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Database: 365,228(8 Aug 2026)
isotope analysis water inrush detection water inrush point mine water organic water chemistry analysis method technology evolution analysis compared water samples hydraulic concrete reinforcement plum germplasm resources medical birth certificate basic dataset
KA/T 6-2023 in English

KA/T 6-2023 in English

VALID

Hydrochemical method for identifying water source of mine water inrush

  • Issued on:2023-10-26
  • Implemented on:2024-01-31
  • File Format:PDF
  • Delivery:Via email within 1~3 business days
Price(USD): $130.00
$127.00
Standard No: KA/T 6-2023
Document status: VALID
Title in English: Hydrochemical method for identifying water source of mine water inrush
Title in Chinese: 矿井突水水源水化学判别方法
Language: English
File Format: Electronic (PDF)
Delivery: Via email within 1~3 business days
Issued on: 2023-10-26
Implemented on: 2024-01-31
ICS Classification: 73.020-Mining and quarrying
Chinese Classification: D14-Hydrogeological survey
Professional Classification: KA-
Related Keywords: isotope analysis water inrush detection
water inrush point
mine water
organic water chemistry analysis method technology evolution analysis compared
water samples
Related Topics: foreign mine water
Discriminant analysis

本文件规定了矿井水化学基础数据库的建立、标准水样水化学资料分析、突水水质检测及矿井突水水源水化学判别方法的技术要求。
本文件适用于矿井突水水源水化学判别。


Introduction

Analysis of the core content of the standard

Technical aspects Key requirements Innovation points
Hydrochemistry database Must include 3 types of information: basic data of water samples, test data, analysis and collation data Replace traditional ledgers and realize dynamic updates
Standard water samples For each aquifer, ≥3 samples need to establish 5 types of characteristic maps such as Piper three-line diagram Introduce trace element and isotope analysis
Water inrush detection The sampling point is ≤5m from the water inrush point, and the detection index is ≥MT/T 672 requirements New organic water chemistry analysis method

Technology evolution analysis

Compared with traditional methods, this standard has three major breakthroughs: 1) Establishment of a dynamic water chemistry database; 2) Introduction of a machine learning discriminant model (Bayes multi-group discrimination); 3) New organic matter feature analysis (TOC, 3DEEM spectrum). In particular, the characteristic ion ratio method specified in Appendix A has increased the accuracy of water source identification by more than 40%.


Implementation suggestions

Application case of a coal mine in Shanxi

After the mine established the discrimination model using clause 7.1.5 of the standard:
1) The response time for identifying old empty water was shortened from 72 hours to 4 hours
2) The error in calculating the proportion of mixed water sources was ≤15%

Operation tips: It is recommended to use Cl- ions for mixing ratio calculation (standard 7.1.4) first. When the mineralization is >3g/L, δD isotope correction is required.

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