Ore image classification based on small deep learning model: Evaluation and optimization of model depth, model structure and data size

Granularity analysis is one of the most essential issues in authenticate under microscope. To improve the efficiency and accuracy of traditional manual work, an convolutional neural network based method is proposed for granularity analysis from thin section image, which chooses and extracts features from image samples while build classifier to recognize granularity of input image samples. 4800 samples from Ordos basin are used for experiments under colour spaces of HSV, YCbCr and RGB respectively. On the test dataset, the correct rate in RGB colour space is 98.5%, and it is believable in HSV and YCbCr colour space. The results show that the convolution neural network can classify the rock images with high reliability.

https://iopscience.iop.org/article/10.1088/1742-6596/887/1/012089/pdf

Rock images classification by using deep convolution neural network


 Petrophysics is a pivotal discipline that bridges engineering and geosciences for reservoir characterization and development. New sensor technologies have enabled real-time streaming of large-volume, multi-scale, and high-dimensional petrophysical data into our databases. Petrophysical data types are extremely diverse, and include numeric curves, arrays, waveforms, images, maps, 3-D volumes, and texts. All data can be indexed with depth (continuous or discrete) or time. Petrophysical data exhibits all the "7V" characteristics of big data, i.e., volume, velocity, variety, variability, veracity, visualization, and value. This paper will give an overview of both theories and applications of machine learning methods as applicable to petrophysical big data analysis.
 Recent publications indicate that petrophysical data-driven analytics (PDDA) has been emerging as an active sub-discipline of petrophysics. Field examples from the petrophysics literature will be used to illustrate the advantages of machine learning in the following technical areas: (1) Geological facies classification or petrophysical rock typing; (2) Seismic rock properties or rock physics modeling; (3) Petrophysical/geochemical/geomechanical properties prediction; (3) Fast physical modeling of logging tools; (4) Well and reservoir surveillance; (6) Automated data quality control; (7) Pseudo data generation; and (8) Logging or coring operation guidance.
 The paper will also review the major challenges that need to be overcome before the potentially game-changing value of machine learning for petrophysics discipline can be realized. First, a robust theoretical foundation to support the application of machine leaning to petrophysical interpretation should be established; second, the utility of existing machine learning algorithms must be evaluated and tested in different petrophysical tasks with different data scenarios; third, procedures to control the quality of data used in machine leaning algorithms need to be implemented and the associated uncertainties need to be appropriately addressed. The paper will outlook the future opportunities of enabling advanced data analytics to solve challenging oilfield problems in the era of the 4th industrial revolution (IR4.0).

When Petrophysics Meets Big Data: What can Machine Do?

https://eartharxiv.org/repository/object/134/download/274/

Fully automated carbonate petrography using deep convolutional neural networks

In the mining process of underground metal mines, the misjudgment of rock types by on-site technicians will have a serious negative impact on the stability evaluation of rock mass and the formulation of support schemes, which will result in the loss of economic benefits and potential safety hazards of mining enterprises. In order to realize the precise and intelligent identification of rock types, the image data of peridotite, basalt, marble, gneiss, conglomerate, limestone, granite, magnetite quartzite are amplified. Under the target detection framework of Faster R-CNN deep learning, the extraction network based on simplified VGG16 is used to extract and learn features of rock images, and finally the rock type identification system is successfully trained. The experimental verification shows that the system is correct for single-type rock image recognition and the accuracy is more than 96%. In order to realize accurate and intelligent identification of the surrounding rock surface under complex lithological conditions, the multi-type rocks hybrid images are also identified. The results show that the recognition effect is great and the accuracy rate is over 80%. Therefore, this system can accurately identify rock types with similar image features, which proves that the model has strong robustness and generalization ability. It has broad application prospects in rock mass stability evaluation and rock classification in underground mining.

/pdf/research-on-intelligent-identification-of-rock-types-based-57dkzf1rcw.pdf

Research on Intelligent Identification of Rock Types Based on Faster R-CNN Method

An intelligent system for mineral identification in thin sections based on a cascade approach

Assessment of seismic hazard including equivalent-linear soil response analysis for Dhaka Metropolitan Region, Bangladesh

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An intelligent system for mineral identification in thin sections based on a cascade approach

Assessment of seismic hazard including equivalent-linear soil response analysis for Dhaka Metropolitan Region, Bangladesh