Automatic 3D illumination-diagnosis method for large-N arrays: Robust data scanner and machine-learning feature provider

Abstract

Automatic 3D illumination-diagnosis method for large-N arrays: robust data scanner and machine-learning feature provider Chamarczuk, M.; Malinowski, M.; Nishitsuji, Yohei; Thorbecke, Jan Willem; Koivisto, E.; Heinonen, S.; Juurela, S.; Mężyk, M.; Draganov, Deyan ABSTRACTThe main issues related to passive-source reflection imaging with seismic interferometry are inadequate acquisition parameters for sufficient spatial wavefield sampling and vulnerability of surface arrays to the dominant influence of the omni-present surface-wave sources.Additionally, long recordings provide large data volumes that require robust and efficient processing methods. We address these problems by developing a two-step wavefield evaluation and detection method (TWEED) of body waves in recorded ambient noise. TWEED evaluates the spatio-temporal characteristics of noise recordings by simultaneous analysis of adjacent receiver lines. We test our method on synthetic data representing transient ambient-noise sources at the surface and in the deeper subsurface. We discriminate between basic types of seismic events by using three adjacent receiver lines. Subsequently, we apply TWEED to 600 hours of ambient noise acquired with ~1000-receiver array deployed over an active underground mine in Eastern Finland. We demonstrate detection of body-wave events related to mine blasts and other routine mining activities using a representative one-hour noise panel.Using TWEED, we successfully detect 1093 body-wave events in the full data set. To increase the computational efficiency, we use slowness parameters derived from the first step of TWEED as input to a support vector machine (SVM) algorithm. Using this approach, we detect 94 percent of the TWEED-evaluated body-wave events indicating the possibility to limit the illumination analysis to only one step and therefore increase the time efficiency at the price of lower detection rate. However, TWEED on a small volume of the recorded data followed by SVM on the rest of the data could be efficiently used for a quick and robust (real-time) scanning for body-wave energy in large data volumes for subsequent application of seismic interferometry for retrieval of reflections.

Virtual Sources of Body Waves from Noise Correlations in a Mineral Exploration Context

“…To characterize the passive seismic wavefield at higher frequencies, we applied a plane-wave cross-correlation beamforming method (similar to illumination diagnosis, see Almagro Vidal et al, 2014;Chamarczuk et al, 2019) using a subarray and 1 min time windows filtered between 15 and 40 Hz (results in Fig. 4).…”

“…For example, when there exists sufficient time-separable sources, such as microearthquakes, they can be used directly for imaging (Reshetnikov et al, 2010;Chaput et al, 2012Chaput et al, , 2015. When sources are not time separable, it is still possible to evaluate noise over time to select periods when there exists high ratios of body-to surface-wave energy based on apparent velocity analysis (Draganov et al, 2013;Almagro Vidal et al, 2014;Cheraghi et al, 2015;Chamarczuk et al, 2019). There are also many studies that utilize trains passing on nearby railways as strong sources of body-wave energy (Quiros et al, 2016;Brenguier et al, 2019).…”

Cost-Effective Seismic Exploration: 2D Reflection Imaging at the Kylylahti Massive Sulfide Deposit, Finland

“…In 1908, geologists found an ore containing boulder some 50 km away from the bedrock hosted copper deposit that later developed into the Outokumpu mine. During the past hundred years, Outokumpu-type sulfide deposits have been discovered in a zone covering an area of about 4500 km 2 . The Kylylahti polymetallic sulfide deposit was discovered in 1984 [11].…”

“…Seismic methods can be used to image subsurface geological features at high resolution down to several kilometers depth but are often considered expensive by the mineral exploration industry. The COGITO-MIN project (COst-effective Geophysical Imaging Techniques for supporting Ongoing MINeral exploration in Europe) investigated and developed cost-effective seismic exploration techniques [1] including passive seismic interferometry [2] and distributed-acoustic sensing vertical seismic profiling [3]. A major part of the cost of seismic surveys is attributed to the seismic source and the large field crews operating and moving the seismic receivers.…”

Automatic Image-Based Event Detection for Large-N Seismic Arrays Using a Convolutional Neural Network

“…In the case of ambient noise recordings focused on body-wave reflection retrieval, the selective-stacking approach can be automated and improved by introducing the algorithms that could sift through massive volumes of continuous data in order to identify coherent noise sources, which contribute constructively to the stacked EGFs. The automatic detection of noise records (noise panels) containing body-wave energy (hereinafter referred to as body-wave events) evaluated using large-N (i.e., large receiver number) array was already tackled by Chamarczuk et al [22], who applied support vector machine (SVM), a supervised machine-learning (ML) algorithm, in combination with a cross-correlated wavefield evaluation in the tau-p (intercept time-slowness) domain and predefined body-wave velocity limits. Recently, deep learning approaches have been also utilized for detection purposes in passive seismic recordings.…”