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Supervised Deep Learning for Detecting and Locating Passive Seismic Events Recorded with DAS: A Case Study.

Emad Al-Hemyari¹, Olivia Collet¹, Konstantin Tertyshnikov¹

¹Centre for Exploration Geophysics, Curtin University, GPO Box U1987, Perth, WA 6845, Australia.

Sensors (Basel, Switzerland)

|November 9, 2024

Summary

Deep learning with synthetic seismic data aids in detecting and locating seismic sources for mining. This method shows promise for subsurface characterization and continuous monitoring applications.

Keywords:

DAS deep learning induced seismicity passive seismic

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Area of Science:

Geophysics
Machine Learning
Seismology

Background:

Acquiring dense seismic data is crucial for exploring shallow mineral resources and monitoring mining operations.
Distributed Acoustic Sensing (DAS) is an effective technology for enhanced seismic data acquisition.
Characterizing the subsurface using passive seismic methods generates large datasets, challenging current computational systems and algorithms, especially for continuous monitoring.

Purpose of the Study:

To investigate a supervised deep-learning neural network methodology for detecting and locating induced seismic sources.
To explore the potential of this methodology for reconstructing subsurface properties.
To address challenges and demonstrate applicability in induced seismicity and mining-related seismic monitoring.

Main Methods:

Utilized synthetic seismic data combined with real noise recordings.
Implemented a supervised deep-learning neural network approach.
Demonstrated the methodology on synthetic data and validated it on field data from the Otway CO2 injection site.

Main Results:

The deep-learning methodology successfully detected and located induced seismic sources.
Performance was evaluated against factors like time shifts, signal-to-noise ratios, and geometry mismatches.
The method's applicability was confirmed on field seismic monitoring data.

Conclusions:

The developed deep-learning approach is a viable method for analyzing large seismic datasets in continuous monitoring scenarios.
The methodology shows potential for subsurface property reconstruction and can be applied to various field data, including seismic while drilling.
This work paves the way for advanced seismic data analysis in mining and geothermal energy exploration.