Advances in Seismic Data Compression via Learning from Data: Compression for Seismic Data Acquisition

Abstract

The next generation of oil and gas exploration technology is moving toward large-scale seismic acquisition, automation, and flexibility. This phenomenon has accelerated the interest in moving away from traditional seismic acquisition systems that are heavily mechanical. Currently, on a daily basis, a seismic survey may require 800 or more crew members to place more than 200,000 prewired geophones over a field of several square miles. As such, the cost of cabling accounts for up to 50% of the total operating cost of a typical land survey, and up to 75% of the total equipment weight. This labor-intensive deployment of the prewired geophones, in addition to cost, prolongs the survey time and places a huge barrier on scaling the seismic acquisition and its adaptation/automation. Therefore, there has been a growing interest to switch from prewired geophones to wireless seismic acquisition. On the other hand, a typical seismic survey may generate tens of terabytes of raw seismic data per day. Hence, wireless communication faces great challenges in light of the enormous amounts of data that must be transmitted from geophones to on-site data collection centers.