Cost Of Seismic Acquisition Research Articles

A Genetic Algorithm Optimized Undersampling Method for Seismic Sparse Acquisition and Reconstruction

The irregular observation region poses challenges to seismic acquisition systems design. The commonly-used parallel-type acquisition system requires that the geophones are located at equally spaced positions and therefore is hard to implement in an irregular observation region. An acquisition system which allows implementation of sparse and irregular observation (e.g., the node-type geometry) followed by a reconstruction procedure is a solution. It can not only fit in irregular observation regions but also make a significant reduction in seismic acquisition costs. The seismic sparse acquisition can be mathematically modeled as a undersampling operator in the seismic reconstruction problem. A suboptimal undersampling pattern will lead to an inferior reconstruction result. To optimize the seismic sparse acquisition, I propose a undersampling method based on bionic intelligence in this study. In the proposed method, a Shannon entropy maximum model is proposed to improve the observed ergodicity and reduce the undersampling artifacts. To solve the maximum problem, an improved version of genetic algorithm is presented. The proposed method is applicable to irregular observation regions and can optimize the subsequent reconstruction performance. I provide the detailed algorithm framework and discuss the undersampling artifacts of different undersampling methods. The application to synthetic and field seismic data validates the effectiveness of the proposed method.

Optimal Seismic Sensor Placement Based on Reinforcement Learning Approach: An Example of OBN Acquisition Design

Seismic acquisition costs are directly associated with the number of sensors used in the survey. Limiting the number of sensors in a seismic survey can be beneficial, especially when sensors are expensive to purchase, deploy, and maintain. This work explores an optimal design method for ocean bottom node (OBN) detector deployment. The proposed method is based on a reinforcement learning approach. We assume access to an initial dataset over the area of study. These data are used to extract an over-complete pre-learned basis library via the proper orthogonal decomposition method, which leads to a fast least-squares seismic data reconstruction algorithm. Then, the sensor selection procedure entails using Q-learning to find the sensor configuration that maximizes the reconstruction quality.

Iterative method for the separation of blended seismic data: discussion on the algorithmic aspects

ABSTRACTRecently much attention has been given to the possibility of shooting in an overlapping fashion, the so‐called blended or simultaneous acquisition. In conventional acquisition the time intervals between successive sources are large enough to avoid interference in time. In blending, a temporal overlap between source responses is allowed. This additional degree of freedom in survey design has the potential to significantly reduce seismic acquisition costs while maintaining or improving the data quality. Deblending is the procedure of retrieving data as if they were acquired in the conventional, unblended way. This is an essential step in the case where standard processing flows are applied. Several methods have been proposed to perform data separation with the majority of them falling into two categories. The first category consists in methods that filter out the blending noise by arranging seismic data in some domain, whereas inversion techniques fall into the second category. We recently introduced an iterative estimation and subtraction algorithm that integrates elements of both categories. This method exploits the fact that the character of the blending noise differs in different domains, e.g., it is coherent in the common source domain but incoherent in the common receiver, common‐offset or common midpoint domains. Up to now, our method relies on the interaction with a human operator. The automation of the thresholding process is addressed in this paper, leading to a hands‐off algorithm for the separation of blended data, optimized for both efficiency and effectiveness. We found that one of the major limiting factors is the edge artefacts generated by the coherence‐pass filter. The effectiveness of the coherence‐pass filter has a considerable influence on the convergence of our deblending algorithm. This is shown by testing different coherence‐pass filters on marine data as well as introducing errors in the coherence‐pass process. We show that these data estimation errors can be handled properly and the best result is obtained using a τ‐p filter as a coherence‐pass filter. Furthermore, very promising results are obtained on numerically‐blended land data.

The synchronized electromagnetic impulsive source

Land seismic acquisition can be challenging, even in the best conditions and terrains. When the terrain changes from rural fields to industrial, urban, or environmentally sensitive areas, the challenges become more significant. A major factor that influences the difficulty and cost of seismic acquisition is the seismic source.