Machine Learning for Seismic Exploration: where are we and how far are we from the Holy Grail?

Machine Learning (ML) applications in seismic exploration are growing faster than applications in other industry fields, mainly due to the large amount of acquired data for the exploration industry. The ML algorithms are constantly being implemented to almost all the steps involved in seismic processing and interpretation workflow, mainly for automation, processing time reduction, efficiency and in some cases for improving the results. We carried out a literature-based analysis of existing ML-based seismic processing and interpretation published in SEG and EAGE literature repositories and derived a detailed overview of the main ML thrusts in different seismic applications. For each publication, we extracted various metadata about ML implementations and performances. The data indicate that current ML implementations in seismic exploration are focused on individual tasks rather than a disruptive change in processing and interpretation workflows. The metadata shows that the main targets of ML applications for seismic processing are denoising, velocity model building and first break picking, whereas for seismic interpretation, they are fault detection, lithofacies classification and geo-body identification. Through the metadata available in publications, we obtained indices related to computational power efficiency, data preparation simplicity, real data test rate of the ML model, diversity of ML methods, etc. and we used them to approximate the level of efficiency, effectivity and applicability of the current ML-based seismic processing and interpretation tasks. The indices of ML-based processing tasks show that current ML-based denoising and frequency extrapolation have higher efficiency, whereas ML-based QC is more effective and applicable compared to other processing tasks. Among the interpretation tasks, ML-based impedance inversion shows high efficiency, whereas high effectivity is depicted for fault detection. ML-based Lithofacies classification, stratigraphic sequence identification and petro/rock properties inversion exhibit high applicability among other interpretation tasks

Artificial intelligence to detect and forecast earthquakes

Precursors to large earthquakes have been widely but not systematically identified. The ability of deep neural networks to solve complex tasks that involve generalisations makes them highly suited to earthquake and precursor detection. Large moment magnitude (Mw) earthquakes and associated tsunamis can have a huge economic and social impact. Detecting precursors could significantly improve seismic hazard preparedness, particularly if precursors can assist, within a more general probabilistic forecasting framework, in reducing the uncertainty interval on expected earthquakes’ timing, location and Mw. Additionally, artificial intelligence has recently been used to improve the detection and location of smaller earthquakes, assisting in the completion and automation of seismic catalogues. This paper is the first to present a deep learning-based solution for detecting and identifying short-term changes in the raw seismic signal, correlated to earthquake occurrence. Deep neural networks (DNNs) were employed to investigate the background seismic signal prior to 31 Mw >= 6 earthquakes in the Japan region. Instantaneous, precursor-related features (features correlated to the investigated earthquakes) were detected as opposed to predicting future values based on previously observed values in the case of time series forecasting. The network achieved a 98% train accuracy and a 96% test accuracy classifying noise unrelated to Mw >= 6 earthquakes from signal immediately prior to the investigated earthquakes. Additionally, the precursor-related features became increasingly systematic (more frequently detected prior to the investigated earthquakes) with earthquake proximity. Discriminative features appeared most dominant over a frequency range of ~ 0.1-0.9 Hz, coinciding with microseismic noise and recent observations of broadband slow earthquake signal (Masuda et al. 2020). In particular, frequencies of ~ 0.16 and ~ 0.21 Hz provided significant precursor-related information. Deep learning successfully detected features of the seismic data correlated to earthquake occurrence. Developing a better understanding of the origin of the precursor-related features and their reliability is the next step towards establishing an earthquake forecasting system

WP2 final report

This document summarises the significant results in work package 2 of the DigiMon project. Detailed descriptions and results from each task can be found in the referenced deliverables and publications

Integrated interpretation of 3D seismic data using seismic attributes to understand the structural control of methane occurrences at deep gold mining levels: West Wits Line Goldfield, South Africa

A thesis submitted to the Faculty of Science, University of the Witwatersrand in fulfilment of the requirements for the degree of Master of Science, School of Geosciences University of the Witwatersrand. 08 November 2017.At a number of gold mines in South Africa, the presence of methane gases has been encountered when drilling into faults and/or dyke structures extending to depths beyond 4.5 km. Methane gas has been reported to have migrated through structures from within the basin to the mine working environments (~3.0 km depths) and caused explosions. The Booysens Shale is considered one of the possible source rocks for hydrocarbons and it forms the footwall to the gold-bearing Ventersdorp Contact Reef (VCR, ~ 1.5 m thick). The Booysens Shale lies at depths between 3.5 km and 4.5 km below land surface and can be best described as the base of the divergent clastic wedge which thickens westward, hosting the quartzite and conglomerate units that sub-crop against the VCR towards the east of the gold mining areas. Geometric attributes (dip and dip azimuth) and instantaneous attributes (phase, frequency and envelope) computed for the Booysens Shale and Ventersdorp Contact Reef horizons (interpreted from 3D prestack time migrated data acquired in the Witwatersrand goldfields) provide insight into structures that extend from the Booysens Shale into the overlying mining level, the Ventersdorp Contact Reef. These attributes provide high-resolution mapping of the structures (faults, dykes, and joints) that have intersected both the Ventersdorp Contact Reef and Booysens Shale horizons. Volumetric fault analysis using the ant-tracking attribute incorporated with methane gas data also show the continuity and connections of the faults and fracture zones possibly linked to methane gas and fluid migration. Correlation between the known occurrence of fissure water and methane with geologically- and seismically-mapped faults show that steeply dipping structures (dip>60°) are most likely to channel fracture water and methane. δ13C and δ2H isotope results suggest that the methane gas (and associated H2 and alkanes) from the goldfields, particularly along seismically delineated faults and dykes, have an abiogenic origin produced by water-rock reactions. Isotopic data derived from adjacent goldfields also suggests the possibility of mixing between microbial hydrocarbons (characterized by highly depleted 2HCH4 values) and abiogenic gases. It is, therefore, possible that the propagation of these structures, as mapped by 3D seismics and enhanced volumetric attributes, between Booysens Shale and Ventersdorp Supergroup provide conduits for mixing of fluids and gases encountered at mining levels. The study may provide new evidence for the notion of hydrocarbons, particularly CH4, having migrated via faults and dykes from depth, within the Witwatersrand Basin, to where they are intersected at mining levels. The research gives new insight into mixing between microbial and abiogenic end-members within hydrogeologically isolated water pockets.LG201

Statistical and deep learning methods for geoscience problems

Machine learning is the new frontier for technology development in geosciences and has developed extremely fast in the past decade. With the increased compute power provided by distributed computing and Graphics Processing Units (GPUs) and their exploitation provided by machine learning (ML) frameworks such as Keras, Pytorch, and Tensorflow, ML algorithms can now solve complex scientific problems. Although powerful, ML algorithms need to be applied to suitable problems conditioned for optimal results. For this reason ML algorithms require not only a deep understanding of the problem but also of the algorithm’s ability. In this dissertation, I show that Simple statistical techniques can often outperform ML-based models if applied correctly. In this dissertation, I show the success of deep learning in addressing two difficult problems. In the first application I use deep learning to auto-detect the leaks in a carbon capture project using pressure field data acquired from the DOE Cranfield site in Mississippi. I use the history of pressure, rates, and cumulative injection volumes to detect leaks as pressure anomaly. I use a different deep learning workflow to forecast high-energy electrons in Earth’s outer radiation belt using in situ measurements of different space weather parameters such as solar wind density and pressure. I focus on predicting electron fluxes of 2 MeV and higher energy and introduce the ensemble of deep learning models to further improve the results as compared to using a single deep learning architecture. I also show an example where a carefully constructed statistical approach, guided by the human interpreter, outperforms deep learning algorithms implemented by others. Here, the goal is to correlate multiple well logs across a survey area in order to map not only the thickness, but also to characterize the behavior of stacked gamma ray parasequence sets. Using tools including maximum likelihood estimation (MLE) and dynamic time warping (DTW) provides a means of generating quantitative maps of upward fining and upward coarsening across the oil field. The ultimate goal is to link such extensive well control with the spectral attribute signature of 3D seismic data volumes to provide a detailed maps of not only the depositional history, but also insight into lateral and vertical variation of mineralogy important to the effective completion of shale resource plays

Investigating the dynamic response of rock mass to reservoir drainage at Grimsel test site, Switzerland, as an analogue for glacial retreat

An effective solution for the geologic disposal of nuclear waste, with no environmental risk (i.e. avoidance of harmful release of radioactive material), is a fundamental issue for the environment protection, and for the future continued reliance on nuclear power. Although geological disposal is considered as the best option, there are still elements of risk to be addressed, such as glacial retreat, which could impact the safety performance of a geological disposal facility. In this project two consecutive annual cycles of a reservoir in the Swiss Alps are used as a small scale analogue of the glacial retreat cycles, in order to investigate the response of granitic rock (as a host rock to a geologic disposal facility) to significant load changes. Assuming that the reservoir’s stress changes cause the fractured and weakened rock slopes to slip, I chose to use microseismic monitoring as a tool to monitor the reservoir induced seismicity. A seismic network was deployed in the tunnels adjacent to the reservoir and recorded continuously ground movement over a 3.5-year period (Nov 2014 – Aug 2018). In order to be able to detect microseismic slips in the acquired real field dataset I explore various algorithms from the literature and develop my own methodology. The two main problems my research focuses on are the length of the dataset (big data issues) and the signal to noise ratio of the events I want to detect (small magnitude events in a varying noisy background). My results show, albeit not all of the seismic signals were possible to locate or characterise, that the reservoir unloading increases the frequency of occurrence of microseismic events for a short time period in the region surrounding the reservoir. It is possible therefore that the construction of a geologic disposal facility will have a similar effect. However, the magnitudes of the induced events are very small and hence unlikely to have a significant effect as part of a safety case for a geologic disposal facility. The contributions of this thesis can be summarised to: (i) using a reservoir as a small-scale test site analogue for exploring the seismic hazard in radioactive deep geologic disposal facilities due to glacial retreat; (ii) sensor deployment design and sensor data cleaning with noise characterisation for microseismic monitoring over several years; (iii) proposal of a new algorithm (NpD) for detecting potential seismic signals under not well-constrained conditions and without requirement of a priori knowledge about the expected signal frequencies and amplitudes; (iv) the NpD detection algorithm and acquired 3.5 years dataset are made freely available; (v) detailed discussion of onset time picking and hypocentre localisation methodologies, where again novelty lies in using, comparing suitability and adjusting a number of well-known approaches for the purposes of my project; (vi) compilation of a seismic catalogue related to the dynamic response of the rock mass to reservoir drainage.An effective solution for the geologic disposal of nuclear waste, with no environmental risk (i.e. avoidance of harmful release of radioactive material), is a fundamental issue for the environment protection, and for the future continued reliance on nuclear power. Although geological disposal is considered as the best option, there are still elements of risk to be addressed, such as glacial retreat, which could impact the safety performance of a geological disposal facility. In this project two consecutive annual cycles of a reservoir in the Swiss Alps are used as a small scale analogue of the glacial retreat cycles, in order to investigate the response of granitic rock (as a host rock to a geologic disposal facility) to significant load changes. Assuming that the reservoir’s stress changes cause the fractured and weakened rock slopes to slip, I chose to use microseismic monitoring as a tool to monitor the reservoir induced seismicity. A seismic network was deployed in the tunnels adjacent to the reservoir and recorded continuously ground movement over a 3.5-year period (Nov 2014 – Aug 2018). In order to be able to detect microseismic slips in the acquired real field dataset I explore various algorithms from the literature and develop my own methodology. The two main problems my research focuses on are the length of the dataset (big data issues) and the signal to noise ratio of the events I want to detect (small magnitude events in a varying noisy background). My results show, albeit not all of the seismic signals were possible to locate or characterise, that the reservoir unloading increases the frequency of occurrence of microseismic events for a short time period in the region surrounding the reservoir. It is possible therefore that the construction of a geologic disposal facility will have a similar effect. However, the magnitudes of the induced events are very small and hence unlikely to have a significant effect as part of a safety case for a geologic disposal facility. The contributions of this thesis can be summarised to: (i) using a reservoir as a small-scale test site analogue for exploring the seismic hazard in radioactive deep geologic disposal facilities due to glacial retreat; (ii) sensor deployment design and sensor data cleaning with noise characterisation for microseismic monitoring over several years; (iii) proposal of a new algorithm (NpD) for detecting potential seismic signals under not well-constrained conditions and without requirement of a priori knowledge about the expected signal frequencies and amplitudes; (iv) the NpD detection algorithm and acquired 3.5 years dataset are made freely available; (v) detailed discussion of onset time picking and hypocentre localisation methodologies, where again novelty lies in using, comparing suitability and adjusting a number of well-known approaches for the purposes of my project; (vi) compilation of a seismic catalogue related to the dynamic response of the rock mass to reservoir drainage

Microearthquake evidence for reaction-driven cracking within the Trans-Atlantic Geotraverse active hydrothermal deposit

Author Posting. © American Geophysical Union, 2014. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research: Solid Earth 119 (2014): 822–839, doi:10.1002/2013JB010110.We detected 32,078 very small, local microearthquakes (average ML = −1) during a 9 month deployment of five ocean bottom seismometers on the periphery of the Trans-Atlantic Geotraverse active mound. Seismicity rates were constant without any main shock-aftershock behavior at ~243 events per day at the beginning of the experiment, 128 events per day after an instrument failed, and 97 events per day at the end of the experiment when whale calls increased background noise levels. The microearthquake seismograms are characterized by durations of <1 s and most have single-phase P wave arrivals (i.e., no S arrivals). We accurately located 6207 of the earthquakes, with hypocenters clustered within a narrow depth interval from ~50 to 125 m below seafloor on the south and west flanks of the deposit. We model the microearthquakes as reaction-driven fracturing events caused by anhydrite deposition in the secondary circulation system of the hydrothermal mound and show that under reasonable modeling assumptions an average event represents a volume increase of 31–58 cm3, yielding an annual (seismogenic) anhydrite deposition rate of 27–51 m3.This work was supported in part by the U.S. National Science Foundation, National Science and Engineering Graduate Fellowship, and the Woods Hole Oceanographic Institution Deep Ocean Exploration Institute.2014-09-1