offering a more efficient and straightforward solution through deghosting. Applications to real datasets demonstrate the method's effectiveness in resolving zero-phasing processing, further validating its practicality. The proposed technique provides a reliable and simplified framework for source wavelet estimation, enhancing the overall quality of seismic data processing.

An investigation was made into the feasibility of compressing complex Synthetic Aperture Radar (SAR) images using MatrixViewTM compression technology to achieve higher compression ratios than previously achieved. Complex SAR images contain both amplitude and phase information that are severely degraded with traditional compression techniques. This phase and amplitude information allows interferometric analysis to detect minute changes between pairs of SAR images, but is highly sensitive to any degradation in image quality. This sensitivity provides a measure to compare capabilities of different compression technologies. The interferometric process of Coherent Change Detection (CCD) is acutely sensitive to any quality loss and, therefore, is a good measure by which to compare compression capabilities of different technologies. The best compression that could be achieved by block adaptive quantization (a classical compression approach) applied to a set of I and Q phased-history samples, was a Compression Ratio (CR) of 2x. Work by Novak and Frost [3] increased this CR to 3-4x using a more complex wavelet-based Set Partitioning In Hierarchical Trees (SPIHT) algorithm (similar in its core to JPEG 2000). In each evaluation as the CR increased, degradation occurred in the reconstituted image measured by the CCD image coherence. The maximum compression was determined at the point the CCD image coherence remained g 0.9. The same investigation approach using equivalent sample data sets was performed using an emerging technology and product called MatrixViewTM. This paper documents preliminary results of MatrixView's compression of an equivalent data set to demonstrate a CR of 10-12x with an equivalent CCD coherence level of g0.9: a 300-400% improvement over SPIHT.© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

A multistage single-sideband generator consisting of a cascade of binary periodically switched phase shifters, e.g. with values of 180 degrees , 90 degrees , and 45 degrees for a double stage unit is described. With a special order of phase shift values, any degree of suppression of the image sideband may be obtained even with imperfect phase shifters merely by increasing the number of stages. This means that any degree of accuracy in the homodyne measurement of amplitude and phase values of a device under test can be obtained using the concept described. The phase and amplitude information is retrieved from a bandpass-filtered low-frequency signal at the mixer output. The receiver seems to be particularly suited for industrial applications of microwaves, where accurate amplitude and phase information is needed and where simplicity and robustness are of prime importance. >

The parabolic Radon transform is one of the most commonly used multiple attenuation methods in seismic data processing. The 2D Radon transform cannot consider the azimuth effect on seismic data when processing 3D common-depth point gathers; hence, the result of applying this transform is unreliable. Therefore, the 3D Radon transform should be applied. The theory of the 3D Radon transform is first introduced. To address sparse sampling in the crossline direction, a lower frequency constraint is introduced to reduce spatial aliasing and improve the resolution of the Radon transform. An orthogonal polynomial transform, which can fit the amplitude variations in different parabolic directions, is combined with the dealiased 3D high-resolution Radon transform to account for the amplitude variations with offset of seismic data. A multiple model can be estimated with superior accuracy, and improved results can be achieved. Synthetic and real data examples indicate that even though our method comes at a higher computational cost than existing techniques, the developed approach provides better attenuation of multiples for 3D seismic data with amplitude variations.

Seismic data usually contain various types of noise. Noise comes from signal collection environment, equipment (current noise), micro seismic, etc. Noise reduces the signal- to-noise ratio and resolution of seismic records, and affects the accuracy of seismic processing and stratigraphic interpretation. Improving the signal-to-noise ratio of seismic data is the primary task of seismic data processing. In practical research, many algorithms for suppressing random noise interference are proposed. In order to achieve better seismic data denoising technology, this paper introduces a new algorithm: improved shearlet transform algorithm. The basic principle of this algorithm is to perform shearlet transform on noisy seismic data, calculate the L2 norm of shearlet coefficients in different directions at each scale according to the distribution law of effective signals at different scales, and remove random noise according to the adaptive threshold in shearlet domain varying with scale and direction. Note: This paper was accepted into the Technical Program but was not presented at IMAGE 2022 in Houston, Texas.

Metal detector is widely used in food security and industrial manufacture. In order to solve the product effect problem which used only amplitude feature, the dual channel digital detecting algorithm was put forward, based on amplitude and phase information. Also, the balance coil mathematical model was built, and the metal signal feature was deeply analyzed. Through the coordinate orientating, the amplitude and phase information of metal could be exactly extracted, product learning and clustering algorithm could efficiently separate the metal signal and product effect signal. The experiment results showed that the phase information could separate different material metal effectively, cooperated with the amplitude information, the dual channel digital detecting algorithm could extract the metal information accurately and detect the metal with strong product effect.

The undulating sand dunes in the Tadong region cause strong interferences in seismic da ta, which leads to poor signal-to-noise (S/N) when targeting the deeply-buried Lower Paleozoic carbonates. With respect to reducing and suppressing this interference from the sand dunes, we present methods for improving the S/N ratio of seismic data, including combining DEM data with a near-surface database for line selection, a point by point sand dune characterized geophone-array elevation difference design, and application of wide-line and a large geophone array together. Combined, these have achieved great improvement in the seismic data quality when applied in the Tadong region.