Probing the subsurface karst features using time-frequency decomposition

2016 ◽  
Vol 4 (4) ◽  
pp. T533-T542 ◽  
Author(s):  
Yangkang Chen
Keyword(s):  
High Resolution ◽  
Seismic Data ◽  
Single Frequency ◽  
Frequency Interval ◽  
Data Set ◽  
Time Frequency ◽  
Frequency Decomposition ◽  
Inherent Frequency ◽  
Resolution Mapping ◽  
Resource Exploration

The high-resolution mapping of karst features is of great importance to hydrocarbon discovery and recovery in the resource exploration field. Currently, however, there are few effective methods specifically tailored for such a task. The 3D seismic data can reveal the existence of karsts to some extent, but a precise characterization cannot be obtained. I have developed an effective framework for accurately probing the subsurface karst features using a well-developed time-frequency decomposition algorithm. More specifically, I have introduced a frequency interval analysis approach for obtaining the best karsts detection result using an optimal frequency interval. A high-resolution time-frequency transform was preferred in the proposed framework to capture the inherent frequency components hidden behind the amplitude map. Although the single-frequency slice could not provide a reliable karst depiction result, the summation over the selected frequency interval could obtain a high-resolution and high-fidelity delineation of subsurface karsts. I used a publicly available 3D field seismic data set as an example to indicate the performance of the proposed method.

A union of spectrum and cepstrum: Recipe for thin-bed delineation

2019 ◽  
Vol 38 (4) ◽  
pp. 298-305
Author(s):  
Prashant Kumar Mishra ◽  
Sanjai Kumar Singh ◽  
Pradip Kumar Chaudhuri
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Dominant Frequency ◽  
Resolution Limit ◽  
Bandwidth Extension ◽  
Data Set ◽  
Time Frequency ◽  
Frequency Decomposition ◽  
Cepstral Analysis ◽  
Resultant Data

The resolution limit of seismic data is an intricate issue that depends not only on frequency and data quality (signal-to-noise ratio) but also on the tools and technology used to analyze seismic response. In this context, the subject of thin-bed delineation is extremely significant for coal-laminated (causing large acoustic impedance contrasts) clastic sequences of the Western Onshore Basin, India. Most of the clastic reservoirs in the area are of subseismic resolution (below 10 m in thickness) due to the low dominant frequency available in seismic data (19–35 Hz). This is where improving seismic resolution is essential for a detailed structural and stratigraphic interpretation. We have implemented a modified workflow with which, by using state-of-the-art techniques of time-frequency decomposition and cepstral analysis, significant seismic bandwidth extension has been achieved. This in turn yields improved vertical resolution of the seismic data with better geologic interpretability. Our approach is named the “syn-cepstral method” after its two integral constituents — synchrosqueezing transform and cepstral analysis. Applying the syn-cepstral method produces better well-to-seismic ties and resolves additional events in comparison to the original seismic data. The validity of syn-cepstral methodology has been demonstrated by 1D and 2D modeling studies followed by application to a 3D seismic data set from the Western Onshore Basin of India. The improvement in thin-bed delineation arising from the increased bandwidth of the resultant data has been validated by well-to-seismic ties and amplitude map interpretation. Thus, while thin clastic reservoir beds in the logs show no discernible presence in the original seismic data, upon application of the syn-cepstral method, the resultant seismic data show improved interpretability of these units.

scholarly journals Land-Streamer vs. Conventional Seismic Data for High-Resolution Near-Surface Surveys

2022 ◽  
Vol 12 (2) ◽  
pp. 584
Author(s):  
Sherif M. Hanafy
Keyword(s):  
High Resolution ◽  
Seismic Data ◽  
Acquisition Time ◽  
Data Set ◽  
Near Surface ◽  
Seismic Surveys ◽  
Time Frequency ◽  
Lower Accuracy ◽  
Recorded Data ◽  
Local Anomalies

Near-surface high-resolution seismic mapping is very important in many applications such as engineering and environmental. However, the conventional setup of the seismic technique requires planting geophones, connecting cables, and then collecting all equipment after completing the survey, which is time-consuming. In this study, we suggest using a land-streamer setup rather than the conventional setup for fast, accurate, and high-resolution near-surface seismic surveys. Only one field data set is recorded using both the conventional and the land-streamer setups. The recorded data is then compared in terms of time, frequency, wavenumber domains, and acquisition time. Following this, we compared the accuracy of the subsurface mapping of both setups using a synthetic example. The results show that the conventional setup can reach deeper depths but with lower accuracy, where the errors in imaging the local anomalies’ widths and thicknesses are 77% to 145% and 35% to 50%, respectively. The land-streamer setup provides accurate near-surface results but shallower penetration depth, here the errors in the anomalies’ widths and thicknesses are 5% to 12% and 10% to 20%, respectively.

Sparse spectral attributes to detect hydrocarbons: A case study from deep dolomite reservoirs

2019 ◽  
Vol 7 (3) ◽  
pp. T701-T711
Author(s):  
Jianhu Gao ◽  
Bingyang Liu ◽  
Shengjun Li ◽  
Hongqiu Wang
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Spectral Amplitude ◽  
Data Set ◽  
Frequency Spectra ◽  
Hydrocarbon Production ◽  
Hydrocarbon Detection ◽  
Time Frequency ◽  
Gas Bearing

Hydrocarbon detection is always one of the most critical sections in geophysical exploration, which plays an important role in subsequent hydrocarbon production. However, due to the low signal-to-noise ratio and weak reflection amplitude of deep seismic data, some conventional methods do not always provide favorable hydrocarbon prediction results. The interesting dolomite reservoirs in Central Sichuan are buried over an average depth of 4500 m, and the dolomite rocks have a low porosity below approximately 4%, which is measured by well-logging data. Furthermore, the dominant system of pores and fractures as well as strong heterogeneity along the lateral and vertical directions lead to some difficulties in describing the reservoir distribution. Spectral decomposition (SD) has become successful in illuminating subsurface features and can also be used to identify potential hydrocarbon reservoirs by detecting low-frequency shadows. However, the current applications for hydrocarbon detection always suffer from low resolution for thin reservoirs, probably due to the influence of the window function and without a prior constraint. To address this issue, we developed sparse inverse SD (SISD) based on the wavelet transform, which involves a sparse constraint of time-frequency spectra. We focus on investigating the applications of sparse spectral attributes derived from SISD to deep marine dolomite hydrocarbon detection from a 3D real seismic data set with an area of approximately [Formula: see text]. We predict and evaluate gas-bearing zones in two target reservoir segments by analyzing and comparing the spectral amplitude responses of relatively high- and low-frequency components. The predicted results indicate that most favorable gas-bearing areas are located near the northeast fault zone in the upper reservoir segment and at the relatively high structural positions in the lower reservoir segment, which are in good agreement with the gas-testing results of three wells in the study area.

Nonstationary local time-frequency transform

Geophysics ◽  
2021 ◽  
Vol 86 (3) ◽  
pp. V245-V254
Author(s):  
Yangkang Chen
Keyword(s):  
Local Time ◽  
Seismic Data ◽  
Low Frequency ◽  
Data Sets ◽  
Frequency Spectra ◽  
Time Frequency ◽  
Data Registration ◽  
Frequency Decomposition ◽  
Temporal Smoothing ◽  
The Time Domain

Time-frequency analysis is a fundamental approach to many seismic problems. Time-frequency decomposition transforms input seismic data from the time domain to the time-frequency domain, offering a new dimension to probe the hidden information inside the data. Considering the nonstationary nature of seismic data, time-frequency spectra can be obtained by applying a local time-frequency transform (LTFT) method that matches the input data by fitting the Fourier basis with nonstationary Fourier coefficients in the shaping regularization framework. The key part of LTFT is the temporal smoother with a fixed smoothing radius that guarantees the stability of the nonstationary least-squares fitting. We have developed a new LTFT method to handle the nonstationarity in all time, frequency, and space ( x and y) directions of the input seismic data by extending fixed-radius temporal smoothing to nonstationary smoothing with a variable radius in all physical dimensions. The resulting time-frequency transform is referred to as the nonstationary LTFT method, which could significantly increase the resolution and antinoise ability of time-frequency transformation. There are two meanings of nonstationarity, i.e., coping with the nonstationarity in the data by LTFT and dealing with the nonstationarity in the model by nonstationary smoothing. We evaluate the performance of our nonstationary LTFT method in several standard seismic applications via synthetic and field data sets, e.g., arrival picking, quality factor estimation, low-frequency shadow detection, channel detection, and multicomponent data registration, and we benchmark the results with the traditional stationary LTFT method.

scholarly journals ON INVESTIGATING EMD PARAMETERS TO SEARCH FOR GRAVITATIONAL WAVES

2013 ◽  
Vol 05 (02) ◽  
pp. 1350010 ◽  
Author(s):  
HIROTAKA TAKAHASHI ◽  
KEN-ICHI OOHARA ◽  
MASATO KANEYAMA ◽  
YUTA HIRANUMA ◽  
JORDAN B. CAMP
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Time Series Analysis ◽  
Gravitational Waves ◽  
Basis Set ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Frequency Decomposition ◽  
Mode Decomposition ◽  
Optimal Values

The Hilbert–Huang transform (HHT) is a novel, adaptive approach to time series analysis. It does not impose a basis set on the data or otherwise make assumptions about the data form, and so the time-frequency decomposition is not limited by spreading due to uncertainty. Because of the high resolution of the time-frequency, we investigate the possibility of the application of the HHT to the search for gravitational waves. It is necessary to determine some parameters in the empirical mode decomposition (EMD), which is a component of the HHT, and in this paper we propose and demonstrate a method to determine the optimal values of the parameters to use in the search for gravitational waves.

Extended stable factor method for the inverse Q-filter

Geophysics ◽  
2020 ◽  
Vol 85 (3) ◽  
pp. T155-T163
Author(s):  
Yong Li ◽  
Gulan Zhang ◽  
Jing Duan ◽  
Chengjie He ◽  
Hao Du ◽  
...  
Keyword(s):  
High Resolution ◽  
Seismic Data ◽  
Signal To Noise Ratio ◽  
High Signal ◽  
Time Frequency ◽  
Stable Factor ◽  
Factor Method ◽  
Data Application ◽  
Two Parameters ◽  
Limit Assumption

The commonly used stable factor methods for the inverse [Formula: see text]-filter achieve good performance in seismic data processing; however, the constant gain-limit assumption in these methods is not associated with the effective frequency band of seismic data and cannot obtain desirable results with high resolution and high signal-to-noise ratio (S/N). Our extended stable factor method for the inverse [Formula: see text]-filter extends these methods by introducing two parameters and constant or self-adaptive gain limit to achieve the desirable high-resolution and high-S/N result. The extended stable factor method for the inverse [Formula: see text]-filter can be implemented in the frequency or time-frequency domain; the latter implementation achieves a higher S/N. Analysis of synthetic signals and field seismic data application illustrate that our method can produce a desirable high-resolution and high-S/N result.

Application of sparse time-frequency decomposition to seismic data

2014 ◽  
Vol 11 (4) ◽  
pp. 447-458 ◽  
Author(s):  
Xiong-Wen Wang ◽  
Hua-Zhong Wang
Keyword(s):  
Seismic Data ◽  
Time Frequency ◽  
Frequency Decomposition
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