Seismic Data Transmission Research Articles

Wavefield Reconstruction of Distributed Acoustic Sensing: Lossy Compression, Wavefield Separation, and Edge Computing

AbstractDistributed acoustic sensing (DAS) presents challenges and opportunities for seismological research and data management. This study explores wavefield reconstruction using deep learning methods for data compression and wavefield separation. We test various architectures to treat DAS data as two‐dimensional arrays, such as the implicit neural representation (INR) models and the SHallow REcurrent Decoder (SHRED) model. The INR models present better data compression ability but do not generalize over space and time, a major practical limitation. On the other hand, SHRED generalizes over space and time for a single optical fiber with data from 20% decimated channels for the reconstruction. Despite good performance in reconstructing long‐wavelength features, the shallow recurrent decoder does not reconstruct transient earthquake wavefields at shorter wavelengths, limiting its usability for seismic data transmission. Nevertheless, we leverage wavefield reconstruction of ocean waves to separate them from the seismic wavefield and improve seismological use cases for earthquake detection and Earth imaging. In summary, as a lightweight deep learning model, SHRED is well suited for wavefield separation and lossy compression of the DAS data.

Seismic communication data processing based on compressed sensing algorithm

AbstractGeophysical prospecting signals encompass subsurface structural information and incorporate textual messages generated in accordance with a specific pattern. These signals can be employed in places without radio access to ensure public and worker safety. Therefore, the use of seismic signals to transmit information through the earth has attracted the attention of researchers in the last decade. Presently, achievements in seismic communication are mainly in the coding, generation, and propagation of seismic signals. Little work has been done on methods to convert seismic signals generated by vibroseis sources into text and broadcast them vocally. Therefore, we built a seismic communication system with 6‐bit code using the American Standard Code for Information Interchange. To better recommend the seismic communication system scheme, the origin, state and principles of seismic communication system are illustrated in detail. Then, a seismic transmitting system is devised with a 500 N vibroseis source, which compiles seismic signals through amplitude modulation. After seismic signals propagate through the earth, they are received by geophones and recorded in seismographs. Through data acquisition based on a compression algorithm, seismic signals are converted into text and voice signals, which significantly reduces the storage and transmission of seismic data.

1-ADM-CNN: A Lightweight In-field Compression Method for Seismic Data

Large-scale seismic acquisition, versatility, flexibility, automation, and scalability are the objectives of future oil and gas exploration technology. An example of emerging technology for seismic monitoring is distributed acoustic sensing (DAS). The significant amount of data produced by DAS is a challenge that necessitates the development of new technologies for its efficient handling and processing. A typical seismic survey, on the one hand, can generate hundreds of terabytes of raw seismic data per day. The demand for wireless seismic data transmission, on the other hand, remains enormous. The massive amount of data transmission from geophones to the on-site data collection center and its storage poses significant challenges. A lightweight compression procedure is required in order to reduce the data traffic and the storage size at the data center without putting an extra burden on a geophone. In this brief, an efficient implementation of a 1D convolutional neural network (CNN) together with 1-bit adaptive delta modulation is presented for in-field seismic data compression. It is worth mentioning here that the training of CNN is done offline on synthetic data set and hence, the proposed approach has potential for real-time implementation. Furthermore, no assumption on the underlying statistics of noise or the seismic signal is imposed and consequently, the proposed method is suitable for a wide range of seismic data. Furthermore, the proposed method works in the time-domain, unlike existing transform-domain methods, making it suitable for quick diagnosis of bad traces at the data center. Simulation results with real data set reveal that the proposed approach achieves a signal-to-noise ratio (SNR) of approximately 30 dB with a compression gain of 35: 1. Finally, significant superiority in terms of compression gain and reconstruction quality is demonstrated when compared to the existing methods.

Using the Baikal-8 recorders for seismic monitoring of buildings and structures in real time in Seismological Branch of the GS RAS

The paper describes the composition of hardware and software parts of the seismic data transmission complex. The complex has been developed as a variant of a practical approach to the implementation of the method of engineering-seismic monitoring developed in SEB GS RAS. The method is based on spectral and quantitative analysis of the amplitude-frequency components of the signal emitted by the studied sources. Examples of the operation of the complex at industrial facilities and in civil construction are given, various approaches to the analysis of forced vibrations caused by the operation of large industrial equipment and normal oscillations of structures are shown.

Mitigating Wireless Channel Impairments in Seismic Data Transmission Using Deep Neural Networks.

The traditional cable-based geophone network is an inefficient way of seismic data transmission owing to the related cost and weight. The future of oil and gas exploration technology demands large-scale seismic acquisition, versatility, flexibility, scalability, and automation. On the one hand, a typical seismic survey can pile up a massive amount of raw seismic data per day. On the other hand, the need for wireless seismic data transmission remains immense. Moving from pre-wired to wireless geophones faces major challenges given the enormous amount of data that needs to be transmitted from geophones to the on-site data collection center. The most important factor that has been ignored in the previous studies for the realization of wireless seismic data transmission is wireless channel effects. While transmitting the seismic data wirelessly, impairments like interference, multi-path fading, and channel noise need to be considered. Therefore, in this work, a novel amalgamation of blind channel identification and deep neural networks is proposed. As a geophone already is responsible for transmitting a tremendous amount of data under tight timing constraints, the proposed setup eschews sending any additional training signals for the purpose of mitigating the channel effects. Note that the deep neural network is trained only on synthetic seismic data without the need to use real data in the training process. Experiments show that the proposed method gives promising results when applied to the real/field data set.

OFDMA-TDMA-Based Seismic Data Transmission Over TV White Space

Traditional cable-based geophone network is not an efficient way of seismic data transmission owing to the related cost and weight. To alleviate these drawbacks, in this work, unlike the existing setup, an OFDMA-TDMA based wireless geophone network is proposed to maximize the throughput for timely delivery of the seismic data from geophones to the data center. Furthermore, a TV white space is utilized for transmission in order to have long-distance links between the wireless geophones and the data center, and ultimately evading the use of intermediate relays. This, in turn, reduces the delivery time of the seismic data. More importantly, the seismic data transmission is modeled using a Markov chain and analytical expressions of throughput and transmission time are derived. Finally, our theoretical findings are substantially corroborated by simulation results.

Hardware-software implementation of the compression and transmission of seismic data

A real-time wireless network architecture for seismic data ac-quisition system based on multi-level radio network is proposed. The sin-gle-chip programmable transceiver Si446x that allows creating the first level of the network is considered. Physical layer with GFSK modulation in the sub-gigahertz band is considered. Data link layer implementation with generic frame structure for a network, operating in synchronous mode, is presented. An algorithm for seismic data accumulating and com-pressing is presented. The comparison of existing and proposed solutions is given.

The Test of Phased Array Antenna for Marine Seismic Data Transmission

Abstract This paper mainly analyzes the test results of a phased array antenna for real-time sea-area seismic datatransmission. The real-time data backhaul effects to the onshore data processing center through the phased array antenna in the sea-area seismic detection process was tested in the specified sea area. The test results show that the phased array antenna can transmit stably the detection data across the sea for 105km. The transmission delay is within 2 to 3 seconds, and the transmission rate reaches 40Kbps while the transmission loss rate is only about 1.5%. 99.5% of the detection data is well received and the power consumption of the whole system is 30W. It can be seen from the test results that the phased array antenna system can satisfactorily meet the real-time seismic databackhaul requirements in the transoceanic seas, and thus can provide technical support for the development of seismic detection

Seismic Exploration Wireless Sensor System Based on Wi-Fi and LTE.

In present seismic exploration wireless sensor systems with large acquisition channels, it is difficult to achieve a high data rate, high reliability and long distance in wireless data transmission simultaneously. In this paper, a wireless seismic exploration system using a dual-layer network is proposed. The dual-layer network is designed based on Wi-Fi and LTE, so that long-distance high-rate seismic data transmission with a high reliability can be achieved. In the proposed system, the sensor array is composed of two kinds of nodes, the gateway node and the collecting node. Based on the proposed nodes, collecting node positioning, seismic data acquisition, seismic local data storage and quasi real-time remote seismic data transmission can be realized. Reliability mechanisms have been put forward to deal with the exceptions. An experiment was carried out to test the data transmission efficiency of the proposed system. The results show that the seismic exploration wireless sensor system with a dual-layer network structure can achieve quasi real-time remote seismic data transmission with no packet loss.

Atom‐based‐segmented MP compression algorithm for seismic data

Data compression is an effective way to improve the seismic data transmission efficiency. The features of seismic exploration are long sampling time and large data quantity, so the compression algorithm should achieve high-fidelity, high-compression ratio (CR) and low-compression time. Since the existing compression algorithms cannot meet the requirements of site-collected seismic data compression, the segmented matching pursuit (SMP) compression algorithm based on new atom dictionary is proposed. This method is based on the principle of MP. A novel-segmented compression structure is adopted. The modified Morlet wave atom dictionary is designed to replace the previous dictionaries. The results of comparative experiments show that the proposed algorithm makes improvements in CR and compression time with the same fidelity requirement.

Global Optimization of Wireless Seismic Sensor Network Based on the Kriging Model and Improved Particle Swarm Optimization Algorithm

This study established the Kriging model to simplify the mathematical model for calculations and to improve the operational efficiency of global optimization in seismic exploration engineering. Accordingly, wireless seismic sensor network (WSSN) was used as an example in this research, and the generated seismic data flow rate and the flow rate of seismic data transmission are the simulation sample points. Thereafter, the Kriging model was constructed and the function was fitted. An improved particle swarm optimization (PSO) was also utilized for the global optimization of the Kriging model of WSSN to determine the optimized network lifetime. Results show that the Kriging model and the improved PSO algorithm significantly enhanced the lift performance and computer operational efficiency of WSSN.

Q Versatile Seismic Imager (QVSI) Successfully Predicts Target Depth and Pore-Pressure in HPHT Environment

Drilling in any environment is challenging as it poses a challenge to drill reservoir targets without losses and minimum casing strings and is even challenging in HPHT (high pressure high temperature) environment. Seismic is the fundamental for pre-drill prognosis and completion design. The target depth prognosis is achieved through depth transformation by using seismic velocities or available velocity logs in the nearby field or block and often has varying degree of uncertainty in target depths depending upon the suitable of the velocity function used. The velocity function used could be affected due to available seismic bandwidth or structure. These uncertainties in target depths often lead to increased well costs as a result of wellbore stability issues & undesired casing strings. Most common issue faced by drillers is the target confirmation & distance to these targets ahead of bit. Vertical seismic profile (VSP) look-ahead at intermediate depths is one of the approaches to mitigate these uncertainties and drill wells safely. VSP help confirm the presence of drilling targets & also predict the depth to top of these targets. Additionally, the predicted interval velocity is used to predict the pre-pressure for next section drilling. In South China Sea, oil & gas operators face a significant risk while drilling over-pressured formations. It is therefore imperative to know the depth to top of these high pressured formations to avoid drilling directly into it and risking the well. It is also important to know the pore-pressure and mud weight for the next section to be drilled for safe drilling & with minimum casing strings [1] [2] [3]. It is more difficult to get this information in the HPHT environment due to the lack of high temperature tools [4]. Schlumberger’s proprietary QVSI*—High pressure, high temperature VSI* (Versatile Seismic Imager) has been successfully used to predict the target depth for casing landing and pore-pressure prediction in HPHT environment. QVSI is the latest generation of VSI* Versatile Seismic Imager tools developed by Schlumberger to acquire high quality tri-axial borehole seismic data in extreme environment wells. The QVSI* tool uses the Q-Technology* singlesensor hardware and software and advanced wireline telemetry for fast digital seismic data transmission from borehole to surface. QVSI is a high-temperature, high-pressure array tool design that focuses on tri-axial vector fidelity and efficient data acquisition, extending the limits in a 4-tool configuration to 500°F (260℃) and 30 kpsi (207 MPa). In this paper, a case study is presented for Well-XX for CNOOC from South China Sea. The well-xx is located in Yanyan Sag, Qiongdongnan Basin and the downhole temperature was 204℃. The main target layer is Lingshui III sandstone, which is controlled by Northwest fault. It is a gas well and critical for the client to land the casing at right depth and know the drilling parameters for the next section ahead. QVSI* predicted the target depth within ±2 m for decision on casing point. The predicted pore pressure was within ±0.1 ppg.

Application of Embedded Technology and CAN Bus in Seismic Data Transmission in the Field

Both the networking mode and data transmission mode of the field mobile seismic station concern such matters as richness, real-time and effectiveness of seismic data. In light of the effectiveness, distance, cost and construction relating to data transmission in the field environment, CAN bus was utilized to connect the scattered seismic collection nodes, and the embedded microprocessor ARM was used to manage collection nodes within a certain scope. In addition, both the multiple task operating system Linux and application framework Qt were employed to construct a seismic network local management system, which significantly simplified the management task of mobile station. Such networking and data transmission mode shows such features as lower cost, easy construction, and higher reliability and instantaneity, also enables the possibility of constructing a mobile collection station with larger scale.

Distributed Wireless Acquisition System for Seismic Signal with Vibration and Noise

This work presented the design and implementation of a wireless acquisition system for seismic signal. 4 distributed signal acquisition stations were integrated to the low-power prototype which composed of 48 acquisition channels, and a star-shaped wireless network was built which is suitable for seismic data transmission using the networking mode of multi-pipe and multi-address switching. A distributed seismic data acquisition system was accomplished which has the function of synchronous control and asynchronous transmission. The key technical problems and solutions of time balance distribution between multi-channel time-sharing switching acquisition and temporary storage, and wireless seismic data transmission is analyzed in detail in this paper. Thus the additional accessories of traditional wired seismograph were greatly reduced, to improve its cable layout trouble and low efficiency of construction in an adverse environment. This system increased efficiency more than 50% and took same exploration effect compared with wired mode in the field application.

Realization of High-Speed Data Transmission in Seismic Data Acquisition

Data transmission is one of key techniques of acquisition station for seismic data acquisition system. With the development of electronics, computer science and communication science, a good realization method has become available for seismic data transmission. The paper focused on the independent design & development of Manchester encoding module, Manchester decoding module and parallel/serial and serial/parallel conversion module by hardware description language (HDL) with FPGA as main control unit and Manchester code and low-voltage differential signaling as theoretical basis. It can realize data transmission speed of 16Mbps between seismic data acquisition stations. Testing results showed low error rate during data transmission to ensure that seismic data acquisition station can read commands sent by power station and convey seismic data correctly.

Computerized geophysical tomography

Computerized tomography is used as an aid in geophysical exploration. With this method, detailed pictures of electromagnetic properties in the regions between pairs of boreholes can be reconstructed. The spatial distribution of attenuation or propagation velocity is calculated from line integrals along rays in the plane between boreholes, and displayed as a digital picture. In principle, the transmission of seismic data can also be analyzed by this method as long as it obeys the line integral model. Iterative solution techniques, similar to those used in medical X-ray tomography are applied to solve the large sets of linear equations relating the line integral data and the remote observables. A straight-line ray optics model was used for energy propagation between boreholes. The performance of the reconstruction algorithm is demonstrated using computer-generated data and it is then applied to experimental data collected by continuous-wave electromagnetic transmission probing. Experimental attenuation reconstructions are presented of a proposed underground urban mass-transit site. Both lateral and vertical variations are displayed using these methods.