The Study of Automatic Picking of P and S Wave Arrival and Identification of Earthquake Sequence Pattern using Scalogram in Obspy (Python)

Initial identification on an earthquake record (seismogram) is something that needs to be done precisely and accurately. Moreover, the discovery of a series of unexpected successive earthquake events has caused unpreparedness for the community and related agencies in tackling these events. Determining the arrival time of the P and S waves becomes an important parameter to finding the location of the earthquake source (hypocenter) as well as further information related to the earthquake event. However, manual steps that are currently often used are considered to be less effective, because it requires a lot of time in the process. Continuous Wavelet Transform (CWT) analysis can be a solution for this problem. With further CWT analysis in the form of a scalogram, can help to determine the arrival time of P and S waves automatically (automatic picking) becomes simpler. In addition, further CWT analysis can also be utilized to help identify the sequence of earthquake events (foreshock, mainshock, aftershock) through the resulting scalogram pattern.

Horizon Picking from SBP Images Using Physicals-Combined Deep Learning

Horizon picking from sub-bottom profiler (SBP) images has great significance in marine shallow strata studies. However, the mainstream automatic picking methods cannot handle multiples well, and there is a need to set a group of parameters manually. Considering the constant increase in the amount of SBP data and the high efficiency of deep learning (DL), we proposed a physicals-combined DL method to pick the horizons from SBP images. We adopted the DeeplabV3+ net to extract the horizons and multiples from SBP images. We generated a training dataset from the Jiaozhou Bay survey (Shandong, China) and the Zhujiang estuary survey (Guangzhou, China) to increase the applicability of the trained model. After the DL processing, we proposed a simulated Radon transform method to eliminate the surface-related multiples from the prediction by combining the designed pseudo-Radon transform and correlation analysis. We verified the proposed method using actual data (not involved in the training dataset) from Jiaozhou Bay and Zhujiang estuary. The positions of picked horizons are accurate, and multiples are suppressed.

Application of a density-based spatial clustering algorithm for fully automatic picking of surface-wave dispersion curves

Surface waves can be used to enhance the characterization of the shallow subsurface in desert environments. A high-resolution shear-wave velocity model is typically obtained by inverting dispersion curves, which correspond to different propagation modes of the surface waves. A common approach to estimate the dispersion curves is to manually pick the magnitude maxima from the frequency-phase velocity spectra of the seismic data. This approach is inefficient, time consuming, highly subjective, and not feasible for large surveys. Automatic picking of dispersion curves has become a topic of interest recently in the oil and gas research community, where many of the developed algorithms were inherited from the fields of image processing and machine learning. By exploring in the area of unsupervised learning, we recently derived an algorithm and workflow for fully automatic picking of surface-wave dispersion curves by employing a density-based spatial clustering technique. Our approach has been tested on the SEG Advanced Modeling Corporation Arid model synthetic data set and a field data set acquired in a desert environment. The results of the synthetic tests show that the estimated dispersion curves match the true dispersion curves with high accuracy, and they can be inverted for shear-wave velocities, successfully recovering the shallow near-surface features. The application of the method to field data provides high-resolution geology-consistent shear-wave velocity information that can be converted into a compressional-wave velocity model in agreement with uphole observations.

Soft Grippers for Automatic Crop Harvesting: A Review

Agriculture 4.0 is transforming farming livelihoods thanks to the development and adoption of technologies such as artificial intelligence, the Internet of Things and robotics, traditionally used in other productive sectors. Soft robotics and soft grippers in particular are promising approaches to lead to new solutions in this field due to the need to meet hygiene and manipulation requirements in unstructured environments and in operation with delicate products. This review aims to provide an in-depth look at soft end-effectors for agricultural applications, with a special emphasis on robotic harvesting. To that end, the current state of automatic picking tasks for several crops is analysed, identifying which of them lack automatic solutions, and which methods are commonly used based on the botanical characteristics of the fruits. The latest advances in the design and implementation of soft grippers are also presented and discussed, studying the properties of their materials, their manufacturing processes, the gripping technologies and the proposed control methods. Finally, the challenges that have to be overcome to boost its definitive implementation in the real world are highlighted. Therefore, this review intends to serve as a guide for those researchers working in the field of soft robotics for Agriculture 4.0, and more specifically, in the design of soft grippers for fruit harvesting robots.