Distributed acoustic sensing (DAS) has emerged as a transformational technology for seismic data acquisition. However, noise remains a major impediment, necessitating advanced denoising techniques. This study pioneers the application of diffusion models, a type of generative model, for DAS vertical seismic profile (VSP) data denoising. The diffusion network is trained on a new generated synthetic dataset that accommodates variations in the acquisition parameters. The trained model is applied to suppress noise in synthetic and field DAS-VSP data. The results demonstrate the model’s effectiveness in removing various noise types with minimal signal leakage, outperforming conventional methods. This research signifies diffusion models’ potential for DAS processing.

Depicting faults in seismic data is one of the key steps in seismic structure interpretation. However, the manual identification of faults is a time-consuming and tedious process. In conventional methods, seismic attributes associated with the reflection continuities or discontinuities of seismic data are extracted for fault detection. In recent years, convolutional neural networks (CNNs) have been introduced to solve geophysical problems. Herein, we have proposed powerful and efficient methods to enhance the performance of CNN-based fault detection. We first introduce human reasoning to improve the performance of a 3D neural network, which is trained with synthetic seismic data. We propose two methods that efficiently use human reasoning. Manually identified faults can be easily added to train an efficient spatial pyramid network (ESPNet) and contextual information can be obtained to improve the continuity of the faults. We propose an unsupervised fault registration framework to fuse the results from commonly used 3D CNNs and ESPNet, which simultaneously preserves high data accuracy and continuity. Furthermore, to use sparse manually identified faults to directly train a 3D CNN, we propose a weakly supervised learning method. Multiple examples find that faults can be more accurately predicted with the proposed methods than with previously established algorithms, including conventional seismic attributes and CNN-based methods.

In the past decade, distributed acoustic sensing (DAS) has enabled many new monitoring applications in diverse fields including hydrocarbon exploration and extraction; induced, local, regional, and global seismology; infrastructure and urban monitoring; and several others. However, to date, the open-source software ecosystem for handling DAS data is relatively immature. Here we introduce DASCore, a Python library for analyzing, visualizing, and managing DAS data. DASCore implements an object-oriented interface for performing common data processing and transformations, reading and writing various DAS file types, creating simple visualizations, and managing file system-based DAS archives. DASCore also integrates with other Python-based tools which enable the processing of massive data sets in cloud environments. DASCore is the foundational package for the broader DAS data analysis ecosystem (DASDAE), and as such its main goal is to facilitate the development of other DAS libraries and applications.

Paleokarst systems, found in carbonate rock formations worldwide, have potential for creating vast reservoirs and facilitating hydrocarbon migration. Thus, studying these systems is essential for the exploration and development of carbonate reservoirs. An approach using convolutional neural networks (CNNs) is introduced to automatically and precisely identify cave features within 3D seismic data. An efficient technique is outlined for generating ample amounts of 3D training data, which is comprised of synthetic seismic data and labels for cave features contained in the seismic data, as a solution to bypass the labeling task for training the CNN. This workflow uses point-spread functions to simulate the cave response in the seismic data and allows us to easily generate realistic and diverse synthetic training data sets with different geologic structures and cave features. By training a CNN with these synthetic data sets, it can effectively learn to detect cave features in field seismic volumes. Upon evaluation using multiple examples, this approach outperforms earlier techniques like seismic attributes and other CNN-based paleokarst characterization methods.

Seismic facies analysis can effectively estimate reservoir properties, and seismic waveform clustering is a useful tool for facies analysis. We have developed a deep-learning-based clustering approach called the modified deep convolutional embedded clustering with adaptive Gaussian mixture model (AGMM-MDCEC) for seismic waveform clustering. Trainable feature extraction and clustering layers in AGMM-MDCEC are implemented using neural networks. We fuse the two independent processes of feature extraction and clustering, such that the extracted features are modified simultaneously with the results of clustering. We use a convolutional autoencoder for extracting features from seismic data and to reduce data redundancy in the algorithm. At the same time, weights of clustering network are fine-tuned through iteration to obtain state-of-the-art clustering results. We apply our new classification algorithm to a data volume acquired in western China to map architectural elements of a complex fluvial depositional system. Our method obtains superior results over those provided by traditional K-means, Gaussian mixture model, and some machine-learning methods, and it improves the mapping of the extent of the distributary system.

The increasing global reliance on offshore wind farms as a sustainable energy source necessitates precise soil assessment for foundation design due to their high foundation costs and complex integration into marine environments. This study explores the use of ultrahigh-resolution seismic (UHRS) data in conjunction with cone penetration test data for soil strength estimation in offshore wind farm development. We develop a convolutional neural network-based approach that leverages UHRS data for direct soil strength estimation, aiming to address the challenges of limited cone penetration test measurements and potential overfitting in complex geologic settings. Our methodology involves preprocessing UHRS and cone penetration test data, interpreting and integrating geologic unit (GU) information, and applying repeated k-fold cross validation to evaluate model performance. The field data results demonstrate the model’s efficacy in accurately predicting cone penetration test curve trends, albeit with some amplitude discrepancies. We highlight the significance of geologic interpretation in predicting soil strength and identify the dependency on valid data within each GU as a limitation.