Utilization of Air Wave to Determine the Quality of Seismic Refraction Data in Near Surface Geophysical Survey

2019 ◽  
Author(s):  
E. M. H. Masyhur ◽  
A. G. M. Rafek ◽  
K. A. M. Noh
Keyword(s):  
Seismic Refraction ◽  
Geophysical Survey ◽  
Near Surface ◽  
Seismic Refraction Data ◽  
Refraction Data

scholarly journals Frequency-dependent traveltime tomography for near-surface seismic refraction data

2016 ◽  
Vol 207 (1) ◽  
pp. 72-88 ◽  
Author(s):  
Colin A. Zelt ◽  
Jianxiong Chen
Keyword(s):  
Seismic Refraction ◽  
Frequency Dependent ◽  
Near Surface ◽  
Traveltime Tomography ◽  
Seismic Refraction Data ◽  
Refraction Data

scholarly journals THE LITHOSTRATIGRAPHY OF THE NEAR-SURFACE IN PART OF SEDIMENTARY KOLMANI FIELD IN NORTHERN BENUE TROUGH, NIGERIA, USING SOIL CORE AND SEISMIC REFRACTION DATA

2020 ◽  
Vol 4 (2) ◽  
pp. 53-59
Author(s):  
Glory G. Akpan ◽  
Etim D. Uko ◽  
Owajiokiche D. Ngerebara
Keyword(s):  
Wave Velocity ◽  
Seismic Refraction ◽  
Compressional Wave ◽  
Soil Samples ◽  
Soil Core ◽  
Compressional Wave Velocity ◽  
Near Surface ◽  
Weathered Layer ◽  
Seismic Refraction Data ◽  
Refraction Data

Soil samples from 31 shallow boreholes were acquired at depths 0m, 1m, 2m, 3m, 4m, 5m, 7m, 10m, 15m, 20m, 25m, 30m, 35m, 40m, 45m, 50m, 55m, and 60m in Pingida (Kolmani Field) in Ako LGA, Gombe State, Nigeria. Using the same boreholes, seismic refraction data was also acquired. The aim of the survey was to delineate the near-surface lithology and velocity layering. The boreholes were drilled using rotary drilling rig and the core samples acquired and described using Wentworth Scale. Seismic refraction data acquired using a single trace Stratavisor NZXP portable digital recorder. The recording spread consisted of a single SM4- 10Hz geophone positioned at depths where the soil samples were taken. A hammer was used as the energy source and placed 3m away from the hole to obtain the first breaks. The refraction data was interpreted using UDISYS Version 1.0.0.0 software. The soil layers in the Kolmani Field have three distinct layers specified as follows, namely, top weathered and sub-consolidated layers made up of intercalation of sandstone, gravel ash clay and muddy coal shale. The lithologic strata do not correlate throughout the field resulting from the highly variable elevation which ranged from 317m and 524m with average of 389.16m. The top weathered layer of laterite intercalated with cobblestones with compressional wave velocity ranging from 342 ms-1 to 517 ms-1 with an average of 405.03 ms-1. Beneath the weathered layer is the sub-consolidated Clay layer intercalated with silt and laterite of compressional wave velocity ranging from 440 ms-1 to 1854 ms-1 of average of 826 ms-1. The underlying consolidated layer is the shale and coal layer having compressional wave velocity ranging from 1518 ms-1 to 4201 ms-1 with an average of 2162.65 ms-1. The dominant lithologic sequences encountered are laterite, clay, silt, sand, gravel, coal and shale. The results of this work can be used for static corrections in seismic reflection processing, planning and assessing risk for engineering structures, and for groundwater exploration. The laterite, clay, silt, sand, gravel, coal and shale can be utilized in agriculture, construction, process industries, and environmental remediation.

scholarly journals Inversion of seismic refraction data using genetic algorithms

Geophysics ◽  
1996 ◽  
Vol 61 (6) ◽  
pp. 1715-1727 ◽  
Author(s):  
Fabio Boschetti ◽  
Mike C. Dentith ◽  
Ron D. List
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Seismic Refraction ◽  
Solution Space ◽  
Optimization Methods ◽  
Data Set ◽  
Weathered Layer ◽  
Seismic Refraction Data ◽  
Refraction Data

The use of genetic algorithms in geophysical inverse problems is a relatively recent development and offers many advantages in dealing with the nonlinearity inherent in such applications. However, in their application to specific problems, as with all algorithms, problems of implementation arise. After extensive numerical tests, we implemented a genetic algorithm to efficiently invert several sets of synthetic seismic refraction data. In particular, we aimed at overcoming one of the main problems in the application of genetic algorithms to geophysical problems: i.e., high dimensionality. The addition of a pseudo‐subspace method to the genetic algorithm, whereby the complexity and dimensionality of a problem is progressively increased during the inversion, improves the convergence of the process. The method allows the region of the solution space containing the global minimum to be quickly found. The use of local optimization methods at the last stage of the search further improves the quality of the inversion. The genetic algorithm has been tested on a field data set to determine the structure and base of the weathered layer (regolith) overlaying a basement of granite and greenstones in an Archaean terrain of Western Australia.

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