钻孔柱状图数字化专利解析

<span data-lang="zh">钻孔柱状图数字化专利解析</span><span data-lang="en">Analysis of Borehole Log Digitization Patents</span>

钻孔柱状图数字化专利解析Analysis of Borehole Log Digitization Patents

Patent Technology Analysis
深入解析钻孔柱状图数字化领域的中国发明专利，梳理各专利的技术路线、核心方法与工程价值。In-depth analysis of Chinese invention patents in borehole log digitization, covering technical routes, core methods, and engineering value.
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专利格局概述Patent Landscape Overview

中国在钻孔柱状图数字化领域已形成较为完整的技术专利布局，覆盖从传统图像识别到前沿大模型技术的多条路线。按技术代际可分为： China has formed a relatively complete patent layout in borehole log digitization, covering multiple routes from traditional image recognition to cutting-edge large model technologies. By generation:

代际Generation 核心技术Core Technology 代表专利Representative Patents 特点Characteristics
第一代1st Gen 几何图元识别+模板匹配Geometric primitive + Template matching 结构模式识别法Structural pattern recognition 基于岩性符号形状特征Based on lithology symbol shape features
第二代2nd Gen CAD解析+区域划分CAD parsing + Zone segmentation CAD工程文件处理法CAD engineering file processing 直接解析矢量图元Direct vector primitive parsing
第三代3rd Gen 字段映射+数据库关联+校核Field mapping + DB association + Verification 多源数据融合法Multi-source data fusion 强调数据质量控制Emphasizes data quality control
第四代4th Gen OCR+深度学习+批量训练OCR + Deep learning + Batch training 扫描图件全要素识别法Scanned image full-element recognition 端到端自动化提取End-to-end automated extraction
第五代5th Gen 多模态大模型+Prompt工程Multimodal LLM + Prompt engineering 多模态大模型识别法Multimodal LLM recognition 泛化能力强、无需大量标注Strong generalization, minimal labeling
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基于结构模式识别的钻孔柱状图数字化方法Structural Pattern Recognition Based Digitization

基于结构模式识别的钻孔柱状图数字化方法Borehole Log Digitization Based on Structural Pattern Recognition
申请单位：中煤科工西安研究院 | 技术代际：第一代 Applicant: CCTEG Xi’an Research Institute | Generation: 1st

技术背景Technical Background

钻孔柱状图中的岩性符号具有固定几何形状（如圆形、三角形、条纹等），传统OCR方法无法识别这些非文字符号。本专利针对岩性符号的几何特征，提出基于结构模式识别的数字化方案。 Lithology symbols in borehole logs have fixed geometric shapes (circles, triangles, stripes, etc.) that traditional OCR cannot recognize. This patent targets the geometric features of lithology symbols with a structural pattern recognition approach.

核心技术方法Core Technical Methods

图像预处理Preprocessing → 符号检测Symbol Detection → 边界分割Boundary Segmentation → 几何特征提取Feature Extraction → 模板匹配Template Matching → 岩性定名Lithology Naming
  1. 岩性符号几何图元识别Lithology Symbol Geometric Primitive Recognition
    通过边缘检测和轮廓提取算法，识别柱状图中的岩性符号区域。将符号分解为基本几何图元（线段、圆弧、闭合区域等），构建符号的几何特征向量。 Identifies lithology symbol regions via edge detection and contour extraction. Decomposes symbols into basic geometric primitives (line segments, arcs, closed regions) to build geometric feature vectors.
  2. 按符号边界分割地层段Stratigraphic Interval Segmentation by Symbol Boundaries
    相邻岩性符号之间的边界即为地层分层界面。通过符号位置自动计算每个地层段的顶底深度。 Boundaries between adjacent lithology symbols serve as stratigraphic layer interfaces. Automatically calculates top/bottom depths for each interval from symbol positions.
  3. 符号-模板匹配Symbol-Template Matching
    建立标准岩性符号模板库，将提取的符号特征向量与模板库进行相似度计算（如Hu矩、形状上下文等），确定岩性名称。 Establishes a standard lithology symbol template library. Computes similarity (Hu moments, shape context, etc.) between extracted symbol feature vectors and templates to determine lithology names.

技术优势与局限Advantages & Limitations

优势：Advantages:
– 不依赖OCR，对低质量扫描件鲁棒性高
– 符号识别准确率较高（标准化符号） – Independent of OCR, robust to low-quality scans
– High symbol recognition accuracy (standardized symbols)
局限：Limitations:
– 仅适用于标准化岩性符号，对非标准手绘符号效果差
– 无法处理文字描述信息 – Only works for standardized lithology symbols; poor for non-standard hand-drawn symbols
– Cannot process text descriptions
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钻孔柱状图数据的处理方法（CAD工程文件法）Borehole Log Data Processing Method (CAD-based)

钻孔柱状图数据的处理方法Borehole Log Data Processing Method
申请单位：中科院广州地球化学研究所 | 技术代际：第二代 Applicant: GIGCAS | Generation: 2nd

技术背景Technical Background

国内地勘行业大量使用AutoCAD绘制钻孔柱状图，这些CAD文件中包含矢量化的几何图元和文字对象，可以直接解析获取精确数据，无需经过OCR环节。 China’s geotechnical industry widely uses AutoCAD for borehole log drafting. These CAD files contain vectorized geometric primitives and text objects that can be directly parsed for accurate data without OCR.

核心技术方法Core Technical Methods

CAD 读取Read → 图面扫描Surface Scan → 区域划分Zone Segmentation → 自动建表Auto Table Build → 质量检查Quality Check → 异常定位Anomaly Location
  1. 读取CAD工程文件Read CAD Engineering File
    直接读取DWG/DXF格式文件，解析其中所有几何图元（LINE、TEXT、MTEXT、POLYLINE等）和属性信息，保留精确坐标和文字内容。 Directly reads DWG/DXF files, parsing all geometric primitives (LINE, TEXT, MTEXT, POLYLINE, etc.) and attributes, preserving exact coordinates and text content.
  2. 扫描图面结构Scan Drawing Surface Structure
    通过空间聚类算法分析图元的空间分布模式，识别钻孔柱状图的栏目结构（如孔口信息栏、地层描述栏、测井曲线栏等）。 Analyzes spatial distribution patterns of primitives via spatial clustering algorithms to identify column structures (collar info, stratigraphic description, log curves, etc.).
  3. 划分结构区域Segment Structural Regions
    根据栏目结构将图面划分为若干逻辑区域，每个区域对应一类数据（基本信息、分层数据、试验数据等）。 Divides the drawing into logical regions by column structure, each corresponding to a data category (basic info, layer data, test data, etc.).
  4. 自动建表提取数据Auto Table Building & Data Extraction
    在每个逻辑区域内，按行列关系组织图元数据，自动构建结构化表格。通过图元的位置关系（如文字对齐、线框包围）确定字段对应关系。 Within each logical region, organizes primitive data by row-column relationships to auto-build structured tables. Determines field mappings through positional relationships (text alignment, line bounding).
  5. 质量检查与异常定位Quality Check & Anomaly Location
    对提取结果进行自动校验：深度递增检查、字段完整性检查、逻辑一致性检查。发现异常时，在CAD图面上精确定位并标注异常位置。 Auto-validates extraction results: depth monotonicity, field completeness, logical consistency. When anomalies are found, precisely locates and marks them on the CAD drawing.

技术优势与局限Advantages & Limitations

优势：Advantages:
– 精度极高，直接读取矢量数据无OCR误差
– 可保留空间位置信息
– 异常定位功能便于人工复核 – Extremely high precision; direct vector data reading without OCR errors
– Preserves spatial position information
– Anomaly location facilitates manual review
局限：Limitations:
– 仅适用于矢量CAD文件，无法处理扫描图片
– 不同单位的CAD绘图规范差异大，适应性有限 – Only works for vector CAD files; cannot process scanned images
– Different organizations’ CAD drafting standards vary widely, limiting adaptability
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基于多源数据融合的钻孔柱状图结构化处理方法Multi-source Data Fusion Based Structured Processing

基于多源数据融合的钻孔柱状图结构化处理方法Structured Processing Method Based on Multi-source Data Fusion
技术代际：第三代 Generation: 3rd

技术背景Technical Background

钻孔数据来源多样（野外记录、实验室报告、测井曲线、设计文档等），格式不统一，深度基准可能不一致。单一来源的数据往往不完整，需要多源融合与交叉验证。 Borehole data comes from diverse sources (field records, lab reports, log curves, design documents) with inconsistent formats and depth references. Single-source data is often incomplete, requiring multi-source fusion and cross-validation.

核心技术方法Core Technical Methods

字段映射Field Mapping → 格式转换Format Conversion → 数据库关联DB Association → 深度对齐Depth Alignment → 多层级校核Multi-level Verification
  1. 字段映射Field Mapping
    建立不同数据源间的字段映射字典，将各来源的同义字段统一为标准字段名。例如将”孔深”、”井深”、”Depth”映射为统一字段”depth”。 Establishes field mapping dictionaries between different data sources, unifying synonymous fields to standard names. E.g., maps “孔深”, “井深”, “Depth” to unified field “depth”.
  2. 格式转换Format Conversion
    将Excel、CSV、PDF表格、数据库记录等多种格式统一转换为中间标准格式（如JSON/XML），保留原始数据来源标记。 Converts Excel, CSV, PDF tables, database records, etc. into an intermediate standard format (JSON/XML), preserving original data source tags.
  3. 数据库关联Database Association
    以钻孔编号为键，将来自不同数据源的记录关联到同一钻孔实体下，建立主从表关系（如钻孔基本信息为主表，地层编录、试验数据为从表）。 Uses borehole ID as key to associate records from different sources under the same borehole entity, establishing master-detail relationships (basic info as master, stratigraphic logs and test data as details).
  4. 地层-测井数据深度对齐Stratigraphy-Log Depth Alignment
    这是核心创新点。地层编录的分层深度与测井曲线的采样深度通常不完全一致，需要基于深度插值或最近邻匹配将两类数据对齐到统一深度网格。 This is the core innovation. Stratigraphic log layer depths and log curve sample depths usually don’t exactly align; depth interpolation or nearest-neighbor matching is used to align both to a unified depth grid.
  5. 多层级校核Multi-level Verification
    设计三级校核机制： A three-level verification mechanism is designed:
    • 字段级：数据类型、取值范围、单位一致性 Field-level: data type, value range, unit consistency
    • 记录级：单孔内地层连续性、深度逻辑 Record-level: intra-borehole stratigraphic continuity, depth logic
    • 项目级：多孔间地层对比一致性、空间分布合理性 Project-level: inter-borehole stratigraphic correlation consistency, spatial distribution plausibility

技术优势与局限Advantages & Limitations

优势：Advantages:
– 多源数据互补，提升数据完整性
– 深度对齐算法解决异构数据融合难题
– 多级校核确保数据质量 – Multi-source data complementarity improves completeness
– Depth alignment solves heterogeneous data fusion challenges
– Multi-level verification ensures data quality
局限：Limitations:
– 依赖结构化输入源，对扫描图片/纸质文档需前置OCR
– 字段映射规则需人工维护 – Depends on structured input sources; scanned images/paper require前置 OCR
– Field mapping rules require manual maintenance
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扫描钻孔柱状图全要素信息自动识别提取方法Scanned Borehole Log Full-Element Auto Recognition

扫描钻孔柱状图全要素信息自动识别提取方法Full-Element Information Auto Extraction from Scanned Borehole Logs
技术代际：第四代 Generation: 4th

技术背景Technical Background

历史钻孔档案以扫描图片为主，包含文字、数字、岩性符号、曲线等多种要素。传统方法需要人工逐项录入，效率极低。本专利提出端到端的自动化识别方案。 Historical drilling archives are mostly scanned images containing text, numbers, lithology symbols, curves, and other elements. Traditional methods require manual entry item by item, which is extremely inefficient. This patent proposes an end-to-end automated recognition solution.

核心技术方法Core Technical Methods

样本批量生成Batch Sample Gen → OCR模型训练OCR Model Training → 特殊符号识别Special Symbol Recognition → 全要素提取Full-element Extraction → XML结构化存储XML Structured Storage
  1. 批量生成文字样本训练OCR模型Batch Text Sample Generation for OCR Training
    针对钻孔柱状图的特定字体和排版风格，批量合成训练样本（包括不同字体、字号、旋转角度、噪声水平），训练专用OCR模型。这比通用OCR模型在该领域准确率更高。 For the specific fonts and layout styles of borehole logs, batch-synthesizes training samples (different fonts, sizes, rotation angles, noise levels) to train domain-specific OCR models, achieving higher accuracy than general OCR.
  2. 特殊符号识别Special Symbol Recognition
    岩性符号、标贯符号、取芯符号等非文字要素，采用目标检测（如YOLO、Faster R-CNN）或传统图像处理方法（连通域分析、形状描述子）进行识别和分类。 Non-text elements such as lithology symbols, SPT symbols, and core recovery symbols are recognized and classified using object detection (YOLO, Faster R-CNN) or traditional image processing (connected component analysis, shape descriptors).
  3. 全要素信息提取Full-Element Information Extraction
    整合OCR文字结果和符号识别结果，通过版面分析确定各要素间的空间关系（如某深度值属于哪一地层段），输出完整的结构化记录。 Integrates OCR text results and symbol recognition results. Uses layout analysis to determine spatial relationships between elements (e.g., which depth value belongs to which stratigraphic interval), outputting complete structured records.
  4. XML结构化存储XML Structured Storage
    将提取结果按预定义的XML Schema存储，包含钻孔基本信息、地层分层列表、试验数据列表、符号识别结果等，便于后续系统集成。 Stores extraction results according to a predefined XML Schema, including basic borehole info, stratigraphic interval list, test data list, symbol recognition results, etc., facilitating subsequent system integration.

技术优势与局限Advantages & Limitations

优势：Advantages:
– 端到端自动化，无需人工干预
– 专用OCR模型针对性强，准确率高
– XML输出标准化，便于集成 – End-to-end automation without manual intervention
– Domain-specific OCR model has strong targeting and high accuracy
– Standardized XML output facilitates integration
局限：Limitations:
– 需要大量标注样本训练专用模型
– 对新格式/新字体的泛化能力有限
– 复杂表格结构的理解仍有挑战 – Requires large amounts of labeled samples to train specialized models
– Limited generalization to new formats/fonts
– Understanding complex table structures remains challenging
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基于多模态大模型的钻孔柱状图信息识别提取方法Multimodal LLM Based Borehole Log Information Extraction

基于多模态大模型的钻孔柱状图信息识别提取方法Borehole Log Information Extraction Based on Multimodal Large Models
技术代际：第五代 Generation: 5th

技术背景Technical Background

传统OCR和专用模型需要为每种新格式重新训练，适应成本高。多模态大模型（如GPT-4V、Claude 3）具备强大的视觉理解能力，可通过自然语言指令直接理解钻孔柱状图内容，无需针对特定格式训练。 Traditional OCR and specialized models require retraining for each new format, with high adaptation costs. Multimodal large models (GPT-4V, Claude 3) have powerful visual understanding capabilities and can directly comprehend borehole log content through natural language instructions without format-specific training.

核心技术方法Core Technical Methods

图像增强预处理Image Enhancement → Prompt微调语料构建Prompt Fine-tuning Corpus → 多模态大模型调优Multimodal LLM Tuning → 数据清洗Data Cleaning → 结构化输出Structured Output
  1. 图像增强预处理Image Enhancement Preprocessing
    对扫描图像进行去噪、对比度增强、几何校正、分辨率统一等预处理，提升大模型的识别效果。对退化严重的历史档案，采用超分辨率重建技术恢复细节。 Applies denoising, contrast enhancement, geometric correction, resolution normalization to scanned images to improve large model recognition. For severely degraded historical archives, uses super-resolution reconstruction to restore details.
  2. 构建Prompt微调语料Build Prompt Fine-tuning Corpus
    设计针对钻孔柱状图的专业Prompt模板，包括： Designs specialized prompt templates for borehole logs, including:
    • 角色设定：”你是一位资深地质工程师…” Role setting: “You are a senior geological engineer…”
    • 输出格式指令：要求按JSON/XML格式输出 Output format instruction: require JSON/XML output
    • 字段说明：明确各字段的取值范围和单位 Field descriptions: clarify value ranges and units for each field
    • 示例学习(Few-shot)：提供标准输入-输出示例 Few-shot learning: provide standard input-output examples
  3. 多模态大模型参数调优Multimodal LLM Parameter Tuning
    通过LoRA、Prompt Tuning等轻量微调技术，在少量标注样本上调整大模型参数，使其更适配钻孔柱状图领域。或采用RAG（检索增强生成）方式，将领域知识库接入推理流程。 Uses lightweight fine-tuning techniques (LoRA, Prompt Tuning) on small labeled datasets to adapt large models to the borehole log domain. Alternatively, uses RAG (Retrieval-Augmented Generation) to integrate domain knowledge bases into the inference pipeline.
  4. 数据清洗输出结构化结果Data Cleaning & Structured Output
    大模型输出可能存在幻觉(hallucination)和格式不一致问题。设计后处理模块：① schema校验确保输出格式正确；② 规则引擎校验地质逻辑（深度递增、厚度为正）；③ 置信度过滤剔除低置信度结果。 Large model outputs may contain hallucinations and format inconsistencies. A post-processing module is designed: ① schema validation ensures correct output format; ② rule engine validates geological logic (depth monotonicity, positive thickness); ③ confidence filtering removes low-confidence results.

技术优势与局限Advantages & Limitations

优势：Advantages:
– 零样本/少样本即可适配新格式，无需重新训练
– 理解能力强，可处理复杂布局和退化图像
– 自然语言交互降低使用门槛 – Zero/few-shot adaptation to new formats without retraining
– Strong comprehension, handles complex layouts and degraded images
– Natural language interaction lowers usage barrier
局限：Limitations:
– 大模型推理成本高，不适合大规模批量处理
– 存在幻觉风险，必须有后处理校验
– 依赖外部API或本地GPU算力 – High inference cost, unsuitable for large-scale batch processing
– Hallucination risk requires post-processing validation
– Depends on external APIs or local GPU resources
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技术路线对比Technical Route Comparison

专利方法特征对比Patent Method Characteristics Comparison

专利Patent 输入类型Input Type 核心技术Core Technology 输出格式Output Format 适用场景Applicable Scenario
结构模式识别Pattern Recognition 扫描图片Scanned image 几何图元+模板匹配Geometric primitive + Template 结构化表格Structured table 标准化符号的岩性柱Lithology columns with standardized symbols
CAD工程文件CAD File Processing DWG/DXFDWG/DXF 矢量图元解析+区域划分Vector parsing + Zone segmentation 结构化表格+异常标注Structured table + Anomaly marks 现有CAD图件数字化Digitizing existing CAD drawings
多源数据融合Multi-source Fusion 多格式结构化数据Multi-format structured data 字段映射+深度对齐+校核Field mapping + Depth alignment + Verification 融合数据库Fused database 多来源数据整合Multi-source data integration
扫描图件OCRScanned OCR 扫描图片Scanned image 专用OCR+目标检测+版面分析Specialized OCR + Object detection + Layout XMLXML 历史扫描档案批量处理Batch processing of historical scanned archives
多模态大模型Multimodal LLM 扫描图片/PDFScanned image / PDF 视觉大模型+Prompt工程+RAGVision LLM + Prompt engineering + RAG JSON/XMLJSON/XML 新格式快速适配/小批量Rapid new format adaptation / Small batches

技术选型建议Technology Selection Recommendations

场景一：大量历史扫描档案数字化Scenario 1: Large-scale Historical Scanned Archive Digitization
推荐：扫描图件OCR法（第四代）为主，多模态大模型法（第五代）处理复杂/退化样本。 Recommendation: Scanned OCR method (4th gen) as primary, multimodal LLM (5th gen) for complex/degraded samples.
场景二：现有CAD图件数据提取Scenario 2: Existing CAD Drawing Data Extraction
推荐：CAD工程文件法（第二代），精度最高、成本最低。 Recommendation: CAD file processing method (2nd gen), highest precision and lowest cost.
场景三：多来源异构数据整合Scenario 3: Multi-source Heterogeneous Data Integration
推荐：多源数据融合法（第三代），解决深度对齐和数据质量瓶颈。 Recommendation: Multi-source fusion method (3rd gen), solves depth alignment and data quality bottlenecks.
场景四：新格式快速适配（样本少）Scenario 4: Rapid New Format Adaptation (Few Samples)
推荐：多模态大模型法（第五代），零样本/少样本即可启动。 Recommendation: Multimodal LLM method (5th gen), can start with zero/few shots.

技术融合趋势Technology Convergence Trends

实际工程中，单一技术往往难以应对复杂场景。未来的技术路线将是多代技术的融合： In practice, single technologies often struggle with complex scenarios. Future routes will be a fusion of multiple generations:

  1. OCR+大模型混合架构OCR + LLM Hybrid Architecture
    常规页面用第四代OCR批量处理，复杂/疑难页面用第五代大模型兜底。 Routine pages processed by 4th-gen OCR in batches, complex/difficult pages handled by 5th-gen LLM as fallback.
  2. CAD解析+数据库融合CAD Parsing + Database Fusion
    第二代CAD提取的数据，经第三代多源融合流程与测井、试验数据整合。 Data extracted by 2nd-gen CAD parsing is integrated with log and test data through 3rd-gen multi-source fusion workflows.
  3. 符号识别+版面理解联合推理Symbol Recognition + Layout Understanding Joint Inference
    第一代的符号识别与第四代的版面分析相结合，实现符号-文字-布局的联合理解。 1st-gen symbol recognition combined with 4th-gen layout analysis enables joint understanding of symbols, text, and layout.

钻孔柱状图专利方法详解 | 基于公开专利文献整理Borehole Log Patent Analysis | Compiled from public patent literature