Mitigating Wireless Channel Impairments in Seismic Data Transmission Using Deep Neural Networks

Area of Science:

• Geophysics
• Wireless Communication
• Machine Learning

Background:

• Traditional cable-based geophone networks are costly and inefficient for large-scale seismic data acquisition.
• Future oil and gas exploration requires versatile, scalable, and automated seismic surveys with massive data transmission needs.
• Wireless seismic data transmission faces challenges due to enormous data volumes and unaddressed wireless channel effects.

Purpose of the Study:

• To propose a novel method for efficient wireless seismic data transmission.
• To address the impact of wireless channel impairments like interference and fading.
• To develop a system that mitigates channel effects without requiring additional training signals.

Main Methods:

• A novel approach combining blind channel identification and deep neural networks (DNNs) was developed.
• The DNN was trained exclusively on synthetic seismic data, avoiding the need for real-world training data.
• The system is designed to operate under tight timing constraints inherent in seismic data transmission.

Main Results:

• The proposed method demonstrates promising performance in mitigating wireless channel effects.
• The system successfully transmitted seismic data wirelessly, overcoming significant technical hurdles.
• Experimental results validated the effectiveness of the approach on a real-world field dataset.

Conclusions:

• The amalgamation of blind channel identification and DNNs offers an effective solution for wireless seismic data transmission.
• This approach enhances the feasibility of large-scale, automated seismic surveys in oil and gas exploration.
• The method provides a robust way to handle wireless channel impairments without compromising data integrity or requiring extra training overhead.