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Normalizing Large Scale Sensor-Based MWD Data: An Automated Method toward A Unified Database.

Abbas Abbaszadeh Shahri1,2, Chunling Shan2,3, Stefan Larsson3

  • 1Johan Lundberg AB, 754 50 Uppsala, Sweden.

Sensors (Basel, Switzerland)
|February 24, 2024
PubMed
Summary
This summary is machine-generated.

Analyzing large measurement-while-drilling (MWD) data for tunneling projects is challenging. A new automated method efficiently normalizes and filters MWD data, improving rock engineering assessments and enabling AI applications.

Keywords:
Swedenfiltering processmeasurement while drilling (MWD)normalizing indexsensor-based datatunneling

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Area of Science:

  • Geotechnical Engineering
  • Data Science
  • Geo-infrastructures

Background:

  • Large-scale sensor-based measurement-while-drilling (MWD) data is crucial for assessing rock engineering conditions in tunneling projects.
  • Handling and processing big MWD data, often affected by multiform stacking and noise, presents significant challenges.
  • Accurate geoengineering interpretations require integrating domain expertise with data science skills.

Purpose of the Study:

  • To develop an automated approach for normalizing and filtering noisy measurement-while-drilling (MWD) data.
  • To introduce a novel normalizing index for classifying large geoengineering datasets.
  • To establish an efficient data management system for MWD and grouting data.

Main Methods:

  • An automated processing approach was developed, integrating stepwise techniques, mode, and percentile gate bands for data normalization and filtering.
  • A novel mathematical normalizing index was presented for classifying large datasets.
  • A relational unified PostgreSQL database was created to store and manage raw and processed MWD data alongside real-time grouting information.

Main Results:

  • The developed automated method efficiently normalizes and filters noisy data from measurement-while-drilling (MWD) datasets.
  • Visualized results demonstrated that single hole-based normalizing is effective in eliminating outliers and noisy data.
  • A cost-effective and efficient PostgreSQL database was established for data extraction and management.

Conclusions:

  • The automated processing approach significantly enhances the efficiency of handling large MWD datasets in geo-infrastructure projects.
  • The novel normalizing index and database facilitate in-depth investigations and the application of AI techniques.
  • This methodology is expected to improve predictions of rock quality and inform the design of appropriate support systems.