Explainable Artificial Intelligence driven mask design for self-supervised seismic denoising

The presence of coherent noise in seismic data leads to errors and uncertainties, and as such it is paramount to suppress noise as early and efficiently as possible. Self-supervised denoising circumvents the common requirement of deep learning procedures of having noisy-clean training pairs. However, self-supervised coherent noise suppression methods require extensive knowledge of the noise statistics. We propose the use of explainable artificial intelligence approaches to see inside the black box that is the denoising network and use the gained knowledge to replace the need for any prior knowledge of the noise itself. This is achieved in practice by leveraging bias-free networks and the direct linear link between input and output provided by the associated Jacobian matrix; we show that a simple averaging of the Jacobian contributions over a number of randomly selected input pixels, provides an indication of the most effective mask to suppress noise present in the data. The proposed method therefore becomes a fully automated denoising procedure requiring no clean training labels or prior knowledge. Realistic synthetic examples with noise signals of varying complexities, ranging from simple time-correlated noise to complex pseudo rig noise propagating at the velocity of the ocean, are used to validate the proposed approach. Its automated nature is highlighted further by an application to two field datasets. Without any substantial pre-processing or any knowledge of the acquisition environment, the automatically identified blind-masks are shown to perform well in suppressing both trace-wise noise in common shot gathers from the Volve marine dataset and colored noise in post stack seismic images from a land seismic survey

Joint Microseismic Event Detection and Location with a Detection Transformer

Microseismic event detection and location are two primary components in microseismic monitoring, which offers us invaluable insights into the subsurface during reservoir stimulation and evolution. Conventional approaches for event detection and location often suffer from manual intervention and/or heavy computation, while current machine learning-assisted approaches typically address detection and location separately; such limitations hinder the potential for real-time microseismic monitoring. We propose an approach to unify event detection and source location into a single framework by adapting a Convolutional Neural Network backbone and an encoder-decoder Transformer with a set-based Hungarian loss, which is applied directly to recorded waveforms. The proposed network is trained on synthetic data simulating multiple microseismic events corresponding to random source locations in the area of suspected microseismic activities. A synthetic test on a 2D profile of the SEAM Time Lapse model illustrates the capability of the proposed method in detecting the events properly and locating them in the subsurface accurately; while, a field test using the Arkoma Basin data further proves its practicability, efficiency, and its potential in paving the way for real-time monitoring of microseismic events

Statistical methods for ambient noise characterisation, modelling and suppression: theory and applications for surface microseismic monitoring.

An ever-present feature in seismic data, noise affects outcomes of processing and imaging algorithms, causing uncertainty in the interpretation of results. Despite abundant evidence that noise is not white, stationary or Gaussian, these assumptions are commonly made when generating noise models and processing data. While synthetic seismic datasets have evolved to include geological complexities, a standardised approach to incorporating realistic noise does not yet exist. The aim of this work is to introduce a noise modelling methodology that avoids the above assumptions. A statistical analysis of three months of pre-injection noise from the vertical components of a 50 station, c.2.5km-wide, cross-shaped array at the Aquistore CO2 storage site, characterises noise sources originating from wellsite activity and passing traffic. A covariance modelling approach is then devised to generate realistic noise models that have close similarity to the recorded noise in both the time and frequency domain, with >65% noise realisations having >50% probability of arising from the same distribution as the recorded noise. The modelling procedure is finally applied to two cases: benchmarking and development of microseismic inversion algorithms on synthetic datasets; and noise suppression. In the former, the source location is correctly estimated at a signal-to-noise ratio of 0.1 with white, Gaussian noise (WGN) but 0.5 was required for realistic noise. Then, applying a microseismic source inversion algorithm, datasets with realistic noise identify pitfalls unobserved under WGN conditions. Thus, in both cases, a WGN assumption gives a misleadingly favourable assessment of efficiency. In the latter, a noise whitening technique that utilises the inverse of the covariance matrix reduces the total noise energy by a factor of 3.5, allowing both imaging of additional microseismic events and greater confidence in identified events. The proposed techniques are illustrated on passive surface data, but offer future applications in both active and passive seismic monitoring

A self-supervised scheme for ground roll suppression

In recent years, self-supervised procedures have advanced the field of seismic noise attenuation, due to not requiring a massive amount of clean labeled data in the training stage, an unobtainable requirement for seismic data. However, current self-supervised methods usually suppress simple noise types, such as random and trace-wise noise, instead of the complicated, aliased ground roll. Here, we propose an adaptation of a self-supervised procedure, namely, blind-fan networks, to remove aliased ground roll within seismic shot gathers without any requirement for clean data. The self-supervised denoising procedure is implemented by designing a noise mask with a predefined direction to avoid the coherency of the ground roll being learned by the network while predicting one pixel's value. Numerical experiments on synthetic and field seismic data demonstrate that our method can effectively attenuate aliased ground roll.Comment: 19 pages, 12 figures

Seismic arrival enhancement through the use of noise whitening

A constant feature in seismic data, noise is particularly troublesome for passive seismic monitoring where noise commonly masks microseismic events. We propose a statistics-driven noise suppression technique that whitens the noise through the calculation and removal of the noise’s covariance. Noise whitening is shown to reduce the noise energy by a factor of 3.5 resulting in microseismic events being observed and imaged at lower signal to noise ratios than originally possible - whilst having negligible effect on the seismic wavelet. The procedure is shown to be highly resistant to most changes in the noise properties and has the flexibility of being used as a stand-alone technique or as a first step before standard random noise attenuation methods

Gene expression profiling of human prostate cancer stem cells reveals a pro-inflammatory phenotype and the importance of extracellular matrix interactions

An expression signature of human prostate cancer stem cells identifies 581 differentially expressed genes and suggests that the JAK-STAT pathway and focal adhesion signaling are important

Qualitative Impact Assessment of Land Management Interventions on Ecosystem Services (“QEIA”). Report-1: Executive Summary: QEIA Evidence Review & Integrated Assessment

The focus of this project was to provide an expert-led, rapid qualitative assessment of land management interventions on Ecosystem Services (ES) proposed for inclusion in Environmental Land Management (ELM) schemes. This involved a review of the current evidence base for 741 land management actions on 33 Ecosystem Services and 53 Ecosystem Service indicators by ten teams involving 45 experts drawn from the independent research community in a consistent series of Evidence Reviews covering the broad topics of: • Air quality • Greenhouse gas emissions • Soils • Water management • Biodiversity: croplands • Biodiversity: improved grassland • Biodiversity: semi-natural habitats • Biodiversity: integrated systems-based actions • Carbon sequestration • Cultural services (including recreation, geodiversity and regulatory services). It should be noted that this piece of work is just one element of the wider underpinning work Defra has commissioned to support the development of the ELM schemes