A method of phase identification for seismic data acquired with the controlled accurate seismic source (CASS)

2020 ◽  
Vol 222 (1) ◽  
pp. 54-68
Author(s):  
Xiaolei Wang ◽  
Bing Xue ◽  
Rensheng Cui ◽  
Guoliang Gu ◽  
Chaoyong Peng ◽  
...  
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Seismic Source ◽  
Phase Identification ◽  
Time Varying ◽  
Central Frequency ◽  
Wave Velocities ◽  
Seismic Wave Velocities ◽  
Potential Applications ◽  
High Repeatability

SUMMARY With the advantages of the little destruction to the deployment site and high repeatability compared with explosive sources, the controlled accurate seismic source (CASS) has many potential applications with respect to the investigation of the crustal structure and seismic wave velocities. However, the signal generated by the CASS rapidly attenuates with the increasing distance because of its poor signal-to-noise ratio (SNR). Consequently, the difficulties in identifying specific seismic phases from the CASS data limit its application and popularization. The aim of this study is to present a new method to improve the accuracy of traveltime estimation and to identify more seismic phases travelling through the crust. We adopt the global seismic phase scanning algorithms (GSPSA) combined with an optimized narrowband time-varying filter, whose central frequency corresponds to the instantaneous frequency of the linear frequency modulation (LFM) signals produced by the CASS. Using the seismic data from the 40-ton CASS in a field experiment around Xinfengjiang reservoir in southeast China, we attain the seismic phases such as Pg, Sg, PmP and SmS at epicentral distances of more than 200 km with GSPSA. To identify and verify these seismic phases information, we also calculate synthetic waveforms. The results demonstrate that the GSPSA method is an effective tool for seismic phase identification of CASS data.

A 1D time-varying median filter for seismic random, spike-like noise elimination

Geophysics ◽  
2009 ◽  
Vol 74 (1) ◽  
pp. V17-V24 ◽  
Author(s):  
Yang Liu ◽  
Cai Liu ◽  
Dian Wang
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Random Noise ◽  
Threshold Value ◽  
Band Pass Filter ◽  
Time Varying ◽  
Noise Elimination ◽  
Pass Filter ◽  
Median Filters

Random noise in seismic data affects the signal-to-noise ratio, obscures details, and complicates identification of useful information. We have developed a new method for reducing random, spike-like noise in seismic data. The method is based on a 1D stationary median filter (MF) — the 1D time-varying median filter (TVMF). We design a threshold value that controls the filter window according to characteristics of signal and random, spike-like noise. In view of the relationship between seismic data and the threshold value, we chose median filters with different time-varying filter windows to eliminate random, spike-like noise. When comparing our method with other common methods, e.g., the band-pass filter and stationary MF, we found that the TVMF strikes a balance between eliminating random noise and protecting useful information. We tested the feasibility of our method in reducing seismic random, spike-like noise, on a synthetic dataset. Results of applying the method to seismic land data from Texas demonstrated that the TVMF method is effective in practice.

scholarly journals A 9C-2D Land Seismic Experiment for Lithology Estimation of a Permian Clastic Reservoir

GeoArabia ◽  
2000 ◽  
Vol 5 (3) ◽  
pp. 427-440 ◽  
Author(s):  
Costas G. Macrides ◽  
Panos G. Kelamis
Keyword(s):  
Saudi Arabia ◽  
Shear Wave ◽  
Seismic Data ◽  
Seismic Source ◽  
Well Logs ◽  
Target Interval ◽  
Wave Velocities ◽  
Shear Wave Velocities ◽  
Seismic Experiment ◽  
Clastic Reservoir

ABSTRACT In 1997, a nine-component shear wave experiment, the first of its kind in the country, was carried out in central Saudi Arabia over the Umm Jurf and Usaylah fields. The seismic source consisted of conventional and shear-wave vibrators. The objective of the experiment was to test the feasibility of using multicomponent seismic data for lithology estimation and differentiation between sand, silt and shale in the clastic Permian Unayzah Formation. The estimates of average ratios of compressional to shear-wave velocities for the target interval are encouraging as they identified lithologic variations within the Unayzah that are in agreement with the available well logs. Specifically, the seismic ratios correlate satisfactorily with sand/(silt+shale) ratios measured in key wells.

Removal of all time-varying sea-surface effects by source deghosting and demultiple

Geophysics ◽  
2019 ◽  
Vol 84 (6) ◽  
pp. T347-T362 ◽  
Author(s):  
Elsa Cecconello ◽  
Endrias G. Asgedom ◽  
Walter Söllner
Keyword(s):  
Seismic Data ◽  
Surface Condition ◽  
Seismic Source ◽  
Sea Surface ◽  
Normal Velocity ◽  
Time Varying ◽  
Data Set ◽  
Marine Seismic ◽  
Rough Sea Surface ◽  
The Impact

Seismic source deghosting and sea-surface-related demultiple have been long-standing problems in marine seismic data processing. Although the receiver ghost problem may be considered as solved by using collocated measurement of pressure and normal velocity wavefields, the source deghosting and demultiple algorithms are still limited by assumptions related to the sea-surface condition. We have investigated the impact of a time-varying rough sea surface on source deghosting and demultiple. Starting from Rayleigh’s reciprocity theorem for time-varying sea surfaces, we uncover a fundamental limitation for source deghosting of time-dependent wavefields, such as marine seismic data that contain a receiver ghost or sea-surface-related multiples. We use simple synthetic examples to study the impact of source deghosting on sea-surface-related multiples. To resolve this limitation, we derive a method for simultaneous source deghosting and sea-surface-related demultiple for time-variant wavefields. Finally, we use the complex geologic model Sigsbee 2B first to illustrate that the source deghosting operation brings significant errors when applied to a data set containing sea-surface multiples. Second, we find that this problem can be resolved by simultaneously performing source deghosting and demultiple operations even in the presence of time-varying sea surfaces.

Combination of the CRS-based pre-stack data regularization and RTM: application to real land data

Geophysics ◽  
2020 ◽  
pp. 1-45
Author(s):  
German Garabito ◽  
Paul L. Stoffa ◽  
Yuri S. F. Bezerra ◽  
João L. Caldeira
Keyword(s):  
Seismic Data ◽  
Model Building ◽  
Signal To Noise Ratio ◽  
Seismic Source ◽  
Velocity Model ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Depth Migration ◽  
Time Migration ◽  
Data Regularization

The application of the reverse time migration (RTM) in land seismic data is still a great challenge due to its low quality, low signal-to-noise ratio, irregular spatial sampling, acquisition gaps, missing traces, etc. Therefore, prior to the application of this kind of depth migration, the input pre-stack data must be conveniently preconditioned, that is, it must be interpolated, regularized, and enhanced. There are several methods for seismic data preconditioning, but for 2D real land data, the regularization of pre-stack data based on common reflection surface (CRS) stack method provides high quality enhanced preconditioned data, which is suitable for pre-stack depth migration and velocity model building. This work demonstrates the potential of RTM combined with CRS-based pre-stack data regularization, applied to real land seismic data with low quality and irregularly sparse spatial sampled, from geologically complex areas with the presence of diabase sills and steep dip reflections. Usually, determining the wavelet of the seismic source from land data is a challenge, because of this, RTM migration is often applied using artificial sources (e.g. Ricker). In this work, from the power spectrum of the pre-stacked data, we determine the wavelet of the seismic source to apply the RTM to real land data. We present applications of the pre-stack data preconditioning based on CRS stack and of the RTM in 2D land data of Tacutu and Parnaiba Basins, Brazil. Comparisons with the standard Kirchhoff depth migration reveals that the RTM improves the quality and resolution of the migrated images.

Coherent noise in marine seismic data

Geophysics ◽  
1983 ◽  
Vol 48 (7) ◽  
pp. 854-886 ◽  
Author(s):  
Ken Larner ◽  
Ron Chambers ◽  
Mai Yang ◽  
Walt Lynn ◽  
Willon Wai
Keyword(s):  
Seismic Data ◽  
Noise Suppression ◽  
Seismic Source ◽  
Coherent Noise ◽  
Critical Data ◽  
Predictive Deconvolution ◽  
Scattered Energy ◽  
Marine Seismic ◽  
The Many ◽  
Water Bottom

Despite significant advances in marine streamer design, seismic data are often plagued by coherent noise having approximately linear moveout across stacked sections. With an understanding of the characteristics that distinguish such noise from signal, we can decide which noise‐suppression techniques to use and at what stages to apply them in acquisition and processing. Three general mechanisms that might produce such noise patterns on stacked sections are examined: direct and trapped waves that propagate outward from the seismic source, cable motion caused by the tugging action of the boat and tail buoy, and scattered energy from irregularities in the water bottom and sub‐bottom. Depending upon the mechanism, entirely different noise patterns can be observed on shot profiles and common‐midpoint (CMP) gathers; these patterns can be diagnostic of the dominant mechanism in a given set of data. Field data from Canada and Alaska suggest that the dominant noise is from waves scattered within the shallow sub‐buttom. This type of noise, while not obvious on the shot records, is actually enhanced by CMP stacking. Moreover, this noise is not confined to marine data; it can be as strong as surface wave noise on stacked land seismic data as well. Of the many processing tools available, moveout filtering is best for suppressing the noise while preserving signal. Since the scattered noise does not exhibit a linear moveout pattern on CMP‐sorted gathers, moveout filtering must be applied either to traces within shot records and common‐receiver gathers or to stacked traces. Our data example demonstrates that although it is more costly, moveout filtering of the unstacked data is particularly effective because it conditions the data for the critical data‐dependent processing steps of predictive deconvolution and velocity analysis.

Automatic phase identification of earthquake based on the UBDN deep network

2021 ◽  
pp. 1-10
Author(s):  
Jianxian Cai ◽  
Xun Dai ◽  
Zhitao Gao ◽  
Yan Shi
Keyword(s):  
Seismic Data ◽  
Short Term Memory ◽  
Long Term Memory ◽  
Phase Identification ◽  
S Wave ◽  
Traditional Methods ◽  
Term Memory ◽  
Seismic Stations ◽  
2008 Wenchuan Earthquake ◽  
Deep Network

Seismic data obtained from seismic stations are the major source of the information used to forecast earthquakes. With the growth in the number of seismic stations, the size of the dataset has also increased. Traditionally, STA/LTA and AIC method have been applied to process seismic data. However, the enormous size of the dataset reduces accuracy and increases the rate of missed detection of the P and S wave phase when using these traditional methods. To tackle these issues, we introduce the novel U-net-Bidirectional Long-Term Memory Deep Network (UBDN) which can automatically and accurately identify the P and S wave phases from seismic data. The U-net based UBDN strongly maintains the U-net’s high accuracy in edge detection for extracting seismic phase features. Meanwhile, it also reduces the missed detection rate by applying the Bidirectional Long Short-Term Memory (Bi-LSTM) mode that processes timing signals to establish the relationship between seismic phase features. Experimental results using the Stanford University seismic dataset and data from the 2008 Wenchuan earthquake aftershock confirm that the proposed UBDN method is very accurate and has a lower rate of missed phase detection, outperforming solutions that adapt traditional methods by an order of magnitude in terms of error percentage.

Sign in / Sign up

Export Citation Format

Share Document