Case study on sediment classification using seismic attributes and 2d high-resolution seismic data

Abstract

This work has the purpose to understand the relation that are between seismic attributes extracted from 2D high-resolution seismic data and the sediment surveyed. To accomplish this goal, we performed a research using 2D high-resolution seismic data acquired on the Continental Shelf of the Antarctic Peninsula, nearby the South Shetland Islands, and sediment samples acquired with gravity corer overlapping each seismic line surveyed. The geologic samples were used as control samples to make the binding between seismic traces - sediment cores and to fulfill the sediment classification software training outline. One computational script was written to extract the seismic attributes from the seismic data and in sequence the attributes were statistically analyzed. The attributes were used as variables during the statistical process and they were: Amplitude (AMP), Instantaneous Phase (PHI), Instantaneous Frequency (FREQ), Envelope (ENV), Time Derivative of the Envelope (DTENV), Second Derivative of the Envelope (DT2ENV) and Acceleration of Phase (ACPHI). The statistical analyses were: Principal Components (PCA), Dendrograms and K-Means Classification. Four kinds of seismic attributes groups were coherently formed on statistical analyses. In theory basis, the PHI shouldn't be grouped with any other attribute because of its physical behavior completely distinct from the others attributes here studied. The sets of attributes formed that presented ACPHI were absolutely coherent, in a way that all others attributes grouped within ACPHI present the same physical and spatial behavior such as DTENV, AMP, ENV and FREQ, this is due to the absorption of both the amplitude and frequency of the wave propagated through the medium. Therefore, it is believed that both more coherent sets of attributes are AMP and ENV identified by all the statistical analyses and DTENV and ACPHI formed by the PCA and Dendrograms analyses, and are both well supported by the other analyses that assured an intrinsic physical and spatial relation between the grouped attributes. The subsequent step on the Semi-Automatic Sediment Classification Process using 2D high-resolution seismic data were to prepare the set of attributes that grouped coherently, to identify the range of values of each attribute grouped that correspond to the location of each core sample, locate on the reflectors in the seismic section where those ranges of values from the set of attributes are found at the same place and finally suppose that on these regions identified by the script we have the same kind of sediment found on the geological samples.