Abstract: A method for processing fused “underground+ground” data in deep formation exploration includes: collecting underground multi-source data by an underground intelligent probing rod, preprocessing the multi-source data to obtain an original digital signal, storing the original digital signal in an underground memory, obtaining continuous depth data by a ground device; performing high-bitrate and low-bitrate compression on the original digital signal, and modulating compressed signals; driving modulated signals by power, so the modulated signal is transmitted in a drilling stem with cable in a form of a power line carrier and then output, or first transmitted in a first section of the drilling stem with cable, re-collected in a short drill pipe with repeater, re-emitted to a second section of the drilling stem with cable, and then output; when a transmission line is disconnected, synchronizing and fusing data; and finally using fused data and the data output by the transmission line as final output data.

Abstract: A method for aligning a laser point cloud and an image based on deep learning includes: collecting data by using a laser ladar and a camera, to obtain laser point cloud data and a two-dimensional (2D) image respectively; dividing preprocessed point cloud data into triangular meshes to obtain a three-dimensional (3D) mesh model of the point cloud data; determining a relative location relationship between a laser radar and the multi-function camera, and constructing a virtual camera in the 3D mesh model to capture a simulated 2D image at a same location and angle as those of the camera; and aligning, by using a convolution neural network (CNN), the 2D image captured by the camera and the simulated 2D image captured by the virtual camera to generate aligned images that map onto each other.

Abstract: A method for mapping a three-dimensional (3D) point cloud model based on deep learning includes: extracting features of image data by using a convolutional neural network; extracting features of point cloud data by using a PointNet point cloud processing network, and constructing a 3D model of the point cloud data by using a triangular mesh; aligning the 3D model and an image spatially and temporally; performing projection mapping on the aligned 3D model and image, and superimposing location information in the point cloud data onto texture information of the image to obtain a first fused image; fusing the features of the point cloud data and the features of the image data to obtain a second fused image with a fused feature; and superimposing the first fused image onto the second fused image, and obtaining a superimposition weight by using the convolutional neural network, to generate a mapped 3D model.

Abstract: An internal peeping detection-while-drilling system includes an intelligent probing rod, cable drill pipes, a signal relay sub, a ground control terminal, a data interpretation and goaf reconstruction imaging software system, and a remote data transmission system, where the intelligent probing rod includes an up-down separated protective transmission mechanism and an instrument cabin; the intelligent probing rod includes a lower end connected to a drilling bit, and an upper end connected to each of the cable drill pipes; the instrument cabin is provided in the up-down separated protective transmission mechanism, and provided with a sonar-lidar-audio/video sensor assembly; the cable drill pipes are connected to each other to form a cable and data transmission channel; the signal relay sub is provided between the cable drill pipes at intervals; and an uppermost end of the cable drill pipe is connected to the ground control terminal.

Abstract: A method for three-dimensional (3D) imaging of an underground goaf includes: obtaining a video image and point cloud data of an underground goaf; determining a posture transformation matrix, and aligning the video image and the point cloud data; identifying a material texture based on the video image to obtain material texture information; dividing the point cloud data into triangular meshes to form an initial 3D model of the underground goaf; mapping the material texture information onto the 3D model to form a target 3D model with a material texture attribute; calculating a bidirectional reflectance distribution function (BRDF) of the target 3D model based on the material texture attribute; simulating global illumination, and constructing a rendering equation; and making a setting to emit ray from a viewpoint, and performing ray tracing based on the rendering equation to obtain a high-brightness real scene image of the underground goaf.

Abstract: The present invention discloses a model test device for ground collapse caused by pipeline leakage, including a sand box, a pipeline water circulation device, a groundwater replenishment device, a water storage tank and a water head measuring pipe set. The sand box includes a sand box body and a mesh sieve plate. There are two mesh sieve plate which divides an inner cavity of the sand box into a penetration cavity and a test cavity. Corresponding positions on the side wall of the test cavity are provided with a tunnel construction hole and a plurality of pipe mounting hole groups, respectively. One side wall of the test cavity is provided with a plurality of rows. There is a plurality of rows of water head measuring hole groups, and each row is provided with a plurality of the water head measuring hole groups.

Abstract: The present invention discloses a model test device for ground collapse caused by pipeline leakage, including a sand box, a pipeline water circulation device, a groundwater replenishment device, a water storage tank and a water head measuring pipe set. The sand box includes a sand box body and a mesh sieve plate. There are two mesh sieve plate which divides an inner cavity of the sand box into a penetration cavity and a test cavity. Corresponding positions on the side wall of the test cavity are provided with a tunnel construction hole and a plurality of pipe mounting hole groups, respectively. One side wall of the test cavity is provided with a plurality of rows. There is a plurality of rows of water head measuring hole groups, and each row is provided with a plurality of the water head measuring hole groups.

Abstract: A high-efficiency pre-drilling pressure meter test apparatus for deep rock mass includes a rigid drill pipe, a probe, a pressurizing device and a signal processing device. The rigid drill pipe, comprising a top end and a tail end opposite to the top end, receives a fluid medium. The probe is connected with the tail end of the rigid drill pipe and communicates with the rigid drill pipe. The probe includes a measuring chamber. The fluid medium flows from the rigid drill pipe to the measuring chamber. The pressurizing device is connected to the top end of the rigid drill pipe and applies pressure to the fluid medium. The signal processing device is electrically connected with the probe to detect deformation of the measuring chamber with the change of the pressure and volume of the fluid medium in the pressurizing device.

Abstract: A high-efficiency pre-drilling pressure meter test apparatus for deep rock mass includes a rigid drill pipe, a probe, a pressurizing device and a signal processing device. The rigid drill pipe, comprising a top end and a tail end opposite to the top end, receives a fluid medium. The probe is connected with the tail end of the rigid drill pipe and communicates with the rigid drill pipe. The probe includes a measuring chamber. The fluid medium flows from the rigid drill pipe to the measuring chamber. The pressurizing device is connected to the top end of the rigid drill pipe and applies pressure to the fluid medium. The signal processing device is electrically connected with the probe to detect deformation of the measuring chamber with the change of the pressure and volume of the fluid medium in the pressurizing device.

Abstract: The present disclosure relates to an automatic system and method for detecting problematic geological formations ahead of tunnel faces. The automatic system includes a data acquisition module configured to acquire data, a data transmission module configured to transmit the data and a control and data analysis module configured to receive and analyze the data and determine the geological formations ahead of the tunnel faces. The data acquisition module includes at least one three-component detector and a processor. The three-component detector is installed in a borehole in a side wall of the tunnel. The data transmission module includes a synchronous communicator and a signal line with shielding properties. The synchronous communicator is connected with the three-component detector via the signal line. The control and data analysis module includes a host and a control and analysis procedure of the host. The host is connected with the synchronous communicator.