Webis also an important feature of lithology recognition, there are three color layers R, G and B. In this way, the core image size of the accurate sample set is 200 * 200 * 3. In the case of the same receptive field, the smaller the convolution kernel is, the smaller the parameters and computational complexity are. WebReceived recognition from CEO and Segment president for service quality, business growth and new technology deployment during this tenure. 2. Delivered a 3x business growth ~ $90m annual revenue in 2015, amid an industry-wide gloom, by leading high-quality service delivery for 13 intl. clients in this competitive, high-volume, high-risk and …
基于DBN模型的火山岩岩性识别方法——以车排子地区为例
Web31 jul. 2001 · The result has greatly enhanced geological understanding and consistency in logging of lithology, alteration, mineralisation and structural domains and subgroups. KGL now recognise two main styles of mineralisation and alteration/metamorphic mineral assemblages: 1. Lower tenor, primary syn-depositional or stratabound disseminated … Web3 feb. 2024 · Abstract: Logging data contains a lot of redundant information that is irrelevant to lithology, and the distribution of various lithology label data is uneven, which substantially impacts the accuracy of lithology recognition.The commonly used classification algorithms cannot effectively solve the problem of imbalance between … cisco catalyst 3650-24ts-e
基于岩石图像深度学习的多尺度岩性识别
Web27 okt. 2024 · In the literature , the Resnet101 model is used for lithology recognition, and it has achieved good recognition accuracy. However, the number and volume of model … Weblithology label for each log sample of d log reading at a given depth in the cored interval of the key well (91. The training data log samples are subdivided into five classes (lithologies) on the basis of core to log correlation. The number of samples for each lithology is shown in Table 2. : • • Table 2 List of lithologies Lithology No ... Web19 mrt. 2024 · Abstract: The recognition and classification of rock lithology is an extremely important task of geological surveys. This paper proposes a new method for quickly identifying multiple types of rocks suitable for geological survey work field. Based on the two lightweight convolutional neural networks (CNNs), SqueezeNet and MobileNets, and … diamond resort phoenix arizona