A Synchronous Triggering Method for Impact Artificial Seismic Source and Seismographs Based on Non-Contact Audio...

Area of Science:

• Geophysics
• Signal Processing
• Machine Learning

Background:

• Impact artificial seismic sources are crucial for reflection seismic exploration.
• Traditional triggering methods face challenges with uncertain delays and electromagnetic interference, leading to data misalignment.
• Existing methods struggle with synchronous triggering for artificial seismic sources.

Purpose of the Study:

• To develop a high-precision synchronous triggering method for impact-type artificial seismic sources.
• To overcome the limitations of traditional triggering methods in seismic exploration.
• To enhance the reliability of seismic exploration data acquired using artificial sources.

Main Methods:

• A non-contact audio detection system utilizing an STM32F4 microcontroller.
• Extraction of acoustic features using Mel-frequency cepstrum coefficients (MFCC).
• Implementation of a lightweight convolutional neural network (CNN) for hammer impact event identification and a synchronization time compensation mechanism.

Main Results:

• High-precision triggering accuracy of up to 94.6% achieved across diverse field environments.
• Compensated triggering time errors controlled within ±125 μs, meeting minimum synchronous triggering error requirements.
• Demonstrated effectiveness in open ground, construction sites, and mining tunnels.

Conclusions:

• The proposed audio detection method significantly enhances the reliability of impact-type artificial seismic source exploration data.
• This approach offers a robust solution for synchronous triggering challenges in seismic exploration.
• The study highlights the potential of sound recognition in engineering surveying and related fields.