Narrow Results

Fault recognition is a difficult problem in seismic exploration data interpretation, and there is still no solution both well in terms of accuracy and signal-to-noise ratio. To solve this problem, based on the region energy algorithm, a novel fault recognition method is proposed, which determines the direction of fault tracking based on region energy when identifying fault points. First, the third-generation coherence cube algorithm is adopted to calculate the coherence attribute of the seismic data volume. Then, fault tracking is performed on each seismic section. When conducting fault tracking, the seismic sample is scanned and identified one by one. If it is a fault point, it is assigned to the corresponding fault in the connected area, and then, track along a certain direction of the current pixel point in the front left, directly ahead, or front right direction. The selection of the tracking directions is based on the energy of the corresponding area in the direction. The direction with the highest energy is tracked in the direction until the complete fault is tracked or the stopping condition is reached. If the point is not judged as a fault point, a certain distance is tracked down continue and the path is stored temporarily. If a fault point is tracked, the tracking path is classified as a fault, otherwise return to continue scanning. When all the sample points on the seismic section are scanned, the fault tracking on the corresponding section is completed. Subsequently, the fault points are fitted using the least squares fitting algorithm, and the fault line is obtained. Finally, comparative experiments were conducted on actual seismic data, and the effectiveness of the novel method was validated.

The equipment operational state detection is important in modern industry for reducing production costs and improving production efficiency. However, due to the complex and continuous nature of the papermaking process, analyzing paper break faults remains challenging. This chapter proposes a paper break fault classification and identification method based on a Stacked Autoencoder (SAE) and a Softmax classifier. A stacked autoencoder model is established and trained to extract deep features from the data, and a Softmax classifier is employed to identify paper breakage faults based on the extracted features. A case study with data collected from a real paper mill demonstrates that the classification model based on SAE feature extraction and Softmax classifier can effectively achieve paper break fault recognition.

A new method of detecting optical sight’s fault is presented based on computer techniques of image processing and pattern recognition theory. The presented technique can detect the fault of the optical sight including dark spots, bright spot, flash and dimmer with high efficiency and precision, as well as, get rid of the traditional estimation model relying on the human eye observation. Image acquisition system, image processing system and image fault recognizing system is mainly discussed in this paper. Using the technique of noise filtering and background noise separation, we successfully realized the division of image fault and background to obtain feature of optical sight fault. On the whole the detection method can carry out the recognition of fault of optical sight effectively.