We present the results of adapting seismic interferometry (SI) for reflection imaging in mineral exploration. We use a unique dataset of one-month ambient-noise recordings acquired with large-N array (~1000 receivers) deployed in a regular grid (50 m receiver interval, 200 m line interval) directly above the known mineralisation and underground mine infrastructure at the Kylylahti polymetallic mine located in Eastern Finland. Ambient-noise in the study area is dominated by the road traffic and mine activities (both surface and underground) providing quasi omni-directional distribution and broad freqeuncy spectrum of the noise sources. We start from the simple 2D forward modelling using existing geological model. Results of the SI applied to field data from selected receiver lines exhibit reflections related to the bottom of the ore body as well as the reflection from the target area confirmed by synthetics. Finally, we develop robust mineral exploration SI workflow (MESI) tailored for reflection imaging and apply it to our 3D ambient-noise dataset. Migrated sections obtained from the MESI-processed data exhibit high reflectivity, compatible with the active-source seismics and directly related to the known geological structures.@en

The main issues related to passive-source reflection imaging with seismic interferometry (SI) are inadequate acquisition parameters for sufficient spatial wavefield sampling and vulnerability of surface arrays to the dominant influence of the omnipresent 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 event detection (TWEED) method of body waves in recorded ambient noise. TWEED evaluates the spatiotemporal 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 h of ambient noise acquired with an approximately 1000-receiver array deployed over an active underground mine in Eastern Finland. We develop the detection of body-wave events related to mine blasts and other routine mining activities using a representative 1 h 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% 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 SI for retrieval of reflections.@en

Here we analyse ambient noise (AN) data generated during drilling of exploration boreholes and recorded using a dense array deployed over one of the numerous shallow iron-ore mineralization targets in the Pilbara region (Western Australia). Drilling and drilling-related operations were reoccurring in a sequence as described by the drillers’ field notes, which created the rare opportunity to analyse AN data in time segments when only one type of technical process was predominantly active. Consequently, most of the recorded AN sources did not overlap in time and space. We extract the recordings in 15-min-long segments matching the time-span of single field-note entry and identify individually acting AN sources associated with specific field operations. The temporal variations of noise spectrograms and AN cross-correlations show dependency on the sequence of a few consecutive field operations and specific frequency–amplitude patterns associated with single field operations. These changes are directly reflected by the events visible in the retrieved virtual-source gathers (VSG), implying significant changes in noise temporal and spatial stationarity. Some VSGs represent the mixed contributions of surface and air waves. To remove the contributions of these arrivals to the reflection imaging, we visually inspect all data and select only field operations acting as stationary-phase sources specifically for the reflection retrieval. This was done for different receiver configurations inside PilbArray, and as a result, we obtain a collection of VSGs containing coherent body-wave reflections. Database of visually inspected VSGs is used to develop and benchmark a semi-automatic curvelet-based method for accurate parametrization of the reflection events retrieved from passive data and to compare the imaging quality of the different field operations. Common-midpoint stacks from manually and automatically selected VSGs show reflectivity consistent with the one obtained from the active-source data and related to the structure hosting shallow iron mineralization. Our results demonstrate the capacity of AN seismic interferometry to retrieve body-wave reflections and image shallow mineralization. They also provide an intermediate step toward automating the passive reflection imaging with similar data sets.@en